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. 2025 Sep;213(2):247-259.
doi: 10.1007/s10549-025-07759-7. Epub 2025 Jul 12.

Genomic landscape of hormone therapy-resistant HR-positive, HER2-negative breast cancer

Rohan Chaubal  1   2   3 , Elizabeth Talker  2   4 , Jaya Chitra  1   2 , Rasika Kadam  2   4 , Nilesh Gardi  2   3   4 , Riddhi Ursekar  2   4 , Anushree Kadam  2   4 , Ankita Singh  2   4 , Suhani Sale  1   2 , Shwetali Pandey  2   4 , Mrudula Madhav  2   4 , Aishwarya Raja  2   3   4 , Rushikesh Mukhare  2   3   4 , Pallavi Parab  3   4 , Nitin Shetty  3   5 , Kunal Gala  3   5 , Suyash Kulkarni  3   5 , Khushboo A Gandhi  2   4 , Seema Gulia  3   4 , Shalaka Joshi  1   2   3 , Tanuja Shet  3   6 , Sudeep Gupta  7   8   9
Affiliations

Genomic landscape of hormone therapy-resistant HR-positive, HER2-negative breast cancer

Rohan Chaubal et al. Breast Cancer Res Treat. 2025 Sep.

Abstract

Purpose: We aimed to characterize the genomic landscape of hormone receptor-positive (HR+)/HER2-negative breast cancer in patients with hormone therapy-resistant and -sensitive phenotypes.

Methods: HR+/HER2-negative patients who were disease-free for ≥2 years were considered hormone therapy-sensitive (n = 19), while those who experienced disease progression within 2 years were considered hormone therapy-resistant (n = 48). Whole-exome sequencing (WES) was performed on paired (treatment-naïve and relapse-site) tumor and germline-derived DNA from resistant patients (n = 19), and targeted next-generation sequencing (NGS) was performed on plasma-derived circulating tumor DNA (ctDNA) from resistant (n = 35) and sensitive (n = 19) patients.

Results: In 19 resistant patients, the mutation burden was higher in relapse-site compared with treatment-naïve samples (median 0.883 vs 0.655 mutations/mb, p = 0.03), there were 64 driver mutations (median treatment-naïve versus relapse-site; 2/sample vs. 3/relapse), of which 21 mutations in 8 genes in 15 (78.9%) patients were classified as actionable, and branching evolutionary trajectories were seen in 18 (94.7%) patients, with the presence of PIK3CA and/or TP53 mutations in stem clones of 13 (68.4%) patients. ctDNA analysis in 35 resistant patients identified 27 actionable hotspot mutations, such as PIK3CA H1047X, AKT1 p.E17K, CDH1 p.R63X, CDKN2A p.X50*, ERBB2 p.D769Y, and ESR1 p.E380Q, in 25 (71.4%) patients. Among 19 patients with hormone therapy-sensitive disease who were in remission at the time of sample collection, ctDNA analysis showed driver mutations in 10 (52.6%) patients, of whom 2 patients subsequently experienced relapse and died.

Conclusion: Hormone therapy-resistant HR+/HER2-negative breast cancers are polyclonal, acquire actionable alterations at relapse, and moderate-depth ctDNA successfully identifies many clonal mutations, suggesting a role for liquid biopsy monitoring in these patients.

Keywords: Actionable mutations; Breast cancer; Circulating tumor DNA; Hormone therapy resistance; Liquid biopsy.

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Conflict of interest statement

Declarations. Conflict of interest: The authors declare no competing interests. Ethics: This study was conducted in accordance with the Declaration of Helsinki. Patients were recruited after obtaining informed consent under the appropriate protocol for each study as approved by the Tata Memorial Hospital (TMH) and ACTREC Institutional Ethics Committee (IEC) for IEC study no. 900239 registered with the Clinical Trials Registry of India (CTRI) as CTRI/2017/11/010553.

Figures

Fig. 1
Fig. 1
A,B Study design, schema, and patient recruitment flowchart
Fig. 2
Fig. 2
Driver mutations identified as actionable by ESCAT analysis on CGI for 19 hormone therapy-resistant patients with WES data of paired treatment-naïve and relapse-site tissue biopsies. Each column is a patient, and each row is a mutation. ESCAT level of evidence is indicated beside each mutation along with treatment
Fig. 3
Fig. 3
Distribution of mutations across each clone in clonal analysis of 19 patients from WES data of paired treatment-naïve and relapse-site tissue biopsies
Fig. 4
Fig. 4
Clonal landscape of 18 patients with WES data of treatment-naïve and relapse-site tissue biopsies. Patients are divided into those with TP53 and PIK3CA in stem clone (A), TP53 alone in stem clone (B), PIK3CA alone in stem clone (C), and those with other driver mutations in stem clone (D)
Fig. 5
Fig. 5
Driver mutations identified as actionable by ESCAT analysis on CGI for the entire cohort of 66 patients, with data from WES and ctDNA combined. Each column is a patient, and each row is a mutation. The black box indicates “yes”. The key indicates color coding for tissues in which mutations have been identified
See this image and copyright information in PMC

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