Abstract 829: Global tumor transcriptional activity reveals aggressiveness across multiple cancers
Bibliographic record
Abstract
Abstract Background: At present, conventional clinical and histopathological evaluations are not sufficient to distinguish biologically indolent cancers from those that will exhibit aggressive behavior. We hypothesize that global transcriptomic activity of tumor cells reflects the end cumulative result of somatic, germline, and epigenetic alterations, as well as additional transcriptional regulatory events. Therefore, it may be more directly associated with clinical outcomes. However, the total number of mRNA molecules is not directly measurable, either in bulk or single-cell RNA sequencing data. To this end, we develop a novel metric: the transcriptional activity score (TAS), to measure the relative global tumor-cell specific transcriptional activity in heterogeneous tumor samples. Materials and Methods: We propose TAS as the ratio of average total transcript proportion over the count proportion of tumor cells versus surrounding non-tumor cells. The transcript proportions are estimated using RNAseq deconvolution method DeMixT and the count proportions are estimated using DNAseq deconvolution methods such as ASCAT and ABSOLUTE. Using matching bulk RNA and DNA sequencing data from the Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC), we calculated TAS for a total of 5,031 patient samples across 15 cancer types. For validation, we obtained TAS for two genomic studies: 1) from patients with early-onset prostate cancer (n=99) as part of the ICGC, and 2) from patients with localized prostate cancer as part of the Canadian Prostate Cancer Genome Network (CPC-GENE, n=144). Results: We found that higher TAS corresponds to a more aggressive state of cancer, as characterized by MYC dysregulation, genome instability, known marker genes, and molecular subtypes. By examining the association between TAS and survival outcomes across cancer types, we also found that TAS refines the prognostic ability of pathologic stage, identifying aggressive early-stage tumors associated with poor survival as well as late-stage tumors with favorable outcomes. In prostate cancer, TAS is linearly associated with progression-free probabilities, useful to rank patients within the median risk group (Gleason score = 7). This added prediction power is consistent in TCGA and two independent validation data (ICGC and CPC-GENE). Conclusion: We have developed a new summary metric using matched DNA and RNA sequencing data from tumor samples, to compute, in vivo and using deconvolution, the relative global gene expression level of tumor cells. The TAS metric evaluates global transcriptional activity, an intrinsic behavior of cells that is well-known, but now for the first time is shown through TAS to be associated with prognosis. TAS may serve as a tractable phenotype to help elucidate the biology that underlies metastasis, prognosis and response to treatment in cancer patients. Citation Format: Shaolong Cao, Jennifer R. Wang, Jonas Demeulemeester, Jingxiao Chen, Kaixian Yu, Peng Yang, Bora Lim, Alfonso Urbanucci, Peter Campbell, Hongtu Zhu, Peter Van loo, Wenyi Wang. Global tumor transcriptional activity reveals aggressiveness across multiple cancers [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 829.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".