Dataset Information

Predicting Relapse in Patients With Triple Negative Breast Cancer (TNBC) Using a Deep-Learning Approach.

ABSTRACT: The abundance and/or location of tumor infiltrating lymphocytes (TILs), especially CD8⁺ T cells, in solid tumors can serve as a prognostic indicator in various types of cancer. However, it is often difficult to select an appropriate threshold value in order to stratify patients into well-defined risk groups. It is also important to select appropriate tumor regions to quantify the abundance of TILs. On the other hand, machine-learning approaches can stratify patients in an unbiased and automatic fashion. Based on immunofluorescence (IF) images of CD8⁺ T lymphocytes and cancer cells, we develop a machine-learning approach which can predict the risk of relapse for patients with Triple Negative Breast Cancer (TNBC). Tumor-section images from 9 patients with poor outcome and 15 patients with good outcome were used as a training set. Tumor-section images of 29 patients in an independent cohort were used to test the predictive power of our algorithm. In the test cohort, 6 (out of 29) patients who belong to the poor-outcome group were all correctly identified by our algorithm; for the 23 (out of 29) patients who belong to the good-outcome group, 17 were correctly predicted with some evidence that improvement is possible if other measures, such as the grade of tumors, are factored in. Our approach does not involve arbitrarily defined metrics and can be applied to other types of cancer in which the abundance/location of CD8⁺ T lymphocytes/other types of cells is an indicator of prognosis.

SUBMITTER: Yu G

PROVIDER: S-EPMC7538858 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Predicting Relapse in Patients With Triple Negative Breast Cancer (TNBC) Using a Deep-Learning Approach.

Yu Guangyuan G Li Xuefei X He Ting-Fang TF Gruosso Tina T Zuo Dongmei D Souleimanova Margarita M Ramos Valentina Muñoz VM Omeroglu Atilla A Meterissian Sarkis S Guiot Marie-Christine MC Yang Li L Yuan Yuan Y Park Morag M Lee Peter P PP Levine Herbert H

Frontiers in physiology 20200923

The abundance and/or location of tumor infiltrating lymphocytes (TILs), especially CD8<sup>+</sup> T cells, in solid tumors can serve as a prognostic indicator in various types of cancer. However, it is often difficult to select an appropriate threshold value in order to stratify patients into well-defined risk groups. It is also important to select appropriate tumor regions to quantify the abundance of TILs. On the other hand, machine-learning approaches can stratify patients in an unbiased and ...[more]

PMID: 33071806

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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.

Project description:BackgroundTriple Negative Breast Cancers (TNBCs) lack the appropriate targets for currently used breast cancer therapies, conferring an aggressive phenotype, more frequent relapse and poorer survival rates. The biological heterogeneity of TNBC complicates the clinical treatment further. We have explored and compared the biological pathways in TNBC and other subtypes of breast cancers, using an in silico approach and the hypothesis that two opposing effects (Yin and Yang) pathways in cancer cells determine the fate of cancer cells. Identifying breast subgroup specific components of these opposing pathways may aid in selecting potential therapeutic targets as well as further classifying the heterogeneous TNBC subtype.MethodsGene expression and patient clinical data from The Cancer Genome Atlas (TCGA) and the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) were used for this study. Gene Set Enrichment Analysis (GSEA) was used to identify the more active pathways in cancer (Yin) than in normal and the more active pathways in normal (Yang) than in cancer. The clustering analysis was performed to compare pathways of TNBC with other types of breast cancers. The association of pathway classified TNBC sub-groups to clinical outcomes was tested using Cox regression model.ResultsAmong 4729 curated canonical pathways in GSEA database, 133 Yin pathways (FDR < 0.05) and 71 Yang pathways (p-value <0.05) were discovered in TNBC. The FOXM1 is the top Yin pathway while PPARα is the top Yang pathway in TNBC. The TNBC and other types of breast cancers showed different pathways enrichment significance profiles. Using top Yin and Yang pathways as classifier, the TNBC can be further subtyped into six sub-groups each having different clinical outcomes.ConclusionWe first reported that the FOMX1 pathway is the most upregulated and the PPARα pathway is the most downregulated pathway in TNBC. These two pathways could be simultaneously targeted in further studies. Also the pathway classifier we performed in this study provided insight into the TNBC heterogeneity.

Dataset Information

Predicting Relapse in Patients With Triple Negative Breast Cancer (TNBC) Using a Deep-Learning Approach.

Publications

Predicting Relapse in Patients With Triple Negative Breast Cancer (TNBC) Using a Deep-Learning Approach.

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