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Unsupervised transcriptomic analysis of paired pre- and post-treatment specimens reveals divergent chemoimmunomodulatory induction trajectories in breast cancer

Lopes de Lima, I.; Molchan, E.; Streeks, K. H.; Coleman, K. L.; Makarem, M. S.; Seligson, N.; Gbadamosi, M. O.

2026-09-28 cancer biology
10.64898/2026.09.24.754178 bioRxiv
Show abstract

The immunomodulatory effects of chemotherapy (chemoimmunomodulation; CIM) are clinically consequential and heterogeneous, yet no systematic framework exists for classifying the immunomodulatory trajectory a tumor follows in response to treatment (CIM trajectory). Here, we present the CIM Induction Classifier (CIMIC), an unsupervised clustering pipeline leveraging delta gene expression across 3,189 CIM-related genes to classify specimens chemoimmunomodulatory trajectory. Applied to two pre- and post-chemotherapy breast cancer (BC) datasets (NKI/SMC, N = 36; NEO, N = 19) and nine epirubicin-perturbed triple-negative BC (TNBC) cell lines, CIMIC identified two divergent CIM trajectories: a functional CIM (Fun-CIM) trajectory, broadly conserved across tumors and cell lines and characterized by induction of inflammatory cell death, antigen presentation, viral mimicry, and adaptive immune activation programs, and a dysfunctional CIM (Dys-CIM) trajectory, characterized by induction of proteostatic and metabolic stress-adaptation programs, reduced immune cell abundances and cytotoxic activity, and enrichment of aggressive BC subtypes. Using survival and longitudinal transcriptomic data in NKI/SMC (N = 20), treatment-induced increases in Fun-CIM-associated genes and ssGSEA scores were associated with reduced recurrence, whereas Dys-CIM-associated genes and scores were associated with increased recurrence. In multivariable analyses within independent chemotherapy-treated BC cohorts (METABRIC, N = 412; SCAN-B, N = 2,462), higher baseline Fun-CIM ssGSEA scores were associated with better outcomes, whereas higher baseline Dys-CIM ssGSEA scores were associated with worse outcomes. These findings establish CIM as a dynamic, trajectory-level process and position CIMIC as a framework for defining CIM trajectories and supporting future efforts to identify predictors, mechanisms, and therapeutic strategies that maximize beneficial CIM.

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