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Distinct tumor microenvironment landscapes of rectal cancer for prognosis and prediction of immunotherapy response

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Abstract

Purpose

Tumor microenvironment (TME) affects the progression of rectal cancer (RC), and the clinical relevance of its immune elements was widely reported. Here we aim to delineate the complete TME landscape, including non-immune features, to improve our understanding of RC heterogeneity and provide a better strategy for precision medicine.

Methods

Single-cell analysis of GSE161277 using Seurat and Cellcall was performed to identify cell-cell interactions. The ssGSEA was employed to quantify the TME elements in TCGA patients, which were further clustered into subtypes by hclust. WGCNA and LASSO were combined to construct a degenerated signature for prognosis, and its performance was validated in two GEO datasets.

Results

We proposed a subtyping strategy based on the abundance of both immune and non-immune components, which divided all RC patients into 4 subtypes (Immune-, Canonical-, Dormant- and Stem-like). Different subtypes exhibited distinct mutation landscapes, biological features, immune characteristics, immunotherapy responses and prognoses. Next, WGCNA and LASSO regression were combined to construct a 10-gene signature based on differentially expressed genes among different subtypes. Subgroups divided by this signature also exhibited different clinical parameters and responses to immune checkpoint blockades. Diverse machine learning algorithms were applied to achieve higher accuracy for survival prediction and a nomogram was further established in combination with M stage and age to provide an accurate and visual prediction of prognosis.

Conclusions

We identified four TME-based RC subtypes with distinct biological and clinical features. Based on those subtypes, we also proposed a degenerated 10-gene signature to predict the prognosis and immunotherapy response.

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Data availability

All sequencing data were public available datasets (TCGA https://portal.gdc.cancer.gov/cart and GEO https://www.ncbi.nlm.nih.gov/geo/). All other data supporting the conclusions of this article are presented within the article and its supplementary files.

References

  1. H. Sung, J. Ferlay, R.L. Siegel, M. Laversanne, I. Soerjomataram, A. Jemal, F. Bray, Global Cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. (2021). https://doi.org/10.3322/caac.21660

  2. D.S. Keller, M. Berho, R.O. Perez, S.D. Wexner, M. Chand, The multidisciplinary management of rectal cancer. Nat. Rev. Gastroenterol. Hepatol. (2020). https://doi.org/10.1038/s41575-020-0275-y

  3. W.J. Tan, H.J. Tan, S.R. Dorajoo, F.J. Foo, C.L. Tang, M.H. Chew, Rectal Cancer surveillance-recurrence patterns and survival outcomes from a cohort followed up beyond 10 years. J. Gastrointest. Cancer (2018). https://doi.org/10.1007/s12029-017-9984-z

  4. R. Braun, L. Anthuber, D. Hirsch, D. Wangsa, J. Lack, N.E. McNeil, K. Heselmeyer-Haddad, I. Torres, D. Wangsa, M.A. Brown, A. Tubbs, N. Auslander, E.M. Gertz, P.R. Brauer, M.C. Cam, D.L. Sackett, J.K. Habermann, A. Nussenzweig, E. Ruppin, et al., Single-cell-derived primary rectal carcinoma cell lines reflect Intratumor heterogeneity associated with treatment response. Clin. Cancer Res. (2020). https://doi.org/10.1158/1078-0432.CCR-19-1984

  5. Z. Lakkis, G. Manceau, V. Bridoux, A. Brouquet, S. Kirzin, L. Maggiori, C. de Chaisemartin, J.H. Lefevre, Y. Panis, Management of rectal cancer: The 2016 French guidelines. Color. Dis. (2017). https://doi.org/10.1111/codi.13550

  6. N.M. Anderson, M.C. Simon, The tumor microenvironment. Curr. Biol. (2020). https://doi.org/10.1016/j.cub.2020.06.081

  7. I. Vitale, G. Manic, L.M. Coussens, G. Kroemer, L. Galluzzi, Macrophages and metabolism in the tumor microenvironment. Cell Metab. (2019). https://doi.org/10.1016/j.cmet.2019.06.001

  8. M. Saxena, N. Bhardwaj, Re-emergence of dendritic cell vaccines for Cancer treatment. Trends Cancer (2018). https://doi.org/10.1016/j.trecan.2017.12.007

  9. J.J. Loh, S. Ma, The role of Cancer-associated fibroblast as a dynamic player in mediating Cancer Stemness in the tumor microenvironment. Front. Cell Dev. Biol. (2021). https://doi.org/10.3389/fcell.2021.727640

  10. S. Sui, X. An, C. Xu, Z. Li, Y. Hua, G. Huang, S. Sui, Q. Long, Y. Sui, Y. Xiong, M. Ntim, W. Guo, M. Chen, W. Deng, X. Xiao, M. Li, An immune cell infiltration-based immune score model predicts prognosis and chemotherapy effects in breast cancer. Theranostics (2020). https://doi.org/10.7150/thno.49451

  11. W. Jiang, D. Zhu, C. Wang, Y. Zhu, An immune relevant signature for predicting prognoses and immunotherapeutic responses in patients with muscle-invasive bladder cancer (MIBC). Cancer Med. (2020). https://doi.org/10.1002/cam4.2942

  12. R. Zhou, J. Zhang, D. Zeng, H. Sun, X. Rong, M. Shi, J. Bin, Y. Liao, W. Liao, Immune cell infiltration as a biomarker for the diagnosis and prognosis of stage I-III colon cancer. Cancer Immunol. Immunother. (2019). https://doi.org/10.1007/s00262-018-2289-7

  13. P. Ge, W. Wang, L. Li, G. Zhang, Z. Gao, Z. Tang, X. Dang, Y. Wu, Profiles of immune cell infiltration and immune-related genes in the tumor microenvironment of colorectal cancer. Biomed. Pharmacother. (2019). https://doi.org/10.1016/j.biopha.2019.109228

  14. M. Binnewies, E.W. Roberts, K. Kersten, V. Chan, D.F. Fearon, M. Merad, L.M. Coussens, D.I. Gabrilovich, S. Ostrand-Rosenberg, C.C. Hedrick, R.H. Vonderheide, M.J. Pittet, R.K. Jain, W. Zou, T.K. Howcroft, E.C. Woodhouse, R.A. Weinberg, M.F. Krummel, Understanding the tumor immune microenvironment (TIME) for effective therapy. Nat. Med. (2018). https://doi.org/10.1038/s41591-018-0014-x

  15. Z. Wu, K. Zhu, Q. Liu, Y. Liu, L. Chen, J. Cui, H. Guo, N. Zhou, Y. Zhu, Y. Li, B. Shi, Profiles of immune infiltration in bladder Cancer and its clinical significance: An integrative genomic analysis. Int. J. Med. Sci. (2020). https://doi.org/10.7150/ijms.42151

  16. J. Liu, Z. Tan, J. He, T. Jin, Y. Han, L. Hu, J. Song, S. Huang, Identification of three molecular subtypes based on immune infiltration in ovarian cancer and its prognostic value. Biosci. Rep. (2020). https://doi.org/10.1042/BSR20201431

  17. P. Hu, Y. Gao, Y. Huang, Y. Zhao, H. Yan, J. Zhang, L. Zhao, Gene expression-based immune cell infiltration analyses of prostate Cancer and their associations with survival outcome. DNA Cell Biol. (2020). https://doi.org/10.1089/dna.2020.5371

  18. S. Yang, T. Liu, Y. Cheng, Y. Bai, G. Liang, Immune cell infiltration as a biomarker for the diagnosis and prognosis of digestive system cancer. Cancer Sci. (2019). https://doi.org/10.1111/cas.14216

  19. X. Liu, S. Wu, Y. Yang, M. Zhao, G. Zhu, Z. Hou, The prognostic landscape of tumor-infiltrating immune cell and immunomodulators in lung cancer. Biomed. Pharmacother. (2017). https://doi.org/10.1016/j.biopha.2017.08.003

  20. N. Kim, H.K. Kim, K. Lee, Y. Hong, J.H. Cho, J.W. Choi, J.I. Lee, Y.L. Suh, B.M. Ku, H.H. Eum, S. Choi, Y.L. Choi, J.G. Joung, W.Y. Park, H.A. Jung, J.M. Sun, S.H. Lee, J.S. Ahn, K. Park, et al., Single-cell RNA sequencing demonstrates the molecular and cellular reprogramming of metastatic lung adenocarcinoma. Nat. Commun. (2020). https://doi.org/10.1038/s41467-020-16164-1

  21. L.M. Becker, J.T. O'Connell, A.P. Vo, M.P. Cain, D. Tampe, L. Bizarro, H. Sugimoto, A.K. McGow, J.M. Asara, S. Lovisa, K.M. McAndrews, R. Zielinski, P.L. Lorenzi, M. Zeisberg, S. Raza, V.S. LeBleu, R. Kalluri, Epigenetic reprogramming of Cancer-associated fibroblasts deregulates glucose metabolism and facilitates progression of breast Cancer. Cell Rep. (2020). https://doi.org/10.1016/j.celrep.2020.107701

  22. A. Butler, P. Hoffman, P. Smibert, E. Papalexi, R. Satija, Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. (2018). https://doi.org/10.1038/nbt.4096

  23. M.C. Cieslak, A.M. Castelfranco, V. Roncalli, P.H. Lenz, D.K. Hartline, T-distributed stochastic neighbor embedding (t-SNE): A tool for eco-physiological transcriptomic analysis. Mar. Genomics (2020). https://doi.org/10.1016/j.margen.2019.100723

  24. Q. Huang, Y. Liu, Y. Du, L.X. Garmire, Evaluation of cell type annotation R packages on single-cell RNA-seq data. Genomics Proteomics Bioinformatics (2021). https://doi.org/10.1016/j.gpb.2020.07.004

  25. Y. Zhang, T. Liu, X. Hu, M. Wang, J. Wang, B. Zou, P. Tan, T. Cui, Y. Dou, L. Ning, Y. Huang, S. Rao, D. Wang, X. Zhao, CellCall: Integrating paired ligand-receptor and transcription factor activities for cell-cell communication. Nucleic Acids Res. (2021). https://doi.org/10.1093/nar/gkab638

  26. S. Mariathasan, S.J. Turley, D. Nickles, A. Castiglioni, K. Yuen, Y. Wang, E.I. Kadel, H. Koeppen, J.L. Astarita, R. Cubas, S. Jhunjhunwala, R. Banchereau, Y. Yang, Y. Guan, C. Chalouni, J. Ziai, Y. Senbabaoglu, S. Santoro, D. Sheinson, et al., TGFbeta attenuates tumour response to PD-L1 blockade by contributing to exclusion of T cells. Nature (2018). https://doi.org/10.1038/nature25501

  27. P. Charoentong, F. Finotello, M. Angelova, C. Mayer, M. Efremova, D. Rieder, H. Hackl, Z. Trajanoski, Pan-cancer Immunogenomic analyses reveal genotype-Immunophenotype relationships and predictors of response to checkpoint blockade. Cell Rep. (2017). https://doi.org/10.1016/j.celrep.2016.12.019

  28. G. Bindea, B. Mlecnik, M. Tosolini, A. Kirilovsky, M. Waldner, A.C. Obenauf, H. Angell, T. Fredriksen, L. Lafontaine, A. Berger, P. Bruneval, W.H. Fridman, C. Becker, F. Pages, M.R. Speicher, Z. Trajanoski, J. Galon, Spatiotemporal dynamics of intratumoral immune cells reveal the immune landscape in human cancer. Immunity (2013). https://doi.org/10.1016/j.immuni.2013.10.003

  29. X. Zhang, Y. Lan, J. Xu, F. Quan, E. Zhao, C. Deng, T. Luo, L. Xu, G. Liao, M. Yan, Y. Ping, F. Li, A. Shi, J. Bai, T. Zhao, X. Li, Y. Xiao, CellMarker: A manually curated resource of cell markers in human and mouse. Nucleic Acids Res. (2019). https://doi.org/10.1093/nar/gky900

  30. S. Hanzelmann, R. Castelo, J. Guinney, GSVA: Gene set variation analysis for microarray and RNA-seq data. Bmc Bioinformatics (2013). https://doi.org/10.1186/1471-2105-14-7

  31. J. Wu, L. Li, H. Zhang, Y. Zhao, H. Zhang, S. Wu, B. Xu, A risk model developed based on tumor microenvironment predicts overall survival and associates with tumor immunity of patients with lung adenocarcinoma. Oncogene (2021). https://doi.org/10.1038/s41388-021-01853-y

  32. R. Cao, B. Ma, G. Wang, Y. Xiong, Y. Tian, L. Yuan, Characterization of hypoxia response patterns identified prognosis and immunotherapy response in bladder cancer. Mol. Ther. Oncolytics (2021). https://doi.org/10.1016/j.omto.2021.06.011

  33. P. Langfelder, S. Horvath, WGCNA: An R package for weighted correlation network analysis. Bmc Bioinformatics (2008). https://doi.org/10.1186/1471-2105-9-559

  34. T. Wu, E. Hu, S. Xu, M. Chen, P. Guo, Z. Dai, T. Feng, L. Zhou, W. Tang, L. Zhan, X. Fu, S. Liu, X. Bo, G. Yu, clusterProfiler 4.0: A universal enrichment tool for interpreting omics data. Innovation (Camb.) (2021). https://doi.org/10.1016/j.xinn.2021.100141

  35. P. Geeleher, N. Cox, R.S. Huang, pRRophetic: An R package for prediction of clinical chemotherapeutic response from tumor gene expression levels. PLoS One (2014). https://doi.org/10.1371/journal.pone.0107468

  36. D. Sun, Y. Zhu, H. Zhao, T. Bian, T. Li, K. Liu, L. Feng, H. Li, H. Hou, Loss of ARID1A expression promotes lung adenocarcinoma metastasis and predicts a poor prognosis. Cell Oncol. (Dordr.) (2021). https://doi.org/10.1007/s13402-021-00616-x

  37. Y. Chen, J. Zhao, Identification of an immune gene signature based on tumor microenvironment characteristics in Colon adenocarcinoma. Cell Transplant. (2021). https://doi.org/10.1177/09636897211001314

  38. Y. Zhao, C. Chen, X. Xu, X. Ge, K. Ding, S. Zheng, J. Wang, L. Sun, An efficient prognostic immune scoring system for colorectal Cancer patients with peritoneal metastasis. Oncoimmunology (2021). https://doi.org/10.1080/2162402X.2021.1901464

  39. W. Chong, L. Shang, J. Liu, Z. Fang, F. Du, H. Wu, Y. Liu, Z. Wang, Y. Chen, S. Jia, L. Chen, L. Li, H. Chen, M(6)a regulator-based methylation modification patterns characterized by distinct tumor microenvironment immune profiles in colon cancer. Theranostics (2021). https://doi.org/10.7150/thno.52717

  40. J. Rao, W. Li, C. Chen, Pyroptosis-mediated molecular subtypes and tumor microenvironment infiltration characterization in Colon Cancer. Front. Cell Dev. Biol. (2021). https://doi.org/10.3389/fcell.2021.766503

  41. W. Song, J. Ren, R. Xiang, C. Kong, T. Fu, Identification of pyroptosis-related subtypes, the development of a prognosis model, and characterization of tumor microenvironment infiltration in colorectal cancer. Oncoimmunology (2021). https://doi.org/10.1080/2162402X.2021.1987636

  42. X. Zhu, X. Tian, L. Ji, X. Zhang, Y. Cao, C. Shen, Y. Hu, J. Wong, J.Y. Fang, J. Hong, H. Chen, A tumor microenvironment-specific gene expression signature predicts chemotherapy resistance in colorectal cancer patients. NPJ Precis. Oncol. (2021). https://doi.org/10.1038/s41698-021-00142-x

  43. C.A. Doubeni, D.A. Corley, V.P. Quinn, C.D. Jensen, A.G. Zauber, M. Goodman, J.R. Johnson, S.J. Mehta, T.A. Becerra, W.K. Zhao, J. Schottinger, V.P. Doria-Rose, T.R. Levin, N.S. Weiss, R.H. Fletcher, Effectiveness of screening colonoscopy in reducing the risk of death from right and left colon cancer: A large community-based study. Gut (2018). https://doi.org/10.1136/gutjnl-2016-312712

  44. R. Yaeger, W.K. Chatila, M.D. Lipsyc, J.F. Hechtman, A. Cercek, F. Sanchez-Vega, G. Jayakumaran, S. Middha, A. Zehir, M. Donoghue, D. You, A. Viale, N. Kemeny, N.H. Segal, Z.K. Stadler, A.M. Varghese, R. Kundra, J. Gao, A. Syed, et al., Clinical sequencing defines the genomic landscape of metastatic colorectal Cancer. Cancer Cell (2018). https://doi.org/10.1016/j.ccell.2017.12.004

  45. S. Talukdar, P. Bhoopathi, L. Emdad, S. Das, D. Sarkar, P.B. Fisher, Dormancy and cancer stem cells: An enigma for cancer therapeutic targeting. Adv. Cancer Res. (2019). https://doi.org/10.1016/bs.acr.2018.12.002

  46. F. Liu, W. Hou, J. Liang, L. Zhu, C. Luo, LRP1B mutation: A novel independent prognostic factor and a predictive tumor mutation burden in hepatocellular carcinoma. J. Cancer (2021). https://doi.org/10.7150/jca.53124

  47. E. Tabouret, M. Labussiere, A. Alentorn, Y. Schmitt, Y. Marie, M. Sanson, LRP1B deletion is associated with poor outcome for glioblastoma patients. J. Neurol. Sci. (2015). https://doi.org/10.1016/j.jns.2015.09.345

  48. P. Li, J. Xiao, B. Zhou, J. Wei, J. Luo, W. Chen, SYNE1 mutation may enhance the response to immune checkpoint blockade therapy in clear cell renal cell carcinoma patients. Aging (Albany NY) (2020). https://doi.org/10.18632/aging.103781

  49. Q. Luo, D. Chen, X. Fan, X. Fu, T. Ma, D. Chen, KRAS and PIK3CA bi-mutations predict a poor prognosis in colorectal cancer patients: A single-site report. Transl. Oncol. (2020). https://doi.org/10.1016/j.tranon.2020.100874

  50. S. Jang, M. Hong, M.K. Shin, B.C. Kim, H.S. Shin, E. Yu, S.M. Hong, J. Kim, S.M. Chun, T.I. Kim, K.C. Choi, Y.W. Ko, J.W. Kim, KRAS and PIK3CA mutations in colorectal adenocarcinomas correlate with aggressive histological features and behavior. Hum. Pathol. (2017). https://doi.org/10.1016/j.humpath.2017.01.010

  51. D.S. Chen, I. Mellman, Elements of cancer immunity and the cancer-immune set point. Nature (2017). https://doi.org/10.1038/nature21349

  52. Y. Chen, Z. Sun, W. Chen, C. Liu, R. Chai, J. Ding, W. Liu, X. Feng, J. Zhou, X. Shen, S. Huang, Z. Xu, The immune subtypes and landscape of gastric Cancer and to predict based on the whole-slide images using deep learning. Front. Immunol. (2021). https://doi.org/10.3389/fimmu.2021.685992

  53. C. Tang, J. Ma, X. Liu, Z. Liu, Identification of four immune subtypes in bladder Cancer based on immune gene sets. Front. Oncol. (2020). https://doi.org/10.3389/fonc.2020.544610

  54. M. Sasaki, N. Miyoshi, S. Fujino, K. Saso, T. Ogino, H. Takahashi, M. Uemura, H. Yamamoto, C. Matsuda, M. Yasui, M. Ohue, T. Mizushima, Y. Doki, H. Eguchi, The meiosis-specific cohesin component stromal antigen 3 promotes cell migration and chemotherapeutic resistance in colorectal cancer. Cancer Lett. (2021). https://doi.org/10.1016/j.canlet.2020.10.006

  55. J.M. Kraveka, L. Li, Z.M. Szulc, J. Bielawski, B. Ogretmen, Y.A. Hannun, L.M. Obeid, A. Bielawska, Involvement of dihydroceramide desaturase in cell cycle progression in human neuroblastoma cells. J. Biol. Chem. (2007). https://doi.org/10.1074/jbc.M700647200

  56. P. Munoz-Guardiola, J. Casas, E. Megias-Roda, S. Sole, H. Perez-Montoyo, M. Yeste-Velasco, T. Erazo, N. Dieguez-Martinez, S. Espinosa-Gil, C. Munoz-Pinedo, G. Yoldi, J.L. Abad, M.F. Segura, T. Moran, M. Romeo, J. Bosch-Barrera, A. Oaknin, J. Alfon, C. Domenech, et al., The anti-cancer drug ABTL0812 induces ER stress-mediated cytotoxic autophagy by increasing dihydroceramide levels in cancer cells. Autophagy (2021). https://doi.org/10.1080/15548627.2020.1761651

  57. S.Z. Xie, L. Garcia-Prat, V. Voisin, R. Ferrari, O.I. Gan, E. Wagenblast, K.B. Kaufmann, A. Zeng, S.I. Takayanagi, I. Patel, E.K. Lee, J. Jargstorf, G. Holmes, G. Romm, K. Pan, M. Shoong, A. Vedi, C. Luberto, M.D. Minden, et al., Sphingolipid modulation activates Proteostasis programs to govern human hematopoietic stem cell self-renewal. Cell Stem Cell (2019). https://doi.org/10.1016/j.stem.2019.09.008

  58. C. Di Malta, D. Siciliano, A. Calcagni, J. Monfregola, S. Punzi, N. Pastore, A.N. Eastes, O. Davis, R. De Cegli, A. Zampelli, L.G. Di Giovannantonio, E. Nusco, N. Platt, A. Guida, M.H. Ogmundsdottir, L. Lanfrancone, R.M. Perera, R. Zoncu, P.G. Pelicci, et al., Transcriptional activation of RagD GTPase controls mTORC1 and promotes cancer growth. Science (2017). https://doi.org/10.1126/science.aag2553

  59. L. Ding, X. Liang, Ras related GTP binding D promotes aerobic glycolysis of hepatocellular carcinoma. Ann. Hepatol. (2021). https://doi.org/10.1016/j.aohep.2021.100307

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Acknowledgements

We acknowledge the owners of TCGA, GEO and all other databases for providing valuable platforms and for making all those meaningful data available. All the contributions to those public datasets are deeply appreciated.

Funding

This work was supported by grants from the Beijing Nova Program of Science and Technology (Z191100001119128), the National Natural Science Foundation of China (82073390, 81702314), the Beijing Municipal Science and Technology Project (Z191100006619081), the Beijing Municipal Administration of Hospitals’ Youth Programme (QML20180108), the Funding Program for Excellent Talents of Beijing (2017000021469G212) and the Digestive Medical Coordinated Development Center of Beijing Municipal Administration of Hospitals (XXZ02, XXZ01). The study sponsors had no role in the design of the study and the preparation of this manuscript.

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  1. Fanqin Bu, Yu Zhao and Yushan Zhao contributed equally to this work.

Authors and Affiliations

  1. Department of Gastroenterology, Beijing Friendship Hospital, Capital Medical University, National Clinical Research Center for Digestive Disease, Beijing Digestive Disease Center, Beijing Key Laboratory for Precancerous Lesion of Digestive Disease, Beijing, 100050, People’s Republic of China

    Fanqin Bu, Yu Zhao, Xiaohan Yang, Shengtao Zhu & Li Min

  2. The State Key Laboratory of Medical Molecular Biology, Department of Molecular Biology and Biochemistry, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, School of Basic Medicine, Peking Union Medical College, Beijing, China

    Yushan Zhao & Yang Chen

  3. Innovation Laboratory of Terahertz Biophysics, National Innovation Institute of Defense Technology, Beijing, 100071, People’s Republic of China

    Lan Sun

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Contributions

Study concept and design: FB and LM. Data acquisition and data cleaning: YZ and FB. Data analysis and interpretation: FB, YSZ, YC and LS. Drafting of the manuscript: FB, XY, YZ and YSZ. Supervision: LM and SZ. Proofreading and revision: LM, SZ, LS, and YC. Critical revision of the manuscript for important intellectual content: All authors.

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Correspondence to Shengtao Zhu or Li Min.

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Bu, F., Zhao, Y., Zhao, Y. et al. Distinct tumor microenvironment landscapes of rectal cancer for prognosis and prediction of immunotherapy response. Cell Oncol. 45, 1363–1381 (2022). https://doi.org/10.1007/s13402-022-00725-1

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