Abstract PO-053: 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. One barrier to understanding metastatic heterogeneity has been a paucity of model systems that capture this natural variation and allow for direct assessment of paired primary tumors and metastases. We previously developed an autochthonous model of PDAC – the KPCX model – that employs multiplexed fluorescence-based labeling to track the contribution of multiple distinct tumor populations to metastasis. Importantly, this technique allows for ascertainment of primary-metastatic lineage relationships in vivo, so that primary tumor clones with substantial metastatic potential can be distinguished with those having poor metastatic potential. To understand the factors underlying differences in metastatic potential, we analyzed paired primary tumors and metastases in the KPCX model and from a cohort of 398 PDAC patients. Genomic and transcriptomic analysis of murine and human metastatic PDAC revealed an association between the highly metastatic state and gene amplification or transcriptional upregulation of MYC and its transcriptional targets. Functional assessments showed that MYC promotes metastasis by recruiting tumor associated macrophages (TAMs), leading to greater bloodstream intravasation. Consistent with these findings, metastatic progression in human PDAC was also associated of MYC signaling pathways and enrichment for MYC amplification in metastasis. Collectively, these results implicate MYC activity as a major determinant of metastatic burden and heterogeneity in advanced PDA. 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 J. 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 Pancreatic Cancer; 2020 Sep 29-30. Philadelphia (PA): AACR; Cancer Res 2020;80(22 Suppl):Abstract nr PO-053.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".