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Integrated germline and somatic features reveal divergent immune pathways driving ICB response.

ABSTRACT: Immune Checkpoint Blockade (ICB) has revolutionized cancer treatment, however mechanisms determining patient response remain poorly understood. Here we used machine learning to predict ICB response from germline and somatic biomarkers and interpreted the learned model to uncover putative mechanisms driving superior outcomes. Patients with higher T follicular helper infiltrates were robust to defects in the class-I Major Histocompatibility Complex (MHC-I). Further investigation uncovered different ICB responses in MHC-I versus MHC-II neoantigen reliant tumors across patients. Despite similar response rates, MHC-II reliant responses were associated with significantly longer durable clinical benefit (Discovery: Median OS=63.6 vs. 34.5 months P=0.0074; Validation: Median OS=37.5 vs. 33.1 months, P=0.040). Characteristics of the tumor immune microenvironment reflected MHC neoantigen reliance, and analysis of immune checkpoints revealed LAG3 as a potential target in MHC-II but not MHC-I reliant responses. This study highlights the value of interpretable machine learning models in elucidating the biological basis of therapy responses.

SUBMITTER: Sears T

PROVIDER: S-EPMC10827124 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature

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Integrated germline and somatic features reveal divergent immune pathways driving ICB response.

Sears Timothy T Pagadala Meghana M Castro Andrea A Lee Ko-Han KH Kong JungHo J Tanaka Kairi K Lippman Scott S Zanetti Maurizio M Carter Hannah H

bioRxiv : the preprint server for biology 20240115

Immune Checkpoint Blockade (ICB) has revolutionized cancer treatment, however mechanisms determining patient response remain poorly understood. Here we used machine learning to predict ICB response from germline and somatic biomarkers and interpreted the learned model to uncover putative mechanisms driving superior outcomes. Patients with higher T follicular helper infiltrates were robust to defects in the class-I Major Histocompatibility Complex (MHC-I). Further investigation uncovered differen ...[more]

PMID: 38293085

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Project description:While a number of genes have been implicated in melanoma susceptibility, the role of protein-coding variation in melanoma development and progression remains underexplored. To better characterize the role of germline coding variation in melanoma, we conducted a whole-exome case-control and somatic-germline interaction study involving 322 skin cutaneous melanoma cases from The Cancer Genome Atlas and 3607 controls of European ancestry. We controlled for cross-platform technological stratification using XPAT and conducted gene-based association tests using VAAST 2. Four established melanoma susceptibility genes achieved nominal statistical significance, MC1R (p = .0014), MITF (p = .0165) BRCA2 (p = .0206), and MTAP (p = .0393). We also observed a suggestive association for FANCA (p = .002), a gene previously implicated in melanoma survival. The association signal for BRCA2 was driven primarily by likely gene disrupting (LGD) variants, with an Odds Ratio (OR) of 5.62 (95% Confidence Interval (CI) 1.03-30.1). In contrast, the association signals for MC1R and MITF were driven primarily by predicted pathogenic missense variants, with estimated ORs of 1.4 to 3.0 for MC1R and 4.1 for MITF. MTAP exhibited an excess of both LGD and predicted damaging missense variants among cases, with ORs of 5.62 and 3.72, respectively, although neither category was significant. For individuals with known or predicted damaging variants, age of disease onset was significantly lower for two of the four genes, MC1R (p = .005) and MTAP (p = .035). In an analysis of germline carrier status and overlapping copy number alterations, we observed no evidence to support a two-hit model of carcinogenesis in any of the four genes. Although MC1R carriers were represented proportionally among the four molecular tumor subtypes, these individuals accounted for 69% of ultraviolet (UV) radiation mutational signatures among triple-wild type tumors (p = .040), highlighting the increased sensitivity to UV exposure among individuals with loss-of-function variants in MC1R.

Project description:BackgroundVarious approaches to calling single-nucleotide variants (SNVs) or insertion-or-deletion (indel) mutations have been developed based on next-generation sequencing (NGS). However, most of them are dedicated to a particular type of mutation, e.g. germline SNVs in normal cells, somatic SNVs in cancer/tumor cells, or indels only. In the literature, efficient and integrated callers for both germline and somatic SNVs/indels have not yet been extensively investigated.ResultsWe present SNVSniffer, an efficient and integrated caller identifying both germline and somatic SNVs/indels from NGS data. In this algorithm, we propose the use of Bayesian probabilistic models to identify SNVs and investigate a multiple ungapped alignment approach to call indels. For germline variant calling, we model allele counts per site to follow a multinomial conditional distribution. For somatic variant calling, we rely on paired tumor-normal pairs from identical individuals and introduce a hybrid subtraction and joint sample analysis approach by modeling tumor-normal allele counts per site to follow a joint multinomial conditional distribution. A comprehensive performance evaluation has been conducted using a diversity of variant calling benchmarks. For germline variant calling, SNVSniffer demonstrates highly competitive accuracy with superior speed in comparison with the state-of-the-art FaSD, GATK and SAMtools. For somatic variant calling, our algorithm achieves comparable or even better accuracy, at fast speed, than the leading VarScan2, SomaticSniper, JointSNVMix2 and MuTect.ConclusionsSNVSniffers demonstrates the feasibility to develop integrated solutions to fast and efficient identification of germline and somatic variants. Nonetheless, accurate discovery of genetic variations is critical yet challenging, and still requires substantially more research efforts being devoted. SNVSniffer and synthetic samples are publicly available at http://snvsniffer.sourceforge.net .

Dataset Information

Integrated germline and somatic features reveal divergent immune pathways driving ICB response.

Publications

Integrated germline and somatic features reveal divergent immune pathways driving ICB response.

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