Abstract 5813: Evolution of the tumor immune microenvironment (TIME) in hepatocellular carcinoma (HCC) with liver metastases treated with atezolizumab-bevacizumab (INTEGRATE)
Bibliographic record
Abstract
Background: HCC is the most common form of primary liver cancer and the 3rd leading cause of cancer death globally. Combination of atezolizumab-bevacizumab (AB) demonstrated an impressive 19-month median survival in 1st line HCC (IMbrave-150) and a HR (0.66) that has not been surpassed by newer combinations and remains an important standard of care with an ORR of 32%. Despite advancements in the treatment of advanced HCC, ∼30% of patients demonstrate no response to therapy. It is theorized that there is a dynamic interplay between the liver TIME and HCC. We performed an exploratory evaluation of the evolution of the TIME in HCC treated prospectively with AB Methods: The study was carried out at Princess Margaret Cancer Centre between July 2021 and June 2024. Patients had a histologically confirmed diagnosis of inoperable HCC, Child’s Pugh Class A and were receiving AB. Biopsies of tumor and normal liver were performed at pre-treatment and at the time of progression. Blood and stool samples were collected at pre-treatment, 3-4 weeks post initiation of therapy and the time of progression. Blood samples were analyzed for cfDNA, alpha-fetoprotein (AFP) and immune cell phenotyping using 30+ marker spectral flow cytometry panels and stool samples were analyzed for microbiome. Results: The analysis included 8 treatment response evaluable patients with paired tumor samples, median age of 63 (46-77) years, 5 patients had hepatitis B/C as a risk factor for HCC, 2 non-alcoholic and 1 alcoholic liver disease. 3 patients had progressive disease at 1st interval CT, with 2 patients had ongoing response after >30 months. Patients with a baseline frequency of circulating cytotoxic CD8+ TEFF cells (effector T cells) above the median value (3.78%) exhibited numerically improved PFS (P=0.0265) compared to those with lower frequencies (median PFS: 12.67 months vs. 2.4 months; HR 0.12, 95% CI: 0.01-1.08). TEFF cells are terminally differentiated (TCF1-CD57+), but not exhausted (PD1lowTIGITlow). Conclusion: A greater knowledge of the TIME and how it changes with therapy is key to understand which patients are likely to respond to immune checkpoint inhibition. Our findings suggest that enrichment of preexisting, circulating cytotoxic CD8+ TEFF cells may be important for mediating treatment responses to immune checkpoint inhibition in HCC patients. Deep immune profiling is being conducted on serial tumor biopsies using combined single-cell TCR sequencing, and transcriptomic and protein expression analysis (CITE-Seq) to characterize tissue-specific immune responses and identify potential virus-specific T cell clones. This analysis will be updated and has the potential to identify mechanistic biomarkers predictive of response or resistance to treatment. Citation Format: Harry Myles Harvey, Stephanie Wong, Selina Melillo, Jehan Vakharia, Simone Stone, Azin Sayad, Ben Wang, Pamela Ohashi, Rachel Garonce-Hediger, Raymond Jang, Robert C. Grant, Eric X. Chen, Jennifer Knox, Adrian Sacher, Leo M. Chan. Evolution of the tumor immune microenvironment (TIME) in hepatocellular carcinoma (HCC) with liver metastases treated with atezolizumab-bevacizumab (INTEGRATE) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 5813.
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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 | 0.001 |
| 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) | 0.001 | 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 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".