Abstract 873: Evaluating immunotherapy effects using preclinical molecular imaging tools for quantitative immune cell tracking
Bibliographic record
Abstract
Abstract Immunotherapies are a promising class of cancer therapeutics, but clinical translation is often hampered by a lack of understanding regarding optimal therapy administration, combination, and reliable biomarkers of success. Traditional metrics such as RECIST, and modern metrics like irRC and PERCIST used for monitoring cancer therapy outcome, have limitations for immunotherapy evaluation and are not always reflective of the underlying immune mechanisms. We aim to better characterize and monitor these immunotherapies, with focus on combination therapy optimization, by tracking immune cell migration in response to these therapies using preclinical magnetic resonance imaging (MRI). MRI is used to obtain anatomical tumor changes, detect and quantify superparamagnetic iron oxide (SPIO)-labeled cells in vivo. Goal: To link immune cell migration to early prognostic biomarkers for immunotherapy success. Methods: C57/BL6 mice (n=40) received an implant of 5x105 C3 cancer cells in the left flank. Mice (n=10/group) were i) untreated or treated with ii) 200µg of anti-PD1/day on days 7, 9, 11, 21 and 25, iii) the peptide-based vaccine DepoVaxTM (DPX) on day 15, or iv) with anti-PD1 and DPX. CD8+ cytotoxic T cells (CD8) and suppressive regulatory T cells (Tregs) were isolated from diseased-matched & treated donor mice for expansion in culture before labeling with SPIO for adoptive cell transfer into mice receiving scans. PET/MRI Data: Anatomical and qualitative SPIO data is collected using a balanced steady-state free precession (bSSFP) sequence. Iron quantification is done using R2* maps from a multi-echo single point imaging sequence (TurboSPI). Tumor metabolism was assessed by 18F-fluorodeoxyglucose uptake during simultaneous acquisition of positron emission tomography (PET) with MRI. Imaging was done 21 and 28 days post-implant. Results: CD8 and Treg cells are consistently recruited to both the tumor and vaccine draining inguinal lymph nodes. CD8 T cells are primarily recruited to the tumor periphery and do not always penetrate the tumor core. Positive therapy outcomes are correlated with an increasing CD8/Treg ratio in the tumor, particularly at earlier time points (21 vs 28 days). In certain cases, CD8 T cells were found within the fat pad between the tumor and lymph node. As expected, DPX & anti-PD1 combination therapy resulted in the best prognosis. Simultaneous acquisition of PET/MRI demonstrated large areas of necrosis in tumor cores. Using TurboSPI, we have begun quantifying CD8 and Tregs cells, evaluating if volumetric tumor changes due to pseudoprogression correlate with Treg or CD8 T cell migration and comparing results to pre-existing biomarkers. Conclusions: Using MRI/PET with quantitative immune cell tracking results in more in-depth, longitudinal, characterization of immunotherapies at the preclinical level, which can be used to optimize therapy combinations. Citation Format: Marie-Laurence Tremblay, Zoe O'Brien-Moran, Christa Davis, Kimberly Brewer. Evaluating immunotherapy effects using preclinical molecular imaging tools for quantitative immune cell tracking [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 873. doi:10.1158/1538-7445.AM2017-873
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".