Abstract IA23: How does the immune system contend with intratumoral heterogeneity in ovarian cancer?
Bibliographic record
Abstract
Abstract The presence of tumor-infiltrating T cells and B cells is associated with markedly prolonged survival in high-grade serous ovarian cancer (HGSC), yet we have only a rudimentary understanding of how the immune system contends with the continually evolving tumor genome. HGSC exhibits extensive spatial heterogeneity at diagnosis, and tumors undergo dramatic changes in response to treatment. In long-term survivors, the immune system presumably deploys mechanisms that successfully respond to these changes. With better understanding, it may be possible to exploit these mechanisms to create more effective immunotherapies. We have shown that the most prognostically favorable immune responses involve both cytolytic and antibody-based mechanisms, suggesting important cooperative interactions between the T cell and B cell branches of the immune system. By studying serial tumor samples, we have uncovered extensive temporal dynamics of the antitumor immune response. We found that neoadjuvant chemotherapy can enhance preexisting immune responses yet generally fails to initiate responses in tumors that lack immune infiltrates at baseline. Indeed, with better understanding, these immunologically inert or “cold” tumors may represent an attractive therapeutic opportunity, as they can express high levels of tumor-specific antigens with corresponding systemic T-cell and antibody responses. Currently, we are looking at the issue of spatial heterogeneity by subjecting tumor samples collected from different anatomical locations to comprehensive genomic and immunologic analyses, including mutational profiling, RNA-seq, TCR/BCR-seq, and multi-plexed immunohistochemistry. This work is providing novel insights into the relationship between the clonal architecture of tumors and antitumor immunity. Our findings to date indicate that tumor deposits with a high degree of clonal heterogeneity generally have low densities of immune infiltrates, suggesting a means by which tumor evolutionary processes are insulated from immunologic attack. Furthermore, TCR-seq experiments are providing early evidence that T-cell clones track with individual tumor clones across space, suggesting that the immune system contends with intratumoral heterogeneity by battling each tumor clone individually. The presentation will address the implications of these findings on the design of more effective immunotherapies for HGSC and related cancers. Citation Format: Brad H. Nelson. How does the immune system contend with intratumoral heterogeneity in ovarian cancer? [abstract]. In: Proceedings of the AACR Conference: Addressing Critical Questions in Ovarian Cancer Research and Treatment; Oct 1-4, 2017; Pittsburgh, PA. Philadelphia (PA): AACR; Clin Cancer Res 2018;24(15_Suppl):Abstract nr IA23.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".