Resistance to Cancer Treatment: The Role of Somatic Genetic Events and the Challenges for Targeted Therapies
Bibliographic record
Abstract
Therapeutic resistance remains a major cause of cancer-related deaths. Resistance can occur from the outset of treatment or as an acquired phenomenon after an initial clinical response. Therapeutic resistance is an almost universal phenomenon in the treatment of metastatic cancers. The advent of molecularly targeted treatments brought greater efficacy in patients whose tumors express a particular target or molecular signature. However, resistance remains a predictable challenge. This article provides an overview of somatic genomic events that confer resistance to cancer therapies. Some examples, including BCR-Abl, EML4-ALK, and the androgen receptor, contain mutations in the target itself, which hamper binding and inhibitory functions of therapeutic agents. There are also examples of somatic genetic changes in other genes or pathways that result in resistance by circumventing the inhibitor, as in resistance to trastuzumab and BRAF inhibitors. Yet other examples results in activation of cytoprotective genes. The fact that all of these mechanisms of resistance are due to somatic changes in the tumor's genome makes targeting them selectively a feasible goal. To identify and validate these changes, it is important to obtain biopsies of clinically resistant tumors. A rational consequence of this evolving knowledge is the growing appreciation that combinations of inhibitors will be needed to anticipate and overcome therapeutic resistance.
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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 | 0.011 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".