KRAS Mutation Detection with (2S,4R)-4-[18F]FGln for Noninvasive PDAC Diagnosis

Pancreatic ductal adenocarcinoma (PDAC), which has a poor prognosis and nonspecific symptoms and progresses rapidly, is the most common pancreatic cancer type. Inhibitors targeting KRAS G12D and G12C mutations have been pivotal in PDAC treatment. Cancer cells with different KRAS mutations exhibit various degrees of glutamine dependency; in particular, cells with KRAS G12D mutations exhibit increased glutamine uptake. (2S,4R)-4-[18F]FGln has recently been developed for clinical cancer diagnosis and tumor cell metabolism analysis. Thus, we verified the heterogeneity of glutamine dependency in PDAC models with different KRAS mutations by a visual and noninvasive method with (2S,4R)-4-[18F]FGln. Two tumor-bearing mouse models (bearing the KRAS G12D or G12C mutation) were injected with (2S,4R)-4-[18F]FGln, and positron emission tomography (PET) imaging features and biodistribution were observed and analyzed. The SUVmax in the regions of interest (ROI) was significantly higher in PANC-1 (G12D) tumors than in MIA PaCa-2 (G12C) tumors. Biodistribution analysis revealed higher tumor accumulation of (2S,4R)-4-[18F]FGln and other metrics, such as T/M and T/B, in the PANC-1 mouse models compared to those in the MIAPaCa-2 mouse models. In conclusion, PDAC cells with the KRAS G12D and G12C mutations exhibit various degrees of (2S,4R)-4-[18F]FGln uptake, indicating that (2S,4R)-4-[18F]FGln might be applied to detect KRAS G12C and G12D mutations and provide treatment guidance.

CVCDAP: an integrated platform for molecular and clinical analysis of cancer virtual cohorts

Recent large-scale multi-omics studies resulted in quick accumulation of an overwhelming amount of cancer-related data, which provides an unprecedented resource to interrogate diverse questions. While certain existing web servers are valuable and widely used, analysis and visualization functions with regard to re-investigation of these data at cohort level are not adequately addressed. Here, we present CVCDAP, a web-based platform to deliver an interactive and customizable toolbox off the shelf for cohort-level analysis of TCGA and CPTAC public datasets, as well as user uploaded datasets. CVCDAP allows flexible selection of patients sharing common molecular and/or clinical characteristics across multiple studies as a virtual cohort, and provides dozens of built-in customizable tools for seamless genomic, transcriptomic, proteomic and clinical analysis of a single virtual cohort, as well as, to compare two virtual cohorts with relevance. The flexibility and analytic competence of CVCDAP empower experimental and clinical researchers to identify new molecular mechanisms and develop potential therapeutic approaches, by building and analyzing virtual cohorts for their subject of interests. We demonstrate that CVCDAP can conveniently reproduce published findings and reveal novel insights by two applications. The CVCDAP web server is freely available at https://omics.bjcancer.org/cvcdap/.