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      FGFR3 Alterations in Bladder Cancer Stimulate Serine Synthesis to Induce Immune-Inert Macrophages That Suppress T-cell Recruitment and Activation

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          Abstract

          Metabolic reprogramming of bladder cancer cells driven by mutant FGFR3 increases serine synthesis that suppresses macrophage immunostimulatory functions to generate an immunosuppressive TME, which can be overcome by targeting PI3K.

          Abstract

          FGFR3 alterations are common in patients with bladder cancer. While the FGFR tyrosine kinase inhibitor erdafitinib has been approved as a targeted therapy for patients with FGFR3-altered (a FGFR3) bladder cancer, the response rate remains suboptimal, prompting development of strategies to improve treatment response. Here, we observed an immune-desert tumor microenvironment (TME) phenotype in human a FGFR3 bladder cancer and demonstrated that mutant FGFR3 indirectly induces a “cold” TME in mouse bladder cancer models. Single-cell RNA sequencing revealed the central role of macrophages in inducing the cold TME of a FGFR3 tumors. Macrophages in a FGFR3 tumors exhibited reduced T-cell recruitment and antigen presentation capabilities. Increased serine synthesis in bladder cancer cells that was induced by mutant FGFR3 activated the PI3K/Akt pathway in macrophages, shifting them to an immune-inert phenotype. Targeting PI3K in a FGFR3 tumors with duvelisib achieved promising efficacy by reversing the macrophage phenotype, and combination therapy with duvelisib and erdafitinib demonstrated increased antitumor activity. Overall, these findings reveal the critical role of enhanced serine synthesis efflux from cancer cells with mutant FGFR3 in shifting macrophages to an immune-inert phenotype. Reversing the macrophage phenotype holds promise for enhancing erdafitinib efficacy.

          Significance:

          Metabolic reprogramming of bladder cancer cells driven by mutant FGFR3 increases serine synthesis that suppresses macrophage immunostimulatory functions to generate an immunosuppressive TME, which can be overcome by targeting PI3K.

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          Cancer statistics, 2022

          Each year, the American Cancer Society estimates the numbers of new cancer cases and deaths in the United States and compiles the most recent data on population-based cancer occurrence and outcomes. Incidence data (through 2018) were collected by the Surveillance, Epidemiology, and End Results program; the National Program of Cancer Registries; and the North American Association of Central Cancer Registries. Mortality data (through 2019) were collected by the National Center for Health Statistics. In 2022, 1,918,030 new cancer cases and 609,360 cancer deaths are projected to occur in the United States, including approximately 350 deaths per day from lung cancer, the leading cause of cancer death. Incidence during 2014 through 2018 continued a slow increase for female breast cancer (by 0.5% annually) and remained stable for prostate cancer, despite a 4% to 6% annual increase for advanced disease since 2011. Consequently, the proportion of prostate cancer diagnosed at a distant stage increased from 3.9% to 8.2% over the past decade. In contrast, lung cancer incidence continued to decline steeply for advanced disease while rates for localized-stage increased suddenly by 4.5% annually, contributing to gains both in the proportion of localized-stage diagnoses (from 17% in 2004 to 28% in 2018) and 3-year relative survival (from 21% to 31%). Mortality patterns reflect incidence trends, with declines accelerating for lung cancer, slowing for breast cancer, and stabilizing for prostate cancer. In summary, progress has stagnated for breast and prostate cancers but strengthened for lung cancer, coinciding with changes in medical practice related to cancer screening and/or treatment. More targeted cancer control interventions and investment in improved early detection and treatment would facilitate reductions in cancer mortality.
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            Integrated analysis of multimodal single-cell data

            Summary The simultaneous measurement of multiple modalities represents an exciting frontier for single-cell genomics and necessitates computational methods that can define cellular states based on multimodal data. Here, we introduce “weighted-nearest neighbor” analysis, an unsupervised framework to learn the relative utility of each data type in each cell, enabling an integrative analysis of multiple modalities. We apply our procedure to a CITE-seq dataset of 211,000 human peripheral blood mononuclear cells (PBMCs) with panels extending to 228 antibodies to construct a multimodal reference atlas of the circulating immune system. Multimodal analysis substantially improves our ability to resolve cell states, allowing us to identify and validate previously unreported lymphoid subpopulations. Moreover, we demonstrate how to leverage this reference to rapidly map new datasets and to interpret immune responses to vaccination and coronavirus disease 2019 (COVID-19). Our approach represents a broadly applicable strategy to analyze single-cell multimodal datasets and to look beyond the transcriptome toward a unified and multimodal definition of cellular identity.
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              TGF-β attenuates tumour response to PD-L1 blockade by contributing to exclusion of T cells

              Therapeutic antibodies that block the programmed death-ligand 1 (PD-L1)/programmed death-1 (PD-1) pathway can induce robust and durable responses in patients with various cancers, including metastatic urothelial cancer (mUC) 1–5 . However, these responses only occur in a subset of patients. Elucidating the determinants of response and resistance is key to improving outcomes and developing new treatment strategies. Here, we examined tumours from a large cohort of mUC patients treated with an anti–PD-L1 agent (atezolizumab) and identified major determinants of clinical outcome. Response was associated with CD8+ T-effector cell phenotype and, to an even greater extent, high neoantigen or tumour mutation burden (TMB). Lack of response was associated with a signature of transforming growth factor β (TGF-β) signalling in fibroblasts, particularly in patients with CD8+ T cells that were excluded from the tumour parenchyma and instead found in the fibroblast- and collagen-rich peritumoural stroma—a common phenotype among patients with mUC. Using a mouse model that recapitulates this immune excluded phenotype, we found that therapeutic administration of a TGF-β blocking antibody together with anti–PD-L1 reduced TGF-β signalling in stromal cells, facilitated T cell penetration into the centre of the tumour, and provoked vigorous anti-tumour immunity and tumour regression. Integration of these three independent biological features provides the best basis for understanding outcome in this setting and suggests that TGF-β shapes the tumour microenvironment to restrain anti-tumour immunity by restricting T cell infiltration.
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                Author and article information

                Journal
                Cancer Res
                Cancer Res
                Cancer Research
                American Association for Cancer Research
                0008-5472
                1538-7445
                15 December 2023
                28 September 2023
                : 83
                : 24
                : 4030-4046
                Affiliations
                [1 ]Department of Urology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, P.R. China.
                [2 ]Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, P.R. China.
                [3 ]Guangdong Provincial Clinical Research Center for Urological Diseases, Guangzhou, P.R. China.
                [4 ]Clinical Medical College & Affiliated Hospital of Chengdu University, Chengdu University, Chengdu, Sichuan, P.R. China.
                [5 ]Cellular & Molecular Diagnostics Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, P.R. China.
                [6 ]Department of Urology, Yan'an Hospital, Kunming Medical University, Kunming, P.R. China.
                Author notes
                [#]

                Y. Ouyang, Z. Ou, and W. Zhong contributed equally to this article.

                [* ] Corresponding Authors: Jian Huang, Sun Yat-sen Memorial Hosptial, Sun Yat-sen University, 107 Yanjiangxi Road, Guangzhou, Guangdong 510120, P.R. China. E-mail: huangj8@ 123456mail.sysu.edu.cn ; Tianxin Lin, lintx@ 123456mail.sysu.edu.cn ; and Wenlong Zhong, zhongwlong3@ 123456mail.sysu.edu.cn

                Cancer Res 2023;83:4030–46

                Author information
                https://orcid.org/0000-0001-7936-6539
                https://orcid.org/0009-0008-1642-7179
                https://orcid.org/0000-0001-5343-4652
                https://orcid.org/0000-0002-5094-7169
                https://orcid.org/0000-0002-5115-2229
                https://orcid.org/0000-0002-7685-7946
                https://orcid.org/0000-0002-9660-9392
                https://orcid.org/0000-0003-0040-7532
                https://orcid.org/0000-0002-6944-4158
                https://orcid.org/0009-0008-5142-458X
                https://orcid.org/0009-0006-6338-2490
                https://orcid.org/0000-0003-3180-8697
                https://orcid.org/0000-0002-1296-9316
                Article
                CAN-23-1065
                10.1158/0008-5472.CAN-23-1065
                10722136
                37768887
                145d4b94-699f-4010-a6c6-63ae528e2d15
                ©2023 The Authors; Published by the American Association for Cancer Research

                This open access article is distributed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) license.

                History
                : 08 April 2023
                : 31 July 2023
                : 26 September 2023
                Page count
                Pages: 17
                Funding
                Funded by: National Natural Science Foundation of China (NSFC), DOI 10.13039/501100001809;
                Award ID: 81825016
                Award Recipient :
                Funded by: National Natural Science Foundation of China (NSFC), DOI 10.13039/501100001809;
                Award ID: 81F961128027
                Award Recipient :
                Funded by: National Natural Science Foundation of China (NSFC), DOI 10.13039/501100001809;
                Award ID: 82373254
                Award Recipient :
                Funded by: Natural Science Foundation of Guangdong Province (廣東省自然科學基金), DOI 10.13039/501100003453;
                Award ID: 2023A1515010258
                Award Recipient :
                Funded by: Key Areas Research and Development Program of Guangdong, DOI 10.13039/;
                Award ID: 2018B010109006
                Award Recipient :
                Categories
                Cell Signaling
                Protein Tyrosine Kinases
                Drug Targets
                Protein Kinase & Phosphatase Drug Targets
                Tumor Microenvironment
                Cancer Immunology

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