Abstract 3192: Aptamers, antibodies and radiotherapy for the treatment of DIPG
Bibliographic record
Abstract
Abstract Diffuse intrinsic pontine gliomas (DIPGs) is one of the most aggressive pediatric brain tumors. Radiotherapy (RT) constitute the standard of care of these tumors being its therapeutic effect just palliative. There is plenty of evidence that RT is able to activate immune responses through the induction of immunogenic cell death. However, it also elicits immunosuppressive effects and enforces immunological tolerance. The result is that in DIPGs the RT effect is just transitory. In this context, immune costimulatory or inhibitory molecules with the capability to boost the immune effect of RT could be amenable agents to use in combination to treat DIPGs. Agonist antibodies has been widely used in immunotherapy. Aptamers are high-affinity single-stranded nucleic acid ligands that exhibit remarkable affinity and specificity to their targets, comparable or exceeding that of antibodies. 4-1BB is a major costimulatory receptor promoting the survival and expansion of activated T cells. TIM-3 is a negative regulator of lymphocyte function that is involved in T-cell exhaustion. To this end, we examined the effect of RT in combination with either an agonist 4-1BB antibody or an aptamer against TIM-3. Importantly, both combined treatments showed a safe profile. Moreover, combination treatment of 4-1BB agonist antibody or TIM-3 aptamer with RT resulted in a significant improvement in the median survival of mice bearing DIPG orthotopic tumors when compared with single treatment in around 20 days (P=0.001 and P=0.04, for the combination of RT and 4-1BB or TIM-3,respectively). In addition, both combination led to long-term survivors (90 days). Rechallenge experiments in these animals showed the generation of memory against the tumors in both combined treatment. Mechanistic studies performed on day 16 showed an increase in CD8 effector cells, a decrease in T-regulators Foxp3+ cells and an increase in INF-gamma expression suggesting the triggering of an antitumor-immune response. Our data underscore that combination of RT with immune-boosting strategies for DIPGs are worth exploring. Citation Format: Naiara Martinez-Velez, Miguel Marigil, Javier Aristu, Luis Ramos, Fernando Pastor, Ana Patiño-García, Marc García-Moure, Juan Fueyo, Candelaria Gomez-Manzano, Ricardo Diez-Valle, Sonia Tejada, Marta M. Alonso. Aptamers, antibodies and radiotherapy for the treatment of DIPG [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3192.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".