Highlights d Novel mouse system to uncouple tumor mutational load and tumor heterogeneity d Lower tumor heterogeneity leads to decreased tumor growth because of immune rejection d Both clone numbers and their genetic diversity mediate tumor growth and rejection d Tumor heterogeneity is linked to patient survival and checkpoint blockade response

Predicting the outcome of immunotherapy treatment in melanoma patients is challenging. Alterations in genes involved in antigen presentation and the interferon gamma (IFNγ) pathway play an important role in the immune response to tumors. We describe here that the overexpression of PSMB8 and PSMB9, two major components of the immunoproteasome, is predictive of better survival and improved response to immune-checkpoint inhibitors of melanoma patients. We study the mechanism underlying this connection by analyzing the antigenic peptide repertoire of cells that overexpress these subunits using HLA peptidomics. We find a higher response of patient-matched tumor infiltrating lymphocytes against antigens diferentially presented after immunoproteasome overexpression. Importantly, we find that PSMB8 and PSMB9 expression levels are much stronger predictors of melanoma patientsʼ immune response to checkpoint inhibitors than the tumors' mutational burden. These results suggest that PSMB8 and PSMB9 expression levels can serve as important biomarkers for stratifying melanoma patients for immune-checkpoint treatment.

The quest for tumor-associated antigens (TAA) and neoantigens is a major focus of cancer immunotherapy. Here, we combine a neoantigen prediction pipeline and human leukocyte antigen (HLA) peptidomics to identify TAAs and neoantigens in 16 tumors derived from seven patients with melanoma and characterize their interactions with their tumor-infiltrating lymphocytes (TIL). Our investigation of the antigenic and T-cell landscapes encompassing the TAA and neoantigen signatures, their immune reactivity, and their corresponding T-cell identities provides the first comprehensive analysis of cancer cell T-cell cosignatures, allowing us to discover remarkable antigenic and TIL similarities between metastases from the same patient. Furthermore, we reveal that two neoantigen-specific clonotypes killed 90% of autologous melanoma cells, both and, showing that a limited set of neoantigen-specific T cells may play a central role in melanoma tumor rejection. Our findings indicate that combining HLA peptidomics with neoantigen predictions allows robust identification of targetable neoantigens, which could successfully guide personalized cancer immunotherapies. As neoantigen targeting is becoming more established as a powerful therapeutic approach, investigating these molecules has taken center stage. Here, we show that a limited set of neoantigen-specific T cells mediates tumor rejection, suggesting that identifying just a few antigens and their corresponding T-cell clones could guide personalized immunotherapy. .

The tumor microenvironment (TME) is a complex mixture of cell types whose interactions affect tumor growth and clinical outcome. To discover such interactions, we developed CODEFACS (COnfident DEconvolution For All Cell Subsets), a tool deconvolving cell type–specific gene expression in each sample from bulk expression, and LIRICS (Ligand–Receptor Interactions between Cell Subsets), a statistical framework prioritizing clinically relevant ligand–receptor interactions between cell types from the deconvolved data. We first demonstrate the superiority of CODEFACS versus the state-of-the-art deconvolution method CIBERSORTx. Second, analyzing The Cancer Genome Atlas, we uncover cell type–specific ligand–receptor interactions uniquely associated with mismatch-repair deficiency across different cancer types, providing additional insights into their enhanced sensitivity to anti–programmed cell death protein 1 (PD-1) therapy compared with other tumors with high neoantigen burden. Finally, we identify a subset of cell type–specific ligand–receptor interactions in the melanoma TME that stratify survival of patients receiving anti–PD-1 therapy better than some recently published bulk transcriptomics-based methods. Significance: This work presents two new computational methods that can deconvolve a large collection of bulk tumor gene expression profiles into their respective cell type–specific gene expression profiles and identify cell type–specific ligand–receptor interactions predictive of response to immune-checkpoint blockade therapy. This article is highlighted in the In This Issue feature, p. 873

Highlights d Unlike promoter-mediated PD-L1 induction by IFN-g, EGFR rapidly stabilizes PD-L1 mRNA d Once induced, PD-L1 enhances metastasis in vivo and chemotaxis toward EGF d PD-L1 physically binds with and enhances activation of phospholipase C-g1 by EGFR d PLC-g1 binds a PD-L1's cytoplasmic segment implicated in protection from cytotoxicity

Guilt-by-association codifies the empirical observation that a gene’s function is informed by its neighborhood in a biological network. This would imply that when a gene’s network context is altered, for instance in disease condition, so could be the gene’s function. Although context-specific changes in biological networks have been explored, the potential changes they may induce on the functional roles of genes are yet to be characterized. Here we analyze, for the first time, the network-induced potential functional changes in breast cancer. Using transcriptomic samples for 1047 breast tumors and 110 healthy breast tissues from TCGA, we derive sample-specific protein interaction networks and assign sample-specific functions to genes via a diffusion strategy. Testing for significant changes in the inferred functions between normal and cancer samples, we find several functions to have significantly gained or lost genes in cancer, not due to differential expression of genes known to perform the function, but rather due to changes in the network topology. Our predicted functional changes are supported by mutational and copy number profiles in breast cancers. Our diffusion-based functional assignment provides a novel characterization of a tumor that is complementary to the standard approach based on functional annotation alone. Importantly, this characterization is effective in predicting patient survival, as well as in predicting several known histopathological subtypes of breast cancer.

2Most carcinomas have characteristic chromosomal aneuploidies specific to the tissue of tumor origin. The reason for this specificity is unknown. As aneuploidies directly affect gene expression, we hypothesized that cancer-type specific aneuploidies, which emerge at early stages of tumor evolution, confer adaptive advantages to the physiological requirements of the tissue of origin. To test this hypothesis, we compared chromosomal aneuploidies reported in the TCGA database to chromosome arm-wide gene expression levels of normal tissues from the GTEx database. We find that cancer-type specific chromosomal aneuploidies mirror differential gene expression levels specific to the respective normal tissues which cannot be explained by copy number alterations of resident cancer driver genes. We show that cancer-type specific aneuploidies "hard-wire" chromosome arm-wide gene expression levels present in normal tissues and propose that the clonal evolution of cancer is initiated by tissue-specific transcriptional requirements.

The tumor microenvironment (TME) is a complex mixture of cell-types that interact with each other to affect tumor growth and clinical outcomes. To accelerate the discovery of such interactions, we developed CODEFACS (COnfident DEconvolution For All Cell Subsets), a deconvolution tool inferring cell-type-specific gene expression in each sample from bulk expression measurements, and LIRICS (LIgand Receptor Interactions between Cell Subsets), a supporting pipeline that analyzes the deconvolved gene expression from CODEFACS to identify clinically relevant ligand-receptor interactions between cell-types. Using 15 benchmark test datasets, we first demonstrate that CODEFACS substantially improves the ability to reconstruct cell-type-specific transcriptomes from individual bulk samples, compared to the state-of-the-art method, CIBERSORTx. Second, analyzing the TCGA, we uncover cell-cell interactions that specifically occur in TME of mismatch-repair-deficient tumors and are associated with their high response rates to anti-PD1 treatment. These results point to specific T-cell co-stimulating interactions that enhance immunotherapy responses in tumors independently of their mutation burden levels. Finally, using machine learning, we identify a subset of cell-cell interactions that predict patient response to anti-PD1 therapy in melanoma better than recently published bulk transcriptomics-based signatures. CODEFACS offers a way to study bulk cancer and normal transcriptomes at a cell type-specific resolution, complementing single-cell transcriptomics.