1197 Harnessing the cancer immunity cycle via machine learning models to generate novel strategies for personalized cancer therapy

<h3>Background</h3> The cancer-immunity cycle (CIC) provides a staged framework for understanding the interplay between immune response and disease (figure 1).¹ Precise quantification of gene expression in each stage and contribution to physiological effects remains elusive. This study aims to personalize CIC profiles and pinpoint CIC stages informing precision therapy and diagnostics. <h3>Methods</h3> Data was anonymized from prospective, randomized trials exploring atezolizumab in advanced urothelial (UC; NCT02951767, NCT02108652 &amp; NCT02302807, n = 1540) and non-small cell lung cancer (NSCLC; NCT02366143 &amp; NCT02367781, n = 1926). The objective was to model CI response outcomes according to CIC stage-specific gene expression profiles using two complementary approaches: Approach 1 assessed gene-specific effects. Genes were mapped to CIC stages after recursive RECIST classification² to identify marker genes as putative drivers (figure 2). Approach 2 assessed multi-gene effects. Immune FITness profiles (IFIT) were inferred as CIC stage-specific activity scores³ followed by Cox regression against overall survival (OS; figure 3). The underlying biology of CIC stages were explained by mapping to validated GENCODE mappings (figure 4).⁴ <h3>Results</h3> In the first approach we reduced 4000 CIC associated genes to 100 RECIST-discriminating markers including TRNT1, CD74, PTN, PHYH, ENO3, ITGA6, SERPINE1, and UNC5B differentially expressed in UC; and DHRS2, JAK3, IFNG, and ERBB3 in NSCLC (𝛂=.05; figure 2). These markers highlight stages 1, 2, 5, 5A/C, 6 and correctly classified UC patient response at 73% accuracy and 60% AUC; whereas, for NSCLC, 72% accuracy and 61% AUC was achieved. In the second approach IFIT profiling showed significant association of CIC-stages 5C and 6 to OS and describing T-cell infiltration via TLA and intra-tumoral cancer recognition, respectively (p&lt;0.01) (figure 3). GENCODE signatures highlighted key factors in antigen processing and presentation for CIC stage 6, as well as PI3K-Act, cytokine-cytokine receptor interaction, chemokine, Wnt, and T-cell receptor signaling pathways in CIC stage 5 (figure 4). <h3>Conclusions</h3> This study identifies robust gene signatures and demonstrates the potential role of the IFIT score for stage-specific risk stratification in cancer treatment. Gene-specific methods identified CIC stage-specific markers warranting further investigation and corroborate our multi-gene approach with differences likely arising due to shared biological processes across stages. Untangling this complexity will be essential to move the CIC framework forward as an analytic tool for precision therapy and diagnostics. Validation is ongoing with expansion to other indications and earlier disease settings. <h3>Acknowledgements</h3> This research was a collaborative effort of the imCORE Network, made possible through support and funding from F. Hoffmann-La Roche and Institut Roche, France. M. L-d-C. and R. A. were supported by the Gobierno de Navarra through projects ANDIA 2021 no. 0011-3947-2021-000023, and ERA PerMed JTC2022 PORTRAIT no. 0011-2750-2022-000000; M.H. and L.A. supported by RYC2021- 033127-I MCIN/AEI/10.13039/501100011033 y por la Unión Europea ‘NextGenerationEU’/PRTR and Gobierno de Navarra, Proyectos Estratégicos GRANATE. <h3>Trial Registration</h3> NCT02108652, NCT02302807, NCT02366143, NCT02367781. <h3>References</h3> Mellman I, Chen DS, Powles T, Turley SJ. The cancer-immunity cycle: indication, genotype, and immunotype. <i>Immunity</i>. 2023 Oct;<b>56</b>(10):2188–2205. López-De-Castro M, García-Galindo A, Armañanzas R. Conformal recursive feature elimination.<i> arXiv</i>. 2024;2405.19429:1–35. Available from: https://arxiv.org/abs/2405.19429 Ruiz-Arenas C, Marín-Goñi I, Wang L, Ochoa I, Pérez-Jurado LA, Hernaez M. NetActivity enhances transcriptional signals by combining gene expression into robust gene set activity scores through interpretable autoencoders. <i>Nucleic Acids Res</i>. 2024 May 22;<b>52</b>(9):e44. Available from: https://doi.org/10.1093/nar/gkae197. Bareche Y, Kelly D, Abbas-Aghababazadeh F, Nakano M, Esfahani PN, Tkachuk D, Stagg J. Leveraging big data of immune checkpoint blockade response identifies novel potential targets. <i>Ann Oncol</i>. 2022;<b>33</b>(12):1304–1317. <h3>Ethics Approval</h3> Patients included in this analysis were enrolled in Roche sponsored studies with atezolizumab and the informed consent obtained for study participation had provision for secondary use of data.