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      Sex-based dimorphism of anticancer immune response and molecular mechanisms of immune evasion

      , MD. 1 , , , MD. 1 , , MSc. 2 , , PhD. 2 , , MD. 1 , , MD 1 , , MD 1 , , MD. 3 , 4 , 5 , 1 , 1 , , MD 1 , , MD 1 , , MD 3 , , MD. PhD. 6 , 7 , , MD. 6 , 7 , , MD. PhD 5 , , MD 8 , , MD 9 , , MD. 10 , , MD 11 , , MD PhD 11 , , MD PhD 12 , , PhD. 13 , , MD. 14 , 15 , , MD. 16 , , , MD. 17
      Clinical cancer research : an official journal of the American Association for Cancer Research

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          We previously demonstrated that sex influences response to immune-checkpoint inhibitors.

          Here we investigate sex-based differences in the molecular mechanisms of anticancer immune-response and immune evasion in patients with NSCLC.

          Experimental Design

          We analyzed a) transcriptome-data of 2575 early-stage NSCLCs from 7 different datasets; b) 327 tumor-samples extensively characterized at the molecular level from the TRACERx lung study; c) two independent cohorts of respectively 329 and 391 patients with advanced NSCLC treated with anti-PD1/anti-PDL1 drugs.


          As compared with men, the tumor microenvironment (TME) of women was significantly enriched for a number of innate and adaptive immune cell-types, including specific T-cell subpopulations.

          NSCLCs of men and women exploited different mechanisms of immune evasion.

          The TME of females was characterized by significantly greater T-cell dysfunction status, higher expression of inhibitory immune-checkpoint molecules and higher abundance of immune-suppressive cells, including Cancer Associated Fibroblasts, MDSCs and Regulatory T-cells.

          By contrast, the TME of males was significantly enriched for a T-cells excluded phenotype.

          We reported data supporting impaired neoantigens presentation to immune system in tumors of men, as molecular mechanism explaining the findings observed.

          Finally, in line with our results, we showed significant sex-based differences in the association between TMB and outcome of patients with advanced NSCLC treated with anti-PD1/PDL1 drugs.


          We demonstrated meaningful sex-based differences of anticancer immune response and immune evasion mechanisms, that may be exploited to improve immunotherapy efficacy for both women and men.

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          Most cited references43

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          Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

          Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
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            The Immune Landscape of Cancer

            We performed an extensive immunogenomic analysis of more than 10,000 tumors comprising 33 diverse cancer types by utilizing data compiled by TCGA. Across cancer types, we identified six immune subtypes-wound healing, IFN-γ dominant, inflammatory, lymphocyte depleted, immunologically quiet, and TGF-β dominant-characterized by differences in macrophage or lymphocyte signatures, Th1:Th2 cell ratio, extent of intratumoral heterogeneity, aneuploidy, extent of neoantigen load, overall cell proliferation, expression of immunomodulatory genes, and prognosis. Specific driver mutations correlated with lower (CTNNB1, NRAS, or IDH1) or higher (BRAF, TP53, or CASP8) leukocyte levels across all cancers. Multiple control modalities of the intracellular and extracellular networks (transcription, microRNAs, copy number, and epigenetic processes) were involved in tumor-immune cell interactions, both across and within immune subtypes. Our immunogenomics pipeline to characterize these heterogeneous tumors and the resulting data are intended to serve as a resource for future targeted studies to further advance the field.
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              Signatures of T cell dysfunction and exclusion predict cancer immunotherapy response

              Cancer treatment by immune checkpoint blockade (ICB) can bring long-lasting clinical benefits, but only a fraction of patients respond to treatment. To predict ICB response, we developed TIDE, a computational method to model two primary mechanisms of tumor immune evasion: the induction of T cell dysfunction in tumors with high infiltration of cytotoxic T lymphocytes (CTL) and the prevention of T cell infiltration in tumors with low CTL level. We identified signatures of T cell dysfunction from large tumor cohorts by testing how the expression of each gene in tumors interacts with the CTL infiltration level to influence patient survival. We also modeled factors that exclude T cell infiltration into tumors using expression signatures from immunosuppressive cells. Using this framework and pre-treatment RNA-Seq or NanoString tumor expression profiles, TIDE predicted the outcome of melanoma patients treated with first-line anti-PD1 or anti-CTLA4 more accurately than other biomarkers such as PD-L1 level and mutation load. TIDE also revealed new candidate ICB resistance regulators, such as SERPINB9 , demonstrating utility for immunotherapy research.

                Author and article information

                Clin Cancer Res
                Clin Cancer Res
                Clinical cancer research : an official journal of the American Association for Cancer Research
                18 June 2021
                01 August 2021
                20 May 2021
                04 August 2021
                : 27
                : 15
                : 4311-4324
                [1 ]Division of Medical Oncology for Melanoma & Sarcoma, IEO, European Institute of Oncology IRCCS, Milan, Italy
                [2 ]Department of Statistics and Quantitative Methods, University of Milan-Bicocca, Milan, Italy
                [3 ]Division of Thoracic Oncology, European Institute of Oncology, IRCCS, Milan, Italy
                [4 ]Division of Medical Oncology, European Institute of Oncology, IRCCS, Milan, Italy
                [5 ]Department of Experimental Oncology, IEO, European Institute of Oncology IRCCS, Milan, Italy
                [6 ]Comprehensive Cancer Center, Fondazione Policlinico Universitario A. Gemelli, IRCCS, Rome, Italy
                [7 ]Medical Oncology, Department of Translational Medicine and Surgery, Università Cattolica Del Sacro Cuore, Roma, Italy
                [8 ]Harvard Medical School, Boston, Massachusetts. Mary Horrigan Connors Center for Women's Health and Gender Biology, Brigham and Women's Hospital, Boston, Massachusetts
                [9 ]Division of Breast Cancer Surgery, IEO, European Institute of Oncology, IRCCS, Milan, Italy; Faculty of Medicine, University of Milan, Milan, Italy
                [10 ]Department of Surgical Oncology and department of Genomic Medicine MD Anderson Cancer Center, Houston, TX, USA
                [11 ]Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, University College London, London, UK
                [12 ]Humanitas Clinical and Research Center IRCCS and Humanitas University, Milan
                [13 ]Department of Data Science, Dana-Farber Cancer Institute, Harvard Medical School, Harvard T.H. Chan School of Public Health, and Frontier Science & Technology Research Foundation, Boston. USA
                [14 ]Department of Pathology, IEO, European Institute of Oncology IRCCS Milan, Italy
                [15 ]University of Milan, Milan, Italy
                [16 ]MultiMedica San Giuseppe Hospital, Milan, Italy
                [17 ]Department of Oncology, Weill Cornel Medicine, New York, USA
                Author notes
                Corresponding Author: Fabio Conforti, MD, Division of Medical Oncology (Melanoma, Sarcoma, and Rare Tumors), European Institute of Oncology (IEO) IRCCS, Via Ripamonti 435, 20141, Milan, Italy. ( fabio.conforti@ 123456ieo.it +393313920679).

                This work is licensed under a CC BY 4.0 International license.



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