Prognostic B-cell Signatures Using mRNA-Seq in Patients with Subtype-Specific Breast and Ovarian Cancer
Bibliographic record
Abstract
PURPOSE: Lymphocytic infiltration of tumors predicts improved survival in patients with breast cancer. Previous studies have suggested that this survival benefit is confined predominantly to the basal-like subtype. Immune infiltration in ovarian tumors is also associated with improved prognosis. Currently, it is unclear what aspects of the immune response mediate this improved outcome. EXPERIMENTAL DESIGN: Using The Cancer Genome Atlas mRNA-seq data and a large microarray dataset, we evaluated adaptive immune gene expression by genomic subtype in breast and ovarian cancer. To investigate B-cells observed to be prognostic within specific subtypes, we developed methods to analyze B-cell population diversity and degree of somatic hypermutation (SHM) from B-cell receptor (BCR) sequences in mRNA-seq data. RESULTS: Improved metastasis-free/progression-free survival was correlated with B-cell gene expression signatures, which were restricted mainly to the basal-like and HER2-enriched breast cancer subtypes and the immunoreactive ovarian cancer subtype. Consistent with a restricted epitope-driven response, a subset of basal-like and HER2-enriched breast tumors and immunoreactive ovarian tumors showed high expression of a low-diversity population of BCR gene segments. More BCR segments showed improved prognosis with increased expression in basal-like breast tumors and immunoreactive ovarian tumors compared with other subtypes. Basal-like and HER2-enriched tumors exhibited more BCR sequence variants in regions consistent with SHM. CONCLUSION: Taken together, these data suggest the presence of a productive and potentially restricted antitumor B-cell response in basal-like breast and immunoreactive ovarian cancers. Immunomodulatory therapies that support B-cell responses may be a promising therapeutic approach to targeting these B-cell infiltrated tumors.
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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 | 0.001 |
| 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) | 0.001 | 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".