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Record W4393095401 · doi:10.1158/1538-7445.am2024-1353

Abstract 1353: Potentiating salvage radiotherapy in radiorecurrent prostate cancer through anti-CTLA4 therapy: Implications from a syngeneic model

2024· article· en· W4393095401 on OpenAlexaff
Hanzhi Wang, Linsey Gong, Xiaoyong Huang, Stephanie D. White, Hanz T. Chung, Danny Vesprini, Tera N. Petchiny, Emmanouil Fokas, Hansen He, Robert S. Kerbel, Stanley K. Liu

Bibliographic record

VenueCancer Research · 2024
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Treatment and Research
Canadian institutionsSunnybrook Health Science CentreUniversity of Toronto
Fundersnot available
KeywordsRadiation therapyProstate cancerMedicineCancerSalvage therapyOncologyProstateInternal medicineCancer researchChemotherapy

Abstract

fetched live from OpenAlex

Abstract Background: Advanced prostate cancer (PCa) is a leading cause in cancer death and can elicit significant morbidity and mortality. A common treatment modality for advanced PCa is radiation therapy (RT). Currently, salvage of local disease recurrence after RT is a major clinical problem. Emerging evidence indicates the importance of the immune system in governing RT response. In support of this, immune checkpoint inhibitors (ICIs), which enhance immune activation, have demonstrated clinical therapeutic promise in combination with RT in certain advanced cancers. Irradiation (IR) combined with ICIs may prime the immune system to recognize and target recurrent cancer. Purpose: Investigate the therapeutic efficacy of ICIs in combination with RT for radiorecurrent PCa in a syngeneic pre-clinical model. Methods: TRAMP-C2 cells were treated with 10 Gy of radiation over 5 fractions (similar to clinical hypofractionated (HF) schedule) to generate TRAMP-C2 HF cells. Immune-competent mice were transplanted subcutaneously with TRAMP-C2 HF cells. Once tumors reached palpable size, mice were administered either ICIs (anti-PDL1 or anti-CTLA4) alone or in combination with RT. Tumor volume was monitored to determine the treatment effect. Correlative studies on excised tumors and secondary lymphoid organs included flow cytometry and NanoString gene expression panel to evaluate immunological mechanisms that contribute to anti-tumor effects. Results: TRAMP-C2 HF cells were validated for radiation resistance and exhibited a more aggressive phenotype (reduction in senescence, increase in clonogenicity) similar to clinically recurrent PCa. Radiation resistance of TRAMP-C2 HF tumors was validated in vivo. Administration of anti-PDL1 and anti-CTLA4 as monotherapy or in combination did not achieve significant tumor growth delay compared to control. IR alone produced an observable tumor growth delay compared to ICIs alone. The combination of anti-PDL1 and IR did not yield additional growth delay compared to IR alone. Strikingly, significant tumor growth delay was seen with the combination of IR and anti-CTLA4 compared to IR alone, while also resulting in complete cure in a third of the mice. mice. Lymph nodes and tumors from mice treated with IR and anti-CTLA4 vs. IR and isotype control demonstrated differential expression of genes in T cell functions and enrichment in both CD4+ and CD8+ T cell populations. Conclusion: We generated the first syngeneic radiorecurrent PCa model and demonstrated that combining anti-CTLA4 and IR results in synergistic tumor response. Combined therapy resulted in augmented immune response, most notably enhancement of CD8 T cell activity. Significance: These findings contribute to our understanding of immunological events associated with RT and ICIs in the context of radiorecurrent PCa and support new avenues for salvage therapy in clinical trials. Citation Format: Hanzhi Wang, Linsey Gong, Xiaoyong Huang, Stephanie D. White, Hanz T. Chung, Danny Vesprini, Tera N. Petchiny, Emmanouil Fokas, Hansen He, Robert S. Kerbel, Stanley K. Liu. Potentiating salvage radiotherapy in radiorecurrent prostate cancer through anti-CTLA4 therapy: Implications from a syngeneic model [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1353.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.146
GPT teacher head0.475
Teacher spread0.329 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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