The integration of mass spectrometry-based proteomics with next-generation DNA and RNA sequencing profiles tumors more comprehensively. Here this "proteogenomics" approach was applied to 122 treatment-naive primary breast cancers accrued to preserve post-translational modifications, including protein phosphorylation and acetylation. Proteogenomics challenged standard breast cancer diagnoses, provided detailed analysis of the ERBB2 amplicon, defined tumor subsets that could benefit from immune checkpoint therapy, and allowed more accurate assessment of Rb status for prediction of CDK4/6 inhibitor responsiveness. Phosphoproteomics profiles uncovered novel associations between tumor suppressor loss and targetable kinases. Acetylproteome analysis highlighted acetylation on key nuclear proteins involved in the DNA damage response and revealed cross-talk between cytoplasmic and mitochondrial acetylation and metabolism. Our results underscore the potential of proteogenomics for clinical investigation of breast cancer through more accurate annotation of targetable pathways and biological features of this remarkably heterogeneous malignancy.

Microscaled proteogenomics was deployed to probe the molecular basis for differential response to neoadjuvant carboplatin and docetaxel combination chemotherapy for triple-negative breast cancer (TNBC). Proteomic analyses of pretreatment patient biopsies uniquely revealed metabolic pathways, including oxidative phosphorylation, adipogenesis, and fatty acid metabolism, that were associated with resistance. Both proteomics and transcriptomics revealed that sensitivity was marked by elevation of DNA repair, E2F targets, G2–M checkpoint, interferon-gamma signaling, and immune-checkpoint components. Proteogenomic analyses of somatic copy-number aberrations identified a resistance-associated 19q13.31–33 deletion where LIG1, POLD1, and XRCC1 are located. In orthogonal datasets, LIG1 (DNA ligase I) gene deletion and/or low mRNA expression levels were associated with lack of pathologic complete response, higher chromosomal instability index (CIN), and poor prognosis in TNBC, as well as carboplatin-selective resistance in TNBC preclinical models. Hemizygous loss of LIG1 was also associated with higher CIN and poor prognosis in other cancer types, demonstrating broader clinical implications. Significance: Proteogenomic analysis of triple-negative breast tumors revealed a complex landscape of chemotherapy response associations, including a 19q13.31–33 somatic deletion encoding genes serving lagging-strand DNA synthesis (LIG1, POLD1, and XRCC1), that correlate with lack of pathologic response, carboplatin-selective resistance, and, in pan-cancer studies, poor prognosis and CIN. This article is highlighted in the In This Issue feature, p. 2483

Unlike estrogen receptor (ER)-negative breast cancer, ER-positive breast cancer outcome is less influenced by lymphocyte content, indicating the presence of immune tolerance mechanisms that may be specific to this disease subset. A supervised analysis of microarray data from the ACOSOG Z1031 (Alliance) neoadjuvant aromatase inhibitor (AI) trial identified upregulated genes in Luminal (Lum) B breast cancers that correlated with AI-resistant tumor proliferation (percentage of Ki67-positive cancer nuclei, Pearson r > 0.4) (33 cases Ki67 > 10% on AI) vs LumB breast cancers that were more AI sensitive (33 cases Ki67 < 10% on AI). Overrepresentation analysis was performed using WebGestalt. All statistical tests were two-sided. Thirty candidate genes positively correlated (r ≥ 0.4) with AI-resistant proliferation in LumB and were upregulated greater than twofold. Gene ontologies identified that the targetable immune checkpoint (IC) components IDO1, LAG3, and PD1 were overrepresented resistance candidates (P ≤ .001). High IDO1 mRNA was associated with poor prognosis in LumB disease (Molecular Taxonomy of Breast Cancer International Consortium, hazard ratio = 1.43, 95% confidence interval = 1.04 to 1.98, P = .03). IDO1 also statistically significantly correlated with STAT1 at protein level in LumB disease (Pearson r = 0.74). As a composite immune tolerance signature, expression of IFN-γ/STAT1 pathway components was associated with higher baseline Ki67, lower estrogen, and progesterone receptor mRNA levels and worse disease-specific survival (P = .002). In a tissue microarray analysis, IDO1 was observed in stromal cells and tumor-associated macrophages, with a higher incidence in LumB cases. Furthermore, IDO1 expression was associated with a macrophage mRNA signature (M1 by CIBERSORT Pearson r = 0.62 ) and by tissue microarray analysis. Targetable IC components are upregulated in the majority of endocrine therapy–resistant LumB cases. Our findings provide rationale for IC inhibition in poor-outcome ER-positive breast cancer.

Understanding the dysregulation of complex biological pathways is essential for uncovering molecular mechanisms and identifying novel therapeutic opportunities for complex diseases. In recent years, deep learning (DL) models have shown great potential in modeling biological multi-omics data; however, their "black box" nature limits their application in biological and clinical translation. Knowledge-guided deep learning, particularly methods based on Pathway-Guided Interpretable Deep Learning Architectures (PGI-DLA), aims to improve model performance and interpretability by integrating prior pathway knowledge into the model structure. Here, we review the current progress in PGI-DLA, focusing on omics compatibility, architectural design, feature interpretation, and biological and clinical applications. For widely used pathway databases, such as the Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Ontology (GO), Reactome, and MSigDB, we summarize their differences in knowledge scope, hierarchical structure, level of detail and curation focus. We discuss how the choice of database impacts model design, performance, and interpretability. This review provides valuable guidance for selecting and optimizing pathway databases to implement PGI-DLA to translate omics data into actionable biological and clinical insights.