Your browser doesn't support javascript.
loading
Deep Learning Artificial Intelligence Predicts Homologous Recombination Deficiency and Platinum Response From Histologic Slides.
Bergstrom, Erik N; Abbasi, Ammal; Díaz-Gay, Marcos; Galland, Loïck; Ladoire, Sylvain; Lippman, Scott M; Alexandrov, Ludmil B.
Afiliação
  • Bergstrom EN; Moores Cancer Center, UC San Diego, La Jolla, CA.
  • Abbasi A; Department of Cellular and Molecular Medicine, UC San Diego, La Jolla, CA.
  • Díaz-Gay M; Department of Bioengineering, UC San Diego, La Jolla, CA.
  • Galland L; Moores Cancer Center, UC San Diego, La Jolla, CA.
  • Ladoire S; Department of Cellular and Molecular Medicine, UC San Diego, La Jolla, CA.
  • Lippman SM; Department of Bioengineering, UC San Diego, La Jolla, CA.
  • Alexandrov LB; Moores Cancer Center, UC San Diego, La Jolla, CA.
J Clin Oncol ; : JCO2302641, 2024 Jul 31.
Article em En | MEDLINE | ID: mdl-39083703
ABSTRACT

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 to 0.85). This performance was confirmed in two independent primary breast cancer cohorts (AUC, 0.76 [95% CI, 0.71 to 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 to 0.93]) with 3.7-fold increase in median PFS (14.4 v 3.9 months; P = .0019) and hazard ratio (HR) of 0.45 (P = .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 = .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 = .030) and neoadjuvant (HR, 0.49; P = .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.

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article