Deep Learning Artificial Intelligence Predicts Homologous Recombination Deficiency and Platinum Response From Histologic Slides
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Purpose
Cancers with homologous recombination deficiency (HRD) can benefit from platinum salts and poly(ADP-ribose) polymerase inhibitors. Standard diagnostic tests for detecting HRD require molecular profiling, which is not universally available.
Methods
We trained DeepHRD, a deep learning platform for predicting HRD from hematoxylin and eosin (H&E)–stained histopathological slides, using primary breast (n = 1,008) and ovarian (n = 459) cancers from The Cancer Genome Atlas (TCGA). DeepHRD was compared with four standard HRD molecular tests using breast (n = 349) and ovarian (n = 141) cancers from multiple independent data sets, including platinum-treated clinical cohorts with RECIST progression-free survival (PFS), complete response (CR), and overall survival (OS) endpoints.
Results
DeepHRD predicted HRD from held-out H&E-stained breast cancer slides in TCGA with an AUC of 0.81 (95% CI 0.77-0.85). This performance was confirmed in two independent primary breast cancer cohorts (AUC 0.76, 95% CI 0.71-0.82). In an external platinum-treated metastatic breast cancer cohort, samples predicted as HRD had higher complete CR (AUC 0.76, 95% CI 0.54-0.93) with 3.7-fold increase in median PFS (14.4 vs 3.9 months, P=0.0019) and hazard ratio (HR) of 0.45 (P=0.0047). There were no significant differences in nonplatinum treatment outcome by predicted HRD status in three breast cancer cohorts, including CR (AUC 0.39) and PFS (HR 0.98, P=0.95) in taxane-treated metastatic breast cancer. Through transfer learning to high-grade serous ovarian cancer, DeepHRD-predicted HRD samples had better OS after first-line (HR 0.46, P=0.030) and neoadjuvant (HR 0.49, P=0.015) platinum therapy in two cohorts.
Conclusion
DeepHRD can predict HRD in breast and ovarian cancers directly from routine H&E slides across multiple external cohorts, slide scanners, and tissue fixation variables. When compared with molecular testing, DeepHRD classified 1.8- to 3.1-fold more patients with HRD, which exhibited better OS in high-grade serous ovarian cancer and platinum-specific PFS in metastatic breast cancer.
Read an interview about the study here.
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Deep Learning Artificial Intelligence Predicts Homologous Recombination Deficiency and Platinum Response From Histologic Slides
Primary Source
Journal of Clinical Oncology
Source Reference: