Whole transcriptome sequencing in metastatic cancer: A review of expression outliers in 113 metastatic breast cancer patients.
Bibliographic record
Abstract
3080 Background: The genomic profiling of breast cancers has led to a greater understanding of the mutational landscape of metastatic breast cancer (MBC) with potential therapeutic implications. Despite these advances, there is a paucity of data regarding the additive value and relevance of gene expression across histological and molecular subtypes, which represents the majority of informative and actionable findings identified in the BC Cancer personalized oncogenomics program (POG). Methods: Informative findings with potential clinical application from whole genome sequencing (WGS) and whole transcriptome sequencing (WTS) in MBC patients between 2012-2018 were reviewed. Variants observed in pathway genes of potential clinical relevance, as defined by a curated list of genes, were examined across histological subtypes. High and low expression outliers relative to TCGA breast cases, defined as expression greater than 98th percentile and FC > 2 compared to Illumina breast dataset and lower than 25th percentile and FC < -2 compared to Illumina breast dataset, respectively, were then analyzed to establish how many outliers were observed in pathways of potential clinical relevance. Results: A total of 113 cases were included. WGS revealed that TP53 was the most frequent single nucleotide variant (SNV) in triple negative breast cancer (23/30, 77%), whereas PIK3CA (37/78, 47%), PTEN (11/78, 14%) and ESR1 (19/78, 24%) were most frequent in ER positive cases and CDKN2A (2/18, 11%) in HER2 positive cases. Across all subtypes, the mTOR and cell cycle pathways were found to have the highest frequency of SNVs, with the identification of 86 and 71 variants, respectively. Expression data for 113 RNA-sequenced patients revealed a high frequency of expression outliers in the mTOR pathway (26 high expression and 424 low expression outlier genes) and cell cycle pathways (35 high expression and 331 low expression outlier genes), but also in the WNT pathway (96 high expression and 490 lower expression outlier genes) and NOTCH pathway (84 high expression and 564 low expression outlier genes). Conclusions: Frequently identified SNVs across histological subtypes were correlated with expression outliers in pathways of clinical relevance in breast cancer. Additional informative findings, in pathways of potential clinical relevance not historically targeted in breast cancer, were identified with WTS. The clinical utility of these findings warrants further study.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".