Allelic expression imbalance of <i>PIK3CA</i> mutations is frequent in breast cancer and prognostically significant
Bibliographic record
Abstract
Abstract PIK3CA mutations are the most common in breast cancer, particularly in the estrogen receptor positive cohort, but the benefit of PI3K inhibitors has had limited success compared with approaches targeting other less common mutations. We found allelic imbalances in the expression of PIK3CA in normal breast tissue and mapped a germline candidate regulatory variant. An imbalance was also frequently observed in the expression of the missense mutant and wild-type PIK3CA alleles in breast tumors from METABRIC and TCGA projects. Moreover, although 60% of tumors preferentially expressed the mutant allele, 10% did preferentially express the wild-type allele. Mechanistically, we show that these imbalances are more frequently due to regulatory variants in cis than altered copy-number and predict that somatic variants have a more significant role than germline ones. We further found that imbalanced allelic expression between mutant and wild-type alleles due to cis- regulatory variants associated with poor prognosis ( p =0.0081). Interestingly, ER + , PR + , and Her2 − tumors expressing very low levels of the mutant allele had the poorest prognosis (DSS <7.5yrs for ER + and PR + tumors and <5yrs for Her2 − tumors). Hence, our work provides compelling evidence to support the clinical utility of PIK3CA allelic expression in breast cancer in identifying this cohort of low mutant allele expressing patients of poorer prognosis, who will unlikely benefit from PI3K inhibitors. Furthermore, our work establishes a new model of differential regulation of critical cancer-promoting genes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".