Genomic approach towards personalized anticancer drug therapy

Machine learning for biomarker identification in cancer research - developments toward its clinical application

The patterns identified from the systematically collected molecular profiles of patient tumor samples, along with clinical metadata, can assist personalized treatments for effective management of cancer patients with similar molecular subtypes. There is an unmet need to develop computational algorithms for cancer diagnosis, prognosis and therapeutics that can identify complex patterns and help in classifications based on plethora of emerging cancer research outcomes in public domain. Machine learning, a branch of artificial intelligence, holds a great potential for pattern recognition in cryptic cancer datasets, as evident from recent literature survey. In this review, we focus on the current status of machine learning applications in cancer research, highlighting trends and analyzing major achievements, roadblocks and challenges toward its implementation in clinics.

Exploiting expert systems in cardiology: a comparative study

An improved Adaptive Neuro-Fuzzy Inference System (ANFIS) in the field of critical cardiovascular diseases is presented. The system stems from an earlier application based only on a Sugeno-type Fuzzy Expert System (FES) with the addition of an Artificial Neural Network (ANN) computational structure. Thus, inherent characteristics of ANNs, along with the human-like knowledge representation of fuzzy systems are integrated. The ANFIS has been utilized into building five different sub-systems, distinctly covering Coronary Disease, Hypertension, Atrial Fibrillation, Heart Failure, and Diabetes, hence aiding doctors of medicine (MDs), guide trainees, and encourage medical experts in their diagnoses centering a wide range of Cardiology. The Fuzzy Rules have been trimmed down and the ANNs have been optimized in order to focus into each particular disease and produce results ready-to-be applied to real-world patients.

Molecular targeted therapies in metastatic melanoma

The advent of personalized medicine has ushered in a new era for cancer therapy with a significant impact on the management of advanced melanoma. Molecular targeted therapies have shown promise in the management of various malignancies, including melanoma, with lower toxicity profiles and better overall survival as compared with conventional therapy. The discovery of BRAF mutations in melanoma led to the development of BRAF inhibitors for the treatment of advanced melanoma. However, growing concerns over drug resistance to molecular targeted therapies including BRAF inhibitors, have spurred efforts to elucidate additional molecular targets for the treatment of advanced melanoma. In this review, we discuss the known molecular aberrations in melanoma, current and novel targeted approaches in its treatment, and drug resistance patterns.

Pharmacogenomics of breast cancer therapy: an update

Clinical and histopathologic characteristics of breast cancer have long played an important role in treatment decision-making. Well-recognized prognostic factors include tumor size, node status, presence or absence of metastases, tumor grade, and hormone receptor expression. High tumor grade, presence of hormone receptors, and HER2-positivity are a few predictive markers of response to chemotherapy, endocrine manipulations, and anti-HER2 agents, respectively. However, there is much heterogeneity of outcomes in patients with similar clinical and pathologic features despite equivalent treatment regimens. Some of the differences in response to specific therapies can be attributed to somatic tumor characteristics, such as degree of estrogen receptor expression and HER2 status. In recent years, there has been great interest in evaluating the role that pharmacogenetics/pharmacogenomics, or variations in germline DNA, play in alteration of drug metabolism and activity, thus leading to disparate outcomes among patients with similar tumor characteristics. The utility of these variations in treatment decision-making remains debated. Here we review the data available to date on genomic variants that may influence response to drugs commonly used to treat breast cancer. While none of the variants reported to date have demonstrated clinical utility, ongoing prospective studies and increasing understanding of pharmacogenetics will allow us to better predict risk of toxicity or likelihood of response to specific treatments and to provide a more personalized therapy.

Introduction into PPPM as a new paradigm of public health service: an integrative view

In the present state of healthcare, usual medical care is generally given to the already diseased person, while the key link—personal health monitoring underlain by predictive, preventive, and personalised medicine (PPPM) techniques that are being intensively elaborated worldwide—is simply missing. It is this link, based on the recognition of subclinical conditions, prediction, and further preventive measures, that is capable of regulating morbidity and diminishing the rates of disability among able-bodied population, thus significantly cutting the traditionally high costs of treating the already diseased people. To achieve the above-mentioned goal—the elaboration of the PPPM concept and its practical implementation—it is necessary to create a fundamentally new strategy based upon the subclinical recognition of the signs—bioindicators of cryptic abnormalities long before the disease clinically manifests itself. The implementation of PPPM programme requires an adjusted technology for the proper interpretation of diagnostic data, which would allow for the current ‘physician-patient’ model to be gradually replaced by a novel model, ‘medical advisor-healthy men-at-risk’. This is the reason for an additional need in organising combinatorial scientific, clinical, training and educational projects in the area of PPPM to elicit the content of this new branch of medicine.

The success of pharmacogenomics in moving genetic association studies from bench to bedside: study design and implementation of precision medicine in the post-GWAS era

Pharmacogenomics is emerging as a popular type of study for human genetics in recent years. This is primarily due to the many success stories and high potential for translation to clinical practice. In this review, the strengths and limitations of pharmacogenomics are discussed as well as the primary epidemiologic, clinical trial, and in vitro study designs implemented. A brief discussion of molecular and analytic approaches will be reviewed. Finally, several examples of bench-to-bedside clinical implementations of pharmacogenetic traits will be described. Pharmacogenomics continues to grow in popularity because of the important genetic associations identified that drive the possibility of precision medicine.