Abstract 2752
Background
The tumor immune microenvironment (TIME) may hold critical information for developing and optimizing immuno-therapeutic approaches, identifying predictive signatures, and selecting the most adequate treatment option for a given patient. Tissue phenomics facilitates the use of the TIME to derive predictive conclusions. The visual information content in histological sections is systematically converted into numerical readouts using artificial intelligence (AI). Resulting quantitative descriptors, phenes, of detected structures are mined to yield local expression profiles; this spatial data aggregation detects categories of local environments, which are correlated to clinical, genomic or other -omics data to identify relevant cohort subpopulations.
Methods
Exploration of this technology is illustrated by various examples on different cohorts of NSCLC patients: A categorization of n = 45 non-IO-treated patients with respect to local immune profiles learned via AI in a hypothesis-free scenario was examined. A deep learning based PD-L1 scoring was compared to 3 pathologist’s scoring on n = 40 durvalumab-treated patients using the cutoff 25% of tumor cells staining positive for PD-L1 at any intensity. The predictive value of a digital signature combining cell densities of PD-L1 and CD8+ was tested on n = 163 durvalumab-treated and n = 199 non-IO-treated samples.
Results
A categorization into biologically interpretable classes learned by AI illustrates the exploratory benefits of tissue phenomics. The scoring algorithm could reproduce survival prediction when compared to pathologist’s visual scoring.The digital signature suggests a predictive value for patient stratification into responders and non-responders for durvalumab, while no prognostic value could be found on the non-IO-treated patients. Kaplan-Meier plots for the 2 latter examples will be presented in the poster.
Conclusions
Tissue phenomics facilitates the quantitative assessment of the tumor geography and may lead to improved tools for biomarker analysis and diagnosis. Analysis on larger and prospective datasets are to be conducted in the future to strengthen the findings.
Clinical trial identification
All of these results have been generated retrospectively from samples unrelated to a trial or related to the durvalumab-trial NCT01693562.
Editorial acknowledgement
Legal entity responsible for the study
The authors.
Funding
Boehringer Ingelheim, MedImmune, Definiens AG.
Disclosure
M. Groher: Full / Part-time employment: Definiens AG. J. Zimmermann: Shareholder / Stockholder / Stock options: AstraZeneca; Full / Part-time employment: Definiens AG. H. Musa: Full / Part-time employment: Boehringer Ingelheim. A. Ackermann: Full / Part-time employment: Boehringer Ingelheim. M. Surace: Shareholder / Stockholder / Stock options, Full / Part-time employment: AstraZeneca. J. Rodriguez-Canales: Shareholder / Stockholder / Stock options, Full / Part-time employment: AstraZeneca. M. Rebelatto: Shareholder / Stackeholder / Stock options: AstraZenec LLC; Full / Part-time employment: AstraZeneca LLC. K. Steele: Shareholder / Stockholder / Stock options, Full / Part-time employment: AstraZeneca; Spouse / Financial dependant: Arcellx LLC. A. Kapil: Full / Part-time employment: Definiens AG. N. Brieu: Shareholder / Stockholder / Stock options, Full / Part-time employment: Definiens AG. L. Rognoni: Full / Part-time employment: Definiens AG. F. Segerer: Full / Part-time employment: Definiens AG. A. Spitzmüller: Full / Part-time employment: Definiens AG. T. Tan: Full / Part-time employment: Definiens AG. A. Schäpe: Full / Part-time employment: Definiens AG. G. Schmidt: Full / Part-time employment: Definiens AG; Shareholder / Stockholder / Stock options: AstraZeneca.
Resources from the same session
2115 - Preclinical in vivo screening to predict responder patients depend on EGFR status
Presenter: Yejin Kim
Session: Poster Display session 3
Resources:
Abstract
3349 - Interplay between miR-17-5p and MALAT-1 Shapes The Cytokine Storm in Triple Negative Breast Cancer (TNBC) Tumor Microenvironment
Presenter: Raghda Soliman
Session: Poster Display session 3
Resources:
Abstract
4014 - Clinical verification on the relationship between lipid metabolism and the immune microenvironment of breast cancer
Presenter: Wataru Goto
Session: Poster Display session 3
Resources:
Abstract
4158 - The clinical and transcriptional signatures of human CD204 reveal an applicable marker for tumor associated macrophage in breast cancer
Presenter: Yunjie He
Session: Poster Display session 3
Resources:
Abstract
5392 - Activated effector T cells co-expressing multiple inhibitory receptors (IRs) are enriched in the tumor immune microenvironment in high grade serous ovarian cancer (HGSOC)
Presenter: Alice Bergamini
Session: Poster Display session 3
Resources:
Abstract
2617 - Oncolytic reovirus as a new anti-tumor strategy in castration resistant prostate cancer
Presenter: Yunlim Kim
Session: Poster Display session 3
Resources:
Abstract
2995 - Dysregulation of helper T lymphocytes in esophageal squamous cell carcinoma (ESCC) patients is highly associated with aberrant production of miR-21
Presenter: Ali Memarian
Session: Poster Display session 3
Resources:
Abstract
3597 - Myeloid derived suppressor cells but not regulatory T cells are associated with adaptive immunity and clinical outcomes in anal squamous cell carcinoma
Presenter: Christophe Borg
Session: Poster Display session 3
Resources:
Abstract
3430 - Evaluation of immune responses among responders (R) and non-responders (non-R) in a humanized mouse model with colorectal cancer (CRC) xenografts treated with combination immunotherapy
Presenter: Juan Marín Jiménez
Session: Poster Display session 3
Resources:
Abstract
1995 - ¬¬Advanced melanoma patients with high CD16+ macrophages have better response and survival to anti-PD-1 based immunotherapy
Presenter: Hansol Lee
Session: Poster Display session 3
Resources:
Abstract