Targeting the CINful genome: Strategies to overcome tumor heterogeneity
Bibliographic record
Abstract
Genomic instability, and more specifically chromosomal instability (CIN), arises from a number of processes that are defective in cancer, such as aberrant mitotic cell division, replication stress, defective DNA damage repair, and ineffective telomere maintenance. CIN is an emerging hallmark of cancer that contributes to tumor heterogeneity through increased rates of genetic alterations. As genetic heterogeneity within a single tumor and between tumors is a key challenge leading to treatment failures, this brings to question, whether therapeutic approaches should aim at the genetic diversity or a specific mutation present within these tumors. Answering this question will determine the future of personalized targeted therapies. Here we discuss, how the genetic diversity associated with CIN in tumor cells can be used as a therapeutic advantage and targeted by exploiting the genetic concepts of synthetic lethality and synthetic dosage lethality. Given that a number of CIN-related pathways work together to fix the DNA damage within our genome and ensure proper segregation of chromosomes, we specifically focus on the genetic interactions amongst these pathways and their potential therapeutic applicability in cancer. We also discuss, how tumor genetic heterogeneity can be targeted in emerging immunotherapeutic approaches.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".