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An Open-Source, Automated Tumor-Infiltrating Lymphocyte Algorithm for Prognosis in Triple-Negative Breast Cancer.
Bai, Yalai; Cole, Kimberly; Martinez-Morilla, Sandra; Ahmed, Fahad Shabbir; Zugazagoitia, Jon; Staaf, Johan; Bosch, Ana; Ehinger, Anna; Nimeus, Emma; Hartman, Johan; Acs, Balazs; Rimm, David L.
Afiliação
  • Bai Y; Department of Pathology, Yale School of Medicine, New Haven, Connecticut.
  • Cole K; Department of Pathology, Yale School of Medicine, New Haven, Connecticut.
  • Martinez-Morilla S; Department of Pathology, Yale School of Medicine, New Haven, Connecticut.
  • Ahmed FS; Department of Pathology, Yale School of Medicine, New Haven, Connecticut.
  • Zugazagoitia J; Department of Pathology, Yale School of Medicine, New Haven, Connecticut.
  • Staaf J; Division of Oncology, Department of Clinical Sciences Lund, Lund University, Medicon Village, SE-22381 Lund, Sweden.
  • Bosch A; Division of Oncology, Department of Clinical Sciences Lund, Lund University, Medicon Village, SE-22381 Lund, Sweden.
  • Ehinger A; Department of Hematology, Oncology and Radiation Physics, Region Skåne, Lund, Sweden.
  • Nimeus E; Department of Genetics and Pathology, Laboratory Medicine, Region Skåne, Lund, Sweden.
  • Hartman J; Division of Oncology, Department of Clinical Sciences Lund, Lund University, Medicon Village, SE-22381 Lund, Sweden.
  • Acs B; Division of Surgery, Department of Clinical Sciences, Lund University, Lund, Sweden.
  • Rimm DL; Department of Oncology and Pathology, Karolinska Institutet, Stockholm, Sweden.
Clin Cancer Res ; 27(20): 5557-5565, 2021 10 15.
Article em En | MEDLINE | ID: mdl-34088723
ABSTRACT

PURPOSE:

Although tumor-infiltrating lymphocytes (TIL) assessment has been acknowledged to have both prognostic and predictive importance in triple-negative breast cancer (TNBC), it is subject to inter and intraobserver variability that has prevented widespread adoption. Here we constructed a machine-learning based breast cancer TIL scoring approach and validated its prognostic potential in multiple TNBC cohorts. EXPERIMENTAL

DESIGN:

Using the QuPath open-source software, we built a neural-network classifier for tumor cells, lymphocytes, fibroblasts, and "other" cells on hematoxylin-eosin (H&E)-stained sections. We analyzed the classifier-derived TIL measurements with five unique constructed TIL variables. A retrospective collection of 171 TNBC cases was used as the discovery set to identify the optimal association of machine-read TIL variables with patient outcome. For validation, we evaluated a retrospective collection of 749 TNBC patients comprised of four independent validation subsets.

RESULTS:

We found that all five machine TIL variables had significant prognostic association with outcomes (P ≤ 0.01 for all comparisons) but showed cell-specific variation in validation sets. Cox regression analysis demonstrated that all five TIL variables were independently associated with improved overall survival after adjusting for clinicopathologic factors including stage, age, and histologic grade (P ≤ 0.0003 for all analyses).

CONCLUSIONS:

Neural net-driven cell classifier-defined TIL variables were robust and independent prognostic factors in several independent validation cohorts of TNBC patients. These objective, open-source TIL variables are freely available to download and can now be considered for testing in a prospective setting to assess clinical utility.See related commentary by Symmans, p. 5446.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Algoritmos / Linfócitos do Interstício Tumoral / Neoplasias de Mama Triplo Negativas Tipo de estudo: Observational_studies / Prognostic_studies Limite: Female / Humans / Middle aged Idioma: En Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Algoritmos / Linfócitos do Interstício Tumoral / Neoplasias de Mama Triplo Negativas Tipo de estudo: Observational_studies / Prognostic_studies Limite: Female / Humans / Middle aged Idioma: En Ano de publicação: 2021 Tipo de documento: Article