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Using MRI to evaluate and predict therapeutic success from depot-based cancer vaccines.

ABSTRACT: In the preclinical development of immunotherapy candidates, understanding the mechanism of action and determining biomarkers that accurately characterize the induced host immune responses is critical to improving their clinical interpretation. Magnetic resonance imaging (MRI) was used to evaluate in vivo changes in lymph node size in response to a peptide-based cancer vaccine therapy, formulated using DepoVax (DPX). DPX is a novel adjuvant lipid-in-oil-based formulation that facilitates enhanced immune responses by retaining antigens at the injection site for extended latencies, promoting increased potentiation of immune cells. C57BL/6 mice were implanted with C3 (HPV) tumor cells and received either DPX or control treatments, 5 days post-implantation. Complete tumor eradication occurred in DPX-vaccinated animals and large volumetric increases were observed in the vaccine-draining right inguinal lymph node (VRILN) in DPX mice, likely corresponding to increased localized immune response to the vaccine. Upon evaluating the relative measure of vaccine-potentiated immune activation to tumor-induced immune response (VRILN/VLILN), receiver-operating characteristic (ROC) curves revealed an area under the curve (AUC) of 0.90 (±0.07), indicating high specificity and sensitivity as a predictive biomarker of vaccine efficacy. We have determined that for this tumor model, early MRI lymph node volumetric changes are predictive of depot immunotherapeutic success.

SUBMITTER: DeBay DR

PROVIDER: S-EPMC4685660 | biostudies-other | 2015

REPOSITORIES: biostudies-other

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Project description:BackgroundAn increased number of resources are allocated on cancer biomarker discovery, but very few of these biomarkers are clinically adopted. To bridge the gap between Biomarker discovery and clinical use, we aim to generate the Biomarker Toolkit, a tool designed to identify clinically promising biomarkers and promote successful biomarker translation.MethodsAll features associated with a clinically useful biomarker were identified using mixed-methodology, including systematic literature search, semi-structured interviews, and an online two-stage Delphi-Survey. Validation of the checklist was achieved by independent systematic literature searches using keywords/subheadings related to clinically and non-clinically utilised breast and colorectal cancer biomarkers. Composite aggregated scores were generated for each selected publication based on the presence/absence of an attribute listed in the Biomarker Toolkit checklist.ResultsSystematic literature search identified 129 attributes associated with a clinically useful biomarker. These were grouped in four main categories including: rationale, clinical utility, analytical validity, and clinical validity. This checklist was subsequently developed using semi-structured interviews with biomarker experts (n=34); and 88.23% agreement was achieved regarding the identified attributes, via the Delphi survey (consensus level:75%, n=51). Quantitative validation was completed using clinically and non-clinically implemented breast and colorectal cancer biomarkers. Cox-regression analysis suggested that total score is a significant driver of biomarker success in both cancer types (BC: p>0.0001, 95.0% CI: 0.869-0.935, CRC: p>0.0001, 95.0% CI: 0.918-0.954).ConclusionsThis novel study generated a validated checklist with literature-reported attributes linked with successful biomarker implementation. Ultimately, the application of this toolkit can be used to detect biomarkers with the highest clinical potential and shape how biomarker studies are designed/performed.

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Using MRI to evaluate and predict therapeutic success from depot-based cancer vaccines.

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