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Computational image features of immune architecture is associated with clinical benefit and survival in gynecological cancers across treatment modalities.
Azarianpour, Sepideh; Corredor, Germán; Bera, Kaustav; Leo, Patrick; Fu, Pingfu; Toro, Paula; Joehlin-Price, Amy; Mokhtari, Mojgan; Mahdi, Haider; Madabhushi, Anant.
Afiliación
  • Azarianpour S; Center for Computational Imaging and Personalized Diagnostics, Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA.
  • Corredor G; Center for Computational Imaging and Personalized Diagnostics, Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA.
  • Bera K; Louis Stokes Cleveland VA Medical Center, Cleveland, Ohio, USA.
  • Leo P; Center for Computational Imaging and Personalized Diagnostics, Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA.
  • Fu P; Center for Computational Imaging and Personalized Diagnostics, Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA.
  • Toro P; Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio, USA.
  • Joehlin-Price A; Department of Pathology, Cleveland Clinic, Cleveland, Ohio, USA.
  • Mokhtari M; Department of Pathology, Cleveland Clinic, Cleveland, Ohio, USA.
  • Mahdi H; Center for Computational Imaging and Personalized Diagnostics, Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA.
  • Madabhushi A; School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran (the Islamic Republic of).
J Immunother Cancer ; 10(2)2022 02.
Article en En | MEDLINE | ID: mdl-35115363
ABSTRACT

BACKGROUND:

We present a computational approach (ArcTIL) for quantitative characterization of the architecture of tumor-infiltrating lymphocytes (TILs) and their interplay with cancer cells from digitized H&E-stained histology whole slide images and evaluate its prognostic role in three different gynecological cancer (GC) types and across three different treatment types (platinum, radiation and immunotherapy).

METHODS:

In this retrospective study, we included 926 patients with GC diagnosed with ovarian cancer (OC), cervical cancer, and endometrial cancer with available digitized diagnostic histology slides and survival outcome information. ArcTIL features quantifying architecture and spatial interplay between immune cells and the rest of nucleated cells (mostly comprised cancer cells) were extracted from the cell cluster graphs of nuclei within the tumor epithelial nests, surrounding stroma and invasive tumor front compartments on H&E-stained slides. A Cox proportional hazards model, incorporating ArcTIL features was fit on the OC training cohort (N=51), yielding an ArcTIL signature. A unique threshold learned from the training set stratified the patients into a low and high-risk group.

RESULTS:

The seven feature ArcTIL classifier was found to significantly correlate with overall survival in chemotherapy and radiotherapy-treated validation cohorts and progression-free survival in an immunotherapy-treated validation cohort. ArcTIL features relating to increased density of TILs in the epithelium and invasive tumor front were found to be associated with better survival outcomes when compared with those patients with an increased TIL density in the stroma. A statistically significant association was found between the ArcTIL signature and signaling pathways for blood vessel morphogenesis, vasculature development, regulation of cell differentiation, cell-substrate adhesion, biological adhesion, regulation of vasculature development, and angiogenesis.

CONCLUSIONS:

This study reveals that computationally-derived features from the spatial architecture of TILs and tumor cells are prognostic in GCs treated with chemotherapy, radiotherapy, and checkpoint blockade and are closely associated with central biological processes that impact tumor progression. These findings could aid in identifying therapy-refractory patients and further enable personalized treatment decision-making.
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Biomarcadores de Tumor / Biología Computacional / Neoplasias de los Genitales Femeninos / Inmunoterapia Tipo de estudio: Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Aged / Female / Humans / Middle aged Idioma: En Revista: J Immunother Cancer Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Biomarcadores de Tumor / Biología Computacional / Neoplasias de los Genitales Femeninos / Inmunoterapia Tipo de estudio: Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Aged / Female / Humans / Middle aged Idioma: En Revista: J Immunother Cancer Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos