Bibliographic record
Abstract
Abstract Ovarian cancer is a challenging disease for which new treatments are urgently needed. High-grade serous ovarian cancer is the most common and lethal subtype, with a five-year survival rate of only 30-40%. Nevertheless, patients with evidence of an immune response against their cancer – in particular the presence of tumor-infiltrating T cells – show markedly prolonged survival. We recently showed that B cells also contribute to tumor immunity, in that tumors infiltrated by both B cells and T cells are associated with higher survival rates than seen with T cells alone. Tumor-infiltrating T cells and B cells co-localize in organized structures resembling lymphoid tissue. These structures also contain dendritic cells and show the hallmarks of active antigen presentation and lymphocyte activation. We are investigating the hypothesis that these lymphoid structures are sites of immune surveillance, where tumor antigens are presented to T cells and B cells allowing the immune system to keep check on the evolving cancer genome. To explore this concept, we are assessing tumor-infiltrating T cells for recognition of mutations identified by whole exome sequencing. In one patient undergoing standard chemotherapy, we found an example of a CD8+ T cell response to a point mutation in the HSDL1 gene. The T cell response emerged during first remission, peaked at first recurrence, and then disappeared by second recurrence, shortly before the patient succumbed to her disease. Thus, the immune system actively surveys the tumor genome in patients undergoing standard treatment, but these responses can fail in the absence of immunologic intervention. With better understanding of the lymphocytes and structures involved, it should be possible to enhance these natural surveillance mechanisms to confer protective immunity and further increase patient survival. Citation Format: Brad Nelson. Protective immune networks in ovarian cancer. [abstract]. In: Proceedings of the AACR Special Conference on Advances in Ovarian Cancer Research: From Concept to Clinic; Sep 18-21, 2013; Miami, FL. Philadelphia (PA): AACR; Clin Cancer Res 2013;19(19 Suppl):Abstract nr IA26.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".