Abstract A003: Developmental parallels in pancreatic cancer subtypes revealed by single cell transcriptomics
Bibliographic record
Abstract
Abstract Recent research into the transcriptional subtypes in pancreatic ductal adenocarcinoma (PDA) has demonstrated their importance in predicting survival and therapeutic response. The Basal-like subtype is enriched in metastases and generally predicts poorer prognosis, while the Classical subtype is defined by gene expression reflective of pancreatic progenitors. The two-subtype scheme can be further subdivided into five subtypes that help better reflect the vast transcriptional heterogeneity found in PDA tumors. Basal-like A tumors fare worse than Basal-like B tumors. KRAS wildtype tumors are enriched in Classical B, which have the best prognosis, while Classical A is more reflective of the two-subtype Classical program. Hybrid tumors, generally a mix of Basal-like B and Classical A, are intermediate in prognosis between the Classical and Basal-like subtypes. Despite the importance of the subtypes, we have yet to fully understand how subtype programs within single cells of a tumor are intrinsically regulated, and how they contribute to tumor-level transcriptional observations. To investigate, we generated and leveraged a single cell RNA sequencing dataset enriched for malignant tumor cells spanning both primary and metastatic disease. Along with the richly annotated COMPASS dataset featuring bulk RNA sequencing of over 300 laser captured samples, we developed a single sample classifier utilizing gene pairs for assigning Basal-like A, Basal-like B, Hybrid, Classical A or Classical B to bulk tumors as well as individual cells, allowing interrogation of the subtypes across modalities. We find that the bulk subtype of the tumor is generally reflected in the dominant cell subtype. However, there are frequently cells of other subtypes within individual tumors, and substantial differences in cell subtype composition across tumors of the same subtype. In addition, we find that the proportion of Basal-like cells is a significant risk prognosticator. Investigating deeper into the transcriptional drivers of the subtypes reveals differences in expression of programs linked to endodermal development. Transcription factors such as FOXC1 and IRX3 generally related to foregut development are enriched in Basal-like A, forming a gradient of high expression to lower expression from Basal-like A, Basal-like B, Hybrid, Classical A to Classical B. Interestingly, midgut transcription factors such as CDX2 and ELF3 are expressed in the reverse order. Integrating data from single cell endoderm RNA sequencing further supports this finding, suggesting that the subtypes may arise from tumor cells exploiting pathways normally expressed early in development. This work provides a stepping stone to better understanding the mechanisms behind transcriptional subtypes in PDA. Citation Format: Sabrina Ge, Gun Ho Jang, Michelle Chan-Seng-Yue, Eugenia Flores Figueroa, Karen Ng, Stephanie Ramotar, Anna Dodd, Grainne M. O'Kane, Julie M. Wilson, Jennifer J. Knox, Sandra E. Fischer, Steven Gallinger, Faiyaz Notta. Developmental parallels in pancreatic cancer subtypes revealed by single cell transcriptomics [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer; 2022 Sep 13-16; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2022;82(22 Suppl):Abstract nr A003.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".