Abstract 2060: Treatment combination strategies to improve radiation efficacy in immunologically cold tumors <i>in vivo</i>
Bibliographic record
Abstract
Abstract Introduction: Radiation therapy (RT) is a promising bladder-sparing therapy in muscle-invasive bladder cancer (MIBC). However, 30% of patients exhibit radioresistant tumors requiring salvage surgery. Combining RT with immune checkpoint inhibitor was reported to have synergic effects on anti-tumor immunity. Yet, it is also met with treatment resistance. In addition, activation of the STING pathway was shown to induce cell death, cancer cell antigens release and presentation, and promote the priming, activation and trafficking of T cells into tumors. Previous in vivo results from our team using the MB49 murine bladder cancer cell line - and immunologically ‘hot’ tumor model - demonstrated significant improvement of survival and immune cell infiltration upon STING agonist treatment, when combined with RT and anti-PDL1 treatment. Oppositely, treating the ‘cold’ tumor model UPPL with a combination of RT and anti-PDL1 treatment did not improve survival compared to RT alone. Consistently, UPPL tumors present low T cell and high neutrophil infiltration in vivo. Thus, the main objective of the present study is to evaluate whether combining RT, anti-PDL-1 and STING agonist treatments can improve the immunogenicity of UPPL tumors. Methods: Seven-week-old male C57BL/6 mice were subcutaneously injected with 5.106 UPPL tumor cells. Once tumors reached 0.1-0.15 cm3, mice were randomized into the following treatment groups : 1) Untreated; 2) PDL-1; 3) STING; 4) PDL-1 + STING; 5) RT; 6) RT + PDL-1; 7) RT + STING; 8) RT + STING + PDL-1. Midpoint (~0.6-0.8 cm3, n=5) and endpoint (2 cm3, n=8) tumors, spleens and draining lymph nodes (dLN) were harvested and dissociated for flow cytometry analysis of TME composition and cytokine production. Results: We report that RT and RT-combined treatments delayed tumor growth and prolonged survival in vivo compared to untreated. Additionally, our combination approaches shifted immune infiltration : compared to untreated, each strategy changed myeloid and T cells infiltrating cells in tumors. Combination of RT with anti-PDL-1 and STING agonist favored macrophages infiltration as well as increased MHC-II expression in dendritic cells (DC). Furthermore, we observed increased infiltration of cytotoxic CD8+ T cells (Granzyme B+ and IFN-γ+ CD8+) in these groups. Conclusions: Our results suggest RT and RT combination treatments in cold tumor affect antigen presentation potency in DCs, fine-tune the functionality of infiltrating CD8+ T cells. This preliminary study shows immunologically cold tumors can be modulated through treatment combination, which has relevance in human treatment-resistant tumors. It adds to the very small body of literature taking a deep dive into the immune modulation of a conventionally used ‘cold’ tumor cell line. Citation Format: Eva Michaud, Gautier Marcq, Sabina Fehric, JiaMin Huang, Jose Joao Mansure, Ciriaco Piccirillo, Wassim Kassouf. Treatment combination strategies to improve radiation efficacy in immunologically cold tumors in vivo [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2060.
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 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".