Deep learning predicts chromosomal instability from histopathology images
Zhuoran Xu,
Akanksha Verma,
Uska Naveed,
Samuel F. Bakhoum,
Pegah Khosravi,
Olivier Elemento
Affiliations
Zhuoran Xu
Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York 10065, USA; Pathology and Laboratory Medicine, Weill Cornell Medicine, New York 10065, USA
Akanksha Verma
Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York 10065, USA
Uska Naveed
Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York 10065, USA
Samuel F. Bakhoum
Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York 10021, USA; Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York 10021, USA
Pegah Khosravi
Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York 10065, USA; Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York 10021, USA
Olivier Elemento
Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York 10065, USA; Corresponding author
Summary: Chromosomal instability (CIN) is a hallmark of human cancer yet not readily testable for patients with cancer in routine clinical setting. In this study, we sought to explore whether CIN status can be predicted using ubiquitously available hematoxylin and eosin histology through a deep learning-based model. When applied to a cohort of 1,010 patients with breast cancer (Training set: n = 858, Test set: n = 152) from The Cancer Genome Atlas where 485 patients have high CIN status, our model accurately classified CIN status, achieving an area under the curve of 0.822 with 81.2% sensitivity and 68.7% specificity in the test set. Patch-level predictions of CIN status suggested intra-tumor heterogeneity within slides. Moreover, presence of patches with high predicted CIN score within an entire slide was more predictive of clinical outcome than the average CIN score of the slide, thus underscoring the clinical importance of intra-tumor heterogeneity.
