Comprehensive pan-cancer analysis of mitochondrial outer membrane permeabilisation activity reveals positive immunomodulation and assists in identifying potential therapeutic targets for immunotherapy resistance

Qingshan Chen , Fenglin Gao , Junwan Wu , Kaiming Zhang , Tian Du , Yuhong Chen , Ruizhao Cai , Dechang Zhao , Rong Deng , Jun Tang

Clinical and Translational Medicine ›› 2024, Vol. 14 ›› Issue (6) : e1735

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Clinical and Translational Medicine ›› 2024, Vol. 14 ›› Issue (6) : e1735 DOI: 10.1002/ctm2.1735
RESEARCH ARTICLE

Comprehensive pan-cancer analysis of mitochondrial outer membrane permeabilisation activity reveals positive immunomodulation and assists in identifying potential therapeutic targets for immunotherapy resistance

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Abstract

Background: Mitochondrial outer membrane permeabilisation (MOMP) plays a pivotal role in cellular death and immune activation. A deeper understanding of the impact of tumour MOMP on immunity will aid in guiding more effective immunotherapeutic strategies.

Methods: A comprehensive pan-cancer dataset comprising 30 cancer-type transcriptomic cohorts, 20 immunotherapy transcriptomic cohorts and three immunotherapy scRNA-seq datasets was collected and analysed to determine the influence of tumour MOMP activity on clinical prognosis, immune infiltration and immunotherapy effectiveness. Leveraging 65 scRNA-Seq datasets, the MOMP signature (MOMP.Sig) was developed to accurately reflect tumour MOMP activity. The clinical predictive value of MOMP.Sig was explored through machine learning models. Integration of the MOMP.Sig model and a pan-cancer immunotherapy CRISPR screen further investigated potential targets to overcome immunotherapy resistance, which subsequently underwent clinical validation.

Results: Our research revealed that elevated MOMP activity reduces mortality risk in cancer patients, drives the formation of an anti-tumour immune environment and enhances the response to immunotherapy. This finding emphasises the potential clinical application value of MOMP activity in immunotherapy. MOMP.Sig, offering a more precise indicator of tumour cell MOMP activity, demonstrated outstanding predictive efficacy in machine-learning models. Moreover, with the assistance of the MOMP.Sig model, FOXO1 was identified as a core modulator that promotes immune resistance. Finally, these findings were successfully validated in clinical immunotherapy cohorts of skin cutaneous melanoma and triple-negative breast cancer patients.

Conclusions: This study enhances our understanding of MOMP activity in immune modulation, providing valuable insights for more effective immunotherapeutic strategies across diverse tumours.

Keywords

immune resistance / immunotherapy / mitochondrial outer membrane permeabilisation / pan-cancer

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Qingshan Chen, Fenglin Gao, Junwan Wu, Kaiming Zhang, Tian Du, Yuhong Chen, Ruizhao Cai, Dechang Zhao, Rong Deng, Jun Tang. Comprehensive pan-cancer analysis of mitochondrial outer membrane permeabilisation activity reveals positive immunomodulation and assists in identifying potential therapeutic targets for immunotherapy resistance. Clinical and Translational Medicine, 2024, 14(6): e1735 DOI:10.1002/ctm2.1735

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References

[1]

Liu H, Tang L, Li Y, et al. Nasopharyngeal carcinoma: current views on the tumor microenvironment's impact on drug resistance and clinical outcomes. Mol Cancer. 2024;23(1):20.

[2]

Zou Y, Ye F, Kong Y, et al. The single-cell landscape of intratumoral heterogeneity and the immunosuppressive microenvironment in liver and brain metastases of breast cancer. Adv Sci (Weinh). 2023;10(5):e2203699.

[3]

Butterfield LH, Najjar YG. Immunotherapy combination approaches: mechanisms, biomarkers and clinical observations. Nat Rev Immunol. 2023:1-18. Published online 6 December.

[4]

Deng Y, Zhao P, Zhou L, et al. Epidemiological trends of tracheal, bronchus, and lung cancer at the global, regional, and national levels: a population-based study. J Hematol Oncol. 2020;13(1):98.

[5]

Yang S, Wu Y, Deng Y, et al. Identification of a prognostic immune signature for cervical cancer to predict survival and response to immune checkpoint inhibitors. Oncoimmunology. 2019;8(12):e1659094.

[6]

Altieri DC. Mitochondria in cancer: clean windmills or stressed tinkerers? Trends Cell Biol. 2023;33(4):293-299.

[7]

Cosentino K, García-Sáez AJ. Mitochondrial alterations in apoptosis. Chemi Phys Lipids. 2014;181:62-75.

[8]

Flores-Romero H, Dadsena S, García-Sáez AJ. Mitochondrial pores at the crossroad between cell death and inflammatory signaling. Mol Cell. 2023;83(6):843-856.

[9]

Vringer E, Tait SWG. Mitochondria and cell death-associated inflammation. Cell Death Differ. 2023;30(2):304-312.

[10]

Dorstyn L, Akey CW, Kumar S. New insights into apoptosome structure and function. Cell Death Differ. 2018;25(7):1194-1208.

[11]

Shi J, Zhao Y, Wang K, et al. Cleavage of GSDMD by inflammatory caspases determines pyroptotic cell death. Nature. 2015;526(7575):660-665.

[12]

Rogers C, Fernandes-Alnemri T, Mayes L, Alnemri D, Cingolani G, Alnemri ES. Cleavage of DFNA5 by caspase-3 during apoptosis mediates progression to secondary necrotic/pyroptotic cell death. Nat Commun. 2017;8:14128.

[13]

Rogers C, Erkes DA, Nardone A, Aplin AE, Fernandes-Alnemri T, Alnemri ES. Gasdermin pores permeabilize mitochondria to augment caspase-3 activation during apoptosis and inflammasome activation. Nat Commun. 2019;10(1):1689.

[14]

Ning X, Wang Y, Jing M, et al. Apoptotic caspases suppress type I interferon production via the cleavage of cGAS, MAVS, and IRF3. Mol Cell. 2019;74(1):19-31.

[15]

Vince JE, Wong WWL, Khan N, et al. IAP antagonists target cIAP1 to induce TNFalpha-dependent apoptosis. Cell. 2007;131(4):682-693.

[16]

Pinar A, Dowling JK, Bitto NJ, et al. PB1-F2 peptide derived from avian influenza A virus H7N9 induces inflammation via activation of the NLRP3 inflammasome. J Biol Chem. 2017;292(3):826-836.

[17]

He WT, Wan H, Hu L, et al. Gasdermin D is an executor of pyroptosis and required for interleukin-1β secretion. Cell Res. 2015;25(12):1285-1298.

[18]

Green DR, Reed JC. Mitochondria and apoptosis. Science. 1998;281(5381):1309-1312.

[19]

Galluzzi L, Kepp O, Kroemer G. Mitochondria: master regulators of danger signalling. Nat Rev Mol Cell Biol. 2012;13(12):780-788.

[20]

Kroemer G, Galluzzi L, Brenner C. Mitochondrial membrane permeabilization in cell death. Physiol Rev. 2007;87(1):99-163.

[21]

Petronilli V, Miotto G, Canton M, et al. Transient and long-lasting openings of the mitochondrial permeability transition pore can be monitored directly in intact cells by changes in mitochondrial calcein fluorescence. Biophys J. 1999;76(2):725-734.

[22]

Crouch SP, Kozlowski R, Slater KJ, Fletcher J. The use of ATP bioluminescence as a measure of cell proliferation and cytotoxicity. J Immunol Methods. 1993;160(1):81-88.

[23]

Bernardi P, Gerle C, Halestrap AP, et al. Identity, structure, and function of the mitochondrial permeability transition pore: controversies, consensus, recent advances, and future directions. Cell Death Differ. 2023;30(8):1869-1885.

[24]

Garofano L, Migliozzi S, Oh YT, et al. Pathway-based classification of glioblastoma uncovers a mitochondrial subtype with therapeutic vulnerabilities. Nat Cancer. 2021;2(2):141-156.

[25]

Xia Y, Li X, Bie N, et al. A method for predicting drugs that can boost the efficacy of immune checkpoint blockade. Nat Immunol. 2024;25(4):659-670.

[26]

Zhang Y, Guo L, Dai Q, et al. A signature for pan-cancer prognosis based on neutrophil extracellular traps. J Immunother Cancer. 2022;10(6):e004210.

[27]

Zhang Z, Chen L, Chen H, et al. Pan-cancer landscape of T-cell exhaustion heterogeneity within the tumor microenvironment revealed a progressive roadmap of hierarchical dysfunction associated with prognosis and therapeutic efficacy. EBioMedicine. 2022;83:104207.

[28]

Basso K, Margolin AA, Stolovitzky G, Klein U, Dalla-Favera R, Califano A. Reverse engineering of regulatory networks in human B cells. Nat Genet. 2005;37(4):382-390.

[29]

Alvarez MJ, Shen Y, Giorgi FM, et al. Functional characterization of somatic mutations in cancer using network-based inference of protein activity. Nat Genet. 2016;48(8):838-847.

[30]

Colaprico A, Silva TC, Olsen C, et al. TCGAbiolinks: an R/Bioconductor package for integrative analysis of TCGA data. Nucleic Acids Res. 2016;44(8):e71.

[31]

Bonneville R, Krook MA, Kautto EA, et al. Landscape of microsatellite instability across 39 cancer types. JCO Precis Oncol. 2017;2017.

[32]

Chen B, Khodadoust MS, Liu CL, Newman AM, Alizadeh AA. Profiling tumor infiltrating immune cells with CIBERSORT. Methods Mol Biol. 2018;1711:243-259.

[33]

Yoshihara K, Shahmoradgoli M, Martínez E, et al. Inferring tumour purity and stromal and immune cell admixture from expression data. Nat Commun. 2013;4(1):2612.

[34]

Charoentong P, Finotello F, Angelova M, et al. Pan-cancer immunogenomic analyses reveal genotype-immunophenotype relationships and predictors of response to checkpoint blockade. Cell Rep. 2017;18(1):248-262.

[35]

Hugo W, Zaretsky JM, Sun L, et al. Genomic and transcriptomic features of response to anti-PD-1 therapy in metastatic melanoma. Cell. 2016;165(1):35-44.

[36]

Riaz N, Havel JJ, Makarov V, et al. Tumor and microenvironment evolution during immunotherapy with nivolumab. Cell. 2017;171(4):934-949.

[37]

Auslander N, Zhang G, Lee JS, et al. Robust prediction of response to immune checkpoint blockade therapy in metastatic melanoma. Nat Med. 2018;24(10):1545-1549.

[38]

Van Allen EM, Miao D, Schilling B, et al. Genomic correlates of response to CTLA-4 blockade in metastatic melanoma. Science. 2015;350(6257):207-211.

[39]

Liu D, Schilling B, Liu D, et al. Integrative molecular and clinical modeling of clinical outcomes to PD1 blockade in patients with metastatic melanoma. Nat Med. 2019;25(12):1916-1927.

[40]

Gide TN, Quek C, Menzies AM, et al. Distinct immune cell populations define response to anti-PD-1 monotherapy and anti-PD-1/anti-CTLA-4 combined therapy. Cancer Cell. 2019;35(2):238-255.

[41]

Jung H, Kim HS, Kim JY, et al. DNA methylation loss promotes immune evasion of tumours with high mutation and copy number load. Nat Commun. 2019;10(1):4278.

[42]

Cho JW, Hong MH, Ha SJ, et al. Genome-wide identification of differentially methylated promoters and enhancers associated with response to anti-PD-1 therapy in non-small cell lung cancer. Exp Mol Med. 2020;52(9):1550-1563.

[43]

Mariathasan S, Turley SJ, Nickles D, et al. TGFβ attenuates tumour response to PD-L1 blockade by contributing to exclusion of T cells. Nature. 2018;554(7693):544-548.

[44]

Rose TL, Weir WH, Mayhew GM, et al. Fibroblast growth factor receptor 3 alterations and response to immune checkpoint inhibition in metastatic urothelial cancer: a real world experience. Br J Cancer. 2021;125(9):1251-1260.

[45]

Ascierto ML, McMiller TL, Berger AE, et al. The intratumoral balance between metabolic and immunologic gene expression is associated with anti-PD-1 response in patients with renal cell carcinoma. Cancer Immunol Res. 2016;4(9):726-733.

[46]

Braun DA, Hou Y, Bakouny Z, et al. Interplay of somatic alterations and immune infiltration modulates response to PD-1 blockade in advanced clear cell renal cell carcinoma. Nat Med. 2020;26(6):909-918.

[47]

Zhao J, Chen AX, Gartrell RD, et al. Immune and genomic correlates of response to anti-PD-1 immunotherapy in glioblastoma. Nat Med. 2019;25(3):462-469.

[48]

Wolf DM, Yau C, Wulfkuhle J, et al. Redefining breast cancer subtypes to guide treatment prioritization and maximize response: predictive biomarkers across 10 cancer therapies. Cancer Cell. 2022;40(6):609-623.

[49]

Kim ST, Cristescu R, Bass AJ, et al. Comprehensive molecular characterization of clinical responses to PD-1 inhibition in metastatic gastric cancer. Nat Med. 2018;24(9):1449-1458.

[50]

Eisenhauer EA, Therasse P, Bogaerts J, et al. New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). Eur J Cancer. 2009;45(2):228-247.

[51]

Hu C, Li T, Xu Y, et al. CellMarker 2.0: an updated database of manually curated cell markers in human/mouse and web tools based on scRNA-seq data. Nucleic Acids Research. 2023;51(D1):D870-D876.

[52]

Johnson WE, Li C, Rabinovic A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics. 2007;8(1):118-127.

[53]

Wang X, Tokheim C, Gu SS, et al. In vivo CRISPR screens identify the E3 ligase Cop1 as a modulator of macrophage infiltration and cancer immunotherapy target. Cell. 2021;184(21):5357-5374.

[54]

Li F, Huang Q, Luster TA, et al. In vivo epigenetic CRISPR screen identifies Asf1a as an immunotherapeutic target in Kras-mutant lung adenocarcinoma. Cancer Discov. 2020;10(2):270-287.

[55]

Manguso RT, Pope HW, Zimmer MD, et al. In vivo CRISPR screening identifies Ptpn2 as a cancer immunotherapy target. Nature. 2017;547(7664):413-418.

[56]

Wang G, Chow RD, Zhu L, et al. CRISPR-GEMM pooled mutagenic screening identifies KMT2D as a major modulator of immune checkpoint blockade. Cancer Discov. 2020;10(12):1912-1933.

[57]

Kearney CJ, Vervoort SJ, Hogg SJ, et al. Tumor immune evasion arises through loss of TNF sensitivity. Sci Immunol. 2018;3(23):eaar3451.

[58]

Horai Y, Mizukawa M, Nishina H, et al. Quantification of histopathological findings using a novel image analysis platform. J Toxicol Pathol. 2019;32(4):319-327.

[59]

Marchi S, Guilbaud E, Tait SWG, Yamazaki T, Galluzzi L. Mitochondrial control of inflammation. Nat Rev Immunol. 2023;23(3):159-173.

[60]

Bock FJ, Tait SWG. Mitochondria as multifaceted regulators of cell death. Nat Rev Mol Cell Biol. 2020;21(2):85-100.

[61]

Miranda A, Hamilton PT, Zhang AW, et al. Cancer stemness, intratumoral heterogeneity, and immune response across cancers. Proc Natl Acad Sci. 2019;116(18):9020-9029.

[62]

Zhou Y, Zhou B, Pache L, et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat Commun. 2019;10(1):1523.

[63]

Jerby-Arnon L, Shah P, Cuoco MS, et al. A cancer cell program promotes T cell exclusion and resistance to checkpoint blockade. Cell. 2018;175(4):984-997.

[64]

Xiong D, Wang Y, You M. A gene expression signature of TREM2hi macrophages and γδ T cells predicts immunotherapy response. Nat Commun. 2020;11(1):5084.

[65]

Bassez A, Vos H, Van Dyck L, et al. A single-cell map of intratumoral changes during anti-PD1 treatment of patients with breast cancer. Nat Med. 2021;27(5):820-832.

[66]

Shukla SA, Bachireddy P, Schilling B, et al. Cancer-germline antigen expression discriminates clinical outcome to CTLA-4 blockade. Cell. 2018;173(3):624-633.

[67]

Thompson JC, Hwang WT, Davis C, et al. Gene signatures of tumor inflammation and epithelial-to-mesenchymal transition (EMT) predict responses to immune checkpoint blockade in lung cancer with high accuracy. Lung Cancer. 2020;139:1-8.

[68]

Ayers M, Lunceford J, Nebozhyn M, et al. IFN-γ-related mRNA profile predicts clinical response to PD-1 blockade. J Clin Invest. 2017;127(8):2930-2940.

[69]

Rooney MS, Shukla SA, Wu CJ, Getz G, Hacohen N. Molecular and genetic properties of tumors associated with local immune cytolytic activity. Cell. 2015;160(1):48-61.

[70]

Ju M, Bi J, Wei Q, et al. Pan-cancer analysis of NLRP3 inflammasome with potential implications in prognosis and immunotherapy in human cancer. Brief Bioinform. 2021;22(4):bbaa345.

[71]

Cui C, Xu C, Yang W, et al. Ratio of the interferon-γ signature to the immunosuppression signature predicts anti-PD-1 therapy response in melanoma. npj Genom Med. 2021;6(1):1-12.

[72]

Jiang P, Gu S, Pan D, et al. Signatures of T cell dysfunction and exclusion predict cancer immunotherapy response. Nat Med. 2018;24(10):1550-1558.

[73]

Dominguez CX, Müller S, Keerthivasan S, et al. Single-Cell RNA sequencing reveals stromal evolution into LRRC15+ myofibroblasts as a determinant of patient response to cancer immunotherapy. Cancer Discov. 2020;10(2):232-253.

[74]

Dadsena S, Jenner A, García-Sáez AJ. Mitochondrial outer membrane permeabilization at the single molecule level. Cell Mol Life Sci. 2021;78(8):3777-3790.

[75]

Goldstein JC, Waterhouse NJ, Juin P, Evan GI, Green DR. The coordinate release of cytochrome c during apoptosis is rapid, complete and kinetically invariant. Nat Cell Biol. 2000;2(3):156-162.

[76]

Rehm M, Düssmann H, Prehn JHM. Real-time single cell analysis of Smac/DIABLO release during apoptosis. J Cell Biol. 2003;162(6):1031-1043.

[77]

Lartigue L, Medina C, Schembri L, et al. An intracellular wave of cytochrome c propagates and precedes Bax redistribution during apoptosis. J Cell Sci. 2008;121(21):3515-3523.

[78]

Bhola PD, Mattheyses AL, Simon SM. Spatial and temporal dynamics of mitochondrial membrane permeability waves during apoptosis. Biophys J. 2009;97(8):2222-2231.

[79]

Rehm M, Huber HJ, Hellwig CT, Anguissola S, Dussmann H, Prehn JHM. Dynamics of outer mitochondrial membrane permeabilization during apoptosis. Cell Death Differ. 2009;16(4):613-623.

[80]

Hänggi K, Ruffell B. Cell death, therapeutics, and the immune response in cancer. Trends Cancer. 2023;9(5):381-396.

[81]

Coussens LM, Zitvogel L, Palucka AK. Neutralizing tumor-promoting chronic inflammation: a magic bullet? Science. 2013;339(6117):286-291.

[82]

Tait SWG, Parsons MJ, Llambi F, et al. Resistance to caspase-independent cell death requires persistence of intact mitochondria. Dev Cell. 2010;18(5):802-813.

[83]

Shim JH, Kim HS, Cha H, et al. HLA-corrected tumor mutation burden and homologous recombination deficiency for the prediction of response to PD-(L)1 blockade in advanced non-small-cell lung cancer patients. Ann Oncol. 2020;31(7):902-911.

[84]

Ling F, Makishima F, Morishima N, Shibata T. A nuclear mutation defective in mitochondrial recombination in yeast. EMBO J. 1995;14(16):4090-4101.

[85]

Malena A, Loro E, Di Re M, Holt IJ, Vergani L. Inhibition of mitochondrial fission favours mutant over wild-type mitochondrial DNA. Hum Mol Genet. 2009;18(18):3407-3416.

[86]

Schumacher TN, Schreiber RD. Neoantigens in cancer immunotherapy. Science. 2015;348(6230):69-74.

[87]

Conway JR, Kofman E, Mo SS, Elmarakeby H, Van Allen E. Genomics of response to immune checkpoint therapies for cancer: implications for precision medicine. Genome Medicine. 2018;10(1):93.

[88]

Giacomello M, Pyakurel A, Glytsou C, Scorrano L. The cell biology of mitochondrial membrane dynamics. Nat Rev Mol Cell Biol. 2020;21(4):204-224.

[89]

Cassidy-Stone A, Chipuk JE, Ingerman E, et al. Chemical inhibition of the mitochondrial division dynamin reveals its role in Bax/Bak-dependent mitochondrial outer membrane permeabilization. Dev Cell. 2008;14(2):193-204.

[90]

Pedregosa F, Varoquaux G, Gramfort A, et al. Scikit-learn: machine Learning in Python. Published online 5 June 2018. Accessed January 16, 2024.

[91]

Gan B, Lim C, Chu G, et al. FoxOs enforce a progression checkpoint to constrain mTORC1-activated renal tumorigenesis. Cancer Cell. 2010;18(5):472-484.

[92]

Li J, Wang E, Rinaldo F, Datta K. Upregulation of VEGF-C by androgen depletion: the involvement of IGF-IR-FOXO pathway. Oncogene. 2005;24(35):5510-5520.

[93]

Guan H, Tan P, Xie L, et al. FOXO1 inhibits osteosarcoma oncogenesis via Wnt/β-catenin pathway suppression. Oncogenesis. 2015;4(9):e166.

[94]

Spranger S, Bao R, Gajewski TF. Melanoma-intrinsic β-catenin signalling prevents anti-tumour immunity. Nature. 2015;523(7559):231-235.

[95]

Kim SY, Yoon J, Ko YS, et al. Constitutive phosphorylation of the FOXO1 transcription factor in gastric cancer cells correlates with microvessel area and the expressions of angiogenesis-related molecules. BMC Cancer. 2011;11:264.

[96]

Delpuech O, Griffiths B, East P, et al. Induction of Mxi1-SR alpha by FOXO3a contributes to repression of Myc-dependent gene expression. Mol Cell Biol. 2007;27(13):4917-4930.

[97]

Fukumura D, Kloepper J, Amoozgar Z, Duda DG, Jain RK. Enhancing cancer immunotherapy using antiangiogenics: opportunities and challenges. Nat Rev Clin Oncol. 2018;15(5):325-340.

[98]

Ren D, Hua Y, Yu B, et al. Predictive biomarkers and mechanisms underlying resistance to PD1/PD-L1 blockade cancer immunotherapy. Molecular Cancer. 2020;19(1):19.

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