Immune infiltration, aggressive pathology, and poor survival outcomes in RECQL helicase deficient breast cancers
Bibliographic record
Abstract
RECQL is essential for genomic stability. Here, we evaluated RECQL in 449 pure ductal carcinomas in situ (DCIS), 152 DCIS components of mixed DCIS/invasive breast cancer (IBC) tumors, 157 IBC components of mixed DCIS/IBC and 50 normal epithelial terminal ductal lobular units (TDLUs). In 726 IBCs, CD8+, FOXP3+, IL17+, PDL1+, PD1+ T-cell infiltration (TILs) were investigated in RECQL deficient and proficient cancers. Tumor mutation burden (TMB) was evaluated in five RECQL germ-line mutation carriers with IBC by genome sequencing. Compared with normal epithelial cells, a striking reduction in nuclear RECQL in DCIS was evident with aggressive pathology and poor survival. In RECQL deficient IBCs, CD8+, FOXP3+, IL17+ or PDL1+ TILs were linked with aggressive pathology and shorter survival. In germline RECQL mutation carriers, increased TMB was observed in 4/5 tumors. We conclude that RECQL loss is an early event in breast cancer and promote immune cell infiltration.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".