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Controversial issues in the neoadjuvant treatment of triple-negative breast cancer.

ABSTRACT: Triple-negative breast cancer (TNBC), as a collective group of heterogenous tumours, displays the highest rate of distant recurrence and lowest survival from metastatic disease across breast cancer subtypes. However, a subset of TNBC display impressive primary tumour response to neoadjuvant chemotherapy, translating to reduction in future relapse and increased overall survival. Maximizing early treatment response is crucial to improving the outlook in this subtype. Numerous systemic therapy strategies are being assessed in the neoadjuvant setting and the current paradigm of generic chemotherapy components in regimens for high-risk breast cancers, regardless of biological subtype, is changing. Therapeutic approaches with evidence of benefit include platinum drugs, polyadenosine diphosphate ribose polymerase (PARP) inhibitors, immunotherapy and second adjuvant therapy for those not achieving pathological complete response. Importantly, molecular testing can identify subgroups within TNBC, such as deoxyribonucleic acid (DNA) homologous recombination repair deficiency, lymphocyte-predominant tumours, and TNBC type 4 molecular subtypes. Clinical trials that address the interaction between these biomarkers and treatment approaches are a priority, to identify subgroups benefiting from additional therapy.

SUBMITTER: Fitzpatrick A

PROVIDER: S-EPMC6826917 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Controversial issues in the neoadjuvant treatment of triple-negative breast cancer.

Fitzpatrick Amanda A Tutt Andrew A

Therapeutic advances in medical oncology 20191101

Triple-negative breast cancer (TNBC), as a collective group of heterogenous tumours, displays the highest rate of distant recurrence and lowest survival from metastatic disease across breast cancer subtypes. However, a subset of TNBC display impressive primary tumour response to neoadjuvant chemotherapy, translating to reduction in future relapse and increased overall survival. Maximizing early treatment response is crucial to improving the outlook in this subtype. Numerous systemic therapy stra ...[more]

PMID: 31700549

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Project description:BackgroundNeoadjuvant chemotherapy (NAC) is the standard treatment for early-stage triple negative breast cancer (TNBC). The primary endpoint of NAC is a pathological complete response (pCR). NAC results in pCR in only 30-40% of TNBC patients. Tumor-infiltrating lymphocytes (TILs), Ki67 and phosphohistone H3 (pH3) are a few known biomarkers to predict NAC response. Currently, systematic evaluation of the combined value of these biomarkers in predicting NAC response is lacking. In this study, the predictive value of markers derived from H&E and IHC stained biopsy tissue was comprehensively evaluated using a supervised machine learning (ML)-based approach. Identifying predictive biomarkers could help guide therapeutic decisions by enabling precise stratification of TNBC patients into responders and partial or non-responders.MethodsSerial sections from core needle biopsies (n = 76) were stained with H&E and immunohistochemically for the Ki67 and pH3 markers, followed by whole-slide image (WSI) generation. The serial section stains in H&E stain, Ki67 and pH3 markers formed WSI triplets for each patient. The resulting WSI triplets were co-registered with H&E WSIs serving as the reference. Separate mask region-based CNN (MRCNN) models were trained with annotated H&E, Ki67 and pH3 images for detecting tumor cells, stromal and intratumoral TILs (sTILs and tTILs), Ki67+, and pH3+ cells. Top image patches with a high density of cells of interest were identified as hotspots. Best classifiers for NAC response prediction were identified by training multiple ML models and evaluating their performance by accuracy, area under curve, and confusion matrix analyses.ResultsHighest prediction accuracy was achieved when hotspot regions were identified by tTIL counts and each hotspot was represented by measures of tTILs, sTILs, tumor cells, Ki67+, and pH3+ features. Regardless of the hotspot selection metric, a complementary use of multiple histological features (tTILs, sTILs) and molecular biomarkers (Ki67 and pH3) resulted in top ranked performance at the patient level.ConclusionsOverall, our results emphasize that prediction models for NAC response should be based on biomarkers in combination rather than in isolation. Our study provides compelling evidence to support the use of ML-based models to predict NAC response in patients with TNBC.

Dataset Information

Controversial issues in the neoadjuvant treatment of triple-negative breast cancer.

Publications

Controversial issues in the neoadjuvant treatment of triple-negative breast cancer.

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