Abstract 2432: The genomic consequences of tumor hypoxia in human cancers
Bibliographic record
Abstract
Abstract Introduction: Localized prostate cancers are classified into risk-groups using clinical measurements like grade and stage to inform treatment decisions. However, these groupings are imprecise: ~30% of intermediate-risk patients suffer relapse of their disease despite precision image-guided radiotherapy or radical prostatectomy. One reason for this variability in response to treatment is the underlying cellular and molecular heterogeneity of tumors. Prostate tumor cells exist within a microenvironment characterized by gradients of oxygen levels and prostate tumors with low levels of oxygen (hypoxia) have poor clinical outcomes. Methods and Results: To understand the correlates of hypoxia in cancer we conducted a pan-cancer analysis of copy number alterations (CNAs) and single nucleotide variants (SNVs) across 19 cancer types. We measured hypoxia using multiple mRNA-based signatures and discovered numerous CNAs and SNVs enriched or depleted in hypoxic tumors, highlighting the role of hypoxia in shaping the genomic landscape of multiple tumor types. Next, we examined 548 patients with localized prostate cancer and statistically assessed the association of hypoxia with CNAs, SNVs, genomic rearrangements, focal genomic events (i.e. kataegis, chromothripsis), telomere length, clinical indices (i.e. grade, stage) and subclonal architecture. Tumor hypoxia is associated with specific CNAs and SNVs in prostate cancer driver genes. To translate these findings into a biomarker for prostate cancer precision medicine, we integrated tumor microenvironmental data with genomic and pathological information to stratify patients into distinct prognostic groups. Impact: These data suggest that the aggressiveness of cancers is driven by the interplay of the tumor microenvironment and its genomic mutational profile. Citation Format: Vinayak Bhandari, Shadrielle M. Espiritu, Lydia Y. Liu, Emilie Lalonde, Takafumi N. Yamaguchi, Lawrence E. Heisler, Julie Livingstone, Vincent Huang, Yu-Jia Shiah, Veronica Y. Sabelnykova, Fouad Yousif, Melvin L. Chua, Michael Fraser, Theodorus van der Kwast, Paul C. Boutros, Robert G. Bristow. The genomic consequences of tumor hypoxia in human cancers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2432.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.004 | 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".