Novel Methods of Personalized Treatment in Colorectal Cancer: A Literature Review
Bibliographic record
Abstract
Introduction: Colorectal cancer (CRC) is a leading global health concern, characterized by a high prevalence and significant mortality rate. Despite the progress in treatment modalities, there remains a critical gap in personalized therapy approaches, particularly in tailoring treatments to individual genetic and molecular profiles across different stages. This literature review aims to bridge this gap by analyzing recent advancements and emerging therapies, focusing on how they address the specific needs of patients at various stages of CRC. The aim is to provide insights into the efficacy of personalized treatments and identify areas requiring further research for optimal therapeutic strategies, Methods: A systematic search of the PubMed database was conducted using keywords such as "metastatic CRC," "single cell analysis," "personalized treatments”, "single point mutation”, “solid cancer”, “genetic mutation” and “gene therapy” restricted to clinical trials published from 2017 to 2024. The search criteria included terms like "CRC" and "stage I, II, III, or IV" to identify relevant articles. This approach aimed to comprehensively review the current state of personalized therapies and identify gaps in knowledge that could inform future research. Research on colorectal cancer from 2017-2024 is crucial due to advancements in treatment, clinical guidelines, epidemiology, risk factors, diagnostic tools, and personalized medicine, providing comprehensive understanding, Results: Recent studies show promising advancements in personalized treatments for CRC, particularly with targeted therapies and immunotherapy. Nanomedicine-based therapies and new agents improve drug delivery precision and efficacy. Immunotherapy, particularly for MSI-H tumors, shows effectiveness but remains variable based on genetic profiles. Combination therapies are emerging as a viable strategy. Discussion: The findings highlight significant progress in personalized medicine for CRC, with targeted therapies and immunotherapy offering new hope. However, the complexity of CRC is highlighted by the variability in treatment responses across different genetic profiles. The review identifies a need for more comprehensive research to optimize these therapies and their long-term impacts. Conclusion: Personalized treatments for CRC are advancing through targeted therapies, nanomedicine, and immunotherapy, but challenges persist in achieving consistent efficacy across diverse patient populations. Further research is needed to refine these approaches and improve outcomes.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".