Genomic and Proteomic Insights into ABC Transporter-Mediated Drug Resistance in Cancer
Bibliographic record
Abstract
ATP-binding cassette (ABC) transporters play a key role in the development of multidrug resistance (MDR) in cancer, as they actively pump chemotherapeutic agents out of tumor cells, thereby limiting drug accumulation and efficacy Of the 48 known human ABC transporters, members such as P-glycoprotein (ABCB1), MRP1 (ABCC1) and BCRP (ABCG2) are indeed implicated in clinical drug resistance across a variety of malignancies. In this review, we will examine the most recent genomic and proteomic studies on the regulation, expression, and function of ABC transporters in cancer. Genomic studies have identified mutations, polymorphisms, and epigenetic factors that affect transporter activity and expression, thereby contributing to variability in drug response among individuals. Proteomic studies have provided detailed identification of post-translational modifications and protein–protein interactions that can affect transporter stability and trafficking. In addition, multi-omics studies have provided new insights into regulators of ABC transporters and novel therapeutic targets to reverse MDR. A thorough understanding of the molecular complexities of each ABC transporter family member is crucial for establishing predictive biomarkers and developing strategies to overcome drug resistance. This synthesis of genomic and proteomic data supports the need to consider how the variability of different ABC transporters contributes to each individual's resistance, which in turn highlights the need for personalized approaches in cancer therapy to optimize the effects while overcoming the specific mechanisms linked to ABC transporter-mediated drug resistance.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".