Abstract A064 Characterizing the immune microenvironment and examining the effect of tumour-targeted MRgHIFU mediated hyperthermia in combination with thermosensitive liposomal doxorubicin in a mouse model of embryonal rhabdomyosarcoma
Bibliographic record
Abstract
Abstract Introduction Embryonal rhabdomyosarcoma (ERMS) is the most common pediatric soft tissue sarcoma. High risk patients have poor survival rates and second line chemotherapies such as doxorubicin cause systemic toxicity. We combined thermosensitive liposomal doxorubicin (TLD) with magnetic resonance-guided high intensity focused ultrasound (MRgHIFU). TLD encapsulates doxorubicin in a thermosensitive liposome, thus releasing doxorubicin only once heated to 40°C. MRgHIFU is a non-invasive, non-ionizing technique used to locally heat tumors for targeted drug release. MRgHIFU+TLD treatment resulted in improved survival in a syngeneic mouse model of ERMS. Hyperthermia (HT) can also modulate the immune response, and the immune microenvironment (IM) of RMS is undercharacterized. Therefore, identifying effective immunotherapeutic targets and increasing tumor immunogenicity is warranted. The aim of this study is to characterize the IM of this ERMS model and understand the effect of chemotherapy and HT on immune infiltrates over time. Methods Mice were divided into either an HT or normothermia (NT) group. The HT group underwent tumor-targeted MRgHIFU, while the NT group received no HT. Both groups received free doxorubicin (FD) and TLD via tail vein resulting in 6 treatment groups: untreated, FD, TLD, HT, HT+FD, and HT+TLD. Tumors were resected at 24 and 168 hours post-treatment (hpt) and immunohistochemistry (IHC) was performed on tumor sections for CD11b (myeloid immune cells), CD3 (T cells) and B220 (B cells). IHC was quantified using HALO image analysis software. Imaging mass cytometry (IMC) was conducted on a subset of tumors using a panel of >10 additional markers to spatially visualize immune cell subpopulations. Observations IHC mice (n=105) showed that most of the tumor infiltrating immune cells were CD11b+, with significantly fewer CD3+, and even fewer B220+ cells (x̄ =2107, 381, 16 cells/mm2 respectively, p≤ 0.0001 for all). TLD treatment alone decreased CD11b+ and CD3+ immune cell presence in tumors at 24 hpt (p=0.001, 0.016). However, HT+TLD treatment increased CD11b+ cells compared to TLD alone at 24 hpt (p=0.008), likely due to HT-mediated release of doxorubicin. FD treatment alone decreased CD11b+ cells at both 24 and 168 hpt (p=0.033, 0.02). The addition of HT at 24 hpt decreased CD3+ cells in the FD treated tumors (p=0.003). HT treatment increased B220+ cells in tumors at 168 hpt (p=0.016). IMC showed a high presence of macrophage specific markers, with low levels of B cells, neutrophils, and cytotoxic/regulatory T cells. Conclusions The IM of this ERMS model is macrophage dominant and lymphocyte deficient. HT, FD, and TLD affected tumor infiltrating immune cells at different time points post-treatment and expanding the IMC cohort will inform how we can harness HT+TLD to create a greater synergistic effect with immunotherapies while reducing toxicity. IM characterization within this model establishes a framework for future immunotherapeutic study within the RMS landscape to pioneer preclinical testing. Citation Format: Julia Nomikos, Claire Wunker, Adam C. Waspe, Yael Babichev, Karolina Piorkowska, Suzanne Wong, Warren Foltz, J. Ted Gerstle, Elizabeth G. Demicco, Abha A. Gupta, Cynthia J. Guidos, James M. Drake, Rebecca A. Gladdy. Characterizing the immune microenvironment and examining the effect of tumour-targeted MRgHIFU mediated hyperthermia in combination with thermosensitive liposomal doxorubicin in a mouse model of embryonal rhabdomyosarcoma [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pediatric Cancer Research; 2024 Sep 5-8; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl):Abstract nr A064.
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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 | 0.001 |
| 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".