Unsupervised transcriptomic analysis of paired pre- and post-treatment specimens reveals divergent chemoimmunomodulatory induction trajectories in breast cancer
- 1University of Florida
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.
Related
- Longitudinal immune trajectories following SARS-CoV-2 vaccination in long-term care facility residents: an unsupervised analysis approach
- Unsupervised clustering of concurrent constitutions in traditional Chinese medicine for personalized treatment
- Trajectories of insomnia and anhedonia during early substance use treatment across opioid, stimulant, and opioid–stimulant co-use
- Treatment-Stratified Immune–Inflammatory Trajectories and Early Response in Patients with Hepatocellular Carcinoma Receiving Systemic Therapy
- Cardiovascular-kidney-metabolic syndrome in the tumor host macroenvironment: organ crosstalk, treatment trajectories, and net clinical benefit
BibTeX
@misc{lima2026unsupervised,
title = {{Unsupervised transcriptomic analysis of paired pre- and post-treatment specimens reveals divergent chemoimmunomodulatory induction trajectories in breast cancer}},
author = {I. Lopes de Lima and E. Molchan and K. H. Streeks and K. L. Coleman and M. S. Makarem and N. Seligson and M. O. Gbadamosi},
howpublished = {bioRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.24.754178},
url = {https://www.biorxiv.org/content/10.64898/2026.09.24.754178v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Unsupervised transcriptomic analysis of paired pre- and post-treatment specimens reveals divergent chemoimmunomodulatory induction trajectories in breast cancer
AU - I. Lopes de Lima
AU - E. Molchan
AU - K. H. Streeks
AU - K. L. Coleman
AU - M. S. Makarem
AU - N. Seligson
AU - M. O. Gbadamosi
PY - 2026
DA - 2026/09/24
PB - bioRxiv
DO - 10.64898/2026.09.24.754178
UR - https://www.biorxiv.org/content/10.64898/2026.09.24.754178v1
AB - 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.
ER -