Abstract B052: Single-cell analysis of multiple cancers in the upper gastrointestinal tract uncovers immune characteristics of tumor microenvironments linked to the predictive biomarkers for immunotherapy in esophageal cancers
Bibliographic record
Abstract
Abstract Esophageal cancer is mainly composed of two subtypes – esophageal squamous cell carcinoma (ESCC) and esophageal adenocarcinoma (EAC) – with distinct risk factors and cancer phenotypes despite arising from the same organ. The recent study by the Cancer Genome Atlas (TCGA) has revealed their distinct genomic characteristics and similarities to subsets of other nearby cancers such as head and neck squamous neck carcinoma (HNSCC) and gastric adenocarcinoma (GAC) for ESCC and EAC, respectively. Furthermore, recent clinical trials with anti-PD-1 monotherapy and combination treatment with anti-PD-1 and anti-CTLA-4 reported varying degrees of responses to immunotherapy for those cancers. Here we performed single-cell RNA sequencing of patients with ESCC, EAC, and HNSCC, and collected additional public datasets for comparative analysis of four cancer types in the upper gastrointestinal tract, especially in connection to responses to immunotherapy. In total, we integrated 35 patient samples from 4 different cohorts to comprehensively analyze malignant cells, stromal/endothelial cells, and immune cells. With the integrative analysis, we confirmed the similarities and differences among those cancer types in the upper gastrointestinal tract and expanded understanding of immune mechanisms at single-cell resolution. For malignant cells, we utilized matrix factorization analysis to identify underlying malignant cell programs related to each cancer type. We confirmed clear separation between malignant cells of squamous epithelial cell origins and glandular epithelial cell origins. We further identified the malignant cell programs related to both cancer cell origins and molecular mechanisms of cancer cells. For stromal and endothelial cells, we identified compositional differences in their cellular subtypes. With biological pathway and signature analyses, we revealed that their distinct cellular subtypes might be connected to distinct immune mechanisms in tumor microenvironments. For immune cells, despite having less compositional difference in cellular subtypes, we identified their underlying immune mechanisms that could explain key differences in responses to immunotherapy. With comprehensive analyses of various immune cell-types and their interactions, we identified several T cell populations and related tumor-associated macrophages that could serve as predictive markers of responses to cancer immunotherapy. In order to validate our findings, we utilized both bulk and single-cell sequencing datasets of various cancer types with treatments of immune checkpoint inhibitors (ICI) from previous studies and confirmed significance of those immune signatures and cellular compositions in responses to the ICI treatment. Citation Format: Seungbyn Baek, Gamin Kim, Sang Jun Ha, Hye Ryun Kim, Seong Yong Park, Insuk Lee. Single-cell analysis of multiple cancers in the upper gastrointestinal tract uncovers immune characteristics of tumor microenvironments linked to the predictive biomarkers for immunotherapy in esophageal cancers [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Tumor Immunology and Immunotherapy; 2023 Oct 1-4; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Cancer Immunol Res 2023;11(12 Suppl):Abstract nr B052.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".