Browsing by Subject "Kras"

Now showing 1 - 3 of 3
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    Biomarker Risk Score Algorithm and Preoperative Stratification of Patients with Pancreatic Cystic Lesions
    (Wolters Kluwer, 2021) Yip-Schneider, Michele T.; Wu, Huangbing; Allison, Hannah R.; Easler, Jeffrey J.; Sherman, Stuart; Al-Haddad, Mohammad A.; Dewitt, John M.; Schmidt, C. Max; Surgery, School of Medicine
    Background: Pancreatic cysts are incidentally detected in up to 13% of patients undergoing radiographic imaging. Of the most frequently encountered types, mucin-producing (mucinous) pancreatic cystic lesions may develop into pancreatic cancer, while nonmucinous ones have little or no malignant potential. Accurate preoperative diagnosis is critical for optimal management, but has been difficult to achieve, resulting in unnecessary major surgery. Here, we aim to develop an algorithm based on biomarker risk scores to improve risk stratification. Study design: Patients undergoing surgery and/or surveillance for a pancreatic cystic lesion, with diagnostic imaging and banked pancreatic cyst fluid, were enrolled in the study after informed consent (n = 163 surgical, 67 surveillance). Cyst fluid biomarkers with high specificity for distinguishing nonmucinous from mucinous pancreatic cysts (vascular endothelial growth factor [VEGF], glucose, carcinoembryonic antigen [CEA], amylase, cytology, and DNA mutation) were selected. Biomarker risk scores were used to design an algorithm to predict preoperative diagnosis. Performance was tested using surgical (retrospective) and surveillance (prospective) cohorts. Results: In the surgical cohort, the biomarker algorithm outperformed the preoperative clinical diagnosis in correctly predicting the final pathologic diagnosis (91% vs 73%; p < 0.000001). Specifically, nonmucinous serous cystic neoplasms (SCN) and mucinous cystic neoplasms (MCN) were correctly classified more frequently by the algorithm than clinical diagnosis (96% vs 30%; p < 0.000008 and 92% vs 69%; p = 0.04, respectively). In the surveillance cohort, the algorithm predicted a preoperative diagnosis with high confidence based on a high biomarker score and/or consistency with imaging from ≥1 follow-up visits. Conclusions: A biomarker risk score-based algorithm was able to correctly classify pancreatic cysts preoperatively. Importantly, this tool may improve initial and dynamic risk stratification, reducing overdiagnosis and underdiagnosis.
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    Genetic Evidence for XPC-KRAS Interactions During Lung Cancer Development.
    (Elsevier, 2015-10-20) Zhang, Xiaoli; He, Nonggao; Gu, Dongsheng; Wickliffe, Jeff; Salazar, James; Boldogh, Istavan; Xie, Jingwu; Department of Pediatrics, IU School of Medicine
    Lung cancer causes more deaths than breast, colorectal and prostate cancers combined. Despite major advances in targeted therapy in a subset of lung adenocarcinomas, the overall 5-year survival rate for lung cancer worldwide has not significantly changed for the last few decades. DNA repair deficiency is known to contribute to lung cancer development. In fact, human polymorphisms in DNA repair genes such as xeroderma pigmentosum group C (XPC) are highly associated with lung cancer incidence. However, the direct genetic evidence for the role of XPC for lung cancer development is still lacking. Mutations of the Kirsten rat sarcoma viral oncogene homolog (Kras) or its downstream effector genes occur in almost all lung cancer cells, and there are a number of mouse models for lung cancer using these mutations. Using activated Kras, KrasLA1, as a driver for lung cancer development in mice, we showed for the first time that mice with KrasLA1 and Xpc knockout had worst outcomes in lung cancer development, and this phenotype was associated with accumulated DNA damage. Using cultured cells, we demonstrated that induced expression of oncogenic KRASG12V led to increased levels of reactive oxygen species (ROS) as well as DNA damage, and both can be suppressed by anti-oxidants. Thus, it appears that XPC may help repair DNA damage caused by KRAS-mediated production of ROS.
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    Low-Coverage Whole Genome Sequencing Using Laser Capture Microscopy with Combined Digital Droplet PCR: An Effective Tool to Study Copy Number and Kras Mutations in Early Lung Adenocarcinoma Development
    (MDPI, 2021-11-06) Mickler, Elizabeth A.; Zhou, Huaxin; Phang, Tzu L.; Geraci, Mark W.; Stearman, Robert S.; Sears, Catherine R.; Medicine, School of Medicine
    Defining detailed genomic characterization of early tumor progression is critical to identifying key regulators and pathways in carcinogenesis as potentially druggable targets. In human lung cancer, work to characterize early cancer development has mainly focused on squamous cancer, as the earliest lesions are more proximal in the airways and often accessible by repeated bronchoscopy. Adenocarcinomas are typically located distally in the lung, limiting accessibility for biopsy of pre-malignant and early stages. Mouse lung cancer models recapitulate many human genomic features and provide a model for tumorigenesis with pre-malignant atypical adenomatous hyperplasia and in situ adenocarcinomas often developing contemporaneously within the same animal. Here, we combined tissue characterization and collection by laser capture microscopy (LCM) with digital droplet PCR (ddPCR) and low-coverage whole genome sequencing (LC-WGS). ddPCR can be used to identify specific missense mutations in Kras (Kirsten rat sarcoma viral oncogene homolog, here focused on Kras Q61) and estimate the percentage of mutation predominance. LC-WGS is a cost-effective method to infer localized copy number alterations (CNAs) across the genome using low-input DNA. Combining these methods, the histological stage of lung cancer can be correlated with appearance of Kras mutations and CNAs. The utility of this approach is adaptable to other mouse models of human cancer.