Abstract SY03-01: Genotypic and phenotypic features of de novo and acquired resistance to cancer therapies.
Bibliographic record
Abstract
Abstract Drug resistance is the single cause of cancer treatment failure resulting in cancer related death. Despite its paramount importance, our mechanistic understanding and means of circumventing cancer drug resistance is limited. In the current era, the evolutionary processes in cancer development and progression have been elucidated and provide insights into the biology of cancer and in particular cancer heterogeneity. However, our understanding of the contribution of evolutionary processes and tumor heterogeneity to the development of drug resistance is limited. As a result, our ability to translate knowledge into effective clinical strategies is similarly limited and remains largely on the use combination chemotherapy based on Goldie and Coldman's principles first described in 1984. Pragmatically, resistance may be de novo (i.e. treatment has no effect) or acquired (i.e. treatment had initial effect but tumors eventually progress due to repopulation by resistant cells). De novo resistance is thus equivalent to primary refractoriness and acquired resistance is emergent in the presence and presumably as a direct response to the selective pressures imposed by therapy. Mechanisms of resistance may be divided into various categories; however, a simple construct is to consider resistance as a manifestation of failure of one or more of the following: drug delivery, drug uptake, drug-target interaction and cellular response and cellular adaptation. Genetic, epigenetic, physiochemical and spatial properties of cancers may contribute to drug resistance and it is likely that different mechanisms of drug resistance develop concomitantly. The most important challenge is how to tackle the interrelated problems of tumor genetic and epigenetic heterogeneity and drug resistance in cancer using rational drug selections. The identification of tumor dependencies driven by dominant oncogenes, hormones, or metabolites may prove vulnerable to regimens designed to intercept them assuming dependencies are identifiable, targets or target pathways are “druggable” and such agents and combinations are tolerable and effective for cancer patients. Although the issues are complex, they can and should be addressed. Serial and comprehensive sampling to identify genetic and epigenetic changes of tumor to guide drug therapy will be required as will identifying susceptibilities such as oncogene addiction, synthetic lethality and genomic instability. Rational selection of drug dose, schedule and combination partners can follow. Treatment advances will occur to the extent that mechanisms of resistance can be identified or predicted and circumvented. Citation Format: Janet E. Dancey. Genotypic and phenotypic features of de novo and acquired resistance to cancer therapies. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr SY03-01. doi:10.1158/1538-7445.AM2013-SY03-01
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".