Abstract 1165: Complementary diagnostics for precision targeting of cellular pathways
Bibliographic record
Abstract
Abstract Precision medicine aims to tailor treatment to an individual patient through understanding the molecular basis of their disease. Targeted therapies have the potential to optimize responses and minimize side effects, while also offering savings to the healthcare system through tailoring treatment and avoiding futile therapies. Regimes are usually designed through identifying DNA-level alterations in the tumour and selecting drugs tailored to that mutation. However, cancer is not a one-pathway disease: not all patients with a particular mutational profile will respond to the same treatment, and patients without a canonical pathway-activating alteration will be excluded from potentially life-saving treatment. To address this, we developed a NanoString assay that combines proteomic and transcriptomic profiling of 4 key cancer-related, actionable pathways (MAPK, PI3K, NF-κB and JAK/STAT). Using RNA-Seq data from gold standard cell lines with defined changes in each pathway we generated minimal sets of genes indicative of pathway activation, and integrated this with total and phospho protein measurements to show pathway activation levels. The combined panel was run on a series of isogenic cell lines and glioma samples with both known and unknown driving alterations. Overall, pathway activation is more variable than would be predicted based on DNA alterations alone, with alternate pathway activation emerging as a potential new target and implying that consideration of proteomic and/or transcriptomic-level information will be important for future therapeutic decision-making. Citation Format: Robert Siddaway, Liana Nobre, Monique Johnson, Scott Milos, Scott Ryall, Javal Sheth, Uri Tabori, Cynthia Hawkins. Complementary diagnostics for precision targeting of cellular pathways [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 1165.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".