Work

TRACER: response-protected cross-domain alignment of cancer pharmacogenomic models with patient tumors

Year: 2026

Type: article

Abstract: Pharmacogenomics integrates tumor molecular features with drug responses to support precision oncology, but biological domain shifts and optimization conflicts hinder transfer from screened cell lines to unlabeled patient tumors. We developed TRACER (Transfer of Response Across domains via Conflict-aware End-to-end Regulation), an end-to-end framework combining supervised drug-response prediction, biologically conditioned neighbor alignment and response-protected gradient regulation. By projecting only conflicting components of the auxiliary alignment gradient, TRACER preserves the response-optimization direction learned from experimental labels. Across ModelID-disjoint, seen-drug splits, TRACER-Full achieved a global Pearson correlation of 0.5125 $\pm $ 0.0192 on per-drug z-standardized test pairs, compared with 0.5179 $\pm $ 0.0193 for Response-only, a mean difference of $-0.0054$. Mean oncogenic Hallmark neighbor consistency increased by $0.1162 \pm 0.0210$ in alignment-excluded The Cancer Genome Atlas audit samples. At the patient level, a source-validation-defined drug partition and uniform stability analysis supported four predicted pharmacological response subtypes (PRSs) in breast invasive carcinoma (BRCA), four in uterine corpus endometrial carcinoma (UCEC) and two in sarcoma (SARC). PRS-construction-excluded drugs characterized distinct predicted mechanism-level profiles, and pan-cancer indication recovery provided a lineage-aware internal consistency assessment of the patient rankings. TRACER provides an interpretable and auditable framework for response-preserving cross-domain alignment and it generates patient pharmacogenomic hypotheses for subsequent functional validation.

Source: Briefings in Bioinformatics

DOI: 10.1093/bib/bbag593

Authors: Zhenxing Wang, Qihe Wang, Guangchao Wang, Guohua Wang, Yuming Zhao

Institutions: Northeast Forestry University, Harbin Institute of Technology

Language: English

Topic: Computational Drug Discovery Methods

Subfield: Drug Discovery

Field: Pharmacology, Toxicology and Pharmaceutics

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $3,885