Abstract 5291: Characterizing the interplay between angiogenic and immunoactive factors of hepatocellular carcinoma
Bibliographic record
Abstract
Abstract Hepatocellular carcinoma (HCC) is the fourth leading cause of cancer-related death globally. Patients typically present at an advanced stage and less than 50% reach the maximum 1-year survival rate, when given as first-line treatment, Sorafenib. This highlights the need for early detection and novel therapeutic targets crucial to increase overall survival (OS) for patients with HCC. Given the important role of angiogenesis in HCC from its early stage and its rich immune composition, anti-angiogenic and immune checkpoint inhibitors (ICI), are two therapeutic approaches when combined marked the first treatment in more than a decade to significantly improve the overall survival and progression-free survival in patients with advanced HCC compared to Sorafenib. While the combination of agents inhibiting angiogenesis and ICI have recently entered the clinic, the interplay between angiogenic factors and immunity in the context of this approach remains poorly understood. Here we focus on understanding the interplay between the vascular state of the tumor and the immune response in HCC. As a first step, we focus on defining the immune and vasculature landscape of the central tumor, peripheral tumor, adjacent liver to the tumor, and distal liver regions of each lesion by immunohistochemistry (IHC). Forty (40) formalin-fixed paraffin-embedded (FFPE) human liver tissue samples containing untreated and non-viral HCC tumors, obtained from the Liver Disease Biobank of the RI-MUHC were used to perform IHC. Images were viewed and scored using the Aperio ImageScope software. With respect to the vasculature, we observe that all tumors have a combination of both angiogenic (CD34/Ki67+ve) and co-optioning (CD31 +ve) features, with no uniform distribution. Our immune markers demonstrate that both adaptive and innate immune cells are present at the interface and different tumors demonstrate different levels of infiltration. Next, to identify the immune subtype populations (ie macrophage M1 vs M2, Treg, etc) present, we will use the NanoString Whole Transcriptome Atlas spatial profiler technology. We will also then link the vasculature to any specific immune profile. For example, it has been shown in other cancer types that macrophages are associated with co-option. This project presents preliminary evidence for the interaction of vascular factors with immune cells, thus providing insight into the biological rationale of why 30% of patients responded to combined angiogenic and immunotherapy treatment. Citation Format: Audrey Kapelanski-Lamoureux, Flemming Kondrup, Lucyna Krzywon, Stephanie K. Petrillo, Anthoula Lazaris, Peter Metrakos. Characterizing the interplay between angiogenic and immunoactive factors of hepatocellular carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5291.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".