Abstract PO-071: MYC influences metastatic heterogeneity in pancreatic cancer
Bibliographic record
Abstract
Abstract Tumor heterogeneity - resulting from genetic and epigenetic alterations acquired during tumor progression - is a critical driver of phenotypic diversity in most cancers. A lethal consequence of tumor heterogeneity is the acquisition of metastatic traits by tumor cells, leading to poor clinical outcomes. This remains a major problem in pancreatic ductal adenocarcinoma (PDAC), which continues to have the worst prognosis of any major cancer type. While most cases of PDAC present with metastatic disease at the time of diagnosis, the patterns and burden of metastasis can vary widely, with some patients exhibiting a limited metastatic burden while others have more extensive spread, which impacts clinical outcomes. However, the biological and functional differences that drive metastatic heterogeneity are poorly understood. Citation Format: Ravikanth Maddipati, Robert J. Norgard, Timour Baslan, Komal S. Rathi, Amy Zhang, Pichai Raman, Max D. Wengyn, Taiji Yamazoe, Jinyang Li, David Balli, Michael LaRiviere, Ian W. Folkert, Ian D. Millstein, Jonathan Bermeo, Erica L. Carpenter, Scott Lowe, Christine Iacobuzio-Donahue, Faiyaz Notta, Ben Z. Stanger. MYC influences metastatic heterogeneity in pancreatic cancer [abstract]. In: Proceedings of the AACR Virtual Special Conference on Tumor Heterogeneity: From Single Cells to Clinical Impact; 2020 Sep 17-18. Philadelphia (PA): AACR; Cancer Res 2020;80(21 Suppl):Abstract nr PO-071.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".