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Developing a Case-Based Blended Learning Ecosystem to Optimize Precision Medicine: Reducing Overdiagnosis and Overtreatment
Published in Shaker A. Mousa, Raj Bawa, Gerald F. Audette, The Road from Nanomedicine to Precision Medicine, 2020
Vivek Podder, Binod Dhakal, Gousia Ummae Salma Shaik, Kaushik Sundar, Madhava Sai Sivapuram, Vijay Kumar Chattu, Rakesh Biswas
Clinical trials are evolving to investigate tumor heterogeneity from patient to patient in their design. The Molecular Analysis for Therapy Choice (NCI-MATCH) is a clinical trial selecting treatments based on genetic features of patients, not traditional tumor histology [32]. Thusfar, 2500 patients in the USA have been enrolled in one of the 24 arms of this trial, representing one half of the recruitment goals [37]. The Molecular Profiling-based Assignment of Cancer Therapy (NCI-MPACT) is another innovative clinical trial to test the hypothesis that targeting an oncogenic driver mutation is more efficacious than not targeting it. NCI-MPACT will recruit advanced cancer patients who have been unresponsive to standard therapeutic options and possess mutations in one of three genetic pathways that include DNA repair, PI3K/mTOR (phosphoinositide-3 kinase/mammalian target of rapamycin), and Ras/Raf/MEK (mitogen-activated protein kinase). The efficacy of diagnosis and therapies using precision medicine could be significantly enhanced, should results deliver the outcomes investigated in these trials.
Single-Cell Analysis in Cancer
Published in Inna Kuperstein, Emmanuel Barillot, Computational Systems Biology Approaches in Cancer Research, 2019
Inna Kuperstein, Emmanuel Barillot
Cancer is a severe disease caused by mutations in the genome accumulated in individual cells. Some of these mutations can provide a proliferation advantage to the cell compared to its neighbours, allowing the clone formed by the cell and its progeny to expand and give rise to a tumour.1 Over the course of time, tumour cells can acquire additional mutations that lead to the formation of subclones.1 This evolutionary process results in genetic diversity within a single tumour, referred to as intra-tumour heterogeneity, which plays a central role in the failure of targeted cancer therapies.2 For example, new resistant mutations may emerge, or subclones that were suppressed before treatment may start to expand. Even for monoclonal tumours, it has been shown that the temporal order of mutation occurrence can be informative for drug therapy.3 Therefore, a better understanding of tumourigenesis is key for more efficient and effective personalized cancer treatment.
Illuminating the cycle of life
Published in Raquel Seruca, Jasjit S. Suri, João M. Sanches, Fluorescence Imaging and Biological Quantification, 2017
Anabela Ferro, Patrícia Carneiro, Maria Sofia Fernandes, Tânia Mestre, Ivan Sahumbaiev, João M. Sanches, Raquel Seruca
Tumor heterogeneity is a major issue in cancer therapy as it leads to chemosensitive and chemoresistant subpopulations within the tumor. Miwa et al. have addressed cell cycle and the fate of single cancer cells in a tumor using the FUCCI imaging system and found heterogeneous responses to UVB irradiation, including cell-cycle arrest, escape from the arrest, mitosis, and apoptosis in individual cells [118]. Similarly, in a previous study, the same group demonstrated that on treatment of cancer cells with chemotherapeutic drugs, the chemoresistant subpopulation of cells could be readily identified by an arrest in S/G2/M [118].
GASN: gamma distribution test for driver genes identification based on similarity networks
Published in Connection Science, 2023
Dazhi Jiang, Runguo Wei, Zhihui He, Senlin Lin, Cheng Liu, Yingqing Lin
One of the most common approaches is to identify the driver genes by analysing a set of genes and finding genes that are frequently mutated in somatic cells. However, gene-specific characteristics such as mutation frequency, mutation type, and mutant gene length may have an impact on the acquisition of mutations in genes. Therefore, methods based on mutation frequency are often compared with background mutation rates (BMR) to identify significantly mutated genes. For example, MuSic (Dees et al., 2012) selected genes with mutation frequencies higher than the BMR as driver genes, and MutSigCV (Lawrence et al., 2013) used patient-specific mutation frequencies and spectra to obtain more accurate BMRs by taking into account tumour heterogeneity and genome. Although some driver genes mutate at high frequencies (>20), most cancer mutations occur at intermediate frequencies (2–20) or lower than expected (Gu et al., 2020). If the evaluation of the background mutation rate is too high, some driver genes with significant mutations are difficult to be identified. By contrast, some genes with non-significant mutations will be misidentified.