Publication-only abstracts (abstract number preceded by an "e"), published in conjunction with the 2019 ASCO Annual Meeting but not presented at the Meeting, can be found online only.
Imaging biomarker phenotyping system (iBiopsy) to accelerate hepatocellular carcinoma (HCC) drug development.
Gastrointestinal (Noncolorectal) Cancer
2019 ASCO Annual Meeting
J Clin Oncol 37, 2019 (suppl; abstr e15671)
Author(s): Yan Liu, Corinne Ramos, Pierre Baudot, Johan Brag, Olivier Lucidarme; Median Technologies, Valbonne, France; Radiology Unit, Pitié Salpétrière Hospital, APHP, Paris, France
Background: Current drug therapies in HCC remain limited because of substantial genomic, cellular and molecular heterogeneity of the liver tumor microenvironment (TME). This heterogeneity has implications for tumor development, immune response and cellular invasion and is compounded by multiple molecular pathways related to the various HCC etiologies. In this context, it is challenging to find biomarkers that are predictive of therapeutic response or outcome. A systems biology approach leveraging the iBiopsy phenotyping platform to automatically detect HCC and TME subtypes using non-invasive medical imaging could help identify specific disease pathways and accelerate HCC drug development. Methods: In this retrospective study, 50 patients with primary HCC were used to extract voxel level image signatures from the segmented livers on CT scans. The automatic classification of liver signatures was obtained by Log Linear Local density peak clustering computed using Wasserstein’s optimal transport distance between Gray Level Co-occurrence matrices of local luminance tiles of the Liver. Through this hypothesis-free clustering, phenotypes representative of tumor and tumor environment were identified and further correlated with clinical data. Results: Nine classes of statistically significantly distant clusters were identified on the 50 patients. Preliminary correlation analysis on individual patient showed that up to 3 clusters were specific to the tumor and characterized phenotype heterogeneity within the tumor. Notably, specific clusters at the tumor front-line and surrounding adjacent non-tumoral tissue were also identified. Conclusions: Preliminary results show that a multiplicity of phenotypes representative of a complex heterogeneous lesion and tissue microenvironment that might have prognostic and predictive value can be identified. This support the use of a non-invasive phenotypic approach for the subtyping of HCC that can be used for the identification of a multiplicity of important biological pathways that can be specifically targeted to treat HCC.