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Somatic mutation counts as a surrogate marker for tumor mutation burden to predict progesterone receptor-positive
Bandana Kumari1, Rijhul Lahariya2
1Department of Biochemistry, All India Institute of Medical Sciences, Patna, Bihar, India.
Objectives:
Invasive ductal carcinoma (IDC), the most prevalent subtype of breast cancer, is characterized by significant genomic heterogeneity. Tumor mutation burden (TMB) has emerged as a predictive biomarker for immunotherapy response, yet its estimation via whole-exome sequencing remains complex and costly. This study aimed to evaluate whether total somatic mutation count can serve as a practical surrogate for TMB and assess its association with progesterone receptor (PR) status in PIK3CA-mutated IDC patients.
Methods:
This retrospective observational study utilized publicly available data from a previously published breast cancer sequencing study. A total of 164 female IDC patients with confirmed PIK3CA mutations and documented PR status were included. Relevant genomic and demographic parameters - TMB, mutation count, and age - were extracted and analyzed. Statistical analyses included correlation, intergroup comparisons by PR status, and binary logistic regression. Predictive performance was assessed using area under the receiver operating characteristic (AUROC) curves.
Results:
Patients with PR-negative status exhibited significantly higher TMB and mutation count than PR-positive patients (p-value<0.001 for both). TMB and mutation count were positively correlated (r=0.61, p-value<0.001), indicating overlapping representation of genomic instability. Logistic regression showed that mutation count was a significant predictor of PR status (p-value=0.01). Mutation count demonstrated a slightly superior predictive performance (AUROC=0.738) compared to TMB (AUROC=0.737).
Conclusion:
Total mutation count shows strong potential as a surrogate biomarker for TMB and a predictive marker for PR status in PIK3CA-mutated IDC, offering a cost-effective genomic tool in personalized breast cancer stratification.
