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Preclinical imaging is a critical component in translational research with significant complexities in workflow and site differences in deployment. Importantly, the National Cancer Institute's (NCI) precision medicine initiative emphasizes the use of translational co-clinical oncology models to address the biological and molecular bases of cancer prevention and treatment. The use of oncology models, such as patient-derived tumor xenografts (PDX) and genetically engineered mouse models (GEMMs), has ushered in an era of co-clinical trials by which preclinical studies can inform clinical trials and protocols, thus bridging the translational divide in cancer research. Similarly, preclinical imaging fills a translational gap as an enabling technology for translational imaging research. Unlike clinical imaging, where equipment manufacturers strive to meet standards in practice at clinical sites, standards are neither fully developed nor implemented in preclinical imaging. This fundamentally limits the collection and reporting of metadata to qualify preclinical imaging studies, thereby hindering open science and impacting the reproducibility of co-clinical imaging research. To begin to address these issues, the NCI co-clinical imaging research program (CIRP) conducted a survey to identify metadata requirements for reproducible quantitative co-clinical imaging. The enclosed consensus-based report summarizes co-clinical imaging metadata information (CIMI) to support quantitative co-clinical imaging research with broad implications for capturing co-clinical data, enabling interoperability and data sharing, as well as potentially leading to updates to the preclinical Digital Imaging and Communications in Medicine (DICOM) standard.

The availability of high-fidelity animal models for oncology research has grown enormously in recent years, enabling preclinical studies relevant to prevention, diagnosis, and treatment of cancer to be undertaken. This has led to increased opportunities to conduct co-clinical trials, which are studies on patients that are carried out parallel to or sequentially with animal models of cancer that mirror the biology of the patients' tumors. Patient-derived xenografts (PDX) and genetically engineered mouse models (GEMM) are considered to be the models that best represent human disease and have high translational value. Notably, one element of co-clinical trials that still needs significant optimization is quantitative imaging. The National Cancer Institute has organized a Co-Clinical Imaging Resource Program (CIRP) network to establish best practices for co-clinical imaging and to optimize translational quantitative imaging methodologies. This overview describes the ten co-clinical trials of investigators from eleven institutions who are currently supported by the CIRP initiative and are members of the Animal Models and Co-clinical Trials (AMCT) Working Group. Each team describes their corresponding clinical trial, type of cancer targeted, rationale for choice of animal models, therapy, and imaging modalities. The strengths and weaknesses of the co-clinical trial design and the challenges encountered are considered. The rich research resources generated by the members of the AMCT Working Group will benefit the broad research community and improve the quality and translational impact of imaging in co-clinical trials.

Prostate cancer is one of the leading causes of cancer-related deaths among men worldwide. Research has shown that genetic variations can increase an individual's risk of developing this disease. In this study, we investigated the potential role of two genetic variants, one within RFX6 and another within SLC22A3, in predisposition to prostate cancer. A genetic association study was conducted, involving a case-control design with 112 prostate cancer cases and 95 individuals affected by benign prostatic hyperplasia who served as controls and had no history of cancer. The genotypes of the two variants, rs339331 in RFX6 and rs9364554 in SLC22A3, were determined using tetra-primer ARMS-PCR. In this study, a multi-stage strategy was employed to analyze the data obtained from genotyping and to assess the association of these two variants with prostate cancer risk. For statistical analysis, Chi-squared, Fisher's exact test and logistic regression were performed to evaluate the association of variants with prostate cancer and Gleason score. The results suggest that the rs9364554 variant in SLC22A3 is not associated with prostate cancer risk under either additive or multiplicative genetic models, while the variant in RFX6 is significantly associated with prostate cancer susceptibility. The TT genotype of rs339331 indicated a possible protective effect against prostate adenocarcinoma (CI 95% = 0/009 - 0/725, P-value = 0/009, OR = 0/083). In conclusion, our study provides evidence that the genetic variant rs339331 within RFX6 is significantly associated with prostate cancer susceptibility and may have a potential protective effect against prostate adenocarcinoma. It should be mentioned that more research is needed to investigate the possible protective effect of the TT genotype of rs9364554 against prostate cancer risk in other populations and various ethnic groups and particularly larger sample sizes are required to confirm this association. [ABSTRACT FROM AUTHOR]

Prostate cancer is the second most prevalent malignancy in men and the fifth leading cause of death worldwide. It is the second most common urinary tract cancer among Iranian men. The aim of this study was to investigate the association between rs10090154, located on the 8q24 locus, and rs1691053 on chromosome 5p15.31, with prostate adenocarcinoma and PSA levels. This study also aimed to identify the potential of these genetic markers as screening factors. This case-control study included 79 patients with prostate adenocarcinoma aged between 48 to 86 years, as well as 98 patients with benign prostatic hyperplasia aged between 47 to 81 years. The Tetra-primer ARMS-PCR method was applied to determine the genotype of each participant. In this study, no significant differences in genotypic distribution between the prostate adenocarcinoma and control groups were discovered for rs10090154 (P-value = 0.608) and rs1691053 (P-value = 0.102) polymorphisms. Moreover, for the study of the additive genetics model of the rs10090154 polymorphism, with the TT genotype as the reference, the CC genotype with P-value = 1 and OR {95%CI} = 0.750, {0.039-14.576} and CT genotype with P-value = 0.324 and OR {95%CI} = 0.577, {0.191-1.739}, were not associated. Correspondingly, for rs1691053, with the CC genotype as the reference, the CT genotype with P-value = 0.176 and OR {95%CI}= 0.196, {0.022-1.793}, and the TT genotype with P-value= 0.464, OR{95%CI}= 0.125, {0.005-3.225}, were not associated. These findings suggest that rs10090154 and rs1691053 may not be associated with prostate cancer among Iranians. However, further research with larger sample sizes and investigation of various Iranian subpopulations are needed to confirm these results. [ABSTRACT FROM AUTHOR]

We investigated the transcriptional profile of whole blood in early and metastatic stages of pancreatic cancer (PaC) patients to identify potential diagnostic factors for early diagnosis. Blood samples from 18 participants (6 healthy individuals, 6 patients in early stage (I/II) PaC, and 6 patients in metastatic PaC) were analyzed by RNA‐sequencing. The expression levels of identified genes were subsequently compared with their expression in pancreatic tumor tissues based on TCGA data reported in UALCAN and GEPIA2 databases. Overall, 331 and 724 genes were identified as differentially expressed genes in early and metastatic stages, respectively. Of these, 146 genes were shared by early and metastatic stages. Upregulation of PTCD3 and UBA52 genes and downregulation of A2M and ARID1B genes in PaC patients were observed from early stage to metastasis. TCGA database showed increasing trend in expression levels of these genes from stage I to IV in pancreatic tumor tissue. Finally, we found that low expression of PTCD3, A2M, and ARID1B genes and high expression of UBA52 gene were positively correlated with PaC patients survival. We identified a four‐gene set (PTCD3, UBA52, A2M, and ARID1B) expressed in peripheral blood of early stage and metastatic PaC patients that may be useful for PaC early diagnosis. [ABSTRACT FROM AUTHOR]