From Static to Dynamic: Adaptive Molecular Subtyping in Treated Breast Cancers—Evidence from Single-Center Retrospective Cohort Study

TL;DR

This study shows that breast cancer molecular profiles change during treatment, suggesting that dynamic reassessment could improve personalized treatment strategies.

Contribution

The study provides evidence for the need of dynamic molecular subtyping in breast cancer through longitudinal profiling.

Findings

Molecular profiles of breast cancer tumors frequently change during treatment, including hormone receptor status and HER2 status.

Circulating tumor DNA analysis reveals additional genomic alterations not detected by tissue sampling alone.

Longitudinal molecular assessment identifies clinically actionable changes that static subtyping misses.

Abstract

Breast cancer treatment decisions are commonly based on tumor characteristics assessed at the time of diagnosis. However, breast cancer is a biologically dynamic disease, and tumor molecular profiles can change substantially under therapeutic pressure. Relying on a single baseline evaluation may therefore fail to capture clinically relevant biological evolution that influences treatment response and resistance. In this study, we investigated treatment-associated molecular dynamics by integrating paired tissue-based and blood-based analyses obtained before and after therapy. We observed frequent changes in hormone receptor expression, proliferative activity, HER2 status, and genomic alterations, highlighting substantial molecular plasticity during treatment. Circulating tumor DNA analysis provided complementary information, revealing additional alterations not captured by tissue sampling alone and reflecting tumor heterogeneity across disease compartments. Rather than proposing a new molecular classification system, this study provides biological and translational evidence for dynamically reassessing breast cancer molecular states to enable more personalized, adaptive treatment strategies.

Background/Objective: Breast cancer (BC) management has traditionally relied on static clinicopathologic and immunohistochemical biomarkers (hormone receptor status, HER2 expression, and proliferative activity assessed at diagnosis). However, these biomarkers are typically evaluated at a single time point and may not reflect therapy-induced molecular evolution. This study evaluates whether longitudinal molecular profiling before and after treatment better characterizes tumor dynamics and provides clinically actionable insights into treatment response, resistance, and prognosis. Methods: Thirty-two patients with invasive breast carcinoma were analyzed using histopathology, immunohistochemistry, tissue-based next-generation sequencing, and plasma circulating tumor DNA (ctDNA) analysis. Paired tumor tissue and plasma samples were collected before and after treatment when available. Changes in biomarker expression, molecular subtype, and genomic alterations were assessed to characterize molecular plasticity under therapeutic pressure. Results: The cohort had a median age of 54 years (range 29–86), predominantly invasive ductal carcinoma (>85%), and high-grade disease. Hormone receptor-positive tumors accounted for 78.1%. Molecular subtypes were Luminal A (34.4%), Luminal B HER2− (40.6%), Luminal B HER2+ (6.3%), HER2-enriched (6.3%), and triple-negative breast cancer (12.5%). Initial tissue sequencing identified PI3K/AKT pathway alterations in 28.1% of cases. Post-treatment analyses revealed substantial molecular discordance, including progesterone receptor loss (33.3%), HER2 status changes (33.3%), and Ki67 variability (77.8%). Plasma ctDNA analysis was informative in 53.1% of patients and identified additional clinically relevant alterations, including FGFR1 amplification and BRCA1/2 variants not detected in tissue. Conclusions: BC molecular profiles are dynamic and frequently altered by therapy. Longitudinal molecular assessment reveals clinically actionable changes overlooked by static subtyping, supporting a dynamic model of molecular classification, highlighting the potential value of adaptive molecular subtyping to improve treatment stratification and resistance monitoring.

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