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
2963 - Analytical performance of the Resolution-HRD plasma assay used to identify mCRPC patients with biallelic disruption of DNA repair genes for treatment with niraparib
Presenter: Ira Pekker
Session: Poster Display session 3
Resources:
Abstract
3523 - Results of a global external quality assessment scheme for EGFR testing on liquid biopsy
Presenter: Nicola Normanno
Session: Poster Display session 3
Resources:
Abstract
3295 - Clinical impact of plasma Next-Generation Sequencing (NGS) in advanced Non-small cell lung cancer (aNSCLC)
Presenter: Laura Bonanno
Session: Poster Display session 3
Resources:
Abstract
5632 - Feasibility study of a ctEGFR prototype assay on the fully automated Idylla™ platform
Presenter: Martin Reijans
Session: Poster Display session 3
Resources:
Abstract
3614 - Enhanced Access to EGFR Molecular Testing in NSCLC using a Cell-Free DNA Tube for Liquid Biopsy
Presenter: Theresa May
Session: Poster Display session 3
Resources:
Abstract
5664 - Analysis of circulating tumor DNA in paired plasma and sputum samples of EGFR-mutated NSCLC patients
Presenter: Christina Grech
Session: Poster Display session 3
Resources:
Abstract
4945 - Liquid biopsy and Array Comparative Genomic Hybridization (aCGH)
Presenter: Panagiotis Apostolou
Session: Poster Display session 3
Resources:
Abstract
5746 - Next-generation sequencing panel verification to detect low frequency single nucleotide and copy number variants from mixing cell line studies
Presenter: Rocio Rosas-Alonso
Session: Poster Display session 3
Resources:
Abstract
5901 - Automated rarefaction analysis for precision B and T cell receptor repertoire profiling from peripheral blood and FFPE-preserved tumor
Presenter: Luca Quagliata
Session: Poster Display session 3
Resources:
Abstract
2027 - A Heptamethine cyanine dye is a potential diagnostic marker for Myeloid-Derived Suppressor Cells
Presenter: Chaeyong Jung
Session: Poster Display session 3
Resources:
Abstract