Prognostic and Predictive Biomarkers: Tools in Personalized Oncology

Abstract

Oncology indispensably leads us to personalized medicine, which allows an individual approach to be taken with each patient. Personalized oncology is based on pharmacogenomics and the effect of genetic differences in individuals (germline and somatic) on the way cancer patients respond to chemotherapeutics. Biomarkers detected using molecular biology tools allow the molecular characterization of cancer signatures and provide information relevant for personalized treatment. Biomarkers can be divided into two main subgroups: prognostic and predictive. The aim of the application of prognostic biomarkers, which provide information on the overall cancer outcome in patients, is to facilitate cancer diagnosis, usually with no need for putting invasive methods into use. Predictive biomarkers help to optimize therapy decisions, as they provide information on the likelihood of response to a given chemotherapeutic. Among the prognostic factors that identify patients with different outcome risks (e.g., recurrence of the disease), the following factors can be distinguished: somatic and germline mutations, changes in DNA methylation that lead to the enhancement or suppression of gene expression, the occurrence of elevated levels of microRNA (miRNA) capable of binding specific messenger RNA (mRNA) molecules, which affects gene expression, as well as the presence of circulating tumor cells (CTCs) in blood, which leads to a poor prognosis for the patient. Biomarkers for personalized oncology are used mainly in molecular diagnostics of chronic myeloid leukemia, colon, breast and lung cancer, and recently in melanoma. They are successfully used in the evaluation of the benefits that can be achieved through targeted therapy or in the evaluation of toxic effects of the chemotherapeutic used in the therapy.

A systematic review and checklist presenting the main challenges for health economic modeling in personalized medicine: towards implementing patient-level models

“…This includes the target disease, the description on how the treatment process was personalized, the used modeling method, the model structure, and the performed analyses. The treatment processes were characterized by the purpose of the stratification (screening, targeting, or monitoring) and whether the stratification was prognostic or predictive [31]. Screening is referred to as the process of diagnosing a patient with a specific disease, whereas treatment targeting relates to selecting a treatment, from a set of treatment options that is expected to be most beneficial to a specific patient, based on patient-specific characteristics or diagnostic information.…”

Challenges and Strategies in Precision Medicine for Non-Small-Cell Lung Cancer

“…The course of therapy administered and the response of the patient will depend upon understanding individual aspects of molecular carcinogenesis, pharmacogenomics and genetic variances [17,18]. There are a broad range of cancer biomarkers that can be used to detect for physiological changes such as proteins, sugars, nucleic acids, small metabolites, and cytokinetic and cytogenetic parameters, in addition to tumour cells found in the biofluids.…”

“…The main predictive biomarkers that are recognised in the clinical setting for the molecular diagnostics of cancer are somatic mutations (point mutations and chromosomal aberrations), of the following genes: EGFR, EML4-ALK, ROS, RET, MET, HER2, KRAS, BRAF, KIT, PDGFRA, and BCR-ABL. Analysis of gene expression or methylation is currently a common approach in research than routinely used for diagnostics, with the exception in breast cancer assessment of specific genes used for predicting treatment response [18]. Nevertheless, there are many NSCLC biomarkers that are emerging from current research-EGFR (overexpression), KRAS, ROS1, RET, AKL, HER2, MET, ERCC, PIK3CA and BRAF.…”

“…The survival rate has been observed to improve when exon 19 is deleted than compared to exon 21 point mutation at position L858R [32]. A Polish cohort study discovered that these mutations (exon 19 and exon 21 L858R substitution) in ~12% of cases with adenocarcinoma through the study of exon 19 deletion and L858R substitution [33] as well as in 13% of patients with the same diagnosis through the study of 29 mutations in exons 18, 19, 20 and 21 of EGFR gene [18,34].…”

The era of nanotechnology and omics sciences

“…Oncologists have been working for decades to identify biomarkers that drive tumor intiation or progression (11). Several well-studied inhibitors, such as tyrosine kinase inhibitors (TKI), anaplastic lymphoma kinase (ALK) and epidermal growth factor receptor (EGFR) are widely used to inhibit cancer development (12).…”