Co-Clinical Imaging Resource Program (CIRP): Bridging the Translational Divide to Advance Precision Medicine

Abstract

The National Institutes of Health’s (National Cancer Institute) precision medicine initiative emphasizes the biological and molecular bases for cancer prevention and treatment. Importantly, it addresses the need for consistency in preclinical and clinical research. To overcome the translational gap in cancer treatment and prevention, the cancer research community has been transitioning toward using animal models that more fatefully recapitulate human tumor biology. There is a growing need to develop best practices in translational research, including imaging research, to better inform therapeutic choices and decision-making. Therefore, the National Cancer Institute has recently launched the Co-Clinical Imaging Research Resource Program (CIRP). Its overarching mission is to advance the practice of precision medicine by establishing consensus-based best practices for co-clinical imaging research by developing optimized state-of-the-art translational quantitative imaging methodologies to enable disease detection, risk stratification, and assessment/prediction of response to therapy. In this communication, we discuss our involvement in the CIRP, detailing key considerations including animal model selection, co-clinical study design, need for standardization of co-clinical instruments, and harmonization of preclinical and clinical quantitative imaging pipelines. An underlying emphasis in the program is to develop best practices toward reproducible, repeatable, and precise quantitative imaging biomarkers for use in translational cancer imaging and therapy. We will conclude with our thoughts on informatics needs to enable collaborative and open science research to advance precision medicine.

Co-clinical FDG-PET radiomic signature in predicting response to neoadjuvant chemotherapy in triple-negative breast cancer

“…We previously identified six TNBC subtypes including 2 basal-like (BL1 and BL2), an immunomodulatory (IM), a mesenchymal (M), a mesenchymal stem-like (MSL), and a luminal androgen receptor (LAR) subtype through molecular signatures of TNBC subtypes [20]. The use of PDX in preclinical imaging offers numerous advantages in translational imaging research, chief among them is retention of human tumor heterogeneity [12,16,21], which can be exploited to develop image metrics of response to therapy. Thus, the objective of this work was to first, optimize and identify robust radiomic features to predict response to therapy in subtype-matched TNBC PDX, and second, implement PDX-optimized image features in the TNBC co-clinical study to predict response to therapy using machine learning (ML) algorithms.…”

Respiratory Motion Mitigation and Repeatability of Two Diffusion-Weighted MRI Methods Applied to a Murine Model of Spontaneous Pancreatic Cancer

“…The repeatability metrics provide variability assessments for each DWI protocol. Such assessments would enable the estimation of the sample size required by each DWI method [ 36 ] and are motivated by increased consensus within the cancer imaging community to report these metrics for quantitative imaging markers [ 14 , 27 , 50 , 51 , 52 ]. Considering the highly motion-susceptible location of the pancreatic tumor in freely breathing mice, our reproducibility metrics of the DW-SE-RAD protocol ( SD ws = 0.12 × 10 −3 mm 2 /s and CV WS = 9%) compare favorably to those reported from tumors located in less motion-susceptible locations—for example, the SD ws of 0.1 × 10 −3 mm 2 /s from breast cancer xenografts grown in the hind flank of mice [ 50 ], SD ws of 0.06 × 10 −3 mm 2 /s, and CV WS of 7% from orthotopically implanted breast tumors in mice with restricted respiration [ 27 ].…”

Co-clinical FDG-PET Radiomic Signature in Predicting Response to Neoadjuvant Chemotherapy in Triple Negative Breast Cancer

“…An underlying premise in the co-clinical study design is that the heterogeneity of the human tumor is retained in PDX. Indeed, tumor genomic and pathological investigations have confirmed that PDX recapitulate the heterogeneity of human tumors [12][13][14][15][16] and that these can be used to a better inform cancer biology, therapeutic design [17][18][19], and therefore by extension imaging studies, albeit with some limitations [21]. With that in mind, in this this work, we exploited the heterogeneity of TNBC PDX subtypes to 1) identify robust radiomic features in preclinical TNBC PDX; 2) optimize RadSig-ML algorithms to predict response to therapy in PDX; and 3) implement PDX-optimized RadSig to predict/assess response to therapy in the corresponding clinical trial.…”

“…We previously identified six TNBC subtypes including 2 basal-like (BL1 and BL2), an immunomodulatory (IM), a mesenchymal (M), a mesenchymal stem–like (MSL), and a luminal androgen receptor (LAR) subtype through molecular signatures of TNBC subtypes [20]. The use of PDX in preclinical imaging offers numerous advantages in translational imaging research, chief among them is retention of human tumor heterogeneity [12, 16, 21], which can be exploited to develop image metrics of response to therapy. Thus, the primary objective of this work was to utilize PDX to optimize robust radiomic features of tumor heterogeneity indicative of response to therapy in preclinical PDX trials.…”