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
4614 - Predictors of Response to Checkpoint Inhibitors in Naïve and Ipilimumab-Refractory Melanoma
Presenter: Domenico Mallardo
Session: Poster Display session 3
Resources:
Abstract
2901 - IFN-γ/IL-10 ratio as predictive biomarker for response to anti-PD-1 therapy in metastatic melanoma patients
Presenter: Emilio Giunta
Session: Poster Display session 3
Resources:
Abstract
2306 - Multiplex Chromogenic Immunohistochemistry (IHC) for Spatial Analysis of Checkpoint-Positive Tumor Infiltrating Lymphocytes (TILs)
Presenter: Scott Ely
Session: Poster Display session 3
Resources:
Abstract
1678 - The role of PD-L1 expression as a predictive biomarker in advanced renal cell carcinoma: a meta-analysis of randomized clinical trials.
Presenter: Alberto Carretero-Gonzalez
Session: Poster Display session 3
Resources:
Abstract
5138 - Radiomic Features as a Non-invasive Biomarker to Predict Response to Immunotherapy in Recurrent or Metastatic Urothelial Carcinoma
Presenter: Kye Jin Park
Session: Poster Display session 3
Resources:
Abstract
5800 - Integrative combination of high-plex digital profiling techniques and cluster analysis to reveal complex immune biology in the tumor microenvironment of mesothelioma
Presenter: Carmen Ballesteros-Merino
Session: Poster Display session 3
Resources:
Abstract
5736 - Predictive factors of response to immunotherapy in 198 patients with metastatic non-microcytic lung cancer (mNSCLC): real world data from 2 university hospitals in Spain
Presenter: Juan Felipe Cordoba Ortega
Session: Poster Display session 3
Resources:
Abstract
5645 - Evaluating Lung CT Density Changes Among Patients with Extensive Stage Small Cell Lung Cancer (ES-SCLC) Treated with Thoracic Radiotherapy (TRT) alone or TRT Followed by Combined Ipilimumab (IPI) and Nivolumab (NIVO).
Presenter: Kujtim Latifi
Session: Poster Display session 3
Resources:
Abstract
1540 - Immuno-oncology therapy biomarkers differences between polyoma-virus positive and negative Merkel cell carcinomas
Presenter: Zoran Gatalica
Session: Poster Display session 3
Resources:
Abstract
4538 - Can we improve patient selection for phase 1 clinical trials (Ph1) based on Immuno-Oncology score prognostic index (VIO)?
Presenter: Ignacio Matos Garcia
Session: Poster Display session 3
Resources:
Abstract