Bibliographic record
Abstract
Abstract Patients with tumors that have substantial T cell infiltration benefit from immunotherapy to survive longer. However, several tumor types lack T cells within the tumor and are hence refractory to immunotherapy. Therefore, therapies that render immune-evading cancers susceptible to cytotoxic T cells are highly desirable. We and others have shown that certain types of cancer cell death can kickstart multiple aspects of the anticancer immunity cycle to inflame “cold” tumors. These immunological outcomes were driven by the secretome emitted by a variety of stress and programmed cell death. Many cytotoxic chemoradiotherapies simultaneously activate multiple types of stress and cell death. Hence, it has been a major clinical challenge to identify what specific types of cancer cell death are efficient at eliciting durable anticancer immunity. The overall goal of my lab’s research program is to identify what types of cancer cell death initiate effective anticancer immunity and expand therapeutic approaches to deadly cancers such as pancreatic and brain cancer. Since the start of my lab in September 2020, we have created synthetic models of cell death where inducible expression of cell death executioner proteins activates distinct types of cancer cell death such as apoptosis, necroptosis, and pyroptosis. Using these models, we identified that the secretomes of apoptotic, necroptotic, and pyroptotic tumors display remarkable differences in previously established markers of immunogenic cell death and novel secretomes as identified by mass spectrometry. In follow-up, in vivo studies, we identified differences in the activation and maturation patterns of antigen-presenting cells, such as type I conventional dendritic cells (cDC1). Consistent with the differences in cDC1 functionality we identified differences in the magnitude, diversity, and functionality of effector CD8+ T cells. All these immunological differences contributed to significant differences in the anticancer protective activity of distinct cancer cell death types. During my talk, I will be presenting these findings and discussing how these basic findings can be integrated into translational platforms for the management of non-T cell inflamed pancreatic and brain tumors. Citation Format: Samuel T Workenhe. A tale of cancer cell death and 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 B031.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".