An integrative approach of digital image analysis and transcriptome profiling to explore potential predictive biomarkers for TGFβ blockade therapy.
10.1016/j.apsb.2022.03.013
- Author:
Robert POMPONIO
1
;
Qi TANG
2
;
Anthony MEI
2
;
Anne CARON
3
;
Bema COULIBALY
3
;
Joachim THEILHABER
4
;
Maximilian ROGERS-GRAZADO
4
;
Michele SANICOLA-NADEL
5
;
Souad NAIMI
3
;
Reza OLFATI-SABER
2
;
Cecile COMBEAU
1
;
Jack POLLARD
4
;
Tun Tun LIN
5
;
Rui WANG
1
Author Information
1. Sanofi Translational Medicine Early Development Cambridge, MA 02142, USA.
2. Sanofi Artificial Intelligence and Deep Analytics Data and Data Sciences, Cambridge, MA 02142, USA.
3. Sanofi Translational Science, Vitry sur Seine 94403, France.
4. Sanofi Precision Oncology Cambridge, MA 02142, USA.
5. Sanofi Oncology Early Development Cambridge, MA 02142, USA.
- Publication Type:Journal Article
- Keywords:
Artificial intelligence;
Digital pathology;
Machine learning;
Predictive biomarker;
T cell infiltration;
TGFβ;
Transcriptomic profiling;
Tumor topography
- From:
Acta Pharmaceutica Sinica B
2022;12(9):3594-3601
- CountryChina
- Language:English
-
Abstract:
Increasing evidence suggests that the presence and spatial localization and distribution pattern of tumor infiltrating lymphocytes (TILs) is associate with response to immunotherapies. Recent studies have identified TGFβ activity and signaling as a determinant of T cell exclusion in the tumor microenvironment and poor response to PD-1/PD-L1 blockade. Here we coupled the artificial intelligence (AI)-powered digital image analysis and gene expression profiling as an integrative approach to quantify distribution of TILs and characterize the associated TGFβ pathway activity. Analysis of T cell spatial distribution in the solid tumor biopsies revealed substantial differences in the distribution patterns. The digital image analysis approach achieves 74% concordance with the pathologist assessment for tumor-immune phenotypes. The transcriptomic profiling suggests that the TIL score was negatively correlated with TGFβ pathway activation, together with elevated TGFβ signaling activity observed in excluded and desert tumor phenotypes. The present results demonstrate that the automated digital pathology algorithm for quantitative analysis of CD8 immunohistochemistry image can successfully assign the tumor into one of three infiltration phenotypes: immune desert, immune excluded or immune inflamed. The association between "cold" tumor-immune phenotypes and TGFβ signature further demonstrates their potential as predictive biomarkers to identify appropriate patients that may benefit from TGFβ blockade.
