Abstract 1189: Deciphering the impact of immune editing on liver cancer clonal evolution using immunogenomics
Bibliographic record
Abstract
Abstract Background and aims: Clonal evolution of a tumor ecosystem depends not only on somatic mutations driving uncontrolled growth, but on a full array of selection pressures, principally immune and resource mediated. We aimed at mapping the spatio-temporal interactions between cancer and immune cells in hepatocellular carcinoma (HCC) by quantifying regional adaptive immune editing, and how this impacts clonal evolution. Methods: We integrated data from RNAseq, targeted DNA sequencing, SNP array and histological evaluation from multiple regions of 10 HCC treatment-naive surgical specimens (55 samples, median of 4 tumor and 2 non-tumor regions per patient), 6 of whom with Hepatitis B virus infection (HBV). Immune cells were assessed with immunofluorescence for T (CD3) and B (CD19) markers. MAGEA3 was down-regulated in 3 liver cancer cell lines using shRNA and proliferation assessed with the MTS assay. Analyses included: intra-tumoral and HBV differential gene expression, prediction of immunogenicity of expressed mutations (i.e., tumor neoantigens) and HBV antigens, T and B cell receptor sequencing and survival and network analysis on the liver cancer TCGA dataset. Results & Conclusions: There is a tumor-driven adaptive immune response contributing to HCC heterogeneity, mainly recruited by subclonal mutations as compared to an interplay between clonal mutations and HBV epitopes. Indeed, we found different regional configurations of tumor infiltrating lymphocytes with higher density of Tertiary Lymphoid Structures in areas enriched in highly immunogenic tumor neoantigens. Furthermore, regional differences in gene expression of heterogeneous tumors can capture stronger prognostic signals than best-in-class single biopsy based predictors tested on TCGA LIHC HCC data. This demonstrates that the breadth of molecular states in a single tumor can easily approach that of a large population sample of tumors. We also found evidence that some tumors' spatiotemporal expression profile is directly correlated with HBV expression, including the extreme case where some regions of a HBV infected patient have zero HBV expression, suggesting regional selection of infected clones via resource constraints. Finally we found that some tumoral evolution is dominated by cancer testis antigen (CTA) dysregulation led by MAGEA3/6, which we determined is a key causal driver of a major regulatory subnetwork from a Bayesian gene interaction network we inferred from TCGA LIHC HCC data. Hypothesizing a oncodriver role for MAGEA3, we confirmed that MAGEA3 downregulation has anti-tumoral effects in HCC experimental models. Citation Format: Bojan Losic, Amanda J. Craig, Sebastiao N. Martins-Filho, Carlos Villacorta-Martin, Nicholas Akers, Xintong Chen, Mehmet E. Ahsen, Ismail Labgaa, Delia D'Avola, Sergio A. Lira, Glaucia C. Furtado, Ashley Stueck, Stphen C. Ward, Maria I. Fiel, Ganesh Gunasekaran, Daniela Sia, Eric E. Schadt, Myron Schwartz, Josep M. Llovet, Swan Thung, Gustavo Stolovitzky, Augusto Villanueva. Deciphering the impact of immune editing on liver cancer clonal evolution using immunogenomics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 1189.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".