Abstract B014: Spatial genomics identifies cancer cell cytokines regulating ovarian cancer immunity
Bibliographic record
Abstract
Abstract Ovarian cancer (OvCa) is among the most aggressive and lethal cancers with a poor response to immune checkpoint blockade (ICB) despite its modest tumor mutational burden and expression of known cancer antigens. Consequently, it is critical to understand the molecular mechanisms of tumor immune escape and resistance to immunotherapies. Intercellular signaling between cancer cells and the tumor microenvironment (TME) is a crucial determinant of tumor immunity and given the immunosuppressive nature of OvCa TME, it is of utmost importance to dissect how this immunosuppressed TME is established. To begin to determine which cancer-cell genes may function cell externally in controlling of OvCa immunity, we employed a first-of-its-kind spatial functional genomics approach we developed, called Perturb-map, which enables dozens of CRISPR gene knock-out (KO) cells to be resolved in a tissue/tumor by imaging, along with cells in the local TME. We used Perturb-map to determine how 35 different genes, identified through ligand-receptor analysis of ovarian tumors, influenced tumor response to anti-PD-1 treatment in an OvCa mouse model. Strikingly, Il4 KO tumors were unique amongst the 35 gene KOs making the tumors significantly more responsive to anti-PD-1 treatment despite not impacting tumor growth in the absence of immunotherapy. Further experiments with single CRISPR KO tumors validated the Perturb-map finding, where Il4 KO combined with anti-PD-1 treatment resulted in a major reduction in tumor growth and a significant increase in survival. Cyclic immunofluorescence (CyCIF) multiplex imaging revealed that loss of cancer cell-derived IL-4 led to a significant reduction in pro-tumor macrophages and an increase in dendritic cell (DC) subsets as well as both progenitor and terminally exhausted CD8 T cells. Upon anti-PD-1 treatment, Il4 KO tumors had significant enrichment for activated CD8 T cells and B cells. Importantly, spatial analysis of the tumor samples identified enrichment of T follicular helper cells, B cells, and DCs in tertiary lymphoid structures in tumors with combined IL-4 loss and PD-1 inhibition. Further, we analyzed a human OvCa single-cell transcriptomics dataset and demonstrated that an IL-4 response signature is expressed in tumor-associated macrophages, indicating active IL-4 signaling in human ovarian tumors. Importantly, we employed a CRISPR-knock-in expression reporter in mouse tumors as well as immunohistochemistry of both human and mouse tumor samples to confirm that IL-4 is expressed by cancer cells. Thus, we identified cancer cell-secreted IL-4 as a potent regulator of OvCa TME and response to ICB. Given that an anti-IL-4-receptor antibody is already in clinical use to treat immune diseases, our findings have the potential to be translated to the clinical management of this dreadful disease. Citation Format: Gurkan Mollaoglu, Alexander Tepper, Hunter Potak, Luisanna Pia, Angelo Amabile, Chiara Falcomata, Jaime Mateus-Tique, Noam Rabinovich, Rachel Brody, Lindsay Browning, Jia-Ren Lin, Peter Sorger, Sandro Santagata, Miriam Merad, Alessia Baccarini, Brian Brown. Spatial genomics identifies cancer cell cytokines regulating ovarian cancer immunity [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Tumor Immunology and Immunotherapy; 2023 Oct 1-4; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Cancer Immunol Res 2023;11(12 Suppl):Abstract nr B014.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 teacher head, 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".