Using single-cell sequencing to identify endothelial expression of immune checkpoint ligands in advanced hepatocellular carcinoma, pre- and post- atezolizumab plus bevacizumab in the phase II INTEGRATE study.
Bibliographic record
Abstract
3129 Background: Atezolizumab (atezo; anti-PD-L1) plus bevacizumab (bev; anti-VEGF-A) became a standard treatment for advanced hepatocellular carcinoma (HCC) after demonstrating an overall survival advantage over sorafenib (inhibitor of VEGFR2 & other kinases) in the phase III clinical trial, IMbrave150. However, the mechanisms of primary and acquired resistance to atezo-bev are poorly understood. VEGFR2 + endothelial cells (ECs) are potential cellular targets of bev and may play a key immunomodulatory role in response to atezo-bev. In this study, we utilized single-cell sequencing to identify potential mediators of resistance within EC subsets. Methods: Eight patients with unresectable HCC were enrolled on the INTEGRATE study, treated with atezo-bev, and underwent intensive biospecimen collection (NCT04563338). Serial tumor biopsies were collected and viably cryopreserved including pre-treatment (n=6), 21-28 days after first dose (n=6), and at disease progression (n=2). Single-cell analysis via cellular indexing of transcriptomes and epitopes (CITEseq) has been performed and data from four patients have been analysed to date. Aggregating their nine biopsies, 8,569 hepatocytes (ALB + FABP1 + FGB + ), 29,072 immune cells (CD45 + ), and 8,028 ECs (CD31 + vWF + KDR + ) were annotated. The differential expression of VEGFR2, PD-L1, and other immune checkpoint ligands by tumor vs. immune vs. endothelial cellswere interrogated (Table). Results: VEGFR2 (receptor for VEGF-A) is predominantly expressed by ECs, at high prevalence & intensity. PD-L1 and PD-L2 (ligands of PD-1) are expressed by ECs at low prevalence & intensity. Galectin3 (LAG3 ligand) is widely expressed by hepatocytes, immune cells and ECs; while L-SECtin (LAG3 ligand) is predominantly expressed by ECs but at low prevalence & intensity. ECs had the highest prevalence of galectin9 (TIM3 ligand) expression. Nectin2 (TIGIT ligand) is expressed by both hepatocytes and ECs at high prevalence & intensity. Conclusions: Liver ECs express a broad array of immune checkpoint ligands, which are more frequent than previously anticipated. These EC subsets may potentially drive resistance by contributing to exhaustion of T cell subsets entering the tumor microenvironment. Complete CITEseq, TCR sequencing, and correlative studies from the full cohort are underway. Clinical trial information: NCT04563338 . Hepatocytes Immune cells ECs VEGFR2 (KDR) 0.06% (<0.01) 0.05% (<0.01) 66% (0.8) PD-L1 (CD274) 0.2% (<0.01) 4% (0.03) 2% (0.01) PD-L2 (PDCD1LG2) 0.02% (<0.01) 2% (0.02) 3% (0.02) L-SECtin (CLEC4G) 0% 0.1% (<0.01) 3% (0.05) Galectin-3 (LGALS3) 68% (0.8) 39% (0.5) 43% (0.5) Galectin-9 (LGALS9) 5% (0.04) 28% (0.3) 34% (0.3) PVR (CD155) 14% (0.1) 1% (<0.01) 18% (0.1) Nectin2 (CD112) 60% (0.5) 5% (0.04) 44% (0.4) % = proportion of cells with positive expression. ( ) = normalized mean expression.
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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 | 0.000 | 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".