RNA-Based Classification of Homologous Recombination Deficiency in Racially Diverse Patients with Breast Cancer

Abstract

Background: Aberrant expression of DNA repair pathways such as homologous recombination (HR) can lead to DNA repair imbalance, genomic instability, and altered chemotherapy response. DNA repair imbalance may predict prognosis, but variation in DNA repair in diverse cohorts of breast cancer patients is understudied. Methods: To identify RNA-based patterns of DNA repair expression, we performed unsupervised clustering on 51 DNA repair-related genes in the Cancer Genome Atlas Breast Cancer [TCGA BRCA (n = 1094)] and Carolina Breast Cancer Study [CBCS (n = 1461)]. Using published DNA-based HR-deficiency (HRD) scores (high-HRD ≥ 42) from TCGA, we trained an RNA-based supervised classifier. Unsupervised and supervised HRD classifiers were evaluated in association with demographics, tumor characteristics, and clinical outcomes. Results: Unsupervised clustering on DNA repair genes identified four clusters of breast tumors, with one group having high expression of HR genes. Approximately 39.7% of CBCS and 29.3% of TCGA breast tumors had this unsupervised high-HRD (U-HRD) profile. A supervised HRD classifier (S-HRD) trained on TCGA had 84% sensitivity and 73% specificity to detect HRD-high samples. Both U-HRD and S-HRD tumors in CBCS had higher frequency of TP53 mutant-like status (45% and 41% enrichment) and Basal-like subtype (63% and 58% enrichment). S-HRD high was more common among Black patients. Among chemotherapy-treated participants, recurrence was associated with S-HRD high (HR: 2.38, 95% CI = 1.50, 3.78). Conclusion: HRD is associated with poor prognosis and enriched in the tumors of Black women. Impact: RNA-level indicators of HRD are predictive of breast cancer outcomes in diverse populations.

Incorporating RNA-based Risk Scores for Genomic Instability to Predict Breast Cancer Recurrence and Immunogenicity in a Diverse Population

“…In this analysis, markers of genomic instability were associated with tumor immune microenvironments across three large studies representing a total of 4,892 patients with breast cancer, including a large study population of Black and younger patients from the CBCS. The latter population is important given higher proportions of TP53 (26) and HRD ( 17 ) signatures in younger women and Black women. TP53 and HRD status were each associated with robust immune response in breast cancer and having either signature (vs. neither) modified the association between immune status and recurrence.…”

“…Specifically, tumors were classified as TP53 mutant-like (Mut-like) or wildtype (WT)-like using a similarity-to-centroid approach ( 26, 37 ). HRD-high status was determined ( 17 ) using a classifier based on TCGA HRD status (which included HRD-LOH, large-scale transitions, and the number of subchromosomal regions with allelic imbalance extending to the telomere). This classifier has been published previously ( 17 ) and was applied to both CBCS and METABRIC datasets ( 17 ).…”

“…As such, TNBC status has been proposed as a candidate biomarker indicating benefit from immune checkpoint inhibitor pembrolizumab; pembrolizumab has been approved for PD-L1–positive metastatic TNBC ( 14 ) and early-stage II/III TNBC ( 15 ). Unfortunately, an exclusive focus on TNBC may miss immunogenic estrogen receptor (ER)-positive breast cancers, a particular concern among Black patients, who have a higher frequency of basal-like breast cancer among ER-borderline ( 16 ) and who have higher rates of some types of genomic instability ( 17, 18 ).…”

“…Furthermore, Black women and younger women have more frequent TP53 mutations and more frequent HRD ( 26, 27 ). We have shown that RNA-based assessment of TP53 and HRD pathways can sensitively detect DNA repair differences ( 17, 26 ). Thus, we evaluated RNA-based signatures for TP53 and HRD in association with tumor immune profiles in 1,942 patients from the Carolina Breast Cancer Study (CBCS; n = 1,026 Black, 916 non-Black), with validation in two publicly available datasets [the Cancer Genome Atlas (TCGA; n = 1,046) and the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC; n = 1,904)].…”

Prognostic and Predictive Value of LIV1 Expression in Early Breast Cancer and by Molecular Subtype

“…We also defined the LAR subtype and the non-LAR subtypes among the TN tumors according to the Lehmann’s signature [ 29 ]. Finally, and to assess the potential vulnerability or actionability of tumor samples to anti-cancer drugs used or in development in breast cancer, we applied to each dataset separately several multigene signatures: E2F4-activation signature predictive for response to hormone therapy [ 30 ], Rbsig signature predictive for resistance to CDK4/6 inhibitors [ 31 ], TIS (T-cell-inflamed signature) [ 32 ] predictive for response to immune checkpoint inhibitors (ICI), and the HRD signature [ 33 ] predictive for response to PARP inhibitors. We also extracted the expression levels of genes encoding for therapeutic targets of ADC (HER2, TACSTD2/TROP2, Nectin4).…”