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Ethics in the Era of Precision Medicine
Published in Lawrence S. Chan, William C. Tang, Engineering-Medicine, 2019
Precision medicine holds out the prospect of reducing adverse drug reactions through advances in pharmacogenomics (such as targeted therapeutics tailored to the specific genomic signature of a particular cancer) and creating personalized or patient-specific health protocols based on genetic markers and individualized assessment of disease risk (Collins and Varmus 2015, Chawla and Davis 2013, Jain 2009). Proponents claim that precision medicine will promote increased effectiveness in healthcare delivery due to reductions in overall healthcare expenditures that will result from increases in the use of effective interventions (and the correlate reduction in ineffective interventions). Further improvements will result from the reduction in patient care burden that adverse drug reactions generate (Jain 2009). In short, precision medicine will promote “the right treatment to the right patient at the right time” (IMI 2014), shifting away from the “one size fits all” model of traditional medicine that relied upon a trial-and-error or blockbuster approach to diagnosis and treatment (Yousif et al. 2016).
Radiogenomics
Published in Jun Deng, Lei Xing, Big Data in Radiation Oncology, 2019
Barry S. Rosenstein, Gaurav Pandey, Corey W. Speers, Jung Hun Oh, Catharine M.L. West, Charles S. Mayo
As noted earlier, efforts to develop a “pan-cancer” genomic signature of radiation response have been reported. In these initial studies, cell line sensitivity to ionizing radiation was evaluated across the NCI-60 panel of cancer cell lines (Torres-Roca et al. 2005; Eschrich et al. 2009). Genes associated with intrinsic radiosensitivity (measured as surviving fraction at 2 Gy, SF2) at the RNA level were then identified. Network analysis was used to identify 10 hub genes from which a radio sensitivity index (RSI) was derived. The group assessed the performance of RSI in various disease types with varying levels of success (Eschrich et al. 2009, 2012; Ahmed et al. 2015; Strom et al. 2015). Importantly, RSI was shown to predict benefit from adjuvant radiotherapy in breast cancer patients and has progressed to prospective evaluation in a clinical trial.
Trial Design for Precision Medicine
Published in Mark Chang, John Balser, Jim Roach, Robin Bliss, Innovative Strategies, Statistical Solutions and Simulations for Modern Clinical Trials, 2019
Mark Chang, John Balser, Jim Roach, Robin Bliss
In the past decade, precision medicine and biomarker-driven clinical trials have been discussed and studied by many authors in the literature, e.g., Hawgood et al. (2015), Collins and Varmus (2015), Jameson and Longo (2015), Bayer and Galea (2015), Mirnezami et al (2012), Simon and Maittournam (2004), Mandrekar and Sargent (2009), Weir and Walley (2006), Simon (2010), and Baker et al. (2012). In practice, biomarker-driven adaptive designs are adaptive designs that allow us to select target populations based on interim data (Simon and Simon, 2013). Simon and Wang (2006) and Freidlin, Jiang and Simon (2010) studied a genomic signature design, Jiang, Freidlin and Simon (2007) proposed Biomarker-adaptive threshold design, Chang (2006, 2007), Wang et al. (2007), Wang, Hung and O’Neill (2009) and Jenkins et al. (2011) studied population enrichment design using biomarkers, which allow an interim decision on the target population to be made based on power or utility. Zhou et al (2008) studied Bayesian adaptive randomization design that provides patients with potentially more effective treatments as the conduct of the trial progresses. Song and Pepe (2004) studied markers for selecting a patient’s treatment. Studies on biomarker-adaptive design were done by Beckman, Clark, and Chen (2011) for oncology trials. Recently, Wang (2013), Wang, Chang, and Menon (2014, 2015) used an adaptive design with hierarchical model to solve the mystery regarding why the first level correlation plays a limited role in biomarker-adaptive design.
DNA Methylation and Gene Expression with Clinical Covariates Explain Variation in Aggressiveness and Survival of Pancreatic Cancer Patients
Published in Cancer Investigation, 2020
Shyamali Mukerjee, Agustin Gonzalez-Reymundez, Sophia Y. Lunt, Ana I. Vazquez
Whole profiles of gene expression (18) and gene expression in combination with methylation (19) in breast cancer explain a substantial proportion of interindividual variation in survival, demonstrating the importance of mRNA and methylation changes in breast tumors associated with survival. Both gene expression and methylation profiles explain a substantial percentage of variability in survival. In glioblastoma multiforme, variation in mRNA appear to play a smaller role in the interindividual variation of patient survival, while methylation was the strongest predictor of this variation in survival (24). Results from this study suggest that omics in tumors may be influential in PC development and proliferation. The use of omics and clinical covariates can help us understand interindividual variability for cancer patients and can offer insights on treatment. A potential explanation could be attributable to genes ADM, ASPM, DCBLD2, E2F7, KRT6A, which appear to be associated with vascular invasion and aggressiveness of the squamous tumor subtype (25). New methods are in the intersection between the search of single biomarkers and whole genome variance estimation, such as Local Bayesian Regressions (26). These methods could shed light on the amount of variation explained by the specific genomic signature as a predictive tool. However, larger datasets are necessary to implement these methods.
Oligoscore: a clinical score to predict overall survival in patients with oligometastatic disease treated with stereotactic body radiotherapy
Published in Acta Oncologica, 2022
Davide Franceschini, Vanessa Polenghi, Ciro Franzese, Tiziana Comito, Pierina Navarria, Giuseppe R. D’Agostino, Francesca Ieva, Marta Scorsetti
Attempts to move beyond the number of metastases are still few. The most promising data are those that aimed at identifying genomic signature to distinguish the prognosis of apparently similar patients with oligometastatic disease. A study from the University of Chicago analyzed specific microRNAs from patients treated with lung resection [10] or stereotactic body radiation therapy (SBRT) [11] for oligometastatic disease, differentiating oligometastatic vs. polymetastatic phenotypes linked to the clinical course of the patients. Similar approaches have been attempted by other authors [12–13], but the results of these studies are still missing validation in larger studies and are quite far from clinical implementation on a larger scale.
In-Silico Analysis of Differentially Expressed Genes and Their Regulating microRNA Involved in Lymph Node Metastasis in Invasive Breast Carcinoma
Published in Cancer Investigation, 2022
Anupama Modi, Purvi Purohit, Ashita Gadwal, Shweta Ukey, Dipayan Roy, Sujoy Fernandes, Mithu Banerjee
This present study aimed to identify the genomic signature involved in LNM in BC through publicly available microarray gene expression datasets from the Gene Expression Omnibus (GEO). We performed Kaplan–Meier survival analysis of the hub genes in-silico to identify their role in BC and lymph node-positive BC prognosis. Further, we identified the targeting miRNAs and transcription factors (TF) for the significantly associated hub genes.