What Are the Reasons for Continuing Failures in Cancer Therapy? Are Misleading/Inappropriate Preclinical Assays to Be Blamed? Might Some Modern Therapies Cause More Harm than Benefit?
Bibliographic record
Abstract
Over 50 years of cancer research has resulted in the generation of massive amounts of information, but relatively little progress has been made in the treatment of patients with solid tumors, except for extending their survival for a few months at best. Here, we will briefly discuss some of the reasons for this failure, focusing on the limitations and sometimes misunderstanding of the clinical relevance of preclinical assays that are widely used to identify novel anticancer drugs and treatment strategies (e.g., "synthetic lethality"). These include colony formation, apoptosis (e.g., caspase-3 activation), immunoblotting, and high-content multiwell plate cell-based assays, as well as tumor growth studies in animal models. A major limitation is that such assays are rarely designed to recapitulate the tumor repopulating properties associated with therapy-induced cancer cell dormancy (durable proliferation arrest) reflecting, for example, premature senescence, polyploidy and/or multinucleation. Furthermore, pro-survival properties of apoptotic cancer cells through phoenix rising, failed apoptosis, and/or anastasis (return from the brink of death), as well as cancer immunoediting and the impact of therapeutic agents on interactions between cancer and immune cells are often overlooked in preclinical studies. A brief review of the history of cancer research makes one wonder if modern strategies for treating patients with solid tumors may sometimes cause more harm than benefit.
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 | 0.045 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".