REFERENCES

1. Topalian SL, Drake CG, Pardoll DM. Immune checkpoint blockade: a common denominator approach to cancer therapy. Cancer Cell. 2015;27:450-61.

2. Fridman WH, Pagès F, Sautès-Fridman C, Galon J. The immune contexture in human tumours: impact on clinical outcome. Nat Rev Cancer. 2012;12:298-306.

3. Nakano O, Sato M, Naito Y, et al. Proliferative activity of intratumoral CD8+ T-lymphocytes as a prognostic factor in human renal cell carcinoma: clinicopathologic demonstration of antitumor immunity. Cancer Res. 2001;61:5132-6.

4. Giraldo NA, Becht E, Pagès F, et al. Orchestration and prognostic significance of immune checkpoints in the microenvironment of primary and metastatic renal cell cancer. Clin Cancer Res. 2015;21:3031-40.

5. 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;26:909-18.

6. Motzer RJ, Escudier B, McDermott DF, et al.; CheckMate 025 Investigators. Nivolumab versus everolimus in advanced renal-cell carcinoma. N Engl J Med. 2015;373:1803-13.

7. Rini BI, Plimack ER, Stus V, et al. Pembrolizumab plus axitinib versus sunitinib for advanced clear cell renal cell carcinoma: 5-year survival and biomarker analyses of the phase 3 KEYNOTE-426 trial. Nat Med. 2025;31:3475-84.

8. Galon J, Costes A, Sanchez-Cabo F, et al. Type, density, and location of immune cells within human colorectal tumors predict clinical outcome. Science. 2006;313:1960-4.

9. Song X, Zhu Y, Geng W, et al. Spatial and single-cell transcriptomics reveal cellular heterogeneity and a novel cancer-promoting Treg cell subset in human clear-cell renal cell carcinoma. J Immunother Cancer. 2025;13:e010183.

10. Qureshi OS, Zheng Y, Nakamura K, et al. Trans-endocytosis of CD80 and CD86: a molecular basis for the cell-extrinsic function of CTLA-4. Science. 2011;332:600-3.

11. Wang G, Khattar M, Guo Z, et al. IL-2-deprivation and TGF-beta are two non-redundant suppressor mechanisms of CD4+CD25+ regulatory T cell which jointly restrain CD4+CD25- cell activation. Immunol Lett. 2010;132:61-8.

12. Greenwald NF, Miller G, Moen E, et al. Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning. Nat Biotechnol. 2022;40:555-65.

13. Chen RJ, Chen C, Li Y, et al. Scaling vision transformers to gigapixel images via hierarchical self-supervised learning. arXiv 2022; arXiv:2206.02647. Available from: https://doi.org/10.48550/arXiv.2206.02647. [accessed 18 May 2026].

14. Dosovitskiy A, Beyer L, Kolesnikov A, et al. An image is worth 16×16 words: transformers for image recognition at scale. arXiv 2020; arXiv:2010.11929. Available from: https://doi.org/10.48550/arXiv.2010.11929. [accessed 18 May 2026].

15. Oquab M, Darcet T, Moutakanni T, et al. DINOv2: learning robust visual features without supervision. arXiv 2023; arXiv:2304.07193. Available from: https://doi.org/10.48550/arXiv.2304.07193. [accessed 18 May 2026].

16. He K, Chen X, Xie S, Li Y, Dollár P, Girshick R. Masked autoencoders are scalable vision learners. arXiv 2021; arXiv:2111.06377. Available from: https://doi.org/10.48550/arXiv.2111.06377. [accessed 18 May 2026].

17. Loshchilov I, Hutter F. Decoupled weight decay regularization. arXiv 2017; arXiv:1711.05101. Available from: https://doi.org/10.48550/arXiv.1711.05101. [accessed 18 May 2026].

18. He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. arXiv 2015; arXiv:1512.03385. Available from: https://doi.org/10.48550/arXiv.1512.03385. [accessed 18 May 2026].

19. Cortes C, Vapnik V. Support-vector networks. Mach Learn. 1995;20:273-97.

20. Breiman L. Random forests. Mach Learn. 2001;45:5-32.

21. Hosmer DW Jr., Lemeshow S, Sturdivant RX. Applied logistic regression. John Wiley & Sons; 2013.

22. Kokhlikyan N, Miglani V, Martin M, et al. Captum: a unified and generic model interpretability library for PyTorch. arXiv 2020; arXiv:2009.07896. Available from: https://doi.org/10.48550/arXiv.2009.07896. [accessed 18 May 2026].

23. Bentley JL. Multidimensional binary search trees used for associative searching. Commun ACM. 1975;18:509-17.

24. Kaplan EL, Meier P. Nonparametric estimation from incomplete observations. J Am Stat Assoc. 1958;53:457.

25. Newman AM, Liu CL, Green MR, et al. Robust enumeration of cell subsets from tissue expression profiles. Nat Methods. 2015;12:453-7.

26. Cui H, Zhao G, Lu Y, et al. TIMER3: an enhanced resource for tumor immune analysis. Nucleic Acids Res. 2025;53:W534-41.

27. Zheng L, Qin S, Si W, et al. Pan-cancer single-cell landscape of tumor-infiltrating T cells. Science. 2021;374:abe6474.

28. Schürch CM, Bhate SS, Barlow GL, et al. Coordinated cellular neighborhoods orchestrate antitumoral immunity at the colorectal cancer invasive front. Cell. 2020;182:1341-59.e19.

29. Miao D, Margolis CA, Gao W, et al. Genomic correlates of response to immune checkpoint therapies in clear cell renal cell carcinoma. Science. 2018;359:801-6.

30. Turajlic S, Litchfield K, Xu H, et al. Insertion-and-deletion-derived tumour-specific neoantigens and the immunogenic phenotype: a pan-cancer analysis. Lancet Oncol. 2017;18:1009-21.

31. Javed A, Milhem M. Role of natural killer cells in uveal melanoma. Cancers. 2020;12:3694.

32. Simoni Y, Becht E, Fehlings M, et al. Bystander CD8+ T cells are abundant and phenotypically distinct in human tumour infiltrates. Nature. 2018;557:575-9.

33. Dai S, Zeng H, Liu Z, et al. Intratumoral CXCL13+CD8+T cell infiltration determines poor clinical outcomes and immunoevasive contexture in patients with clear cell renal cell carcinoma. J Immunother Cancer. 2021;9:e001823.

34. Qi Y, Xia Y, Lin Z, et al. Tumor-infiltrating CD39+CD8+ T cells determine poor prognosis and immune evasion in clear cell renal cell carcinoma patients. Cancer Immunol Immunother. 2020;69:1565-76.

35. Granier C, Dariane C, Combe P, et al. Tim-3 expression on tumor-infiltrating PD-1+CD8+ T cells correlates with poor clinical outcome in renal cell carcinoma. Cancer Res. 2017;77:1075-82.

36. Yakirevich E, Patel NR. Tumor mutational burden and immune signatures interplay in renal cell carcinoma. Ann Transl Med. 2020;8:269.

37. Murakami T, Tanaka N, Takamatsu K, et al. Multiplexed single-cell pathology reveals the association of CD8 T-cell heterogeneity with prognostic outcomes in renal cell carcinoma. Cancer Immunol Immunother. 2021;70:3001-13.

38. Campbell JR, McDonald BR, Mesko PB, et al. Fc-optimized anti-CCR8 antibody depletes regulatory T cells in human tumor models. Cancer Res. 2021;81:2983-94.

39. Tannir NM, Albigès L, McDermott DF, et al. Nivolumab plus ipilimumab versus sunitinib for first-line treatment of advanced renal cell carcinoma: extended 8-year follow-up results of efficacy and safety from the phase III CheckMate 214 trial. Ann Oncol. 2024;35:1026-38.

40. Sharma P, Siddiqui BA, Anandhan S, et al. The next decade of immune checkpoint therapy. Cancer Discov. 2021;11:838-57.

41. Diab A, Tannir NM, Bentebibel SE, et al. Bempegaldesleukin (NKTR-214) plus nivolumab in patients with advanced solid tumors: phase I dose-escalation study of safety, efficacy, and immune activation (PIVOT-02). Cancer Discov. 2020;10:1158-73.

42. Dai J, Cui Y, Liang X, et al. PBRM1 mutation as a predictive biomarker for immunotherapy in multiple cancers. Front Genet. 2022;13:1066347.

43. Friedhoff J, Schneider F, Jurcic C, et al. BAP1 and PTEN mutations shape the immunological landscape of clear cell renal cell carcinoma and reveal the intertumoral heterogeneity of T cell suppression: a proof-of-concept study. Cancer Immunol Immunother. 2023;72:1603-18.

44. Batlle E, Massagué J. Transforming growth factor-β signaling in immunity and cancer. Immunity. 2019;50:924-40.

45. Wang J, Ioan-Facsinay A, van der Voort EI, Huizinga TW, Toes RE. Transient expression of FOXP3 in human activated nonregulatory CD4+ T cells. Eur J Immunol. 2007;37:129-38.

46. Patsoukis N, Wang Q, Strauss L, Boussiotis VA. Revisiting the PD-1 pathway. Sci Adv. 2020;6:eabd2712.

47. Zhang Y, Yang Y, Kong Y, Zhong B, Nakai K, Lu H. Toward graph-based decoding of tumor evolution: spatial inference of copy number variations. Diagnostics. 2025;15:3169.

48. Kong Y, Lu H. Biomechanics-driven 3D architecture inference from histology using CellSqueeze3D. Adv Sci. 2026;13:e18706.

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