Abstract B032: Novel immunotherapy approach for metastatic triple-negative breast cancer
Bibliographic record
Abstract
Abstract Currently, there are no curative therapies for metastatic triple-negative breast cancer (TNBC), which accounts for 10–15% of all breast cancers and has a higher rate of early recurrences. Durable responses to therapy of metastatic breast cancer are enhanced when an anti-tumor immune response is activated. However, not all patients treated with immunotherapies demonstrate durable responses. Therefore, it is essential to develop novel treatment regimens that are less toxic and more effective. We suggest that it is possible to immunize patients against their own TNBC metastases by using Focused Ultrasound (FUS) therapy to augment tumor antigen release. FUS is an emerging non-invasive, non-toxic treatment modality for localized treatment of cancers. Damaged tumor cells have been shown to release endogenous danger signals, which could stimulate an immune response, implying that the patient’s dying cancer cells serve as a therapeutic vaccine that stimulates an anti-tumor immune response. We hypothesize that FUS will augment tumor antigen release and danger signals to mediate the rejection of both FUS-treated and untreated tumors. We believe that this approach will increase the percentage of patients who will respond to immunotherapies by stimulating a TNBC-specific immune response. However, the optimum parameters to simulate this response are mostly unknown. Therefore, we conducted experiments to compare different FUS parameters with biological responses. Female Balb/c mice were inoculated orthotopically (1 or 2 mammary glands) with 4T1 cancer cells. When tumors became palpable, 1 mammary tumor received FUS treatments using a 1.14 MHz transducer (FUS Instruments, Canada). FUS was applied either in pulsed (44 W), or in continuous mode (20W). Following FUS treatment, necrosis, apoptosis, cytokines, gene expression, temperature, tumor growth, and metastasis were measured. We observed an increase in both necrosis (H&E) and apoptosis (Caspase-3) with FUS treatment when compared to untreated tumors. Next, we evaluated high mobility group box 1 (HMGB1) protein release in 4T1 cells in vitro to assess changes likely to result in signaling related to immunogenic cell death (ICD). We found that thermal dosing was capable of eliciting ICD, as indicated by the release of HMGB1, with an effect that peaked near 60°C. Our experiments also showed that there is a decrease in the size of secondary tumors as well as lung metastases in FUS-treated animals when compared to the controls (abscopal effect). The volcanic plots and heat maps showed that there are differences in gene expression between the control and FUS-treated groups. Serum Luminex analysis showed several cytokines (e.g., GM-CSF, MIP-1, IL-1a, IL-6, G-CSF) vital for immune responses changed in the FUS-treated group. These data suggest that FUS leads to increased immune responses and a decrease in the tumor size and the number of metastases. This paradigm shift has the potential to open treatment for metastatic TNBC. Citation Format: Paul Griffin, Riya Jain, Sean Le, Kyle Rajapakse, Wilson Miller, Kumari Andarawewa. Novel immunotherapy approach for metastatic triple-negative breast cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Tumor Immunology and Immunotherapy; 2024 Oct 18-21; Boston, MA. Philadelphia (PA): AACR; Cancer Immunol Res 2024;12(10 Suppl):Abstract nr B032.
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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 | 0.001 |
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".