Deep Learning Predicts Underlying Features on Pathology Images with Therapeutic Relevance for Breast and Gastric Cancer

Cancers

RafaelAndres Rosales Mitrowsky¹  CynthiaAparecida Bueno de Toledo Osório²  Lucas Amaro³  AdrianaPassos Bueno³  IsraelTojal da Silva³  Renan Valieris³  DirceMaria Carraro⁴  DianaNoronha Nunes⁵  Emmanuel Dias-Neto⁵ 

[1] Department of Computation and Mathematics, University of São Paulo, Ribeirão Preto 14040-901, Brazil;Department of Pathology, CIPE/A.C. Camargo Cancer Center, São Paulo 01525-001, Brazil;Laboratory of Computational Biology Bioinformatics, CIPE/A.C. Camargo Cancer Center, São Paulo 01508-010, Brazil;Laboratory of Genomics and Molecular Biology, CIPE/A.C. Camargo Cancer Center, São Paulo 01508-010, Brazil;Medical Genomics Laboratory, CIPE/A.C. Camargo Cancer Center, São Paulo 01525-001, Brazil;

关键词: digital pathology;    deep learning;    mutational signature;    biomarker;    DNA repair deficiency;   

DOI  :  10.3390/cancers12123687

来源: DOAJ

【 摘 要 】

DNA repair deficiency (DRD) is an important driver of carcinogenesis and an efficient target for anti-tumor therapies to improve patient survival. Thus, detection of DRD in tumors is paramount. Currently, determination of DRD in tumors is dependent on wet-lab assays. Here we describe an efficient machine learning algorithm which can predict DRD from histopathological images. The utility of this algorithm is demonstrated with data obtained from 1445 cancer patients. Our method performs rather well when trained on breast cancer specimens with homologous recombination deficiency (HRD), AUC (area under curve) = 0.80. Results for an independent breast cancer cohort achieved an AUC = 0.70. The utility of our method was further shown by considering the detection of mismatch repair deficiency (MMRD) in gastric cancer, yielding an AUC = 0.81. Our results demonstrate the capacity of our learning-base system as a low-cost tool for DRD detection.

【 授权许可】

Unknown