Biology subjects

Schaffer, A. A.

Publications and source records attributed to Schaffer, A. A..

At least 19 recordsLinked to original sources

bioRxiv2026

Qombucha: Reconstructing unobserved progenitor methylation profiles reveals distinct developmental programs in glioblastoma

Glioblastoma (GBM) is a highly aggressive brain cancer characterized by substantial intratumoral heterogeneity. Previous research demonstrates that GBM may have complex cell origins. To elucidate the interplay between brain development and GBM progression, we developed Qombucha (Quadratic prOgraMming Based tUmor deConvolution with cell HierArchy), a computational framework that uses DNA methylation data to infer tumor cell-type composition and profiles of unobserved progenitor cells. Unprecedentedly, Qombucha incorporates a developmental cell hierarchy that models mature brain cell types and their progenitors. Applied to a large TCGA GBM dataset spanning the RTK I, RTK II, and MES TYP subtypes, Qombucha identifies a distinct cell type composition profile for each subtype and recapitulates known biological patterns, including elevated microglia infiltration in MES TYP tumors. It also identifies subtype-specific developmental programs and shows that higher progenitor-cell abundance is associated with poorer survival. Qombucha-imputed cell fractions map methylation profiles of tumor samples to a compact, 11-dimensional latent space; in an independent NCI GBM cohort, this compact representation improves subtype clustering and enables accurate subtype classification, achieving performance comparable to state-of-the-art models based on full methylation profiles with much higher dimensionality. These results suggest that tumor cellular composition captures the core biological axes along which GBM subtypes diverge.

bioinformatics↗

bioRxiv2026

Proteomic Profiling Identifies CLDN3 as a Tumor-Selective Therapeutic Target in Small Cell Lung Cancer

Small cell lung cancer (SCLC) remains a highly lethal disease with limited targetable surface antigens beyond delta-like ligand 3 (DLL3). We sought to systematically identify and validate tumor-selective cell-surface targets in relapsed SCLC. To this end, we developed an integrated proteogenomic pipeline combining single-cell RNA sequencing, combinatorial optimization, mass spectrometry-based proteomics, and immunohistochemistry to systematically map the SCLC surfaceome of 49 tumors across 25 patients with relapsed SCLC. Using this approach we found claudin-3 (CLDN3) to be a consistently expressed and tumor-selective antigen, with broader coverage than DLL3, which is the current clinical benchmark. CLDN3 was highly expressed across various treatment states, while maintaining low expression in most nonmalignant tissues. Functional validation using a novel CLDN3-specific monoclonal antibody (ABN501) demonstrated NK cell-mediated cytotoxicity in vitro and tumor regression in vivo, as well as a favorable safety profile. These findings support clinical development of CLDN3-directed therapies and demonstrate the utility of integrated proteogenomic approaches for antigen discovery in solid tumors.

cancer biology↗

bioRxiv2026

Graph-based pangenome provides insights into the adaptive evolution of Cucurbita pepo

Understanding how crops respond to environmental variation is crucial for biodiversity conservation and food security. Cucurbita pepo (pumpkin, squash, gourd) is one of the first domesticated crop species and exhibits remarkable phenotypic and ecological diversity, making it a powerful system for investigating the genomic basis of adaptation. Here, we constructed a graph-based C. pepo pangenome using nine chromosome-level assemblies and identified 229,431 high-confidence structural variants (SVs) that were genotyped across 206 wild and cultivated accessions. Our results demonstrate that C. pepo underwent parallel domestication, with two deeply diverged gene pools independently giving rise to the pepo and ovifera cultivated lineages, followed by expansion into diverse environments that produced strong signatures of differentiation largely mediated by young adaptive alleles. Single-nucleotide polymorphisms (SNPs) and SVs contribute complementary dimensions of environmental responsiveness. Biogeographical modeling predicts continued range contraction of wild relatives and elevated genetic offset in Eastern North American populations under projected future climates. Populations with higher genetic load harbor fewer adaptive variants and exhibit greater predicted maladaptation, indicating that deleterious mutations constrain adaptive potential. These findings highlight how genomic variants, adaptive diversity, and genetic load together shape environmental adaptation and inform conservation and crop improvement.

genomics↗

bioRxiv2026

Epistasis of two classical color genes, B and L-2, synergistically controls carotenoid accumulation in squash

Carotenoid accumulation underlies fruit color and nutritional quality in squash (Cucurbita pepo). One pair of dominant genes, B and L-2, have been long known to interact epistatically, substantially boosting carotenoid accumulation and producing intensely orange-fleshed fruit. However, their molecular identities and regulatory mechanism are unknown. Here, we show that B encodes a truncated H subunit of magnesium chelatase (CpCHLHB) and L-2 encodes a homolog of Arabidopsis Pseudo-Response Regulator 2 (CpAPRR2-A). Significantly, expression of phytoene synthase (CpPSY-A), which encodes the major rate-limiting enzyme in carotenoid biosynthesis, was dramatically upregulated in fruit of B/B L-2/L-2 plants compared with b/b L-2/L-2 or B/B l-2/l-2, showing that the B and L-2 interaction affects CpPSY-A transcription. A similar upregulation was also observed in Arabidopsis gun5 L-2 transgenic plants, where gun5 is a genetic mimic of the C. pepo B gene. The wild-type CpCHLHb physically interacted with CpAPRR2-A, attenuating the CpAPRR2-A-mediated activation of CpPSY-A. In contrast, the truncated CpCHLHB lost its ability to interact with CpAPRR2-A, enabling CpAPRR2-A to activate CpPSY-A and produce intensely orange fruit. These findings uncover the mechanism underlying the epistatic interaction through which B and L-2 act synergistically to boost carotenoid production, offering novel mechanistic insights and key targets for improving crop quality. One-sentence summarySynergistic epistasis between B and L-2 arises from loss of interaction between their encoded proteins, resulting in dramatically upregulating the key rate-limiting enzyme in carotenoid biosynthesis pathway to produce intensely orange-fleshed fruit in squash.

plant biology↗

bioRxiv2025

AI-based supervised treatment response prediction from tumor transcriptomics: A large-scale pan-cancer study

Precision oncology aims to guide treatment decisions using biomarkers. While DNA-based panels are increasingly applied, RNA transcriptomics remain underused due to limited datasets and the absence of robust models. We assembled the largest transcriptomic resource for drug response prediction to date, spanning 91 cohorts, 5,675 patients, nine cancer types, and six frontline therapies: anti-PD-1/PD-L1 immune-checkpoint inhibitors, trastuzumab, bevacizumab, BRAF inhibitors, paclitaxel, and FAC/FEC (Fluorouracil-Adriamycin-Cyclophosphamide/Fluorouracil-Epirubicin-Cyclophosphamide) chemotherapy. We developed EXPRESSO (EXpression-Profile-RESponSe-Optimizer), a supervised machine-learning framework that predicts treatment response from pre-treatment transcriptomes by integrating drug targets and context-specific biomarkers. EXPRESSO achieves mean ROC-AUCs of 0.62-0.73 and median odds ratios of 2.4-4.6 across therapies, outperforming 20 published transcriptomic signatures and other machine learning methods. Prospective validation on 22 independent cohorts confirms that performance generalizes beyond cross-validation. The EXPRESSO signature additionally stratifies progression-free survival in immune checkpoint blockade-treated cohorts, demonstrating prognostic value beyond binary response prediction. Robustness analysis reveals that predictive performance plateaued for some therapies with increasing training cohorts but continued to improve for others. These findings suggest inherent limits of supervised brute-force learning for certain treatments, but additional data and deeper mechanistic modeling may further enhance transcriptomics-based predictors. SIGNIFICANCEEXPRESSO forms the next step in studying the feasibility of harnessing bulk transcriptomic data to inform therapeutic decision-making, advancing the role of transcriptomics from exploratory biomarker discovery to actionable predictive modeling.

cancer biology↗

bioRxiv2025

Single-cell-guided identification of logic-gated antigen combinations for designing effective and safe CAR therapy

Chimeric antigen receptor (CAR) T-cell therapy has revolutionized the treatment of hematological malignancies. However, its application in solid tumors remains limited because single targets are unlikely to suffice due to tumor antigen heterogeneity and off-tumor toxicities. To overcome these obstacles, we developed LogiCAR designer, a computational approach that utilizes single-cell transcriptomics data from patient tumors to systematically identify the cancer-specific antigen circuits with logic gates ("AND," "OR," and "NOT") that target the majority of cancer cells in a tumor while sparing normal cells and tissues as much as possible. LogiCAR designer efficiently scales to higher-order antigen combinations involving up to five genes. Applied to a large-scale dataset encompassing approximately 2 million cells (including > 620k tumor cells) from 342 clinical patient samples across all major breast cancer subtypes, LogiCAR designer identified antigen circuits with enhanced tumor-targeting efficacy and improved safety profiles compared to both previously reported circuits and single-target therapies in clinical trials. However, even these optimized shared circuits still proved insufficient for some patients. We hence systematically studied LogiCAR designers ability to identify highly effective CAR circuits that are individualized to each patient. Remarkably, such personalized CAR circuits provide estimated tumor-targeting efficacy tantamount to complete response in 76% of patients and partial response for all patients. Taken together, this analysis is the first systematic quantification of the efficacy and safety of all possible CAR circuits, showing that: (a) the quality of existing solutions leaves much to be desired; (b) the ability of shared circuits optimized across many patients is moderate, and finally, (c) individually tailored circuits offer significantly higher tumor-targeting efficacies for patients. LogiCAR designer offers a rigorous, data-driven way to facilitate the rational design of safe and effective CAR-based immunotherapies for cancer. Statement of Significance Development of a computational approach that efficiently identifies logic-gated CAR target combinations, called circuits, from single-cell transcriptomics, addressing a critical unmet clinical need. Application to the largest ensemble of breast cancer datasets to date, comprising [~]2 million cells (> 620k tumor cells) from 17 clinical cohorts, to identify CAR circuits predicted to be effective. Comprehensive safety profiling of candidate circuits spanning major tissues at both RNA and protein levels. Logic-gated CAR circuits generated by our pipeline address tumor heterogeneity and achieve efficacy and safety scores that surpass clinical trial and previously computationally identified circuits. Individualized rational CAR design offers a transformative approach to deliver precision-engineered CAR therapies with unprecedented efficacy.

bioinformatics↗

bioRxiv2024

IMMClock reveals immune aging and T cell function at single-cell resolution

The aging of the immune system substantially impacts individual immune responses, yet accurately quantifying immune age remains a complex challenge. Here we developed IMMClock, a novel immune aging clock that uses gene expression data to predict the biological age of individual CD8 T cells, CD4 T cells, and NK cells. The accuracy of IMMClock is first validated across multiple independent datasets, demonstrating its robustness. Second, utilizing the IMMClock, we find that intrinsic cellular aging processes are more strongly altered during immune aging than differentiation processes. Thirdly, our analysis confirms the strong associations between immune aging and established processes such as cellular senescence, exhaustion, and telomere length at the single cell level. Furthermore, immune aging is accelerated under several disease conditions such as type 2 diabetes, heart disease, and cancer. Finally, we apply IMMClock to analyze a perturb-seq gene activation screen of T cell functionality. We find that the post-perturbation immune age of individual T cells is strongly correlated with their pre-perturbation immune age. Furthermore, the immune age at resting state of individual T cells is strongly predictive of their post-stimulation activation state. Overall, IMMClock advances our understanding of immune aging by providing precise, single-cell level age estimations. Its future applications hold promise for identifying interventions that concomitantly rejuvenate and activate T cells, potentially enhancing efforts to counteract age-related immune decline.

bioinformatics↗

bioRxiv2024

Fair molecular feature selection unveils universally tumor lineage-informative methylation sites in colorectal cancer

In the era of precision medicine, performing comparative analysis over diverse patient populations is a fundamental step towards tailoring healthcare interventions. However, the critical aspect of equitably selecting molecular features across multiple patients is often overlooked. To address this challenge, we introduce FALAFL (FAir muLti-sAmple Feature seLection), an algorithmic approach based on combinatorial optimization. FALAFL is designed to bridge the gap between molecular feature selection and algorithmic fairness, ensuring a fair selection of molecular features from all patient samples in a cohort. We have applied FALAFL to the problem of selecting lineage-informative CpG sites within a cohort of colorectal cancer patients subjected to low read coverage single-cell methylation sequencing. Our results demonstrate that FALAFL can rapidly and robustly determine the optimal set of CpG sites, which are each well covered by cells across the vast majority of the patients, while ensuring that in each patient a high proportion of these sites have good read coverage. An analysis of the FALAFL-selected sites reveals that their tumor lineage-informativeness exhibits a strong correlation across a spectrum of diverse patient profiles. Furthermore, these universally lineage-informative sites are highly enriched in the inter CpG island regions. FALAFL integrates equity considerations into the molecular feature selection from single-cell sequencing data obtained from a patient cohort. We hope that it will help propel equitable healthcare data science practices and contribute to the advancement of our understanding of complex diseases.

bioinformatics↗

bioRxiv2024

Chromosome 7 to the rescue: overcoming chromosome 10 loss in gliomas

The co-occurrence of chromosome 10 loss and chromosome 7 gain in gliomas is the most frequent loss-gain co-aneuploidy pair in human cancers, a phenomenon that has been investigated without resolution since the late 1980s. Expanding beyond previous gene-centric studies, we investigate the co-occurrence in a genome-wide manner taking an evolutionary perspective. First, by mining large tumor aneuploidy data, we predict that the more likely order is 10 loss followed by 7 gain. Second, by analyzing extensive genomic and transcriptomic data from both patients and cell lines, we find that this co-occurrence can be explained by functional rescue interactions that are highly enriched on 7, which can possibly compensate for any detrimental consequences arising from the loss of 10. Finally, by analyzing transcriptomic data from normal, non-cancerous, human brain tissues, we provide a plausible reason why this co-occurrence happens preferentially in cancers originating in certain regions of the brain.

cancer biology↗

bioRxiv2023

Temporal genomic analysis of melanoma rejection identifies regulators of tumor immune evasion

Decreased intra-tumor heterogeneity (ITH) correlates with increased patient survival and immunotherapy response. However, even highly homogenous tumors may display variability in their aggressiveness, and how immunologic-factors impinge on their aggressiveness remains understudied. Here we studied the mechanisms responsible for the immune-escape of murine tumors with low ITH. We compared the temporal growth of homogeneous, genetically-similar single-cell clones that are rejected vs. those that are not- rejected after transplantation in-vivo using single-cell RNA sequencing and immunophenotyping. Non- rejected clones showed high infiltration of tumor-associated-macrophages (TAMs), lower T-cell infiltration, and increased T-cell exhaustion compared to rejected clones. Comparative analysis of rejection- associated gene expression programs, combined with in-vivo CRISPR knockout screens of candidate mediators, identified Mif (macrophage migration inhibitory factor) as a regulator of immune rejection. Mif knockout led to smaller tumors and reversed non-rejection-associated immune composition, particularly, leading to the reduction of immunosuppressive macrophage infiltration. Finally, we validated these results in melanoma patient data. Statement of significanceHere, we uncover the association of Mif expression with tumor growth and aggressiveness, specifically in low ITH tumors. These findings could facilitate the development of new strategies to treat patients with homogeneous, high-MIF expressing tumors that are unresponsive to immune checkpoint therapy.

cancer biology↗

bioRxiv2022

Fraction of copy-number alterations significantly predicts survival following immunotherapy in a few cancers

Various studies have shown that high tumor mutation burden (TMB) may predict response to immune checkpoint therapy, at least in some cancer types 1,2. However, identifying patients with low TMB that are still likely to respond to cancer immunotherapy is an important open challenge. Recently, Spurr et al. 3 reported that the tumor aneuploidy score (AS), defined as the fraction of chromosome arms with arm-level copy number alterations in a sample, is predictive of survival following immunotherapy in low-TMB patients across multiple cancers. By re-analyzing the same data set by performing survival analysis in individual cancer types separately, we find that AS only significantly predicts survival in one single cancer indication. We further find that another metric conceptually related to the AS, the fraction of genome encompassed by copy number alterations (FGA), if called with a conventional copy number calling cutoff, has stronger predictive power than the AS proposed in 3, and that this observation holds even if the FGA and AS thresholds for presence/absence of copy number events are set comparably. However, with the current available data, even FGA can predict survival following immunotherapy in only a few cancer indications.

cancer biology↗

bioRxiv2022

Melon pan-genome and multi-parental framework for high-resolution trait dissection

Linking between genotype and phenotype is a fundamental goal in biology and requires robust data for both layers. The prominent increase in plant genome sequencing and comparisons of multiple related individuals, exposed the abundance of structural genomic variation and suggest that a single reference genome cannot represent the complete sequence diversity of a crop species, leading to the expansion of the pan-genome concept. For high-resolution forward genetics, this unprecedented access to genomic variation should be paralleled by availability and phenotypic characterization of genetic diversity, and effective integration between these layers. Here, we describe a multi-parental framework for trait dissection in melon, leveraging a novel pan-genome constructed for this crop. Melon (Cucumis melo L.) is an important crop from the Cucurbitaceae family, which display extensive phenotypic variation available for breeding. A diverse core set of 25 founder lines (MelonCore25) was sequenced using a combination of short and long-read technologies and their genomes were assembled de novo. The construction of a melon pan-genome exposed substantial variation in genome size and structure, including detection of ~300,000 structural variants and ~9 million SNPs. A half-diallel derived set of 300 F2 populations representing all possible MelonCore25 parental combinations was constructed as framework for trait dissection through integration with the pan-genome. We demonstrate the potential of this unified framework for genetic analysis of various melon traits, including rind color and mottling pattern, fruit sugar content and resistance to fungal diseases. We anticipate that utilization of this integrated resource will enhance genetic dissection of important traits and accelerate melon breeding. Significance statementPan-genomes aim to address the abundance of genome structural variation within species for improved genomic analyses. New pan-genome, constructed from de novo genome assemblies of 25 diverse melon (Cucumis melo L.) accessions is integrated with a half-diallel derived set of 300 F2 populations representing all possible parental combinations. The potential of this unified multi-parental trait dissection framework for melon genetics and breeding is presented.

genetics↗

bioRxiv2022

Characterizing the landscape of viral expression in cancer by deep learning

About 15% of human cancer cases are attributed to viral infections. To date, virus expression in tumor tissues has been mostly studied by aligning tumor RNA sequencing reads to databases of known viruses. To allow identification of divergent viruses and rapid characterization of the tumor virome, we develop viRNAtrap, an alignment-free pipeline to identify viral reads and assemble viral contigs. We utilize viRNAtrap, which is based on a deep learning model trained to discriminate viral RNAseq reads, to explore viral expression in cancers and apply it to 14 cancer types from The Cancer Genome Atlas (TCGA). Using viRNAtrap, we uncover expression of unexpected and divergent viruses that have not previously been implicated in cancer and disclose human endogenous viruses whose expression is associated with poor overall survival. The viRNAtrap pipeline provides a way forward to study viral infections associated with different clinical conditions.

bioinformatics↗

bioRxiv2022

Clinically oriented prediction of patient response to targeted and immunotherapies from the tumor transcriptome

BackgroundPrecision oncology is gradually advancing into mainstream clinical practice, demonstrating significant survival benefits. However, eligibility and response rates remain limited in many cases, calling for better predictive biomarkers. MethodsWe present ENLIGHT, a transcriptomics-based computational approach that identifies clinically relevant genetic interactions and uses them to predict a patients response to a variety of therapies in multiple cancer types, without training on previous treatment response data. We study ENLIGHT in two translationally oriented scenarios: Personalized Oncology (PO), aimed at prioritizing treatments for a single patient, and Clinical Trial Design (CTD), selecting the most likely responders in a patient cohort. FindingsEvaluating ENLIGHTs performance on 21 blinded clinical trial datasets in the PO setting, we show that it can effectively predict a patients treatment response across multiple therapies and cancer types. Its prediction accuracy is better than previously published transcriptomics-based signatures and is comparable to that of supervised predictors developed for specific indications and drugs. In combination with the IFN-{gamma}signature, ENLIGHT achieves an odds ratio larger than 4 in predicting response to immune checkpoint therapy. In the CTD scenario, ENLIGHT can potentially enhance clinical trial success for immunotherapies and other monoclonal antibodies by excluding non-responders, while overall achieving more than 90% of the response rate attainable under an optimal exclusion strategy. ConclusionENLIGHT demonstrably enhances the ability to predict therapeutic response across multiple cancer types from the bulk tumor transcriptome. FundingThis research was supported in part by the Intramural Research Program, NIH and by the Israeli Innovation Authority.

bioinformatics↗

bioRxiv2022

Predicting patient treatment response and resistance via single-cell transcriptomics of their tumors

Tailoring the best treatments to cancer patients is an important open challenge. Here, we build a precision oncology data science and software framework for PERsonalized single-Cell Expression-based Planning for Treatments In Oncology (PERCEPTION). Our approach capitalizes on recently published matched bulk and single-cell transcriptome profiles of large-scale cell-line drug screens to build treatment response models from patients single-cell (SC) tumor transcriptomics. First, we show that PERCEPTION successfully predicts the response to monotherapy and combination treatments in screens performed in cancer and patient-tumor-derived primary cells based on SC-expression profiles. Second, it successfully stratifies responders to combination therapy based on the patients tumors SC-expression in two very recent multiple myeloma and breast cancer clinical trials. Thirdly, it captures the development of clinical resistance to five standard tyrosine kinase inhibitors using tumor SC-expression profiles obtained during treatment in a lung cancer patients cohort. Notably, PERCEPTION outperforms state-of-the-art bulk expression-based predictors in all three clinical cohorts. In sum, this study provides a first-of-its-kind conceptual and computational method that is predictive of response to therapy in patients, based on the clonal SC gene expression of their tumors.

bioinformatics↗

bioRxiv2021

Repurposing CAR targets in solid tumors via a pan-cancer single-cell transcriptome analysis

BACKGROUND: Chimeric antigen receptor (CAR) T cell therapies have yielded transformative clinical success for patients with blood tumors, but their full potential remains to be unleashed against solid tumors. One challenge is finding selective targets: cell surface proteins that are expressed widely by cancer cells and minimally by healthy cells in the tumor microenvironment and other normal tissues. METHODS: Analyzing pan-cancer patient tumor single cell transcriptomics data, we first define and quantify selectivity and safety scores of existing CAR targets for indications in which they are in clinical trials or approved. Selectivity scores are computed by the ability of a given surfaceome gene to classify tumor from nontumor cells in the tumor microenvironment. Safety scores are computed by mining healthy tissue transcriptomics and proteomics atlas data. Second, we identify new candidate cell surface CAR targets that have better selectivity and safety scores than the leading targets among those currently being tested, in an indication-specific manner. RESULTS: Remarkably, in almost all cancer types, we cannot find such better targets, testifying to the overall near optimality of the current target space. However, in HPV-negative head and neck squamous cell carcinoma (HNSC), for which there is currently a dearth of existing CAR targets, we find five new targets that have both superior selectivity and safety scores. Among the HNSC new targets, we find a few that additionally are strongly essentiality in HNSC cell lines.

bioinformatics↗

bioRxiv2021

Using a recently approved tumor mutational burden biomarker to stratify patients for immunotherapy may introduce a sex bias

The U.S. Food and Drug Administration (FDA) recently approved the treatment with pembrolizumab, an immune checkpoint inhibitor (ICI) targeting PD1 (anti-PD1), for patients with advanced solid tumors with a high tumor mutational burden (TMB) (defined as TMB [≥]10 mutations/Mb). However, following recent studies suggest that TMB levels and response to ICI treatment may differ between male and female melanoma patients, we investigated whether using this high-TMB threshold for selecting patients for anti-PD1 treatment may induce a sex-dependent bias. We analyzed a large ICI cohort of 1,286 patients across nine cancer types treated with anti-PD1/PDL1. We find that using this threshold would indeed result in an unwarranted sex bias in melanoma, successfully stratifying female but not male patients. While this threshold is currently not a regulatory prerequisite for ICI treatment in melanoma, it is important to raise awareness to this bias. Notably, no sex-dependent significant differences were observed in the response of melanoma patients to anti-CTLA4 therapies, different chemotherapies or combination therapies. Beyond melanoma, the high-TMB threshold additionally introduces a sex bias of considerable magnitude in glioblastoma and in patients with cancers of unknown origin, however, these results are not statistically significant. A power analysis shows that these biases may become significant with larger sample size, warranting further careful testing in larger cohorts.

cancer biology↗

bioRxiv2021

Immune determinants of the association between tumor mutational burden and immunotherapy response across cancer types

The FDA has recently approved high tumor mutational burden (TMB), defined by [≥]10 mutations/Mb, as a biomarker for the treatment of solid tumors with pembrolizumab, an immune checkpoint inhibitor (ICI) that targets PD1. However, recent studies testify that high TMB levels are only able to stratify ICI responders in a subset of cancer types, where the mechanisms underlying this observation have remained unknown. We hypothesized that the tumor immune microenvironment (TME) may modulate the stratification power of TMB (termed TMB power) in a cancer type, leading to this observation. To systematically study this hypothesis, we analyzed TCGA expression data to infer the levels of 31 immune-related factors characteristic of the TME of different cancer types. We integrated this information with TMB and response data of 2,277 patients treated with anti-PD1 or anti-PD-L1 ICI to identify the key immune factors that can determine TMB power across 14 different cancer types. We find that high levels of M1 macrophages and low resting dendritic cells in the TME characterize cancer types with high TMB power. A model based on these two immune factors is strongly predictive of the TMB power in a given cancer type (Spearman Rho=0.76, P<3.6x10-04). Using this model, we provide predictions of the TMB power in nine additional cancer types, including rare cancers, for which TMB and ICI response data are not yet publicly available on a large scale. Our analysis indicates that TMB-High may be highly predictive of ICI response in cervical squamous cell carcinoma, suggesting that such a study should be prioritized.

cancer biology↗