Analysis

There is evidence that the evolutionary divergence of HLA class I genotype impacts the efficacy of cancer immunotherapy.

Read more about HLA class-I allele divergence in Nature Medicine and in Molecular Biology And Evolution .

If you use this tool, please cite the following two papers.

Chowell, D., Krishna, C., Pierini, F., Makarov, V., Rizvi, N. A., Kuo, F., … Chan, T. A. (2019). Evolutionary divergence of HLA class I genotype impacts efficacy of cancer immunotherapy. Nature Medicine, 25(11), 1715–1720. https://doi.org/10.1038/s41591-019-0639-4

Pierini, F., & Lenz, T. L. (2018). Divergent Allele Advantage at Human MHC Genes: Signatures of Past and Ongoing Selection. Molecular Biology and Evolution, 35(9), 2145–2158. Retrieved from http://dx.doi.org/10.1093/molbev/msy116

Allele Pair

Select any two HLA class I alleles to calculate the evolutionary divergence between them.


The HED between and is .

Loci A, B, C & Mean

Select HLA class I alleles for loci A, B, and C to calculate the evolutionary divergence between at each locus as well as the mean evolutionary divergence.


The HED between and is .

The HED between and is .

The HED between and is .

The mean HED is .

Batch Processing

Use the panel below to upload a TSV file for batch processing of HED values. The file should match the format below.

Sample	HLAI
Pt01	A3303,A3201,B4501,B4402,C0704,C1601
Pt02	A0201,A3101,B4403,B4501,C1601,C0501
Pt03	A6801,A3101,B1301,B3503,C0403,C0401

The output will match the format below.

Sample	Alleles	HED_A	HED_B	HED_C	Mean_HED
Pt01	A3303,A3201,B4501,B4402,C0704,C1601	6.05525	4.93923	4.96685	5.32044
Pt02	A0201,A3101,B4403,B4501,C1601,C0501	7.51934	5.88950	4.12155	6.76796
Pt03	A6801,A3101,B1301,B3503,C0403,C0401	4.49171	6.74033	2.96133	4.73112


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About

Evolutionary divergence of HLA class I genotype impacts efficacy of cancer immunotherapy

Chowell, D., Krishna, C., Pierini, F. et al. Evolutionary divergence of HLA class I genotype impacts efficacy of cancer immunotherapy. Nat Med 25, 1715–1720 (2019). https://doi.org/10.1038/s41591-019-0639-4


Functional diversity of the highly polymorphic human leukocyte antigen class I (HLA-I) genes underlies successful immunologic control of both infectious disease and cancer. The divergent allele advantage hypothesis dictates that an HLA-I genotype with two alleles with sequences that are more divergent enables presentation of more diverse immunopeptidomes1,2,3. However, the effect of sequence divergence between HLA-I alleles—a quantifiable measure of HLA-I evolution—on the efficacy of immune checkpoint inhibitor (ICI) treatment for cancer remains unknown. In the present study the germline HLA-I evolutionary divergence (HED) of patients with cancer treated with ICIs was determined by quantifying the physiochemical sequence divergence between HLA-I alleles of each patient’s genotype. HED was a strong determinant of survival after treatment with ICIs. Even among patients fully heterozygous at HLA-I, patients with an HED in the upper quartile respond better to ICIs than patients with a low HED. Furthermore, HED strongly impacts the diversity of tumor, viral and self-immunopeptidomes and intratumoral T cell receptor clonality. Similar to tumor mutation burden, HED is a fundamental metric of diversity at the major histocompatibility complex–peptide complex, which dictates ICI efficacy. The data link divergent HLA allele advantage to immunotherapy efficacy and unveil how ICI response relies on the evolved efficiency of HLA-mediated immunity.

Divergent Allele Advantage at Human MHC Genes: Signatures of Past and Ongoing Selection

Federica Pierini, Tobias L Lenz, Divergent Allele Advantage at Human MHC Genes: Signatures of Past and Ongoing Selection, Molecular Biology and Evolution, Volume 35, Issue 9, September 2018, Pages 2145–2158, https://doi.org/10.1093/molbev/msy116


The highly polymorphic genes of the major histocompatibility complex (MHC) play a key role in adaptive immunity. Divergent allele advantage, a mechanism of balancing selection, is proposed to contribute to their exceptional polymorphism. It assumes that MHC genotypes with more divergent alleles allow for broader antigen-presentation to immune effector cells, by that increasing immunocompetence. However, the direct correlation between pairwise sequence divergence and the corresponding repertoire of bound peptides has not been studied systematically across different MHC genes. Here, we investigated this relationship for five key classical human MHC genes (human leukocyte antigen; HLA-A, -B, -C, -DRB1, and -DQB1), using allele-specific computational binding prediction to 118,097 peptides derived from a broad range of human pathogens. For all five human MHC genes, the genetic distance between two alleles of a heterozygous genotype was positively correlated with the total number of peptides bound by these two alleles. In accordance with the major antigen-presentation pathway of MHC class I molecules, HLA-B and HLA-C alleles showed particularly strong correlations for peptides derived from intracellular pathogens. Intriguingly, this bias coincides with distinct protein compositions between intra- and extracellular pathogens, possibly suggesting adaptation of MHC I molecules to present specifically intracellular peptides. Eventually, we observed significant positive correlations between an allele’s average divergence and its population frequency. Overall, our results support the divergent allele advantage as a meaningful quantitative mechanism through which pathogen-mediated selection leads to the evolution of MHC diversity.

Timothy Chan

Dr. Timothy Chan

Center for Immunotherapy and Precision Immuno-Oncology, Cleveland Clinic
Immunogenomics and Precision Oncology Platform, Memorial Sloan Kettering Cancer Center

Tim is the director of the Center for Immunotherapy and Precision Immuno-Oncology at Cleveland Clinic.

As a physician-scientist he focuses on the characterization of genetic determinants underlying response and resistance to cancer therapeutics.


Tobias Lenz

Dr. Tobias L. Lenz

Research Group for Evolutionary Immunogenomics, Max Planck Institute for Evolutionary Biology

Tobias is group leader of the Research Group for Evolutionary Immunogenomics at the Max Planck Institute for Evolutionary Biology.

His research focuses on evolutionary trade-offs in adaptive immunity and genetic variation underlying individual differences in immunocompetence.


Diego Chowell

Dr. Diego Chowell

Immunogenomics and Precision Oncology Platform, Memorial Sloan Kettering Cancer Center

Diego is a Research Scholar in the Timothy Chan lab at MSKCC.

His research interests include cancer immunogenomics, coevolution of cancer and the immune system, risk prediction, and virus dynamics.


Chirag Krishna

Chirag Krishna

Immunogenomics and Precision Oncology Platform, Memorial Sloan Kettering Cancer Center

Chirag is a PhD student in the Timothy Chan lab at MSKCC.

His research interests include immunogenetics and single cell genomics.


Vlad Makarov

Dr. Vlad Makarov

Immunogenomics and Precision Oncology Platform, Memorial Sloan Kettering Cancer Center

Vlad is a Bioinformatics Engineer III in the Timothy Chan lab at MSKCC.

His computational research efforts are focused on cancer genomics and immuno-oncology


Federica Pierini

Dr. Federica Pierini

Research Group for Evolutionary Immunogenomics, Max Planck Institute for Evolutionary Biology
Computer Science Laboratory, CNRS, University of Paris-Saclay

Federica was a PhD student/postdoc with Tobias Lenz at the Max Planck Institute for Evolutionary Biology and is now a postdoc working with Flora Jay (CNRS - University of Paris-Saclay, France), Emilia Huerta-Sanchez (Brown University, USA) and Maria Avila Arcos (UNAM, Mexico) in the Human Frontier Science Program (HFSP).

Her research interests include evolutionary immunogenomics, evolutionary medicine, and human evolution.


Stephen Petrides

Stephen Petrides

Immunogenomics and Precision Oncology Platform, Memorial Sloan Kettering Cancer Center

Stephen is a Software Engineer in the Timothy Chan lab at MSKCC.

His duties include designing and implementing applications and tools for immuno-oncology data analysis.

Releases

Current Version: v1.0.0

This project adheres to Semantic Versioning .

v1.0.0 - 2020-05-18

Added
  • Added three analysis features: Pair, Loci & Mean, and Batch.
  • Added About page with photos of contributors.
  • Added Release page with update changelog.