Economic evaluations of pharmacogenetic and pharmacogenomic screening tests : a systematic review : second update of the literature

Objective : Due to extended application of pharmacogenetic and pharmacogenomic screening (PGx) tests it is important to assess whether they provide good value for money. This review provides an update of the literature. Methods : A literature search was performed in PubMed and papers published between August 2010 and September 2014, investigating the cost-effectiveness of PGx screening tests, were included. Papers from 2000 until July 2010 were included via two previous systematic reviews. Studies' overall quality was assessed with the Quality of Health Economic Studies (QHES) instrument. Results : We found 38 studies, which combined with the previous 42 studies resulted in a total of 80 included studies. An average QHES score of 76 was found. Since 2010, more studies were funded by pharmaceutical companies. Most recent studies performed cost-utility analysis, univariate and probabilistic sensitivity analyses, and discussed limitations of their economic evaluations. Most studies indicated favorable cost-effectiveness. Majority of evaluations did not provide information regarding the intrinsic value of the PGx test. There were considerable differences in the costs for PGx testing. Reporting of the direction and magnitude of bias on the cost-effectiveness estimates as well as motivation for the chosen economic model and perspective were frequently missing. Conclusions : Application of PGx tests was mostly found to be a cost-effective or cost-saving strategy. We found that only the minority of recent pharmacoeconomic evaluations assessed the intrinsic value of the PGx tests. There was an increase in the number of studies and in the reporting of quality associated characteristics. To improve future evaluations, scenario analysis including a broad range of PGx tests costs and equal costs of comparator drugs to assess the intrinsic value of the PGx tests, are recommended. In addition, robust clinical evidence regarding PGx tests' efficacy remains of utmost importance

HE4 in the differential diagnosis of ovarian masses

Ovarian masses, a common finding among pre- and post-menopausal women, can be benign or malignant. Ovarian cancer is the leading cause of death from gynecologic malignancy among women living in industrialized countries. According to the current guidelines, measurement of CA125 tumor marker remains the gold standard in the management of ovarian cancer. Recently, HE4 has been proposed as emerging biomarker in the differential diagnosis of adnexal masses and in the early diagnosis of ovarian cancer. Discrimination of benign and malignant ovarian tumors is very important for correct patient referral to institutions specializing in care and management of ovarian cancer. Tumor markers CA125 and HE4 are currently incorporated into the Risk of Ovarian Malignancy Algorithm” (ROMA) with menopausal status for discerning malignant from benign pelvic masses. The availability of a good biomarker such as HE4, closely associated with the differential and early diagnosis of ovarian cancer, could reduce medical costs related to more expensive diagnostic procedures. Finally, it is important to note that HE4 identifies platinum non-responders thus enabling a switch to second line chemotherapy and improved survival

Machine Learning and Integrative Analysis of Biomedical Big Data.

Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues

EPMA position paper in cancer:current overview and future perspectives

At present, a radical shift in cancer treatment is occurring in terms of predictive, preventive, and personalized medicine (PPPM). Individual patients will participate in more aspects of their healthcare. During the development of PPPM, many rapid, specific, and sensitive new methods for earlier detection of cancer will result in more efficient management of the patient and hence a better quality of life. Coordination of the various activities among different healthcare professionals in primary, secondary, and tertiary care requires well-defined competencies, implementation of training and educational programs, sharing of data, and harmonized guidelines. In this position paper, the current knowledge to understand cancer predisposition and risk factors, the cellular biology of cancer, predictive markers and treatment outcome, the improvement in technologies in screening and diagnosis, and provision of better drug development solutions are discussed in the context of a better implementation of personalized medicine. Recognition of the major risk factors for cancer initiation is the key for preventive strategies (EPMA J. 4(1):6, 2013). Of interest, cancer predisposing syndromes in particular the monogenic subtypes that lead to cancer progression are well defined and one should focus on implementation strategies to identify individuals at risk to allow preventive measures and early screening/diagnosis. Implementation of such measures is disturbed by improper use of the data, with breach of data protection as one of the risks to be heavily controlled. Population screening requires in depth cost-benefit analysis to justify healthcare costs, and the parameters screened should provide information that allow an actionable and deliverable solution, for better healthcare provision

Integrative analysis of the colorectal cancer proteome : potential clinical impact

Peer reviewedPostprin

An interaction-based modeling approach to predict response to cancer drugs

In oncology, predictive biomarkers define patient subgroups that are likely to benefit from a specific cancer treatment. Since clinical studies entail high costs and low success rates, pre-clinical model systems like cancer cell lines are needed to generate biomarker hypotheses. Existing computational methods to predict drug response have several limitations. First, models often include large numbers of altered genes which contrasts with clinical predictive biomarkers that mostly include single altered genes. Second, models often assume that the effects of individual alterations are independent, although many biological processes rely on the interplay of multiple molecular components. We developed an analytical framework to investigate the role of interactions in drug response based on linear regression models. Using data from two large cancer cell line panels, we conducted an exhaustive analysis of models with up to three genomic alterations. To increase model size, we constructed mutation interaction networks and applied module search algorithms to select subsets of mutations for drug response prediction models. We summarized important covariates as background models that served as a reference to evaluate the performance of models with genomic alterations. We observed that including interactions increased the performance and robustness of drug response prediction models. Moreover, we identified several candidate interactions with consistent association patterns in two large cancer cell line panels. For example, we observed that cancer cell lines with BRAF and TP53 mutations showed worse response to BRAF inhibitors than cell lines with only BRAF mutations. Clinical data supports the resistance interaction between BRAF and TP53 mutations since patients with BRAF and TP53 mutations respond worse to the BRAF inhibitor Vemurafenib than patients with only BRAF mutations. This suggests that inhibition of the oncoprotein BRAF and reactivation of the tumor suppressor protein TP53 could be a promising combination therapy. Our analytical framework moreover allows to distinguish tissue-specific mutation associations from associations that are generalizable across tissues. In addition, we identified synthetic lethal triplets where the simultaneous mutation of two genes sensitizes cells to a drug. Our network-based approach outperformed a standard method for drug response prediction, the regularized regression algorithm elastic net. Based on 14 million models of different size, seven mutations were determined as the optimal model size. In summary, we show that considering interactions in drug response prediction models unlocks a large predictive potential. Our interaction-based modeling approach contributes to a system-level understanding of the factors that mediate drug response

PD-L1 testing for lung cancer in the UK: recognizing the challenges for implementation.

A new approach to the management of non-small-cell lung cancer (NSCLC) has recently emerged that works by manipulating the immune checkpoint controlled by programmed death receptor 1 (PD-1) and its ligand programmed death ligand 1 (PD-L1). Several drugs targeting PD-1 (pembrolizumab and nivolumab) or PD-L1 (atezolizumab, durvalumab, and avelumab) have been approved or are in the late stages of development. Inevitably, the introduction of these drugs will put pressure on healthcare systems, and there is a need to stratify patients to identify those who are most likely to benefit from such treatment. There is evidence that responsiveness to PD-1 inhibitors may be predicted by expression of PD-L1 on neoplastic cells. Hence, there is considerable interest in using PD-L1 immunohistochemical staining to guide the use of PD-1-targeted treatments in patients with NSCLC. This article reviews the current knowledge about PD-L1 testing, and identifies current research requirements. Key factors to consider include the source and timing of sample collection, pre-analytical steps (sample tracking, fixation, tissue processing, sectioning, and tissue prioritization), analytical decisions (choice of biomarker assay/kit and automated staining platform, with verification of standardized assays or validation of laboratory-devised techniques, internal and external quality assurance, and audit), and reporting and interpretation of the results. This review addresses the need for integration of PD-L1 immunohistochemistry with other tests as part of locally agreed pathways and protocols. There remain areas of uncertainty, and guidance should be updated regularly as new information becomes available

The Emergent Landscape of Detecting EGFR Mutations Using Circulating Tumor DNA in Lung Cancer.

The advances in targeted therapies for lung cancer are based on the evaluation of specific gene mutations especially the epidermal growth factor receptor (EGFR). The assays largely depend on the acquisition of tumor tissue via biopsy before the initiation of therapy or after the onset of acquired resistance. However, the limitations of tissue biopsy including tumor heterogeneity and insufficient tissues for molecular testing are impotent clinical obstacles for mutation analysis and lung cancer treatment. Due to the invasive procedure of tissue biopsy and the progressive development of drug-resistant EGFR mutations, the effective initial detection and continuous monitoring of EGFR mutations are still unmet requirements. Circulating tumor DNA (ctDNA) detection is a promising biomarker for noninvasive assessment of cancer burden. Recent advancement of sensitive techniques in detecting EGFR mutations using ctDNA enables a broad range of clinical applications, including early detection of disease, prediction of treatment responses, and disease progression. This review not only introduces the biology and clinical implementations of ctDNA but also includes the updating information of recent advancement of techniques for detecting EGFR mutation using ctDNA in lung cancer