Breast Cancer

Key clinical point: Nanoparticle albumin-bound paclitaxel (nab-paclitaxel) was associated with a higher incidence of chemotherapy-induced peripheral neuropathy (CIPN) than docetaxel or paclitaxel in women with invasive breast cancer (BC).

Major finding: The risk for patient-reported CIPN was lower in the paclitaxel (hazard ratio [HR] 0.59; P = .008) and docetaxel (HR 0.65; P = .02) groups compared with the nab-paclitaxel group, with lesser sensory discomfort being reported by patients receiving paclitaxel (HR 0.44) or docetaxel (HR 0.52; both P < .001) vs nab-paclitaxel.

Study details: Findings are from a prospective cohort study including 1234 patients with invasive BC who received taxane-containing chemotherapy, of which 23.9%, 41.7%, and 34.4% of patients received nab-paclitaxel, paclitaxel, and docetaxel, respectively.

Disclosures: This study was supported by the Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences. Dr. Xu declared receiving personal fees from several sources.

Source: Mo H et al. Association of taxane type with patient-reported chemotherapy-induced peripheral neuropathy among patients with breast cancer. JAMA Netw Open. 2022;5(11):e2239788 (Nov 2). Doi: 10.1001/jamanetworkopen.2022.39788

Key clinical point: Nanoparticle albumin-bound paclitaxel (nab-paclitaxel) was associated with a higher incidence of chemotherapy-induced peripheral neuropathy (CIPN) than docetaxel or paclitaxel in women with invasive breast cancer (BC).

Major finding: The risk for patient-reported CIPN was lower in the paclitaxel (hazard ratio [HR] 0.59; P = .008) and docetaxel (HR 0.65; P = .02) groups compared with the nab-paclitaxel group, with lesser sensory discomfort being reported by patients receiving paclitaxel (HR 0.44) or docetaxel (HR 0.52; both P < .001) vs nab-paclitaxel.

Study details: Findings are from a prospective cohort study including 1234 patients with invasive BC who received taxane-containing chemotherapy, of which 23.9%, 41.7%, and 34.4% of patients received nab-paclitaxel, paclitaxel, and docetaxel, respectively.

Disclosures: This study was supported by the Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences. Dr. Xu declared receiving personal fees from several sources.

Source: Mo H et al. Association of taxane type with patient-reported chemotherapy-induced peripheral neuropathy among patients with breast cancer. JAMA Netw Open. 2022;5(11):e2239788 (Nov 2). Doi: 10.1001/jamanetworkopen.2022.39788

Key clinical point: Nanoparticle albumin-bound paclitaxel (nab-paclitaxel) was associated with a higher incidence of chemotherapy-induced peripheral neuropathy (CIPN) than docetaxel or paclitaxel in women with invasive breast cancer (BC).

Major finding: The risk for patient-reported CIPN was lower in the paclitaxel (hazard ratio [HR] 0.59; P = .008) and docetaxel (HR 0.65; P = .02) groups compared with the nab-paclitaxel group, with lesser sensory discomfort being reported by patients receiving paclitaxel (HR 0.44) or docetaxel (HR 0.52; both P < .001) vs nab-paclitaxel.

Study details: Findings are from a prospective cohort study including 1234 patients with invasive BC who received taxane-containing chemotherapy, of which 23.9%, 41.7%, and 34.4% of patients received nab-paclitaxel, paclitaxel, and docetaxel, respectively.

Disclosures: This study was supported by the Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences. Dr. Xu declared receiving personal fees from several sources.

Source: Mo H et al. Association of taxane type with patient-reported chemotherapy-induced peripheral neuropathy among patients with breast cancer. JAMA Netw Open. 2022;5(11):e2239788 (Nov 2). Doi: 10.1001/jamanetworkopen.2022.39788

Key clinical point: Invasive lobular carcinomas (ILC) are often detected at more advanced stages and have worse survival outcomes than invasive ductal carcinomas (IDC).

Major finding: ILCs vs IDC were more frequently diagnosed at later stages (stage III and IV; 20.7% vs 10.4%), with more lymph node involvement (N2 and 3; 9.9% vs 5.5%), and at larger sizes (T3 and 4; 14.7% vs 4.0%; all P < .001). Among patients with estrogen receptor-positive and human epidermal growth factor receptor 2-negative breast cancer (BC), ILC vs IDC were associated with worse overall survival (hazard ratio [HR] 1.32; P < .001) and disease-free survival (HR 1.18; P = .03).

Study details: Findings are from a retrospective cohort study, the Great Lakes Breast Cancer Consortium, including 33,662 patients with BC, of which 10.7% of patients had ILC and 89.3% of patients had IDC.

Disclosures: This work was supported by the Breast Cancer Research Foundation and other sources. The authors declared no conflicts of interest.

Source: Oesterreich S et al. Clinicopathological features and outcomes comparing patients with invasive ductal and lobular breast cancer. J Natl Cancer Inst. 2022 (Oct 14). Doi: 10.1093/jnci/djac157

Key clinical point: Invasive lobular carcinomas (ILC) are often detected at more advanced stages and have worse survival outcomes than invasive ductal carcinomas (IDC).

Major finding: ILCs vs IDC were more frequently diagnosed at later stages (stage III and IV; 20.7% vs 10.4%), with more lymph node involvement (N2 and 3; 9.9% vs 5.5%), and at larger sizes (T3 and 4; 14.7% vs 4.0%; all P < .001). Among patients with estrogen receptor-positive and human epidermal growth factor receptor 2-negative breast cancer (BC), ILC vs IDC were associated with worse overall survival (hazard ratio [HR] 1.32; P < .001) and disease-free survival (HR 1.18; P = .03).

Study details: Findings are from a retrospective cohort study, the Great Lakes Breast Cancer Consortium, including 33,662 patients with BC, of which 10.7% of patients had ILC and 89.3% of patients had IDC.

Disclosures: This work was supported by the Breast Cancer Research Foundation and other sources. The authors declared no conflicts of interest.

Source: Oesterreich S et al. Clinicopathological features and outcomes comparing patients with invasive ductal and lobular breast cancer. J Natl Cancer Inst. 2022 (Oct 14). Doi: 10.1093/jnci/djac157

Key clinical point: Invasive lobular carcinomas (ILC) are often detected at more advanced stages and have worse survival outcomes than invasive ductal carcinomas (IDC).

Major finding: ILCs vs IDC were more frequently diagnosed at later stages (stage III and IV; 20.7% vs 10.4%), with more lymph node involvement (N2 and 3; 9.9% vs 5.5%), and at larger sizes (T3 and 4; 14.7% vs 4.0%; all P < .001). Among patients with estrogen receptor-positive and human epidermal growth factor receptor 2-negative breast cancer (BC), ILC vs IDC were associated with worse overall survival (hazard ratio [HR] 1.32; P < .001) and disease-free survival (HR 1.18; P = .03).

Study details: Findings are from a retrospective cohort study, the Great Lakes Breast Cancer Consortium, including 33,662 patients with BC, of which 10.7% of patients had ILC and 89.3% of patients had IDC.

Disclosures: This work was supported by the Breast Cancer Research Foundation and other sources. The authors declared no conflicts of interest.

Source: Oesterreich S et al. Clinicopathological features and outcomes comparing patients with invasive ductal and lobular breast cancer. J Natl Cancer Inst. 2022 (Oct 14). Doi: 10.1093/jnci/djac157

Key clinical point: The prespecified second interim analysis of the OlympiA trial revealed a significant improvement in overall survival (OS) with adjuvant olaparib vs placebo in patients with pathogenic variants in BRCA1 or BRCA2 (gBRCA1/2pv) and high-risk, human epidermal growth factor receptor 2-negative (HER2−), early breast cancer (BC).

Major finding: After a median follow-up of 3.5 years, OS improved significantly (hazard ratio 0.68; P = .009) and improvement in 4-year invasive disease-free survival was maintained (82.7% vs 75.4%) in the olaparib vs placebo group. No new safety signals were identified.

Study details: Findings are from the double-blind, phase 3, OlympiA study including 1836 patients with gBRCA1/2pv-associated high-risk, HER2−, early BC who were randomly assigned to receive olaparib or placebo in the adjuvant setting.

Disclosures: This work was supported by the US National Institutes of Health. Some authors declared receiving research funding, honoraria, consulting fees, compensation, accommodations and travel expenses, and royalties from and having other ties with several sources.

Source: Geyer CE Jr et al. Overall survival in the OlympiA phase III trial of adjuvant olaparib in patients with germline pathogenic variants in BRCA1/2 and high risk, early breast cancer. Ann Oncol. 2022 (Oct 10). Doi: 10.1016/j.annonc.2022.09.159

Key clinical point: The prespecified second interim analysis of the OlympiA trial revealed a significant improvement in overall survival (OS) with adjuvant olaparib vs placebo in patients with pathogenic variants in BRCA1 or BRCA2 (gBRCA1/2pv) and high-risk, human epidermal growth factor receptor 2-negative (HER2−), early breast cancer (BC).

Major finding: After a median follow-up of 3.5 years, OS improved significantly (hazard ratio 0.68; P = .009) and improvement in 4-year invasive disease-free survival was maintained (82.7% vs 75.4%) in the olaparib vs placebo group. No new safety signals were identified.

Study details: Findings are from the double-blind, phase 3, OlympiA study including 1836 patients with gBRCA1/2pv-associated high-risk, HER2−, early BC who were randomly assigned to receive olaparib or placebo in the adjuvant setting.

Disclosures: This work was supported by the US National Institutes of Health. Some authors declared receiving research funding, honoraria, consulting fees, compensation, accommodations and travel expenses, and royalties from and having other ties with several sources.

Source: Geyer CE Jr et al. Overall survival in the OlympiA phase III trial of adjuvant olaparib in patients with germline pathogenic variants in BRCA1/2 and high risk, early breast cancer. Ann Oncol. 2022 (Oct 10). Doi: 10.1016/j.annonc.2022.09.159

Key clinical point: The prespecified second interim analysis of the OlympiA trial revealed a significant improvement in overall survival (OS) with adjuvant olaparib vs placebo in patients with pathogenic variants in BRCA1 or BRCA2 (gBRCA1/2pv) and high-risk, human epidermal growth factor receptor 2-negative (HER2−), early breast cancer (BC).

Major finding: After a median follow-up of 3.5 years, OS improved significantly (hazard ratio 0.68; P = .009) and improvement in 4-year invasive disease-free survival was maintained (82.7% vs 75.4%) in the olaparib vs placebo group. No new safety signals were identified.

Study details: Findings are from the double-blind, phase 3, OlympiA study including 1836 patients with gBRCA1/2pv-associated high-risk, HER2−, early BC who were randomly assigned to receive olaparib or placebo in the adjuvant setting.

Disclosures: This work was supported by the US National Institutes of Health. Some authors declared receiving research funding, honoraria, consulting fees, compensation, accommodations and travel expenses, and royalties from and having other ties with several sources.

Source: Geyer CE Jr et al. Overall survival in the OlympiA phase III trial of adjuvant olaparib in patients with germline pathogenic variants in BRCA1/2 and high risk, early breast cancer. Ann Oncol. 2022 (Oct 10). Doi: 10.1016/j.annonc.2022.09.159

Key clinical point: Patients who had survived breast cancer (BC) were more likely to develop soft tissue sarcoma if they received radiotherapy.

Major finding: A very small fraction of BC survivors (~0.1%) developed thoracic soft tissue sarcoma, with radiotherapy being the strongest risk factor in the Kaiser Permanente (KP) cohort (relative risk [RR] 8.1; P = .0052) and the Surveillance, Epidemiology, and End Results (SEER) 13 registries cohort (RR 3.0; P < .0001).

Study details: This retrospective study of data sourced from two cohorts, the KP cohort (n = 15,940) and the SEER 13 registries cohort (n = 457,300) and included patients who had BC surgery and survived ≥1 year after BC diagnosis.

Disclosures: This study was supported by the US National Cancer Institute and National Institutes of Health. The authors declared no conflicts of interest.

Source: Veiga LHS et al. Treatment-related thoracic soft tissue sarcomas in US breast cancer survivors: A retrospective cohort study. Lancet Oncol. 2022;23(11):1451-1464 (Oct 11). Doi: 10.1016/S1470-2045(22)00561-7

Key clinical point: Patients who had survived breast cancer (BC) were more likely to develop soft tissue sarcoma if they received radiotherapy.

Major finding: A very small fraction of BC survivors (~0.1%) developed thoracic soft tissue sarcoma, with radiotherapy being the strongest risk factor in the Kaiser Permanente (KP) cohort (relative risk [RR] 8.1; P = .0052) and the Surveillance, Epidemiology, and End Results (SEER) 13 registries cohort (RR 3.0; P < .0001).

Study details: This retrospective study of data sourced from two cohorts, the KP cohort (n = 15,940) and the SEER 13 registries cohort (n = 457,300) and included patients who had BC surgery and survived ≥1 year after BC diagnosis.

Disclosures: This study was supported by the US National Cancer Institute and National Institutes of Health. The authors declared no conflicts of interest.

Source: Veiga LHS et al. Treatment-related thoracic soft tissue sarcomas in US breast cancer survivors: A retrospective cohort study. Lancet Oncol. 2022;23(11):1451-1464 (Oct 11). Doi: 10.1016/S1470-2045(22)00561-7

Key clinical point: Patients who had survived breast cancer (BC) were more likely to develop soft tissue sarcoma if they received radiotherapy.

Major finding: A very small fraction of BC survivors (~0.1%) developed thoracic soft tissue sarcoma, with radiotherapy being the strongest risk factor in the Kaiser Permanente (KP) cohort (relative risk [RR] 8.1; P = .0052) and the Surveillance, Epidemiology, and End Results (SEER) 13 registries cohort (RR 3.0; P < .0001).

Study details: This retrospective study of data sourced from two cohorts, the KP cohort (n = 15,940) and the SEER 13 registries cohort (n = 457,300) and included patients who had BC surgery and survived ≥1 year after BC diagnosis.

Disclosures: This study was supported by the US National Cancer Institute and National Institutes of Health. The authors declared no conflicts of interest.

Source: Veiga LHS et al. Treatment-related thoracic soft tissue sarcomas in US breast cancer survivors: A retrospective cohort study. Lancet Oncol. 2022;23(11):1451-1464 (Oct 11). Doi: 10.1016/S1470-2045(22)00561-7

Key clinical point: Breast cancer (BC) surgery may be eliminated in patients with early-stage triple-negative BC (TNBC) or human epidermal growth factor receptor 2-positive (HER2+) BC who have achieved pathological complete response (pCR) after neoadjuvant systemic therapy (NST).

Major finding: After a median follow-up of 26.4 months, there were no incidences of ipsilateral breast tumor recurrence in the 31 patients who had achieved a pCR on percutaneous image-guided vacuum-assisted core biopsy (VACB) after NST.

Study details: Findings are from a multicenter, single-arm, phase 2 study including 50 patients with invasive HER2+ BC or TNBC who received percutaneous image-guided VACB after NST.

Disclosures: This study was funded by the US National Cancer Institute. Some authors declared serving in leadership roles or receiving consulting fees, honorarium, royalties, or research funding from several sources.

Source: Kuerer HM et al. Eliminating breast surgery for invasive breast cancer in exceptional responders to neoadjuvant systemic therapy: A multicentre, single-arm, phase 2 trial. Lancet Oncol. 2022 (Oct 25). Doi: 10.1016/S1470-2045(22)00613-1

Key clinical point: Breast cancer (BC) surgery may be eliminated in patients with early-stage triple-negative BC (TNBC) or human epidermal growth factor receptor 2-positive (HER2+) BC who have achieved pathological complete response (pCR) after neoadjuvant systemic therapy (NST).

Major finding: After a median follow-up of 26.4 months, there were no incidences of ipsilateral breast tumor recurrence in the 31 patients who had achieved a pCR on percutaneous image-guided vacuum-assisted core biopsy (VACB) after NST.

Study details: Findings are from a multicenter, single-arm, phase 2 study including 50 patients with invasive HER2+ BC or TNBC who received percutaneous image-guided VACB after NST.

Disclosures: This study was funded by the US National Cancer Institute. Some authors declared serving in leadership roles or receiving consulting fees, honorarium, royalties, or research funding from several sources.

Source: Kuerer HM et al. Eliminating breast surgery for invasive breast cancer in exceptional responders to neoadjuvant systemic therapy: A multicentre, single-arm, phase 2 trial. Lancet Oncol. 2022 (Oct 25). Doi: 10.1016/S1470-2045(22)00613-1

Key clinical point: Breast cancer (BC) surgery may be eliminated in patients with early-stage triple-negative BC (TNBC) or human epidermal growth factor receptor 2-positive (HER2+) BC who have achieved pathological complete response (pCR) after neoadjuvant systemic therapy (NST).

Major finding: After a median follow-up of 26.4 months, there were no incidences of ipsilateral breast tumor recurrence in the 31 patients who had achieved a pCR on percutaneous image-guided vacuum-assisted core biopsy (VACB) after NST.

Study details: Findings are from a multicenter, single-arm, phase 2 study including 50 patients with invasive HER2+ BC or TNBC who received percutaneous image-guided VACB after NST.

Disclosures: This study was funded by the US National Cancer Institute. Some authors declared serving in leadership roles or receiving consulting fees, honorarium, royalties, or research funding from several sources.

Source: Kuerer HM et al. Eliminating breast surgery for invasive breast cancer in exceptional responders to neoadjuvant systemic therapy: A multicentre, single-arm, phase 2 trial. Lancet Oncol. 2022 (Oct 25). Doi: 10.1016/S1470-2045(22)00613-1

A new model for predicting breast cancer risk that is based on standard clinical measures could be used to develop personalized breast screening strategies that are more effective than the current “one size fits all” approach, say researchers.

“Several breast cancer risk prediction models have been created, but we believe this is one of the first models designed to guide breast screening strategies over a person’s lifetime using real data from a screening program,” said study author Javier Louro, PhD, Hospital del Mar Medical Research Institute, Barcelona, Spain.

“Our model might be considered a key for designing personalized screening aimed at reducing the harms and increasing the benefits of mammographic screening,” he said in a statement.

Someone with a low risk “might be offered screening with standard mammography every 3 or 4 years instead of 2 years,” Dr. Louro explained.

“Someone with medium risk might be offered screening with advanced 3D mammography every 3 years, while those at a high risk might be offered a new screening test with mammography or MRI every year.”

However, he cautioned that “all of these strategies are still theoretical and should be studied with regard to their effectiveness.”

Dr. Louro was talking about the new model at the 13th European Breast Cancer Conference.
 

Details of the new prediction model

To develop the new model, Louro and colleagues conducted a retrospective study of 57,411 women who underwent mammography in four counties in Norway between 2007 and 2019 as part of the BreastScreen Norway program, and followed them up to 2022.

The team gathered data on age, breast density, family history of breast cancer, body mass index, age at menarche, alcohol habit, exercise, pregnancy, hormone replacement therapy, and benign breast disease, and compared women with and those without a breast cancer diagnosis.

All of these 10 variables used were found to significantly explain part of the variability in the breast cancer risk.

Overall, the 4-year breast cancer risk predicted by the resulting model varied across the participants, from 0.22% to 7.43%, at a median of 1.10%.

Bootstrap resampling analysis revealed that the model overestimated the risk for breast cancer, at an expected-to-observed ratio of 1.10.

The largest effect on risk was from breast density on mammography. Women with dense breasts were at much higher risk: the adjusted hazard ratio was 1.71 for women with Volpara Density Grade 4 vs Grade 2 and was 1.37 when compared with Grade 3.

Exercise had a large impact on breast cancer risk, the researchers found. Women who exercised for 4 or more hours per week had an adjusted hazard ratio of 0.65 for breast cancer risk compared with women who never exercised. Although this effect of exercise reducing the risk for breast cancer is now widely known, it is not usually included in models that predict breast cancer risk, the team pointed out.

The team concluded that their prediction model could be used to personalize breast screening for women according to their risk assessment, although they acknowledge that more work is needed. This work is based on one screening program in one country, and similar studies in different settings are needed.

Reacting to the findings, Laura Biganzoli, MD, co-chair of the European Breast Cancer Conference and director of the Breast Centre at Santo Stefano Hospital, Prato, Italy, commented, “We know that breast screening programs are beneficial, but we also know that some people will experience potential harms caused by false-positives or overdiagnosis.”

“This research shows how we might be able to identify people with a high risk of breast cancer, but equally how we could identify those with a low risk. So it’s an important step toward personalized screening,” Dr. Biganzoli said.

This study was supported by a grant from Instituto de Salud Carlos III FEDER (grant PI/00047). No relevant financial relationships declared.

A version of this article first appeared on Medscape.com.

A new model for predicting breast cancer risk that is based on standard clinical measures could be used to develop personalized breast screening strategies that are more effective than the current “one size fits all” approach, say researchers.

“Several breast cancer risk prediction models have been created, but we believe this is one of the first models designed to guide breast screening strategies over a person’s lifetime using real data from a screening program,” said study author Javier Louro, PhD, Hospital del Mar Medical Research Institute, Barcelona, Spain.

“Our model might be considered a key for designing personalized screening aimed at reducing the harms and increasing the benefits of mammographic screening,” he said in a statement.

Someone with a low risk “might be offered screening with standard mammography every 3 or 4 years instead of 2 years,” Dr. Louro explained.

“Someone with medium risk might be offered screening with advanced 3D mammography every 3 years, while those at a high risk might be offered a new screening test with mammography or MRI every year.”

However, he cautioned that “all of these strategies are still theoretical and should be studied with regard to their effectiveness.”

Dr. Louro was talking about the new model at the 13th European Breast Cancer Conference.
 

Details of the new prediction model

To develop the new model, Louro and colleagues conducted a retrospective study of 57,411 women who underwent mammography in four counties in Norway between 2007 and 2019 as part of the BreastScreen Norway program, and followed them up to 2022.

The team gathered data on age, breast density, family history of breast cancer, body mass index, age at menarche, alcohol habit, exercise, pregnancy, hormone replacement therapy, and benign breast disease, and compared women with and those without a breast cancer diagnosis.

All of these 10 variables used were found to significantly explain part of the variability in the breast cancer risk.

Overall, the 4-year breast cancer risk predicted by the resulting model varied across the participants, from 0.22% to 7.43%, at a median of 1.10%.

Bootstrap resampling analysis revealed that the model overestimated the risk for breast cancer, at an expected-to-observed ratio of 1.10.

The largest effect on risk was from breast density on mammography. Women with dense breasts were at much higher risk: the adjusted hazard ratio was 1.71 for women with Volpara Density Grade 4 vs Grade 2 and was 1.37 when compared with Grade 3.

Exercise had a large impact on breast cancer risk, the researchers found. Women who exercised for 4 or more hours per week had an adjusted hazard ratio of 0.65 for breast cancer risk compared with women who never exercised. Although this effect of exercise reducing the risk for breast cancer is now widely known, it is not usually included in models that predict breast cancer risk, the team pointed out.

The team concluded that their prediction model could be used to personalize breast screening for women according to their risk assessment, although they acknowledge that more work is needed. This work is based on one screening program in one country, and similar studies in different settings are needed.

Reacting to the findings, Laura Biganzoli, MD, co-chair of the European Breast Cancer Conference and director of the Breast Centre at Santo Stefano Hospital, Prato, Italy, commented, “We know that breast screening programs are beneficial, but we also know that some people will experience potential harms caused by false-positives or overdiagnosis.”

“This research shows how we might be able to identify people with a high risk of breast cancer, but equally how we could identify those with a low risk. So it’s an important step toward personalized screening,” Dr. Biganzoli said.

This study was supported by a grant from Instituto de Salud Carlos III FEDER (grant PI/00047). No relevant financial relationships declared.

A version of this article first appeared on Medscape.com.

A new model for predicting breast cancer risk that is based on standard clinical measures could be used to develop personalized breast screening strategies that are more effective than the current “one size fits all” approach, say researchers.

“Several breast cancer risk prediction models have been created, but we believe this is one of the first models designed to guide breast screening strategies over a person’s lifetime using real data from a screening program,” said study author Javier Louro, PhD, Hospital del Mar Medical Research Institute, Barcelona, Spain.

“Our model might be considered a key for designing personalized screening aimed at reducing the harms and increasing the benefits of mammographic screening,” he said in a statement.

Someone with a low risk “might be offered screening with standard mammography every 3 or 4 years instead of 2 years,” Dr. Louro explained.

“Someone with medium risk might be offered screening with advanced 3D mammography every 3 years, while those at a high risk might be offered a new screening test with mammography or MRI every year.”

However, he cautioned that “all of these strategies are still theoretical and should be studied with regard to their effectiveness.”

Dr. Louro was talking about the new model at the 13th European Breast Cancer Conference.
 

Details of the new prediction model

To develop the new model, Louro and colleagues conducted a retrospective study of 57,411 women who underwent mammography in four counties in Norway between 2007 and 2019 as part of the BreastScreen Norway program, and followed them up to 2022.

The team gathered data on age, breast density, family history of breast cancer, body mass index, age at menarche, alcohol habit, exercise, pregnancy, hormone replacement therapy, and benign breast disease, and compared women with and those without a breast cancer diagnosis.

All of these 10 variables used were found to significantly explain part of the variability in the breast cancer risk.

Overall, the 4-year breast cancer risk predicted by the resulting model varied across the participants, from 0.22% to 7.43%, at a median of 1.10%.

Bootstrap resampling analysis revealed that the model overestimated the risk for breast cancer, at an expected-to-observed ratio of 1.10.

The largest effect on risk was from breast density on mammography. Women with dense breasts were at much higher risk: the adjusted hazard ratio was 1.71 for women with Volpara Density Grade 4 vs Grade 2 and was 1.37 when compared with Grade 3.

Exercise had a large impact on breast cancer risk, the researchers found. Women who exercised for 4 or more hours per week had an adjusted hazard ratio of 0.65 for breast cancer risk compared with women who never exercised. Although this effect of exercise reducing the risk for breast cancer is now widely known, it is not usually included in models that predict breast cancer risk, the team pointed out.

The team concluded that their prediction model could be used to personalize breast screening for women according to their risk assessment, although they acknowledge that more work is needed. This work is based on one screening program in one country, and similar studies in different settings are needed.

Reacting to the findings, Laura Biganzoli, MD, co-chair of the European Breast Cancer Conference and director of the Breast Centre at Santo Stefano Hospital, Prato, Italy, commented, “We know that breast screening programs are beneficial, but we also know that some people will experience potential harms caused by false-positives or overdiagnosis.”

“This research shows how we might be able to identify people with a high risk of breast cancer, but equally how we could identify those with a low risk. So it’s an important step toward personalized screening,” Dr. Biganzoli said.

This study was supported by a grant from Instituto de Salud Carlos III FEDER (grant PI/00047). No relevant financial relationships declared.

A version of this article first appeared on Medscape.com.

A woman who had breast cancer for 23 years, and who had gone through 12 different lines of therapy, has shown a dramatic response to a novel cancer vaccine in a clinical trial.

A recent 6-month follow-up showed no evidence of new or recurrent disease, and scans showed regression of a distant bulky left adrenal metastasis, as well as at other sites.

A small site of residual hypermetabolism remains in the sternum, but this is thought to be related to scar tissue.

The patient, Stephanie Gangi, told Medscape Medical News that, before she entered into the trial for the novel cancer vaccine, she was “mentally and physically exhausted.” She had benefited from being diagnosed with hormone-positive breast cancer just as its treatment was evolving and progressing, which meant that, every time a treatment failed, “there was the next thing to try, which was great and kept me going.”

“But I will admit that, by age 66, and more than 20 years of cancer treatments, I was exhausted.”

Ms. Gangi, a New York City-based poet, essayist, and fiction writer, said she was “cautiously optimistic” about the cancer vaccine, but the “overriding thought was I wanted to avoid chemotherapy.”

“I was not really signing on for great outcomes, I was signing on for something that might keep chemo at bay. The biggest impact so far for me has been that, for the first time in more than a decade, I am not on any medication. That’s really amazing…and that means no side effects,” she said.

Ms. Gangi stopped the vaccine treatment this past July, and just over 3 months later, she is still “wrapping her head around” the fact that her cancer has regressed. “I’ve had breast cancer a long time,” she said, “and you can’t just snap your fingers and be fine.”

Although the two scans that she has had since the trial ended have been “astonishing,” she underlined that this is not about a ‘cure,’ but rather “clearing tumors for the first time in many years.”

“Cancer is sneaky and sinister, and it figures out how to circumvent all kinds of treatments,” she said, adding nevertheless that she is “happy and hopeful, and my family is thrilled, of course.”

Ms. Gangi was classed as having had a partial response to the cancer vaccine, one of a few in a small phase 1/2 trial at the Icahn School of Medicine at Mount Sinai in New York. One other patient also had a partial response, and one patient had a complete response.

However, six patients have progressive disease, and one has stable disease.  

These results come from an interim analysis of 10 patients from the trial, and show a 30% response rate. They were presented at the recent annual meeting of the Society for Immunotherapy of Cancer.

The vaccine that was being tested combines local low-dose radiation, intramural Flt3L, which stimulates dendritic cells, and intravenous poly-ICLC, an immune stimulating factor, with the PD-1 inhibitor pembrolizumab (Keytruda).

The result is that, instead of making a vaccine in a laboratory and administering it, “we’re actually formulating it within the body,” lead author Thomas Marron, MD, PhD, professor of medicine (hematology and medical oncology) at Mount Sinai, said in an interview.

“What people don’t realize,” he said, is that bulky tumor sites contain “a lot of dead tumor, because they grow so fast and in a haphazard way.” This means that the immune system can be recruited to recognize the dead tumor and “gobble up the dead stuff that’s already there,” he added.

The hope is that the immune system will then kill not only “the tumor you are injecting into, but also tumors elsewhere in the body,” Dr. Marron said. “So you’re basically using your body’s own immune system and on and off switches to vaccinate the patient against their cancer.”

Another patient in the trial who had a complete response to the vaccine was William Morrison, with non-Hodgkin lymphoma (NHL).

Mr. Morrison was diagnosed in 2017, at which time he was enrolled onto a phase 1 trial of an earlier version of this novel vaccine treatment regimen. “Basically, they didn’t get the results they were hoping for, and I still had the lymphoma,” he said. In 2018, his indolent follicular lymphoma transformed into an aggressive diffuse large B-cell lymphoma, for which Mr. Morrison was given six cycles of chemotherapy. This put him into remission and cleared his lymphoma.

“But the remission lasted for maybe a little over a year,” he said.

The cancer came back, and at that point he was given the opportunity to enroll in the Mount Sinai trial. At the end of the treatment, “everything was clear.”

“I’ve been for PET scans every 6 months, and I just had a scan done the other week, and everything has been fine…I’ve been pretty excited. I was pretty lucky.”

“This recent one really has worked wonders,” he said, “When they gave me the good news the other day. I felt like a big weight had been lifted.”

Mr. Morrison also said that he did not experience any serious adverse events while being treated with the vaccine. “Other than a few minor things, I tolerated it pretty well,” he said.

In contrast, Ms. Gangi said she experienced “intense” flu-like symptoms that started in the first few days after the treatment and lasted for a couple of days.
 

Need to improve response rate

The current trial achieved responses in 30% of patients, which “is great, [but] we want to be at 100%,” said Dr. Marron.

“What we’re doing in the laboratory right now is using this as an opportunity to study what it is that’s special about those three people who responded and what’s not happening in the other seven people, and we have some initial data that we’re analyzing,” he said.

“We are seeing that the patients who responded have a much more robust response to the Ft3L in particular…and that could suggest that maybe we need a better Ft3L, or we could think about other ways to potentially manipulate this vaccine.

“Most of the patients who are referred to me are people who have run out of options…and that usually means they’ve had many different types of chemotherapy,” Dr. Marron commented. For example, Ms. Gangi had already been through 12 different chemotherapy regimens.

Chemotherapy suppresses the immune system, but it’s not only that — also having an effect are all the other treatments aimed at reducing nausea and allergic reactions to the anti-cancer therapy, Dr. Marron explained.

“By the time that I see a patient,” Dr. Marron said, “oftentimes their immune system is not optimal. So another way in which we would hope to see better responses is by moving this vaccine earlier in the treatment paradigm, and administering it to patients as their first or second treatment.”

Senior author Joshua Brody, MD, director of the Lymphoma Immunotherapy Program at Mount Sinai’s Tisch Cancer Institute, added that it “might be easy” to incorporate the vaccine into earlier lines of therapy.

He said in an interview that both immunotherapy and radiation therapy are “standard” treatments, and the key is “adding multiple ingredients together that don’t have cumulative toxicity.”

“You can’t just chemo one plus chemo two, because they have some of the same toxicities, but the delightful thing here is this therapy had been quite safe.

“So in theory it would be fairly easy to incorporate this into earlier lines of therapy, once we can get a bit more proof of principle,” Dr. Brody said.

Approached for comment, Ann W. Silk, MD, said that the results are “particularly impressive because we know anti-PD-1 plus radiation therapy does not work in hormone-positive breast cancer or lymphoma.”

Dr. Silk, an oncologist at the Dana-Farber Cancer Institute and assistant professor of medicine at Harvard Medical School in Boston, said in an interview that one advantage of this vaccine is that it “is not restricted to a certain number of antigens and does not rely on an algorithm.”

“I would love to see more data in hormone-positive metastatic breast cancer patients,” she added. “I would use this approach after the hormonal treatments stop working, but before chemotherapy.”

Dr. Silk also said that the safety profile “looks quite good, and I imagine this approach would result in a much better quality of life for patients as compared to chemotherapy.”
 

Details of the trial and results

The Mount Sinai researchers had previously developed a personalized genomic cancer vaccine, PGV-001, which showed promise in a phase 1 trial in 13 patients with solid tumors or multiple myeloma and a high risk of recurrence after surgery or autologous stem cell transplant.

Next, they worked to develop the concept further to turn the tumor into its own vaccine, which involved inducing anti-tumor responses in indolent NHL, which typically responds poorly to checkpoint blockade, by combining Ft3L, low-dose irradiation, and poly-ICLC.

The next phase 1 trial showed that this approach was feasible, but preclinical modeling suggested that the addition of PD-1 blockade could improve the cure rates. The researchers therefore conducted the current trial, recruiting 10 patients with indolent NHL, metastatic breast cancer, or head and neck squamous cell carcinoma (HNSCC).

Patients were given local radiation therapy on days 1 and 2, and intramural Ft3L to the same tumor on day 9, followed by eight intravenous injections of poly-ICLC over 6 weeks. On day 23, they received their first of eight doses of pembrolizumab.

Dr. Marron explained that the radiotherapy increases the amount of dead material for the immune system to work on by “killing some of the tumor cells,” adding: “We’re not trying to kill the whole tumor with the radiation…it just starts the process of releasing some more of that dead stuff.”

He explained that Ft3L is a human growth factor that simulates dendritic cells, “which I always say are the professor cells of the immune system,” as they tell the body “what’s good and what’s bad.”

The poly-ICLC is “basically like a fake virus,” Dr. Marron said, as it “turns on those immune cells that have taken up the tumor antigen in the neighborhood” of the tumor, so they “teach the immune system that there is something bad”.

Finally, the pembrolizumab is there to “take the foot off the brake of the immune system” and “grease the wheels a bit more,” he added, even though it does not work in all patients, or in all tumor types, including indolent NHL.

The trial was planned in two phases. In the first part, six patients were enrolled to assess the safety of the approach; the phase 2 stage of the trial followed a Simon’s Two-Stage design, with the aim of recruiting seven patients of each tumor type, followed by a further 12 patients if they showed a response.

The current interim analysis that was presented at the SITC meeting focused on the first 10 patients in the phase 2 part, who were enrolled between April 2019 and July 2022. This included six patients with metastatic breast cancer, three with indolent NHL, and one with HNSCC, all of whom completed their first disease response assessment.

All patients experienced treatment-related adverse events, largely comprising low-grade injection site reactions and flu-like symptoms linked to the poly-ICLC injections.

One patient experienced grade 3 pembrolizumab-related colitis, while another had self-resolving grade 3 fever following poly-ICLC injection.

The study was sponsored by Icahn School of Medicine at Mount Sinai and conducted in collaboration with Merck Sharp & Dohme LLC and Celldex Therapeutics. No relevant financial relationships were reported.

A version of this article first appeared on Medscape.com.

A woman who had breast cancer for 23 years, and who had gone through 12 different lines of therapy, has shown a dramatic response to a novel cancer vaccine in a clinical trial.

A recent 6-month follow-up showed no evidence of new or recurrent disease, and scans showed regression of a distant bulky left adrenal metastasis, as well as at other sites.

A small site of residual hypermetabolism remains in the sternum, but this is thought to be related to scar tissue.

The patient, Stephanie Gangi, told Medscape Medical News that, before she entered into the trial for the novel cancer vaccine, she was “mentally and physically exhausted.” She had benefited from being diagnosed with hormone-positive breast cancer just as its treatment was evolving and progressing, which meant that, every time a treatment failed, “there was the next thing to try, which was great and kept me going.”

“But I will admit that, by age 66, and more than 20 years of cancer treatments, I was exhausted.”

Ms. Gangi, a New York City-based poet, essayist, and fiction writer, said she was “cautiously optimistic” about the cancer vaccine, but the “overriding thought was I wanted to avoid chemotherapy.”

“I was not really signing on for great outcomes, I was signing on for something that might keep chemo at bay. The biggest impact so far for me has been that, for the first time in more than a decade, I am not on any medication. That’s really amazing…and that means no side effects,” she said.

Ms. Gangi stopped the vaccine treatment this past July, and just over 3 months later, she is still “wrapping her head around” the fact that her cancer has regressed. “I’ve had breast cancer a long time,” she said, “and you can’t just snap your fingers and be fine.”

Although the two scans that she has had since the trial ended have been “astonishing,” she underlined that this is not about a ‘cure,’ but rather “clearing tumors for the first time in many years.”

“Cancer is sneaky and sinister, and it figures out how to circumvent all kinds of treatments,” she said, adding nevertheless that she is “happy and hopeful, and my family is thrilled, of course.”

Ms. Gangi was classed as having had a partial response to the cancer vaccine, one of a few in a small phase 1/2 trial at the Icahn School of Medicine at Mount Sinai in New York. One other patient also had a partial response, and one patient had a complete response.

However, six patients have progressive disease, and one has stable disease.  

These results come from an interim analysis of 10 patients from the trial, and show a 30% response rate. They were presented at the recent annual meeting of the Society for Immunotherapy of Cancer.

The vaccine that was being tested combines local low-dose radiation, intramural Flt3L, which stimulates dendritic cells, and intravenous poly-ICLC, an immune stimulating factor, with the PD-1 inhibitor pembrolizumab (Keytruda).

The result is that, instead of making a vaccine in a laboratory and administering it, “we’re actually formulating it within the body,” lead author Thomas Marron, MD, PhD, professor of medicine (hematology and medical oncology) at Mount Sinai, said in an interview.

“What people don’t realize,” he said, is that bulky tumor sites contain “a lot of dead tumor, because they grow so fast and in a haphazard way.” This means that the immune system can be recruited to recognize the dead tumor and “gobble up the dead stuff that’s already there,” he added.

The hope is that the immune system will then kill not only “the tumor you are injecting into, but also tumors elsewhere in the body,” Dr. Marron said. “So you’re basically using your body’s own immune system and on and off switches to vaccinate the patient against their cancer.”

Another patient in the trial who had a complete response to the vaccine was William Morrison, with non-Hodgkin lymphoma (NHL).

Mr. Morrison was diagnosed in 2017, at which time he was enrolled onto a phase 1 trial of an earlier version of this novel vaccine treatment regimen. “Basically, they didn’t get the results they were hoping for, and I still had the lymphoma,” he said. In 2018, his indolent follicular lymphoma transformed into an aggressive diffuse large B-cell lymphoma, for which Mr. Morrison was given six cycles of chemotherapy. This put him into remission and cleared his lymphoma.

“But the remission lasted for maybe a little over a year,” he said.

The cancer came back, and at that point he was given the opportunity to enroll in the Mount Sinai trial. At the end of the treatment, “everything was clear.”

“I’ve been for PET scans every 6 months, and I just had a scan done the other week, and everything has been fine…I’ve been pretty excited. I was pretty lucky.”

“This recent one really has worked wonders,” he said, “When they gave me the good news the other day. I felt like a big weight had been lifted.”

Mr. Morrison also said that he did not experience any serious adverse events while being treated with the vaccine. “Other than a few minor things, I tolerated it pretty well,” he said.

In contrast, Ms. Gangi said she experienced “intense” flu-like symptoms that started in the first few days after the treatment and lasted for a couple of days.
 

Need to improve response rate

The current trial achieved responses in 30% of patients, which “is great, [but] we want to be at 100%,” said Dr. Marron.

“What we’re doing in the laboratory right now is using this as an opportunity to study what it is that’s special about those three people who responded and what’s not happening in the other seven people, and we have some initial data that we’re analyzing,” he said.

“We are seeing that the patients who responded have a much more robust response to the Ft3L in particular…and that could suggest that maybe we need a better Ft3L, or we could think about other ways to potentially manipulate this vaccine.

“Most of the patients who are referred to me are people who have run out of options…and that usually means they’ve had many different types of chemotherapy,” Dr. Marron commented. For example, Ms. Gangi had already been through 12 different chemotherapy regimens.

Chemotherapy suppresses the immune system, but it’s not only that — also having an effect are all the other treatments aimed at reducing nausea and allergic reactions to the anti-cancer therapy, Dr. Marron explained.

“By the time that I see a patient,” Dr. Marron said, “oftentimes their immune system is not optimal. So another way in which we would hope to see better responses is by moving this vaccine earlier in the treatment paradigm, and administering it to patients as their first or second treatment.”

Senior author Joshua Brody, MD, director of the Lymphoma Immunotherapy Program at Mount Sinai’s Tisch Cancer Institute, added that it “might be easy” to incorporate the vaccine into earlier lines of therapy.

He said in an interview that both immunotherapy and radiation therapy are “standard” treatments, and the key is “adding multiple ingredients together that don’t have cumulative toxicity.”

“You can’t just chemo one plus chemo two, because they have some of the same toxicities, but the delightful thing here is this therapy had been quite safe.

“So in theory it would be fairly easy to incorporate this into earlier lines of therapy, once we can get a bit more proof of principle,” Dr. Brody said.

Approached for comment, Ann W. Silk, MD, said that the results are “particularly impressive because we know anti-PD-1 plus radiation therapy does not work in hormone-positive breast cancer or lymphoma.”

Dr. Silk, an oncologist at the Dana-Farber Cancer Institute and assistant professor of medicine at Harvard Medical School in Boston, said in an interview that one advantage of this vaccine is that it “is not restricted to a certain number of antigens and does not rely on an algorithm.”

“I would love to see more data in hormone-positive metastatic breast cancer patients,” she added. “I would use this approach after the hormonal treatments stop working, but before chemotherapy.”

Dr. Silk also said that the safety profile “looks quite good, and I imagine this approach would result in a much better quality of life for patients as compared to chemotherapy.”
 

Details of the trial and results

The Mount Sinai researchers had previously developed a personalized genomic cancer vaccine, PGV-001, which showed promise in a phase 1 trial in 13 patients with solid tumors or multiple myeloma and a high risk of recurrence after surgery or autologous stem cell transplant.

Next, they worked to develop the concept further to turn the tumor into its own vaccine, which involved inducing anti-tumor responses in indolent NHL, which typically responds poorly to checkpoint blockade, by combining Ft3L, low-dose irradiation, and poly-ICLC.

The next phase 1 trial showed that this approach was feasible, but preclinical modeling suggested that the addition of PD-1 blockade could improve the cure rates. The researchers therefore conducted the current trial, recruiting 10 patients with indolent NHL, metastatic breast cancer, or head and neck squamous cell carcinoma (HNSCC).

Patients were given local radiation therapy on days 1 and 2, and intramural Ft3L to the same tumor on day 9, followed by eight intravenous injections of poly-ICLC over 6 weeks. On day 23, they received their first of eight doses of pembrolizumab.

Dr. Marron explained that the radiotherapy increases the amount of dead material for the immune system to work on by “killing some of the tumor cells,” adding: “We’re not trying to kill the whole tumor with the radiation…it just starts the process of releasing some more of that dead stuff.”

He explained that Ft3L is a human growth factor that simulates dendritic cells, “which I always say are the professor cells of the immune system,” as they tell the body “what’s good and what’s bad.”

The poly-ICLC is “basically like a fake virus,” Dr. Marron said, as it “turns on those immune cells that have taken up the tumor antigen in the neighborhood” of the tumor, so they “teach the immune system that there is something bad”.

Finally, the pembrolizumab is there to “take the foot off the brake of the immune system” and “grease the wheels a bit more,” he added, even though it does not work in all patients, or in all tumor types, including indolent NHL.

The trial was planned in two phases. In the first part, six patients were enrolled to assess the safety of the approach; the phase 2 stage of the trial followed a Simon’s Two-Stage design, with the aim of recruiting seven patients of each tumor type, followed by a further 12 patients if they showed a response.

The current interim analysis that was presented at the SITC meeting focused on the first 10 patients in the phase 2 part, who were enrolled between April 2019 and July 2022. This included six patients with metastatic breast cancer, three with indolent NHL, and one with HNSCC, all of whom completed their first disease response assessment.

All patients experienced treatment-related adverse events, largely comprising low-grade injection site reactions and flu-like symptoms linked to the poly-ICLC injections.

One patient experienced grade 3 pembrolizumab-related colitis, while another had self-resolving grade 3 fever following poly-ICLC injection.

The study was sponsored by Icahn School of Medicine at Mount Sinai and conducted in collaboration with Merck Sharp & Dohme LLC and Celldex Therapeutics. No relevant financial relationships were reported.

A version of this article first appeared on Medscape.com.

A woman who had breast cancer for 23 years, and who had gone through 12 different lines of therapy, has shown a dramatic response to a novel cancer vaccine in a clinical trial.

A recent 6-month follow-up showed no evidence of new or recurrent disease, and scans showed regression of a distant bulky left adrenal metastasis, as well as at other sites.

A small site of residual hypermetabolism remains in the sternum, but this is thought to be related to scar tissue.

The patient, Stephanie Gangi, told Medscape Medical News that, before she entered into the trial for the novel cancer vaccine, she was “mentally and physically exhausted.” She had benefited from being diagnosed with hormone-positive breast cancer just as its treatment was evolving and progressing, which meant that, every time a treatment failed, “there was the next thing to try, which was great and kept me going.”

“But I will admit that, by age 66, and more than 20 years of cancer treatments, I was exhausted.”

Ms. Gangi, a New York City-based poet, essayist, and fiction writer, said she was “cautiously optimistic” about the cancer vaccine, but the “overriding thought was I wanted to avoid chemotherapy.”

“I was not really signing on for great outcomes, I was signing on for something that might keep chemo at bay. The biggest impact so far for me has been that, for the first time in more than a decade, I am not on any medication. That’s really amazing…and that means no side effects,” she said.

Ms. Gangi stopped the vaccine treatment this past July, and just over 3 months later, she is still “wrapping her head around” the fact that her cancer has regressed. “I’ve had breast cancer a long time,” she said, “and you can’t just snap your fingers and be fine.”

Although the two scans that she has had since the trial ended have been “astonishing,” she underlined that this is not about a ‘cure,’ but rather “clearing tumors for the first time in many years.”

“Cancer is sneaky and sinister, and it figures out how to circumvent all kinds of treatments,” she said, adding nevertheless that she is “happy and hopeful, and my family is thrilled, of course.”

Ms. Gangi was classed as having had a partial response to the cancer vaccine, one of a few in a small phase 1/2 trial at the Icahn School of Medicine at Mount Sinai in New York. One other patient also had a partial response, and one patient had a complete response.

However, six patients have progressive disease, and one has stable disease.  

These results come from an interim analysis of 10 patients from the trial, and show a 30% response rate. They were presented at the recent annual meeting of the Society for Immunotherapy of Cancer.

The vaccine that was being tested combines local low-dose radiation, intramural Flt3L, which stimulates dendritic cells, and intravenous poly-ICLC, an immune stimulating factor, with the PD-1 inhibitor pembrolizumab (Keytruda).

The result is that, instead of making a vaccine in a laboratory and administering it, “we’re actually formulating it within the body,” lead author Thomas Marron, MD, PhD, professor of medicine (hematology and medical oncology) at Mount Sinai, said in an interview.

“What people don’t realize,” he said, is that bulky tumor sites contain “a lot of dead tumor, because they grow so fast and in a haphazard way.” This means that the immune system can be recruited to recognize the dead tumor and “gobble up the dead stuff that’s already there,” he added.

The hope is that the immune system will then kill not only “the tumor you are injecting into, but also tumors elsewhere in the body,” Dr. Marron said. “So you’re basically using your body’s own immune system and on and off switches to vaccinate the patient against their cancer.”

Another patient in the trial who had a complete response to the vaccine was William Morrison, with non-Hodgkin lymphoma (NHL).

Mr. Morrison was diagnosed in 2017, at which time he was enrolled onto a phase 1 trial of an earlier version of this novel vaccine treatment regimen. “Basically, they didn’t get the results they were hoping for, and I still had the lymphoma,” he said. In 2018, his indolent follicular lymphoma transformed into an aggressive diffuse large B-cell lymphoma, for which Mr. Morrison was given six cycles of chemotherapy. This put him into remission and cleared his lymphoma.

“But the remission lasted for maybe a little over a year,” he said.

The cancer came back, and at that point he was given the opportunity to enroll in the Mount Sinai trial. At the end of the treatment, “everything was clear.”

“I’ve been for PET scans every 6 months, and I just had a scan done the other week, and everything has been fine…I’ve been pretty excited. I was pretty lucky.”

“This recent one really has worked wonders,” he said, “When they gave me the good news the other day. I felt like a big weight had been lifted.”

Mr. Morrison also said that he did not experience any serious adverse events while being treated with the vaccine. “Other than a few minor things, I tolerated it pretty well,” he said.

In contrast, Ms. Gangi said she experienced “intense” flu-like symptoms that started in the first few days after the treatment and lasted for a couple of days.
 

Need to improve response rate

The current trial achieved responses in 30% of patients, which “is great, [but] we want to be at 100%,” said Dr. Marron.

“What we’re doing in the laboratory right now is using this as an opportunity to study what it is that’s special about those three people who responded and what’s not happening in the other seven people, and we have some initial data that we’re analyzing,” he said.

“We are seeing that the patients who responded have a much more robust response to the Ft3L in particular…and that could suggest that maybe we need a better Ft3L, or we could think about other ways to potentially manipulate this vaccine.

“Most of the patients who are referred to me are people who have run out of options…and that usually means they’ve had many different types of chemotherapy,” Dr. Marron commented. For example, Ms. Gangi had already been through 12 different chemotherapy regimens.

Chemotherapy suppresses the immune system, but it’s not only that — also having an effect are all the other treatments aimed at reducing nausea and allergic reactions to the anti-cancer therapy, Dr. Marron explained.

“By the time that I see a patient,” Dr. Marron said, “oftentimes their immune system is not optimal. So another way in which we would hope to see better responses is by moving this vaccine earlier in the treatment paradigm, and administering it to patients as their first or second treatment.”

Senior author Joshua Brody, MD, director of the Lymphoma Immunotherapy Program at Mount Sinai’s Tisch Cancer Institute, added that it “might be easy” to incorporate the vaccine into earlier lines of therapy.

He said in an interview that both immunotherapy and radiation therapy are “standard” treatments, and the key is “adding multiple ingredients together that don’t have cumulative toxicity.”

“You can’t just chemo one plus chemo two, because they have some of the same toxicities, but the delightful thing here is this therapy had been quite safe.

“So in theory it would be fairly easy to incorporate this into earlier lines of therapy, once we can get a bit more proof of principle,” Dr. Brody said.

Approached for comment, Ann W. Silk, MD, said that the results are “particularly impressive because we know anti-PD-1 plus radiation therapy does not work in hormone-positive breast cancer or lymphoma.”

Dr. Silk, an oncologist at the Dana-Farber Cancer Institute and assistant professor of medicine at Harvard Medical School in Boston, said in an interview that one advantage of this vaccine is that it “is not restricted to a certain number of antigens and does not rely on an algorithm.”

“I would love to see more data in hormone-positive metastatic breast cancer patients,” she added. “I would use this approach after the hormonal treatments stop working, but before chemotherapy.”

Dr. Silk also said that the safety profile “looks quite good, and I imagine this approach would result in a much better quality of life for patients as compared to chemotherapy.”
 

Details of the trial and results

The Mount Sinai researchers had previously developed a personalized genomic cancer vaccine, PGV-001, which showed promise in a phase 1 trial in 13 patients with solid tumors or multiple myeloma and a high risk of recurrence after surgery or autologous stem cell transplant.

Next, they worked to develop the concept further to turn the tumor into its own vaccine, which involved inducing anti-tumor responses in indolent NHL, which typically responds poorly to checkpoint blockade, by combining Ft3L, low-dose irradiation, and poly-ICLC.

The next phase 1 trial showed that this approach was feasible, but preclinical modeling suggested that the addition of PD-1 blockade could improve the cure rates. The researchers therefore conducted the current trial, recruiting 10 patients with indolent NHL, metastatic breast cancer, or head and neck squamous cell carcinoma (HNSCC).

Patients were given local radiation therapy on days 1 and 2, and intramural Ft3L to the same tumor on day 9, followed by eight intravenous injections of poly-ICLC over 6 weeks. On day 23, they received their first of eight doses of pembrolizumab.

Dr. Marron explained that the radiotherapy increases the amount of dead material for the immune system to work on by “killing some of the tumor cells,” adding: “We’re not trying to kill the whole tumor with the radiation…it just starts the process of releasing some more of that dead stuff.”

He explained that Ft3L is a human growth factor that simulates dendritic cells, “which I always say are the professor cells of the immune system,” as they tell the body “what’s good and what’s bad.”

The poly-ICLC is “basically like a fake virus,” Dr. Marron said, as it “turns on those immune cells that have taken up the tumor antigen in the neighborhood” of the tumor, so they “teach the immune system that there is something bad”.

Finally, the pembrolizumab is there to “take the foot off the brake of the immune system” and “grease the wheels a bit more,” he added, even though it does not work in all patients, or in all tumor types, including indolent NHL.

The trial was planned in two phases. In the first part, six patients were enrolled to assess the safety of the approach; the phase 2 stage of the trial followed a Simon’s Two-Stage design, with the aim of recruiting seven patients of each tumor type, followed by a further 12 patients if they showed a response.

The current interim analysis that was presented at the SITC meeting focused on the first 10 patients in the phase 2 part, who were enrolled between April 2019 and July 2022. This included six patients with metastatic breast cancer, three with indolent NHL, and one with HNSCC, all of whom completed their first disease response assessment.

All patients experienced treatment-related adverse events, largely comprising low-grade injection site reactions and flu-like symptoms linked to the poly-ICLC injections.

One patient experienced grade 3 pembrolizumab-related colitis, while another had self-resolving grade 3 fever following poly-ICLC injection.

The study was sponsored by Icahn School of Medicine at Mount Sinai and conducted in collaboration with Merck Sharp & Dohme LLC and Celldex Therapeutics. No relevant financial relationships were reported.

A version of this article first appeared on Medscape.com.

Artificial intelligence (AI) is impacting many aspects of health care, and radiation oncology is no exception. It has the potential to cut costs and streamline work flows ranging from image analysis to treatment plan formulation, but its specific place in clinical practice is still being debated.

In a session at the annual meeting of the American Society for Radiation Oncology, researchers discussed some of the ways that AI is or will soon be relevant to the clinic. The general consensus was that AI provides a useful supplement but is no threat to replace the role of radiation oncologists.

In his talk, Sanjay Aneja, MD focused on practical applications of AI that are in the clinic or close to being ready. One example is image classification. “There has been recent evidence that suggests in a variety of different kind of scenarios, deep-learning models can be very good at image classification in automated ways,” said Dr. Aneja, who is a professor of radiology at Yale University, New Haven, Conn. He described one study that used AI to classify 14 different pathologies on chest x-ray images.

Dr. Aneja described the open-source nnU-net tool, which automatically configures itself and segments biomedical images for research or clinical purposes, including therapy planning support, intraoperative support, and tumor growth monitoring. The researchers who developed it also created a “recipe” to systematize configuration of nnU-net, making it useful as an out-of-the-box tool for image segmentation.

He predicted that AI will improve radiology oncology by assisting in the determination of disease extent, including microscopic areas of disease. It could also help plan treatment volume and monitor treatment response. “I think that these are the types of things that will be moving toward the clinic in the future; very specific applications and models trained on very specific scenarios that will help us answer a very important clinical question,” Dr. Aneja said.

He expects AI to contribute to auto-segmenting and clinical contouring, “but I will caution everyone that these algorithms have not been proven to be better than physician contours. They very frequently fail in the specific use cases when anatomy is distorted by, I don’t know, say a tumor. And so a lot of times, we don’t actually have the ability to just make it an automated process. I think it’ll be something that physicians will use to help them but not necessarily replace their contouring ability,” Dr. Aneja said.

Another, potentially more useful application, is in adaptive radiation planning. “I think that AI auto-contouring will be very helpful in establishing contours in a situation in which a physician doing them would not be feasible. We need to have nimble and computationally efficient auto segmentation algorithms that will be able to be easily deployed at the linear accelerator,” he said.
 

AI in pathology and treatment selection

In another talk, Osama Mohamad, MD talked about AI in pathology, and specifically treatment selection. He described research from his group that digitized pathology data from 5,500 patients drawn from five randomized, clinical trials. They used AI on data from four of the clinical trials to identify a prognostic biomarker for distant metastasis, then validated it on data from the remaining clinical trial, which compared radiation versus radiation plus short-term hormone therapy in prostate cancer.

The results suggested that most patients should receive hormone therapy, but the AI suggested a more nuanced answer. “Patients who had AI biomarker negative do not see any benefit from adding 4 months of hormone therapy ... whereas patients who have biomarker positive have significant difference and improvement in distant metastasis at 10 years and 15 years. This means that we can save a significant proportion of patients from getting [androgen deprivation therapy], which is hormonal therapy and has very well-known side effects, because they simply they will not benefit,” said Dr. Mohamad, who is an assistant professor of radiation oncology at University of California, San Francisco.

That study relied on the ArteraAI prostate cancer test, which is available through a Clinical Laboratory Improvement Amendment–certified laboratory in Florida.

Another example of AI used to plan treatment is On-line Real-time Benchmarking Informatics Technology for Radiotherapy (ORBIT-RT), developed at the University of California, San Diego. It focuses on radiotherapy treatment plan quality control, and has two main components: creating clinically validated plan routines and a free radiotherapy plan quality control system.

No matter how impressive the technical advances may be, AI contributions won’t impact clinical practice if radiation oncologists, physicians, and patients don’t accept AI. Dr. Aneja’s group surveyed patients about which health field they would feel more comfortable with AI having an important role. Most said they were extremely uncomfortable when it came to cancer. “Now, does that mean that we can’t use AI in oncology? No, I think it just means that we have to be a little bit more nuanced in our approach and how we develop AI solutions for cancer patients,” Dr. Aneja said.

Physicians also show reluctance, according to Alejandro Berlin, MD, who is an affiliate scientist at Princess Margaret Cancer Centre in Toronto. He discussed some research looking at physician acceptance of machine learning. His group looked at physician acceptance of treatment plans for prostate cancer that were generated by physicians and in parallel by machine learning. In a theoretical phase, physicians generally agreed that the machine learning plans were better, but when it came to a phase of the study in which physicians chose which plan to implement in a real patient, the acceptance of machine learning-generated plans dropped by 20%.

This tendency to trust humans over machines is what Dr. Berlin called “automation bias,” and he called for a more collaborative approach to implement AI. “In some cases, [machine learning] is going to be good and sufficient. And in some cases, you will need the expertise of a human.”

Dr. Aneja, who also moderated the session, expressed a similar sentiment when summing up the day’s talks: “I do feel like it’s a disruptive technology ... but I think there will still be a need for us to have people who are trained in order to evaluate and make sure that these algorithms are working correctly and efficiently.”

Dr. Aneja, Dr. Mohamad, and Dr. Berlin have no relevant financial disclosures.

* This article was updated on Nov. 15, 2022.

Artificial intelligence (AI) is impacting many aspects of health care, and radiation oncology is no exception. It has the potential to cut costs and streamline work flows ranging from image analysis to treatment plan formulation, but its specific place in clinical practice is still being debated.

In a session at the annual meeting of the American Society for Radiation Oncology, researchers discussed some of the ways that AI is or will soon be relevant to the clinic. The general consensus was that AI provides a useful supplement but is no threat to replace the role of radiation oncologists.

In his talk, Sanjay Aneja, MD focused on practical applications of AI that are in the clinic or close to being ready. One example is image classification. “There has been recent evidence that suggests in a variety of different kind of scenarios, deep-learning models can be very good at image classification in automated ways,” said Dr. Aneja, who is a professor of radiology at Yale University, New Haven, Conn. He described one study that used AI to classify 14 different pathologies on chest x-ray images.

Dr. Aneja described the open-source nnU-net tool, which automatically configures itself and segments biomedical images for research or clinical purposes, including therapy planning support, intraoperative support, and tumor growth monitoring. The researchers who developed it also created a “recipe” to systematize configuration of nnU-net, making it useful as an out-of-the-box tool for image segmentation.

He predicted that AI will improve radiology oncology by assisting in the determination of disease extent, including microscopic areas of disease. It could also help plan treatment volume and monitor treatment response. “I think that these are the types of things that will be moving toward the clinic in the future; very specific applications and models trained on very specific scenarios that will help us answer a very important clinical question,” Dr. Aneja said.

He expects AI to contribute to auto-segmenting and clinical contouring, “but I will caution everyone that these algorithms have not been proven to be better than physician contours. They very frequently fail in the specific use cases when anatomy is distorted by, I don’t know, say a tumor. And so a lot of times, we don’t actually have the ability to just make it an automated process. I think it’ll be something that physicians will use to help them but not necessarily replace their contouring ability,” Dr. Aneja said.

Another, potentially more useful application, is in adaptive radiation planning. “I think that AI auto-contouring will be very helpful in establishing contours in a situation in which a physician doing them would not be feasible. We need to have nimble and computationally efficient auto segmentation algorithms that will be able to be easily deployed at the linear accelerator,” he said.
 

AI in pathology and treatment selection

In another talk, Osama Mohamad, MD talked about AI in pathology, and specifically treatment selection. He described research from his group that digitized pathology data from 5,500 patients drawn from five randomized, clinical trials. They used AI on data from four of the clinical trials to identify a prognostic biomarker for distant metastasis, then validated it on data from the remaining clinical trial, which compared radiation versus radiation plus short-term hormone therapy in prostate cancer.

The results suggested that most patients should receive hormone therapy, but the AI suggested a more nuanced answer. “Patients who had AI biomarker negative do not see any benefit from adding 4 months of hormone therapy ... whereas patients who have biomarker positive have significant difference and improvement in distant metastasis at 10 years and 15 years. This means that we can save a significant proportion of patients from getting [androgen deprivation therapy], which is hormonal therapy and has very well-known side effects, because they simply they will not benefit,” said Dr. Mohamad, who is an assistant professor of radiation oncology at University of California, San Francisco.

That study relied on the ArteraAI prostate cancer test, which is available through a Clinical Laboratory Improvement Amendment–certified laboratory in Florida.

Another example of AI used to plan treatment is On-line Real-time Benchmarking Informatics Technology for Radiotherapy (ORBIT-RT), developed at the University of California, San Diego. It focuses on radiotherapy treatment plan quality control, and has two main components: creating clinically validated plan routines and a free radiotherapy plan quality control system.

No matter how impressive the technical advances may be, AI contributions won’t impact clinical practice if radiation oncologists, physicians, and patients don’t accept AI. Dr. Aneja’s group surveyed patients about which health field they would feel more comfortable with AI having an important role. Most said they were extremely uncomfortable when it came to cancer. “Now, does that mean that we can’t use AI in oncology? No, I think it just means that we have to be a little bit more nuanced in our approach and how we develop AI solutions for cancer patients,” Dr. Aneja said.

Physicians also show reluctance, according to Alejandro Berlin, MD, who is an affiliate scientist at Princess Margaret Cancer Centre in Toronto. He discussed some research looking at physician acceptance of machine learning. His group looked at physician acceptance of treatment plans for prostate cancer that were generated by physicians and in parallel by machine learning. In a theoretical phase, physicians generally agreed that the machine learning plans were better, but when it came to a phase of the study in which physicians chose which plan to implement in a real patient, the acceptance of machine learning-generated plans dropped by 20%.

This tendency to trust humans over machines is what Dr. Berlin called “automation bias,” and he called for a more collaborative approach to implement AI. “In some cases, [machine learning] is going to be good and sufficient. And in some cases, you will need the expertise of a human.”

Dr. Aneja, who also moderated the session, expressed a similar sentiment when summing up the day’s talks: “I do feel like it’s a disruptive technology ... but I think there will still be a need for us to have people who are trained in order to evaluate and make sure that these algorithms are working correctly and efficiently.”

Dr. Aneja, Dr. Mohamad, and Dr. Berlin have no relevant financial disclosures.

* This article was updated on Nov. 15, 2022.

Artificial intelligence (AI) is impacting many aspects of health care, and radiation oncology is no exception. It has the potential to cut costs and streamline work flows ranging from image analysis to treatment plan formulation, but its specific place in clinical practice is still being debated.

In a session at the annual meeting of the American Society for Radiation Oncology, researchers discussed some of the ways that AI is or will soon be relevant to the clinic. The general consensus was that AI provides a useful supplement but is no threat to replace the role of radiation oncologists.

In his talk, Sanjay Aneja, MD focused on practical applications of AI that are in the clinic or close to being ready. One example is image classification. “There has been recent evidence that suggests in a variety of different kind of scenarios, deep-learning models can be very good at image classification in automated ways,” said Dr. Aneja, who is a professor of radiology at Yale University, New Haven, Conn. He described one study that used AI to classify 14 different pathologies on chest x-ray images.

Dr. Aneja described the open-source nnU-net tool, which automatically configures itself and segments biomedical images for research or clinical purposes, including therapy planning support, intraoperative support, and tumor growth monitoring. The researchers who developed it also created a “recipe” to systematize configuration of nnU-net, making it useful as an out-of-the-box tool for image segmentation.

He predicted that AI will improve radiology oncology by assisting in the determination of disease extent, including microscopic areas of disease. It could also help plan treatment volume and monitor treatment response. “I think that these are the types of things that will be moving toward the clinic in the future; very specific applications and models trained on very specific scenarios that will help us answer a very important clinical question,” Dr. Aneja said.

He expects AI to contribute to auto-segmenting and clinical contouring, “but I will caution everyone that these algorithms have not been proven to be better than physician contours. They very frequently fail in the specific use cases when anatomy is distorted by, I don’t know, say a tumor. And so a lot of times, we don’t actually have the ability to just make it an automated process. I think it’ll be something that physicians will use to help them but not necessarily replace their contouring ability,” Dr. Aneja said.

Another, potentially more useful application, is in adaptive radiation planning. “I think that AI auto-contouring will be very helpful in establishing contours in a situation in which a physician doing them would not be feasible. We need to have nimble and computationally efficient auto segmentation algorithms that will be able to be easily deployed at the linear accelerator,” he said.
 

AI in pathology and treatment selection

In another talk, Osama Mohamad, MD talked about AI in pathology, and specifically treatment selection. He described research from his group that digitized pathology data from 5,500 patients drawn from five randomized, clinical trials. They used AI on data from four of the clinical trials to identify a prognostic biomarker for distant metastasis, then validated it on data from the remaining clinical trial, which compared radiation versus radiation plus short-term hormone therapy in prostate cancer.

The results suggested that most patients should receive hormone therapy, but the AI suggested a more nuanced answer. “Patients who had AI biomarker negative do not see any benefit from adding 4 months of hormone therapy ... whereas patients who have biomarker positive have significant difference and improvement in distant metastasis at 10 years and 15 years. This means that we can save a significant proportion of patients from getting [androgen deprivation therapy], which is hormonal therapy and has very well-known side effects, because they simply they will not benefit,” said Dr. Mohamad, who is an assistant professor of radiation oncology at University of California, San Francisco.

That study relied on the ArteraAI prostate cancer test, which is available through a Clinical Laboratory Improvement Amendment–certified laboratory in Florida.

Another example of AI used to plan treatment is On-line Real-time Benchmarking Informatics Technology for Radiotherapy (ORBIT-RT), developed at the University of California, San Diego. It focuses on radiotherapy treatment plan quality control, and has two main components: creating clinically validated plan routines and a free radiotherapy plan quality control system.

No matter how impressive the technical advances may be, AI contributions won’t impact clinical practice if radiation oncologists, physicians, and patients don’t accept AI. Dr. Aneja’s group surveyed patients about which health field they would feel more comfortable with AI having an important role. Most said they were extremely uncomfortable when it came to cancer. “Now, does that mean that we can’t use AI in oncology? No, I think it just means that we have to be a little bit more nuanced in our approach and how we develop AI solutions for cancer patients,” Dr. Aneja said.

Physicians also show reluctance, according to Alejandro Berlin, MD, who is an affiliate scientist at Princess Margaret Cancer Centre in Toronto. He discussed some research looking at physician acceptance of machine learning. His group looked at physician acceptance of treatment plans for prostate cancer that were generated by physicians and in parallel by machine learning. In a theoretical phase, physicians generally agreed that the machine learning plans were better, but when it came to a phase of the study in which physicians chose which plan to implement in a real patient, the acceptance of machine learning-generated plans dropped by 20%.

This tendency to trust humans over machines is what Dr. Berlin called “automation bias,” and he called for a more collaborative approach to implement AI. “In some cases, [machine learning] is going to be good and sufficient. And in some cases, you will need the expertise of a human.”

Dr. Aneja, who also moderated the session, expressed a similar sentiment when summing up the day’s talks: “I do feel like it’s a disruptive technology ... but I think there will still be a need for us to have people who are trained in order to evaluate and make sure that these algorithms are working correctly and efficiently.”

Dr. Aneja, Dr. Mohamad, and Dr. Berlin have no relevant financial disclosures.

* This article was updated on Nov. 15, 2022.

A third dose of coronavirus booster vaccine is effective in reducing death and hospitalization among people with cancer, though this population still suffers higher risks than those of the general population, according to a new large-scale observational study out of the United Kingdom.

People living with lymphoma and those who underwent recent systemic anti-cancer treatment or radiotherapy are at the highest risk, according to study author Lennard Y.W. Lee, PhD. “Our study is the largest evaluation of a coronavirus third dose vaccine booster effectiveness in people living with cancer in the world. For the first time we have quantified the benefits of boosters for COVID-19 in cancer patients,” said Dr. Lee, UK COVID Cancer program lead and a medical oncologist at the University of Oxford, England.

The research was published in the November issue of the European Journal of Cancer.

Despite the encouraging numbers, those with cancer continue to have a more than threefold increased risk of both hospitalization and death from coronavirus compared to the general population. “More needs to be done to reduce this excess risk, like prophylactic antibody therapies,” Dr. Lee said.

Third dose efficacy was lower among cancer patients who had been diagnosed within the past 12 months, as well as those with lymphoma, and those who had undergone systemic anti-cancer therapy or radiotherapy within the past 12 months.

The increased vulnerability among individuals with cancer is likely due to compromised immune systems. “Patients with cancer often have impaired B and T cell function and this study provides the largest global clinical study showing the definitive meaningful clinical impact of this,” Dr. Lee said. The greater risk among those with lymphoma likely traces to aberrant white cells or immunosuppressant regimens, he said.

“Vaccination probably should be used in combination with new forms of prevention and in Europe the strategy of using prophylactic antibodies is going to provide additional levels of protection,” Dr. Lee said.

Overall, the study reveals the challenges that cancer patients face in a pandemic that remains a critical health concern, one that can seriously affect quality of life. “Many are still shielding, unable to see family or hug loved ones. Furthermore, looking beyond the direct health risks, there is also the mental health impact. Shielding for nearly 3 years is very difficult. It is important to realize that behind this large-scale study, which is the biggest in the world, there are real people. The pandemic still goes on for them as they remain at higher risk from COVID-19 and we must be aware of the impact on them,” Dr. Lee said.

The study included data from the United Kingdom’s third dose booster vaccine program, representing 361,098 individuals who participated from December 2020 through December 2021. It also include results from all coronavirus tests conducted in the United Kingdom during that period. Among the participants, 97.8% got the Pfizer-BioNTech vaccine as a booster, while 1.5% received the Moderna vaccine. Overall, 8,371,139 individuals received a third dose booster, including 230,666 living with cancer. The researchers used a test-negative case-controlled analysis to estimate vaccine efficacy.

The booster shot had a 59.1% efficacy against breakthrough infections, 62.8% efficacy against symptomatic infections, 80.5% efficacy versus coronavirus hospitalization, and 94.5% efficacy against coronavirus death. Patients with solid tumors benefited from higher efficacy versus breakthrough infections 66.0% versus 53.2%) and symptomatic infections (69.6% versus 56.0%).

Patients with lymphoma experienced just a 10.5% efficacy of the primary dose vaccine versus breakthrough infections and 13.6% versus symptomatic infections, and this did not improve with a third dose. The benefit was greater for hospitalization (23.2%) and death (80.1%).

Despite the additional protection of a third dose, patients with cancer had a higher risk than the population control for coronavirus hospitalization (odds ratio, 3.38; P < .000001) and death (odds ratio, 3.01; P < .000001).

Dr. Lee has no relevant financial disclosures.

A third dose of coronavirus booster vaccine is effective in reducing death and hospitalization among people with cancer, though this population still suffers higher risks than those of the general population, according to a new large-scale observational study out of the United Kingdom.

People living with lymphoma and those who underwent recent systemic anti-cancer treatment or radiotherapy are at the highest risk, according to study author Lennard Y.W. Lee, PhD. “Our study is the largest evaluation of a coronavirus third dose vaccine booster effectiveness in people living with cancer in the world. For the first time we have quantified the benefits of boosters for COVID-19 in cancer patients,” said Dr. Lee, UK COVID Cancer program lead and a medical oncologist at the University of Oxford, England.

The research was published in the November issue of the European Journal of Cancer.

Despite the encouraging numbers, those with cancer continue to have a more than threefold increased risk of both hospitalization and death from coronavirus compared to the general population. “More needs to be done to reduce this excess risk, like prophylactic antibody therapies,” Dr. Lee said.

Third dose efficacy was lower among cancer patients who had been diagnosed within the past 12 months, as well as those with lymphoma, and those who had undergone systemic anti-cancer therapy or radiotherapy within the past 12 months.

The increased vulnerability among individuals with cancer is likely due to compromised immune systems. “Patients with cancer often have impaired B and T cell function and this study provides the largest global clinical study showing the definitive meaningful clinical impact of this,” Dr. Lee said. The greater risk among those with lymphoma likely traces to aberrant white cells or immunosuppressant regimens, he said.

“Vaccination probably should be used in combination with new forms of prevention and in Europe the strategy of using prophylactic antibodies is going to provide additional levels of protection,” Dr. Lee said.

Overall, the study reveals the challenges that cancer patients face in a pandemic that remains a critical health concern, one that can seriously affect quality of life. “Many are still shielding, unable to see family or hug loved ones. Furthermore, looking beyond the direct health risks, there is also the mental health impact. Shielding for nearly 3 years is very difficult. It is important to realize that behind this large-scale study, which is the biggest in the world, there are real people. The pandemic still goes on for them as they remain at higher risk from COVID-19 and we must be aware of the impact on them,” Dr. Lee said.

The study included data from the United Kingdom’s third dose booster vaccine program, representing 361,098 individuals who participated from December 2020 through December 2021. It also include results from all coronavirus tests conducted in the United Kingdom during that period. Among the participants, 97.8% got the Pfizer-BioNTech vaccine as a booster, while 1.5% received the Moderna vaccine. Overall, 8,371,139 individuals received a third dose booster, including 230,666 living with cancer. The researchers used a test-negative case-controlled analysis to estimate vaccine efficacy.

The booster shot had a 59.1% efficacy against breakthrough infections, 62.8% efficacy against symptomatic infections, 80.5% efficacy versus coronavirus hospitalization, and 94.5% efficacy against coronavirus death. Patients with solid tumors benefited from higher efficacy versus breakthrough infections 66.0% versus 53.2%) and symptomatic infections (69.6% versus 56.0%).

Patients with lymphoma experienced just a 10.5% efficacy of the primary dose vaccine versus breakthrough infections and 13.6% versus symptomatic infections, and this did not improve with a third dose. The benefit was greater for hospitalization (23.2%) and death (80.1%).

Despite the additional protection of a third dose, patients with cancer had a higher risk than the population control for coronavirus hospitalization (odds ratio, 3.38; P < .000001) and death (odds ratio, 3.01; P < .000001).

Dr. Lee has no relevant financial disclosures.

A third dose of coronavirus booster vaccine is effective in reducing death and hospitalization among people with cancer, though this population still suffers higher risks than those of the general population, according to a new large-scale observational study out of the United Kingdom.

People living with lymphoma and those who underwent recent systemic anti-cancer treatment or radiotherapy are at the highest risk, according to study author Lennard Y.W. Lee, PhD. “Our study is the largest evaluation of a coronavirus third dose vaccine booster effectiveness in people living with cancer in the world. For the first time we have quantified the benefits of boosters for COVID-19 in cancer patients,” said Dr. Lee, UK COVID Cancer program lead and a medical oncologist at the University of Oxford, England.

The research was published in the November issue of the European Journal of Cancer.

Despite the encouraging numbers, those with cancer continue to have a more than threefold increased risk of both hospitalization and death from coronavirus compared to the general population. “More needs to be done to reduce this excess risk, like prophylactic antibody therapies,” Dr. Lee said.

Third dose efficacy was lower among cancer patients who had been diagnosed within the past 12 months, as well as those with lymphoma, and those who had undergone systemic anti-cancer therapy or radiotherapy within the past 12 months.

The increased vulnerability among individuals with cancer is likely due to compromised immune systems. “Patients with cancer often have impaired B and T cell function and this study provides the largest global clinical study showing the definitive meaningful clinical impact of this,” Dr. Lee said. The greater risk among those with lymphoma likely traces to aberrant white cells or immunosuppressant regimens, he said.

“Vaccination probably should be used in combination with new forms of prevention and in Europe the strategy of using prophylactic antibodies is going to provide additional levels of protection,” Dr. Lee said.

Overall, the study reveals the challenges that cancer patients face in a pandemic that remains a critical health concern, one that can seriously affect quality of life. “Many are still shielding, unable to see family or hug loved ones. Furthermore, looking beyond the direct health risks, there is also the mental health impact. Shielding for nearly 3 years is very difficult. It is important to realize that behind this large-scale study, which is the biggest in the world, there are real people. The pandemic still goes on for them as they remain at higher risk from COVID-19 and we must be aware of the impact on them,” Dr. Lee said.

The study included data from the United Kingdom’s third dose booster vaccine program, representing 361,098 individuals who participated from December 2020 through December 2021. It also include results from all coronavirus tests conducted in the United Kingdom during that period. Among the participants, 97.8% got the Pfizer-BioNTech vaccine as a booster, while 1.5% received the Moderna vaccine. Overall, 8,371,139 individuals received a third dose booster, including 230,666 living with cancer. The researchers used a test-negative case-controlled analysis to estimate vaccine efficacy.

The booster shot had a 59.1% efficacy against breakthrough infections, 62.8% efficacy against symptomatic infections, 80.5% efficacy versus coronavirus hospitalization, and 94.5% efficacy against coronavirus death. Patients with solid tumors benefited from higher efficacy versus breakthrough infections 66.0% versus 53.2%) and symptomatic infections (69.6% versus 56.0%).

Patients with lymphoma experienced just a 10.5% efficacy of the primary dose vaccine versus breakthrough infections and 13.6% versus symptomatic infections, and this did not improve with a third dose. The benefit was greater for hospitalization (23.2%) and death (80.1%).

Despite the additional protection of a third dose, patients with cancer had a higher risk than the population control for coronavirus hospitalization (odds ratio, 3.38; P < .000001) and death (odds ratio, 3.01; P < .000001).

Dr. Lee has no relevant financial disclosures.

A new retrospective study provides more evidence that previous breast cancer diagnoses don’t disrupt the health of mothers and newborns in pregnancy: Women who became pregnant at least 12 months after breast cancer diagnosis weren’t more likely than a control group to have preterm births or suffer maternal/neonatal morbidity – even though they were more likely to undergo cesarean section.

“For patients who are more than 1 year out from the diagnosis of breast cancer, it may be safe and reasonable to consider pregnancy without significantly increased odds of maternal or neonatal complications,” said study lead author Kirsten Jorgensen, MD, a gynecologic oncology fellow at the University of Texas Houston School of Public Health, in an interview. “This study does not suggest there is no risk, but it does place the risk that exists in context with the risk that is associated with any pregnancy.”

The study appears in Obstetrics & Gynecology.

The researchers launched the analysis because “there is relatively little data to help guide patients, their oncologists, and their obstetricians as they navigate the potential for pregnancy after a cancer diagnosis,” Dr. Jorgensen said. “There have been prior studies looking at birth outcomes, but they often include people who become pregnant very shortly after diagnosis, which may skew results.”

Researchers used databases to track 30,021 women in California aged 18-45 who were diagnosed with breast cancer from 2000 to 2012. Of those, only 553 met the study criteria and conceived at least 1 year after a stage I-III breast cancer diagnosis (median age at delivery = 36; 50.6% non-Hispanic White, 23.9% Hispanic, 6.0% Black; 83.2% private insurance).

Study authors compared these women to a matched control group of 1,659 women without breast cancer.

After adjustment for various factors, there was no significant difference between the groups in terms of maternal outcomes – preterm birth at less than 37 weeks of gestation (12.5% in the breast cancer group vs. 10.0% in the control group; odds ratio = 1.29; 95% confidence interval, 0.95-1.74) or preterm birth at less than 32 weeks of gestation (1.3% vs. 1.6%, respectively, OR = 0.77; 95% CI, 0.34-1.79).

Researchers didn’t find a significant difference in neonatal outcomes either – small for gestational age (less than the 5th percentile, 3.1% vs. 5.0%, respectively; OR = 0.60, 95% CI, 0.35-1.03; less than the 10th percentile: 9.4% vs. 10%, respectively; OR = 0.94; 95% CI, 0.68-1.30), or neonatal morbidity (8.7% vs. 7.7%, respectively; OR = 1.15; 95% CI, 0.81-1.62).

“It is possible that breast cancer may have little impact because some breast cancer is treated only with surgery or radiation to the chest,” Dr. Jorgensen said. “These treatments likely do not impact fertility and may not impact a developing pregnancy.”

There were neonatal deaths: one in the breast cancer group and four in the control group. The researchers said the small number of deaths limited their ability to interpret the data.

Researchers found no evidence that treatment with chemotherapy affected outcomes. They did turn up a difference between the groups: those who’d had breast cancer were more likely to undergo cesarean delivery (45.6% in the breast cancer group, and 40.1% in the control group; OR = 1.25; 95% CI 1.03-1.53), However, offspring of women in the cesarean group weren’t more likely to have neonatal morbidity (OR = 1.15; 95% CI 0.81-1.62).

It’s hard to explain the higher rate of cesarean deliveries in the breast cancer group, Dr. Jorgensen said. “Overall, among our study population and the matched controls there was a high rate of cesarean section. It is possible there was bias on the provider side. Perhaps they intervened with cesarean section earlier among those with a history of breast cancer – a type of bias due to knowing the history of the patient. We attempted to match for other comorbidities that impact obstetric outcomes, but it is possible that we did not account for all of them.”

In an interview, Patricia A. Ganz, MD, director of cancer prevention and control research at University of California, Los Angeles, praised the new research.

It’s “a well-conducted study with state-of-the-art analysis and interpretation,” she said. “Based on my experience with patients I have cared for with breast cancer, there were no surprises here. Most have had uncomplicated pregnancies. This should be reassuring for women who wish to have children after treatment for breast cancer and clinicians should support this decision.”

As for the higher rate of cesarean delivery in breast-cancer survivors, she said “there may be a tendency to think of these as ‘high risk’ pregnancies, and C-sections may be selected at a more frequent rate as a result.”

The study was funded by the National Institutes of Health, including grants from the National Cancer Institute and the National Center for Advancing Translational Sciences. Dr. Jorgensen has no disclosures. Other authors disclosed advisory board service (Delfina Care) and payments from the NIH, Guidepoint, the Schlesinger Group, and Johnson & Johnson. Dr. Ganz has no disclosures.

A new retrospective study provides more evidence that previous breast cancer diagnoses don’t disrupt the health of mothers and newborns in pregnancy: Women who became pregnant at least 12 months after breast cancer diagnosis weren’t more likely than a control group to have preterm births or suffer maternal/neonatal morbidity – even though they were more likely to undergo cesarean section.

“For patients who are more than 1 year out from the diagnosis of breast cancer, it may be safe and reasonable to consider pregnancy without significantly increased odds of maternal or neonatal complications,” said study lead author Kirsten Jorgensen, MD, a gynecologic oncology fellow at the University of Texas Houston School of Public Health, in an interview. “This study does not suggest there is no risk, but it does place the risk that exists in context with the risk that is associated with any pregnancy.”

The study appears in Obstetrics & Gynecology.

The researchers launched the analysis because “there is relatively little data to help guide patients, their oncologists, and their obstetricians as they navigate the potential for pregnancy after a cancer diagnosis,” Dr. Jorgensen said. “There have been prior studies looking at birth outcomes, but they often include people who become pregnant very shortly after diagnosis, which may skew results.”

Researchers used databases to track 30,021 women in California aged 18-45 who were diagnosed with breast cancer from 2000 to 2012. Of those, only 553 met the study criteria and conceived at least 1 year after a stage I-III breast cancer diagnosis (median age at delivery = 36; 50.6% non-Hispanic White, 23.9% Hispanic, 6.0% Black; 83.2% private insurance).

Study authors compared these women to a matched control group of 1,659 women without breast cancer.

After adjustment for various factors, there was no significant difference between the groups in terms of maternal outcomes – preterm birth at less than 37 weeks of gestation (12.5% in the breast cancer group vs. 10.0% in the control group; odds ratio = 1.29; 95% confidence interval, 0.95-1.74) or preterm birth at less than 32 weeks of gestation (1.3% vs. 1.6%, respectively, OR = 0.77; 95% CI, 0.34-1.79).

Researchers didn’t find a significant difference in neonatal outcomes either – small for gestational age (less than the 5th percentile, 3.1% vs. 5.0%, respectively; OR = 0.60, 95% CI, 0.35-1.03; less than the 10th percentile: 9.4% vs. 10%, respectively; OR = 0.94; 95% CI, 0.68-1.30), or neonatal morbidity (8.7% vs. 7.7%, respectively; OR = 1.15; 95% CI, 0.81-1.62).

“It is possible that breast cancer may have little impact because some breast cancer is treated only with surgery or radiation to the chest,” Dr. Jorgensen said. “These treatments likely do not impact fertility and may not impact a developing pregnancy.”

There were neonatal deaths: one in the breast cancer group and four in the control group. The researchers said the small number of deaths limited their ability to interpret the data.

Researchers found no evidence that treatment with chemotherapy affected outcomes. They did turn up a difference between the groups: those who’d had breast cancer were more likely to undergo cesarean delivery (45.6% in the breast cancer group, and 40.1% in the control group; OR = 1.25; 95% CI 1.03-1.53), However, offspring of women in the cesarean group weren’t more likely to have neonatal morbidity (OR = 1.15; 95% CI 0.81-1.62).

It’s hard to explain the higher rate of cesarean deliveries in the breast cancer group, Dr. Jorgensen said. “Overall, among our study population and the matched controls there was a high rate of cesarean section. It is possible there was bias on the provider side. Perhaps they intervened with cesarean section earlier among those with a history of breast cancer – a type of bias due to knowing the history of the patient. We attempted to match for other comorbidities that impact obstetric outcomes, but it is possible that we did not account for all of them.”

In an interview, Patricia A. Ganz, MD, director of cancer prevention and control research at University of California, Los Angeles, praised the new research.

It’s “a well-conducted study with state-of-the-art analysis and interpretation,” she said. “Based on my experience with patients I have cared for with breast cancer, there were no surprises here. Most have had uncomplicated pregnancies. This should be reassuring for women who wish to have children after treatment for breast cancer and clinicians should support this decision.”

As for the higher rate of cesarean delivery in breast-cancer survivors, she said “there may be a tendency to think of these as ‘high risk’ pregnancies, and C-sections may be selected at a more frequent rate as a result.”

The study was funded by the National Institutes of Health, including grants from the National Cancer Institute and the National Center for Advancing Translational Sciences. Dr. Jorgensen has no disclosures. Other authors disclosed advisory board service (Delfina Care) and payments from the NIH, Guidepoint, the Schlesinger Group, and Johnson & Johnson. Dr. Ganz has no disclosures.

A new retrospective study provides more evidence that previous breast cancer diagnoses don’t disrupt the health of mothers and newborns in pregnancy: Women who became pregnant at least 12 months after breast cancer diagnosis weren’t more likely than a control group to have preterm births or suffer maternal/neonatal morbidity – even though they were more likely to undergo cesarean section.

“For patients who are more than 1 year out from the diagnosis of breast cancer, it may be safe and reasonable to consider pregnancy without significantly increased odds of maternal or neonatal complications,” said study lead author Kirsten Jorgensen, MD, a gynecologic oncology fellow at the University of Texas Houston School of Public Health, in an interview. “This study does not suggest there is no risk, but it does place the risk that exists in context with the risk that is associated with any pregnancy.”

The study appears in Obstetrics & Gynecology.

The researchers launched the analysis because “there is relatively little data to help guide patients, their oncologists, and their obstetricians as they navigate the potential for pregnancy after a cancer diagnosis,” Dr. Jorgensen said. “There have been prior studies looking at birth outcomes, but they often include people who become pregnant very shortly after diagnosis, which may skew results.”

Researchers used databases to track 30,021 women in California aged 18-45 who were diagnosed with breast cancer from 2000 to 2012. Of those, only 553 met the study criteria and conceived at least 1 year after a stage I-III breast cancer diagnosis (median age at delivery = 36; 50.6% non-Hispanic White, 23.9% Hispanic, 6.0% Black; 83.2% private insurance).

Study authors compared these women to a matched control group of 1,659 women without breast cancer.

After adjustment for various factors, there was no significant difference between the groups in terms of maternal outcomes – preterm birth at less than 37 weeks of gestation (12.5% in the breast cancer group vs. 10.0% in the control group; odds ratio = 1.29; 95% confidence interval, 0.95-1.74) or preterm birth at less than 32 weeks of gestation (1.3% vs. 1.6%, respectively, OR = 0.77; 95% CI, 0.34-1.79).

Researchers didn’t find a significant difference in neonatal outcomes either – small for gestational age (less than the 5th percentile, 3.1% vs. 5.0%, respectively; OR = 0.60, 95% CI, 0.35-1.03; less than the 10th percentile: 9.4% vs. 10%, respectively; OR = 0.94; 95% CI, 0.68-1.30), or neonatal morbidity (8.7% vs. 7.7%, respectively; OR = 1.15; 95% CI, 0.81-1.62).

“It is possible that breast cancer may have little impact because some breast cancer is treated only with surgery or radiation to the chest,” Dr. Jorgensen said. “These treatments likely do not impact fertility and may not impact a developing pregnancy.”

There were neonatal deaths: one in the breast cancer group and four in the control group. The researchers said the small number of deaths limited their ability to interpret the data.

Researchers found no evidence that treatment with chemotherapy affected outcomes. They did turn up a difference between the groups: those who’d had breast cancer were more likely to undergo cesarean delivery (45.6% in the breast cancer group, and 40.1% in the control group; OR = 1.25; 95% CI 1.03-1.53), However, offspring of women in the cesarean group weren’t more likely to have neonatal morbidity (OR = 1.15; 95% CI 0.81-1.62).

It’s hard to explain the higher rate of cesarean deliveries in the breast cancer group, Dr. Jorgensen said. “Overall, among our study population and the matched controls there was a high rate of cesarean section. It is possible there was bias on the provider side. Perhaps they intervened with cesarean section earlier among those with a history of breast cancer – a type of bias due to knowing the history of the patient. We attempted to match for other comorbidities that impact obstetric outcomes, but it is possible that we did not account for all of them.”

In an interview, Patricia A. Ganz, MD, director of cancer prevention and control research at University of California, Los Angeles, praised the new research.

It’s “a well-conducted study with state-of-the-art analysis and interpretation,” she said. “Based on my experience with patients I have cared for with breast cancer, there were no surprises here. Most have had uncomplicated pregnancies. This should be reassuring for women who wish to have children after treatment for breast cancer and clinicians should support this decision.”

As for the higher rate of cesarean delivery in breast-cancer survivors, she said “there may be a tendency to think of these as ‘high risk’ pregnancies, and C-sections may be selected at a more frequent rate as a result.”

The study was funded by the National Institutes of Health, including grants from the National Cancer Institute and the National Center for Advancing Translational Sciences. Dr. Jorgensen has no disclosures. Other authors disclosed advisory board service (Delfina Care) and payments from the NIH, Guidepoint, the Schlesinger Group, and Johnson & Johnson. Dr. Ganz has no disclosures.