Method may predict likelihood of GVHD

Scientist at a computer

Credit: Darren Baker

Researchers say that computer modeling of next-generation DNA sequencing data can help us understand the variable outcomes of stem cell transplant and provide a theoretical framework to make transplant a possibility for more patients who don’t have a related donor.

The team analyzed data obtained from whole-exome sequencing of 9 donor-recipient pairs (DRPs) and found it’s possible to predict the risk of graft-vs-host disease (GVHD).

This finding could one day help physicians tailor immunosuppressive therapies to possibly improve transplant outcomes.

The investigators say their data provide evidence that the way a patient’s immune system rebuilds itself following transplant is representative of a dynamical system, a system in which the current state determines what future state will follow.

“The immune system seems chaotic, but that is because there are so many variables involved,” said Amir Toor, MD, of the Virginia Commonwealth University in Richmond.

“We have found evidence of an underlying order. Using next-generation DNA sequencing technology, it may be possible to account for many of the molecular variables that eventually determine how well a donor’s immune system will graft to a patient.”

Dr Toor and his colleagues describe this work in two articles in Frontiers in Immunology.

In the first paper, the researchers recount how they used whole-exome sequencing to examine variation in minor histocompatibility antigens (mHAs) of transplant DRPs.

Using advanced computer-based analysis, the investigators examined potential interactions between mHAs and HLAs and discovered a high level of mHA variation in HLA-matched DRPs that could potentially contribute to GVHD.

These findings may help explain why many HLA-matched recipients experience GVHD, but why some HLA-mismatched recipients do not develop GVHD remains a mystery.

The researchers offer an explanation for this seeming paradox in a companion article. In this paper, they suggest that by inhibiting peptide generation through immunosuppressive therapies in the earliest weeks following stem cell transplant, antigen presentation to donor T cells could be diminished, which reduces the risk of GVHD as the recipients reconstitute their T-cell repertoire.

In previous research, Dr Toor and his colleagues discovered a fractal pattern in the DNA of recipients’ T-cell repertoires. (Fractals are self-similar patterns that repeat themselves at every scale.)

Based on their data, the researchers believe that the presentation of mHAs following transplant helps shape the development of T-cell clonal families.

Thus, inhibiting this antigen presentation through immunosuppressive therapies in patients who have high mHA variation can potentially reduce the risk of GVHD by influencing the development of their T-cell repertoire. This is supported by data from clinical studies showing immune suppression soon after transplant improves outcomes in unrelated DRPs.

The investigators suggest that an equation such as the logistic model of growth, a mathematical formula used to explain population growth, could be employed to predict the evolution of T-cell clones and determine a patient’s future risk of GVHD.

“Currently, we rely on population-based outcomes derived from probabilistic studies to determine the best way to perform stem cell transplants,” Dr Toor said. “The development of accurate mathematical models that account for the key variables influencing transplant outcomes may allow us to treat patients using a systematic and personalized approach.”

“We plan to keep exploring this concept in hopes that we can tailor the transplantation process to each individual in order to improve outcomes and make transplantation an option for more patients.”

Scientist at a computer

Credit: Darren Baker

Researchers say that computer modeling of next-generation DNA sequencing data can help us understand the variable outcomes of stem cell transplant and provide a theoretical framework to make transplant a possibility for more patients who don’t have a related donor.

The team analyzed data obtained from whole-exome sequencing of 9 donor-recipient pairs (DRPs) and found it’s possible to predict the risk of graft-vs-host disease (GVHD).

This finding could one day help physicians tailor immunosuppressive therapies to possibly improve transplant outcomes.

The investigators say their data provide evidence that the way a patient’s immune system rebuilds itself following transplant is representative of a dynamical system, a system in which the current state determines what future state will follow.

“The immune system seems chaotic, but that is because there are so many variables involved,” said Amir Toor, MD, of the Virginia Commonwealth University in Richmond.

“We have found evidence of an underlying order. Using next-generation DNA sequencing technology, it may be possible to account for many of the molecular variables that eventually determine how well a donor’s immune system will graft to a patient.”

Dr Toor and his colleagues describe this work in two articles in Frontiers in Immunology.

In the first paper, the researchers recount how they used whole-exome sequencing to examine variation in minor histocompatibility antigens (mHAs) of transplant DRPs.

Using advanced computer-based analysis, the investigators examined potential interactions between mHAs and HLAs and discovered a high level of mHA variation in HLA-matched DRPs that could potentially contribute to GVHD.

These findings may help explain why many HLA-matched recipients experience GVHD, but why some HLA-mismatched recipients do not develop GVHD remains a mystery.

The researchers offer an explanation for this seeming paradox in a companion article. In this paper, they suggest that by inhibiting peptide generation through immunosuppressive therapies in the earliest weeks following stem cell transplant, antigen presentation to donor T cells could be diminished, which reduces the risk of GVHD as the recipients reconstitute their T-cell repertoire.

In previous research, Dr Toor and his colleagues discovered a fractal pattern in the DNA of recipients’ T-cell repertoires. (Fractals are self-similar patterns that repeat themselves at every scale.)

Based on their data, the researchers believe that the presentation of mHAs following transplant helps shape the development of T-cell clonal families.

Thus, inhibiting this antigen presentation through immunosuppressive therapies in patients who have high mHA variation can potentially reduce the risk of GVHD by influencing the development of their T-cell repertoire. This is supported by data from clinical studies showing immune suppression soon after transplant improves outcomes in unrelated DRPs.

The investigators suggest that an equation such as the logistic model of growth, a mathematical formula used to explain population growth, could be employed to predict the evolution of T-cell clones and determine a patient’s future risk of GVHD.

“Currently, we rely on population-based outcomes derived from probabilistic studies to determine the best way to perform stem cell transplants,” Dr Toor said. “The development of accurate mathematical models that account for the key variables influencing transplant outcomes may allow us to treat patients using a systematic and personalized approach.”

“We plan to keep exploring this concept in hopes that we can tailor the transplantation process to each individual in order to improve outcomes and make transplantation an option for more patients.”

Scientist at a computer

Credit: Darren Baker

Researchers say that computer modeling of next-generation DNA sequencing data can help us understand the variable outcomes of stem cell transplant and provide a theoretical framework to make transplant a possibility for more patients who don’t have a related donor.

The team analyzed data obtained from whole-exome sequencing of 9 donor-recipient pairs (DRPs) and found it’s possible to predict the risk of graft-vs-host disease (GVHD).

This finding could one day help physicians tailor immunosuppressive therapies to possibly improve transplant outcomes.

The investigators say their data provide evidence that the way a patient’s immune system rebuilds itself following transplant is representative of a dynamical system, a system in which the current state determines what future state will follow.

“The immune system seems chaotic, but that is because there are so many variables involved,” said Amir Toor, MD, of the Virginia Commonwealth University in Richmond.

“We have found evidence of an underlying order. Using next-generation DNA sequencing technology, it may be possible to account for many of the molecular variables that eventually determine how well a donor’s immune system will graft to a patient.”

Dr Toor and his colleagues describe this work in two articles in Frontiers in Immunology.

In the first paper, the researchers recount how they used whole-exome sequencing to examine variation in minor histocompatibility antigens (mHAs) of transplant DRPs.

Using advanced computer-based analysis, the investigators examined potential interactions between mHAs and HLAs and discovered a high level of mHA variation in HLA-matched DRPs that could potentially contribute to GVHD.

These findings may help explain why many HLA-matched recipients experience GVHD, but why some HLA-mismatched recipients do not develop GVHD remains a mystery.

The researchers offer an explanation for this seeming paradox in a companion article. In this paper, they suggest that by inhibiting peptide generation through immunosuppressive therapies in the earliest weeks following stem cell transplant, antigen presentation to donor T cells could be diminished, which reduces the risk of GVHD as the recipients reconstitute their T-cell repertoire.

In previous research, Dr Toor and his colleagues discovered a fractal pattern in the DNA of recipients’ T-cell repertoires. (Fractals are self-similar patterns that repeat themselves at every scale.)

Based on their data, the researchers believe that the presentation of mHAs following transplant helps shape the development of T-cell clonal families.

Thus, inhibiting this antigen presentation through immunosuppressive therapies in patients who have high mHA variation can potentially reduce the risk of GVHD by influencing the development of their T-cell repertoire. This is supported by data from clinical studies showing immune suppression soon after transplant improves outcomes in unrelated DRPs.

The investigators suggest that an equation such as the logistic model of growth, a mathematical formula used to explain population growth, could be employed to predict the evolution of T-cell clones and determine a patient’s future risk of GVHD.

“Currently, we rely on population-based outcomes derived from probabilistic studies to determine the best way to perform stem cell transplants,” Dr Toor said. “The development of accurate mathematical models that account for the key variables influencing transplant outcomes may allow us to treat patients using a systematic and personalized approach.”

“We plan to keep exploring this concept in hopes that we can tailor the transplantation process to each individual in order to improve outcomes and make transplantation an option for more patients.”