ErbB polymorphisms: insights and implications for response to targeted cancer therapeutics
Bibliographic record
Abstract
Advances in high-throughput genomic-scanning have expanded the repertory of genetic variations in DNA sequences encoding ErbB tyrosine kinase receptors in humans, including single nucleotide polymorphisms (SNPs), polymorphic repetitive elements, microsatellite variations, small-scale insertions and deletions. The ErbB family members: EGFR, ErbB2, ErbB3, and ErbB4 receptors are established as drivers of many aspects of tumor initiation and progression to metastasis. This knowledge has provided rationales for the development of an arsenal of anti-ErbB therapeutics, ranging from small molecule kinase inhibitors to monoclonal antibodies. Anti-ErbB agents are becoming the cornerstone therapeutics for the management of cancers that overexpress hyperactive variants of ErbB receptors, in particular ErbB2-positive breast cancer and non-small cell lung carcinomas. However, their clinical benefit has been limited to a subset of patients due to a wide heterogeneity in drug response despite the expression of the ErbB targets, attributed to intrinsic (primary) and to acquired (secondary) resistance. Somatic mutations in ErbB tyrosine kinase domains have been extensively investigated in preclinical and clinical setting as determinants for either high sensitivity or resistance to anti-ErbB therapeutics. In contrast, only scant information is available on the impact of SNPs, which are widespread in genes encoding ErbB receptors, on receptor structure and activity, and their predictive values for drug susceptibility. This review aims to briefly update polymorphic variations in genes encoding ErbB receptors based on recent advances in deep sequencing technologies, and to address challenging issues for a better understanding of the functional impact of single versus combined SNPs in ErbB genes to receptor topology, receptor-drug interaction, and drug susceptibility. The potential of exploiting SNPs in the era of stratified targeted therapeutics is discussed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".