Bibliographic record
Abstract
The stability and function of many oncogenic mutant proteins depend on heat shock protein 90 (HSP90). This unique activity has inspired the exploration of HSP90 as an anticancer target for over two decades. Unfortunately, while clinical trials of highly optimized HSP90 inhibitors have demonstrated modest benefit for patients with advanced cancers, most commonly stabilization of disease, no HSP90 inhibitor has demonstrated sufficient efficacy to achieve FDA approval to date. This review discusses potential reasons for the limited success of these agents and how our increasingly sophisticated understanding of HSP90 suggests alternative, potentially more effective strategies for targeting it to treat cancers. First, we focus on insights gained from model organisms that suggest a fundamental role for HSP90 in supporting the adaptability and heterogeneity of cancers, key factors underlying their ability to evolve and acquire drug resistance. Second, we examine how HSP90’s role in promoting the stability of mutant proteins might be targeted in genetically unstable tumor cells to reveal their aberrant, foreign proteome to the immune system. Both of these emerging aspects of HSP90 biology suggest that the most effective use of HSP90 inhibitors may not be at high doses with the intent to kill cancer cells, but rather in combination with other molecularly targeted therapies at modest, non-heat shock-inducing exposures that limit the adaptive capacity of cancers.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".