Destabilizing the genome as a therapeutic strategy to enhance response to immune checkpoint blockade: a systematic review of clinical trials evidence from solid and hematological tumors
Bibliographic record
Abstract
Background: Genomic instability is increased alterations in the genome during cell division and is common among most cancer cells. Genome instability enhances the risk of initial carcinogenic transformation, generating new clones of tumor cells, and increases tumor heterogeneity. Although genome instability contributes to malignancy, it is also an “Achilles’ heel” that constitutes a therapeutically-exploitable weakness—when sufficiently advanced, it can intrinsically reduce tumor cell survival by creating DNA damage and mutation events that overwhelm the capacity of cancer cells to repair those lesions. Furthermore, it can contribute to extrinsic survival-reducing events by generating mutations that encode new immunogenic antigens capable of being recognized by the immune system, particularly when anti-tumor immunity is boosted by immunotherapy drugs. Here, we describe how genome-destabilization can induce immune activation in cancer patients and systematically review the induction of genome instability exploited clinically, in combination with immune checkpoint blockade. Methods: We performed a systematic review of clinical trials that exploited the combination approach to successfully treat cancers patients. We systematically searched PubMed, Cochrane Central Register of Controlled Trials, Clinicaltrials.gov , and publication from the reference list of related articles. The most relevant inclusion criteria were peer-reviewed clinical trials published in English. Results: We identified 1,490 studies, among those 164 were clinical trials. A total of 37 clinical trials satisfied the inclusion criteria and were included in the study. The main outcome measurements were overall survival and progression-free survival. The majority of the clinical trials (30 out of 37) showed a significant improvement in patient outcome. Conclusion: The majority of the included clinical trials reported the efficacy of the concept of targeting DNA repair pathway, in combination with immune checkpoint inhibitors, to create a “ring of synergy” to treat cancer with rational combinations.
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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 | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".