5.9
CiteScore
5.9
Impact Factor
Volume 48 Issue 5
May  2021
Turn off MathJax
Article Contents

Immu-Mela: An open resource for exploring immunotherapy-related multidimensional genomic profiles in melanoma

doi: 10.1016/j.jgg.2021.03.016
Funds:

Cancer Center Support Grant (2P30 CA068485-19 to Y.S.). Funding for open access charge:National Cancer Institute (5U01 CA163056-05 to Y.S.).

The authors would like to acknowledge funding from National Cancer Institute (5U01 CA163056-05, U2C CA233291 and U54 CA217450 to Y.S.)

  • Received Date: 2020-12-15
  • Revised Date: 2021-03-15
  • Accepted Date: 2021-03-17
  • Publish Date: 2021-05-20
  • There are increasing studies aimed to reveal genomic hallmarks predictive of immune checkpoint blockade (ICB) treatment response, which generated a large number of data and provided an unprecedented opportunity to identify response-related features and evaluate their robustness across cohorts. However, those valuable data sets are not easily accessible to the research community. To take full advantage of existing large-scale immuno-genomic profiles, we developed Immu-Mela (http://bioinfo.vanderbilt.edu/database/Immu-Mela/), a multidimensional immuno-genomic portal that provides interactive exploration of associations between ICB responsiveness and multi-omics features in melanoma, including genetic, transcriptomics, immune cells, and single-cell populations. Immu-Mela also enables integrative analysis of any two genomic features. We demonstrated the value of Immu-Mela by identifying known and novel genomic features associated with ICB response. In addition, Immu-Mela allows users to upload their data sets (unrestricted to any cancer types) and co-analyze with existing data to identify and validate signatures of interest. Immu-Mela reduces barriers between researchers and complex genomic data, facilitating discoveries in cancer immunotherapy.
  • loading
  • Alexandrov, L.B., Nik-Zainal, S., Wedge, D.C., Aparicio, S.A., Behjati, S., Biankin, A.V., Bignell, G.R., Bolli, N., Borg, A., Borresen-Dale, A.L., et al., 2013. Signatures of mutational processes in human cancer. Nature 500, 415-421.
    Auslander, N., Zhang, G., Lee, J.S., Frederick, D.T., Miao, B., Moll, T., Tian, T., Wei, Z., Madan, S., Sullivan, R.J., et al., 2018. Robust prediction of response to immune checkpoint blockade therapy in metastatic melanoma. Nat. Med. 24, 1545-1549.
    Bao, X., Shi, R., Zhao, T., Wang, Y., Anastasov, N., Rosemann, M., Fang, W., 2020. Integrated analysis of single-cell RNA-seq and bulk RNA-seq unravels tumour heterogeneity plus m2-like tumour-associated macrophage infiltration and aggressiveness in tnbc. Cancer Immunol. Immunother. 70, 189-202.
    Butler, A., Hoffman, P., Smibert, P., Papalexi, E., Satija, R., 2018. Integrating singlecell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 36, 411-420.
    Cerami, E., Gao, J., Dogrusoz, U., Gross, B.E., Sumer, S.O., Aksoy, B.A., Jacobsen, A., Byrne, C.J., Heuer, M.L., Larsson, E., et al., 2012. The cbio cancer genomics portal:an open platform for exploring multidimensional cancer genomics data. Canc. Discov. 2, 401-404.
    Chang, C.H., Pearce, E.L., 2016. Emerging concepts of T cell metabolism as a target of immunotherapy. Nat. Immunol. 17, 364-368.
    Charoentong, P., Finotello, F., Angelova, M., Mayer, C., Efremova, M., Rieder, D., Hackl, H., Trajanoski, Z., 2017. Pan-cancer immunogenomic analyses reveal genotype-immunophenotype relationships and predictors of response to checkpoint blockade. Cell Rep. 18, 248-262.
    Chen, B., Khodadoust, M.S., Liu, C.L., Newman, A.M., Alizadeh, A.A., 2018. Profiling tumor infiltrating immune cells with cibersort. Methods Mol. Biol. 1711, 243-259.
    Chen, P.L., Roh, W., Reuben, A., Cooper, Z.A., Spencer, C.N., Prieto, P.A., Miller, J.P., Bassett, R.L., Gopalakrishnan, V., Wani, K., et al., 2016. Analysis of immune signatures in longitudinal tumor samples yields insight into biomarkers of response and mechanisms of resistance to immune checkpoint blockade. Canc. Discov. 6, 827-837.
    Cristescu, R., Mogg, R., Ayers, M., Albright, A., Murphy, E., Yearley, J., Sher, X., Liu, X.Q., Lu, H., Nebozhyn, M., et al., 2018. Pan-tumor genomic biomarkers for PD-1 checkpoint blockade-based immunotherapy. Science 362, eaar3593.
    Daud, A.I., Wolchok, J.D., Robert, C., Hwu, W.J., Weber, J.S., Ribas, A., Hodi, F.S., Joshua, A.M., Kefford, R., Hersey, P., et al., 2016. Programmed death-ligand 1 expression and response to the anti-programmed death 1 antibody pembrolizumab in melanoma. J. Clin. Oncol. 34, 4102-4109.
    Eddy, J.A., Thorsson, V., Lamb, A.E., Gibbs, D.L., Heimann, C., Yu, J.X., Chung, V., Chae, Y., Dang, K., Vincent, B.G., et al., 2020. Cri iatlas:an interactive portal for immuno-oncology research. F1000Res 9, 1028.
    Gibney, G.T., Weiner, L.M., Atkins, M.B., 2016. Predictive biomarkers for checkpoint inhibitor-based immunotherapy. Lancet Oncol. 17, e542-e551.
    Gide, T.N., Quek, C., Menzies, A.M., Tasker, A.T., Shang, P., Holst, J., Madore, J., Lim, S.Y., Velickovic, R., Wongchenko, M., et al., 2019. Distinct immune cell populations define response to anti-PD-1 monotherapy and anti-PD-1/anti-CTLA-4 combined therapy. Canc. Cell 35, 238-255. e6.
    Griss, J., Bauer, W., Wagner, C., Simon, M., Chen, M., GrabmeierPfistershammer, K., Maurer-Granofszky, M., Roka, F., Penz, T., Bock, C., et al., 2019. B cells sustain inflammation and predict response to immune checkpoint blockade in human melanoma. Nat. Commun. 10, 4186.
    Hamid, O., Robert, C., Daud, A., Hodi, F.S., Hwu, W.J., Kefford, R., Wolchok, J.D., Hersey, P., Joseph, R.W., Weber, J.S., et al., 2013. Safety and tumor responses with lambrolizumab (anti-PD-1) in melanoma. N. Engl. J. Med. 369, 134-144.
    Havel, J.J., Chowell, D., Chan, T.A., 2019. The evolving landscape of biomarkers for checkpoint inhibitor immunotherapy. Nat. Rev. Cancer 19, 133-150.
    Hugo, W., Zaretsky, J.M., Sun, L., Song, C., Moreno, B.H., Hu-Lieskovan, S., BerentMaoz, B., Pang, J., Chmielowski, B., Cherry, G., et al., 2016. Genomic and transcriptomic features of response to anti-PD-1 therapy in metastatic melanoma. Cell 165, 35-44.
    Isaeva, O.I., Sharonov, G.V., Serebrovskaya, E.O., Turchaninova, M.A., Zaretsky, A.R., Shugay, M., Chudakov, D.M., 2019. Intratumoral immunoglobulin isotypes predict survival in lung adenocarcinoma subtypes. J. Immunother. Cancer 7, 279.
    Jerby-Arnon, L., Shah, P., Cuoco, M.S., Rodman, C., Su, M.J., Melms, J.C., Leeson, R., Kanodia, A., Mei, S., Lin, J.R., et al., 2018. A cancer cell program promotes T cell exclusion and resistance to checkpoint blockade. Cell 175, 984-997. e24.
    Jiang, P., Gu, S., Pan, D., Fu, J., Sahu, A., Hu, X., Li, Z., Traugh, N., Bu, X., Li, B., et al., 2018. Signatures of T cell dysfunction and exclusion predict cancer immunotherapy response. Nat. Med. 24, 1550-1558.
    Johnson, D.B., Frampton, G.M., Rioth, M.J., Yusko, E., Xu, Y., Guo, X., Ennis, R.C., Fabrizio, D., Chalmers, Z.R., Greenbowe, J., et al., 2016. Targeted next generation sequencing identifies markers of response to PD-1 blockade. Cancer Immunol. Res. 4, 959-967.
    Kim, S.S., Shen, S., Miyauchi, S., Sanders, P.D., Franiak-Pietryga, I., Mell, L., Gutkind, J.S., Cohen, E.E.W., Califano, J.A., Sharabi, A.B., 2020. B cells improve overall survival in HPV-associated squamous cell carcinomas and are activated by radiation and PD-1 blockade. Clin. Canc. Res. 26, 3345-3359.
    Kirkwood, J.M., Tarhini, A.A., Panelli, M.C., Moschos, S.J., Zarour, H.M., Butterfield, L.H., Gogas, H.J., 2008. Next generation of immunotherapy for melanoma. J. Clin. Oncol. 26, 3445-3455.
    Kluger, H.M., Zito, C.R., Barr, M.L., Baine, M.K., Chiang, V.L., Sznol, M., Rimm, D.L., Chen, L., Jilaveanu, L.B., 2015. Characterization of PD-L1 expression and associated T-cell infiltrates in metastatic melanoma samples from variable anatomic sites. Clin. Canc. Res. 21, 3052-3060.
    Krieg, C., Nowicka, M., Guglietta, S., Schindler, S., Hartmann, F.J., Weber, L.M., Dummer, R., Robinson, M.D., Levesque, M.P., Becher, B., 2018. High-dimensional single-cell analysis predicts response to anti-PD-1 immunotherapy. Nat. Med. 24, 144-153.
    Le, D.T., Uram, J.N., Wang, H., Bartlett, B.R., Kemberling, H., Eyring, A.D., Skora, A.D., Luber, B.S., Azad, N.S., Laheru, D., et al., 2015. PD-1 blockade in tumors with mismatch-repair deficiency. N. Engl. J. Med. 372, 2509-2520.
    Lesterhuis, W.J., Bosco, A., Millward, M.J., Small, M., Nowak, A.K., Lake, R.A., 2017. Dynamic versus static biomarkers in cancer immune checkpoint blockade:unravelling complexity. Nat. Rev. Drug Discov. 16, 264-272.
    Li, H., van der Leun, A.M., Yofe, I., Lubling, Y., Gelbard-Solodkin, D., van Akkooi, A.C.J., van den Braber, M., Rozeman, E.A., Haanen, J., Blank, C.U., et al., 2019. Dysfunctional CD8 T cells form a proliferative, dynamically regulated compartment within human melanoma. Cell 176, 775-789. e18.
    Li, T., Fan, J., Wang, B., Traugh, N., Chen, Q., Liu, J.S., Li, B., Liu, X.S., 2017. Timer:a web server for comprehensive analysis of tumor-infiltrating immune cells. Cancer Res. 77, e108-e110.
    Liu, C., He, H., Li, X., Su, M.A., Cao, Y., 2019. Dynamic metrics-based biomarkers to predict responders to anti-PD-1 immunotherapy. Br. J. Canc. 120, 346-355.
    Mockler, M.B., Conroy, M.J., Lysaght, J., 2014. Targeting T cell immunometabolism for cancer immunotherapy; understanding the impact of the tumor microenvironment. Front. Oncol. 4, 107.
    Nathanson, T., Ahuja, A., Rubinsteyn, A., Aksoy, B.A., Hellmann, M.D., Miao, D., Van Allen, E., Merghoub, T., Wolchok, J.D., Snyder, A., et al., 2017. Somatic mutations and neoepitope homology in melanomas treated with CTLA-4 blockade. Cancer Immunol. Res. 5, 84-91.
    Newman, A.M., Liu, C.L., Green, M.R., Gentles, A.J., Feng, W., Xu, Y., Hoang, C.D., Diehn, M., Alizadeh, A.A., 2015. Robust enumeration of cell subsets from tissue expression profiles. Nat. Methods 12, 453-457.
    Ni, L., Lu, J., 2018. Interferon gamma in cancer immunotherapy. Cancer Med. 7, 4509-4516.
    Nishino, M., Ramaiya, N.H., Hatabu, H., Hodi, F.S., 2017. Monitoring immunecheckpoint blockade:response evaluation and biomarker development. Nat. Rev. Clin. Oncol. 14, 655-668.
    Orta-Mascaro, M., Consuegra-Fernandez, M., Carreras, E., Roncagalli, R., CarrerasSureda, A., Alvarez, P., Girard, L., Simoes, I., Martinez-Florensa, M., Aranda, F., et al., 2016. CD6 modulates thymocyte selection and peripheral T cell homeostasis. J. Exp. Med. 213, 1387-1397.
    Pardoll, D.M., 2012. The blockade of immune checkpoints in cancer immunotherapy. Nat. Rev. Cancer 12, 252-264.
    Prat, A., Navarro, A., Pare, L., Reguart, N., Galvan, P., Pascual, T., Martinez, A., Nuciforo, P., Comerma, L., Alos, L., et al., 2017. Immune-related gene expression profiling after PD-1 blockade in non-small cell lung carcinoma, head and neck squamous cell carcinoma, and melanoma. Cancer Res. 77, 3540-3550.
    Riaz, N., Havel, J.J., Makarov, V., Desrichard, A., Urba, W.J., Sims, J.S., Hodi, F.S., Martin-Algarra, S., Mandal, R., Sharfman, W.H., et al., 2017. Tumor and microenvironment evolution during immunotherapy with nivolumab. Cell 171, 934-949. e16.
    Robert, C., Schachter, J., Long, G.V., Arance, A., Grob, J.J., Mortier, L., Daud, A., Carlino, M.S., McNeil, C., Lotem, M., et al., 2015. Pembrolizumab versus ipilimumab in advanced melanoma. N. Engl. J. Med. 372, 2521-2532.
    Rosenthal, R., McGranahan, N., Herrero, J., Taylor, B.S., Swanton, C., 2016. Deconstructsigs:delineating mutational processes in single tumors distinguishes DNA repair deficiencies and patterns of carcinoma evolution. Genome Biol. 17, 31.
    Sade-Feldman, M., Yizhak, K., Bjorgaard, S.L., Ray, J.P., de Boer, C.G., Jenkins, R.W., Lieb, D.J., Chen, J.H., Frederick, D.T., Barzily-Rokni, M., et al., 2018. Defining T cell states associated with response to checkpoint immunotherapy in melanoma. Cell 175, 998-1013 e20.
    Salem, J.E., Manouchehri, A., Moey, M., Lebrun-Vignes, B., Bastarache, L., Pariente, A., Gobert, A., Spano, J.P., Balko, J.M., Bonaca, M.P., et al., 2018. Cardiovascular toxicities associated with immune checkpoint inhibitors:an observational, retrospective, pharmacovigilance study. Lancet Oncol. 19, 1579-1589.
    Samstein, R.M., Lee, C.H., Shoushtari, A.N., Hellmann, M.D., Shen, R., Janjigian, Y.Y., Barron, D.A., Zehir, A., Jordan, E.J., Omuro, A., et al., 2019. Tumor mutational load predicts survival after immunotherapy across multiple cancer types. Nat. Genet. 51, 202-206.
    Sharma, P., Allison, J.P., 2015. The future of immune checkpoint therapy. Science 348, 56-61.
    Snyder, A., Makarov, V., Merghoub, T., Yuan, J., Zaretsky, J.M., Desrichard, A., Walsh, L.A., Postow, M.A., Wong, P., Ho, T.S., et al., 2014. Genetic basis for clinical response to CTLA-4 blockade in melanoma. N. Engl. J. Med. 371, 2189-2199.
    Taube, J.M., Anders, R.A., Young, G.D., Xu, H., Sharma, R., McMiller, T.L., Chen, S., Klein, A.P., Pardoll, D.M., Topalian, S.L., et al., 2012. Colocalization of inflammatory response with B7-h1 expression in human melanocytic lesions supports an adaptive resistance mechanism of immune escape. Sci. Transl. Med. 4, 127ra137.
    Taube, J.M., Klein, A., Brahmer, J.R., Xu, H., Pan, X., Kim, J.H., Chen, L., Pardoll, D.M., Topalian, S.L., Anders, R.A., 2014. Association of PD-1, PD-1 ligands, and other features of the tumor immune microenvironment with response to anti-pd-1 therapy. Clin. Canc. Res. 20, 5064-5074.
    Thommen, D.S., 2019. The first shall (be) last:understanding durable T cell responses in immunotherapy. Immunity 50, 6-8.
    Tirosh, I., Izar, B., Prakadan, S.M., Wadsworth 2nd, M.H., Treacy, D., Trombetta, J.J., Rotem, A., Rodman, C., Lian, C., Murphy, G., et al., 2016. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science 352, 189-196.
    Van Allen, E.M., Miao, D., Schilling, B., Shukla, S.A., Blank, C., Zimmer, L., Sucker, A., Hillen, U., Foppen, M.H.G., Goldinger, S.M., et al., 2015. Genomic correlates of response to CTLA-4 blockade in metastatic melanoma. Science 350, 207-211.
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Article Metrics

    Article views (197) PDF downloads (8) Cited by ()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return