Abstract IA-014: Strategies to enhance the immunogenicity of radiation therapy
Bibliographic record
Abstract
Abstract Radiation is an ideal partner to enhance cancer immunogenicity. In response to DNA damage, cytosolic DNA released from the nucleus and mitochondria activates sensors like cGAS/STING, leading to release of IFNβ, that recruits and activates BAFT3+ dendritic cells, for cross-presentation and cross-priming of CD8+ T cells (Deng L et al, Immunity 2014; Vanpouille-Box C et al, Nature Communications 2017; Yamazaki T et al, Nat Immunol. 2020 Aug 3). Radiation also increases the trafficking of activated CD8+ T cells by releasing CXCL16, a chemokine that binds to CXCR6 (Matsumura et al., J Immunol, 2008). Recent evidence has demonstrated how as part of DNA damage response to radiation, genes mutated in cancer are expressed, availing neoantigens to the patient’s immune system (Formenti S et al,Nature Medicine, 2018). By recruiting both the innate and adaptive immune response in combination with immune checkpoint blockade (ICB) radiation can convert the irradiated tumor into an in situ vaccine. While ongoing translational and clinical research is testing radiation immunogenicity with current immunotherapy, the focus of this presentation is on how to best apply the immunogenic effects in combination with standard systemic cancer therapies. Most standard cancer therapies also have relevant effects on the immune system (for example see Ameratunga M et al, Clin Cancer Res 2019): deciphering the immunological effects that accompany the cytocidal effects of available cancer therapies can guide their optimal integration. With regards to RT, the relevance to an immune response of selecting specific treatment fields, lymphatic sparing, specific radiation dose and fractionation as well as optimal sequencing iare rapidly emerging. For instance, in the setting of metastatic disease, cancer heterogeneity (De Mattos-Arruda L, et al. Cell, 2019) requires radiation targeting of all detectable metastatic deposits. A multi-institutional Canadian trial on oligometastatic cancer patients (with up to 5 metastases) has demonstrated a promising improvement in time-to-progression and survival after stereotactic body radiotherapy (SBRT) to each metastasis (Palma DA et al, Lancet, 2019). In addition to targeting heterogeneity, maximal reduction of tumor burden by multi-site SBRT can enable an immunological equilibrium and potentially prolong survival. Conversely, preclinical evidence discourages the inclusion of draining nodal stations in the field of radiotherapy (Marciscano A et al, Clin Cancer Res; 2018). Finally, data about the optimal timing of integration of SBRT with systemic therapy will be presented, by describing the example of oligometastatic ER+ breast cancer. The preclinical results of different sequencing of focal radiation with with CDK4/6 inhibitors and endocrine therapy in a murine model of ER+ breast cancer will be presented (Petroni G. et al, Clinical Cancer Research, in press), as well as the emerging evidence for the type of selective remodeling of TME that radiotherapy induces when given before this systemic therapy combination. Citation Format: Silvia C. Formenti. Strategies to enhance the immunogenicity of radiation therapy [abstract]. In: Proceedings of the AACR Virtual Special Conference on Radiation Science and Medicine; 2021 Mar 2-3. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(8_Suppl):Abstract nr IA-014.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".