Browsing by Author "Sharma, V."

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In silico disease models of breast cancer
(Springer India, 2014) Munshi, Anjana; Sharma, V.
Breast cancer is a highly heterogeneous disease as a consequence of multiple cells and genetic aberrations. It is the second leading cause of death among women in Western countries. It has been reported that approximately 1 in 8 women is affected by breast cancer and one-third of women die from breast cancer every year. The most common type of breast cancer is infiltrating ductal carcinoma, which represents around 80 % of all malignancies. Recent advances in the area of breast cancer have increased the survival rate of women with breast cancer. The post-genomic area has provided information regarding gene mutations and their effect on pathogenesis as well as on the outcome of breast cancer. A number of interacting biomarkers belonging to different pathways have been reported to influence the progression of breast cancer. However, we need more authenticated and sophisticated technology for early diagnosis and effective treatment in the area of breast cancer. In the past few years, computational modeling or in silico modeling and simulation of disease processes has gained momentum. Computational models of breast cancer have been developed to aid both biological mechansims and oncologists. The development of in silico models is facilitated by experimental and analytical tools which generate required information and data. Statistical models of cancer at the pathway levels, genomics, and transcriptomics have been proven to be effective in developing prognostics/diagnostics. Statistically inferred network models have been proven to be useful for avoiding data overfitting. Signaling and metabolic models with the knowledge of the biochemical processes involved and metabolism, derived from research studies, can also be reconstructed. At longer length scales, agent-based and continuum models of the breast cancer microenvironment and other tissue-level interactions would enable modeling of cancer cells and predictions of tumor progression. Even though breast cancer has been studied using genomics, transcriptomics, and systems approaches, significant challenges yet remain in order to translate the enormous potential of in silico cancer biology for the betterment of patients suffering with breast cancer, thus shifting the paradigm from conventional population-based to patient-specific cancer medicine. ? 2014 Springer India. All rights reserved.
Lessons Learned from Cohort Studies, and Hospital-Based Studies and Their Implications in Precision Medicine
(Elsevier Inc., 2017) Munshi, Anjana; Sharma, V.; Sharma, S.
Advances in human genomics and cutting-edge technologies have created space for integrating personalized or precise medicine into the practice of medicine and thereby improving the public health. Achieving the goal of quantitatively improving the quality and effectiveness of health care for all requires both knowledge of classical, genomics, and pharmacogenomic studies carried out worldwide on different subsets of population. Since the disease outcome and therapeutic effectiveness may vary according to geographical areas, ethnicity, race, and genetic makeup, the complex pathways, including gene-environment, gene-drug, and gene-gene interactions, play a significant role in the development of disease as well as in determining an individual's response toward prescribed medicine. The decision-making process in selecting or identifying precise medicine via clinicians depends on the large effort carried out by researchers and pharmaceutical companies conducting clinical trials, cohort studies, and hospital-based retrospective and prospective studies (e.g., The Strengthening the Reporting of Genetic Association studies (STREGA) initiative builds on the STrengthening the Reporting of Observational Studies in Epidemiology (STROBE) study. However, the evidence based on gene-disease associations is fraught with certain methodological problems such as inadequate sample size, reporting of results, even from well-conducted studies, hampers the assessment of a study's strengths and weaknesses and hence the integration of evidence into clinical practice (Little et al., 2009).Precision Medicine Initiative, a research cohort, launched by NIH, including more than a million Americans, is one of the brilliant steps toward precision medicine which will contain genomic, clinical, and other health-related information. This U.S. Research Cohort may provide a platform for scientists to conduct rapid and efficient randomized trials that use cohorts and registries for further research (Baker et al., 2015). Appropriate decision making on the basis of previously established studies or trials and with available evidence in hand, based on genomic profiling have revolutionized the current paradigm of clinical practice. Now, the trend of giving importance to therapies rather than to diagnostics may need to be revisited to ensure and to speed up innovation in the area of personalized or precision medicine. ? 2017 Elsevier Inc.
Time to Educate Physicians and Hospital Staff in Electronic Medical Records for Precision Medicine
(Elsevier Inc., 2017) Sharma, S.; Munshi, Anjana; Sharma, V.
Over the past decade, inexpensive and dense sequencing technologies have led to many genetic discoveries. Single nucleotide polymorphisms associated with over 250 different phenotypes have been identified by genome-wide association studies. Lack of large cohorts with adequately defined phenotypes has hindered further progress. This hindrance in genetic research can be overcome by electronic health records. These electronic health records have been recognized as a viable and efficient model for genetic research. The drawback of currently existing digitalized data and information in multiple unstructured formats continue to generate huge amount of information leading to difficulty in accessing invaluable and newly discovered knowledge. Connecting molecular data, individual genome sequence, patient phenotype, experimental data, and follow-up details is a big task. Road block to this monumental task of integration and interoperability are ethical, legal, and logistics. Data security and protection of patient rights are a must to maintain public support. In this chapter we have highlighted the advantages and challenges of using electronic health record data for genetic research as well as novel approaches and significant initiatives contributing toward precision medicine. ? 2017 Elsevier Inc.