Paying for personalized care: cancer biomarkers and comparative effectiveness

Molecular and genetic markers in hepatocellular carcinoma: In silico analysis to clinical validation (current limitations and future promises)

Hepatocellular carcinoma (HCC) is the second cause of cancer-related mortality. The diagnosis of HCC depends mainly on -fetoprotein, which is limited in its diagnostic and screening capabilities. There is an urgent need for a biomarker that detects early HCC to give the patients a chance for curative treatment. New targets of therapy could enhance survival and create future alternative curative methods. In silico analysis provides both; discovery of biomarkers, and understanding of the molecular pathways, to pave the way for treatment development. This review discusses the role of in silico analysis in the discovery of biomarkers, molecular pathways, and the role the author has contributed to this area of research. It also discusses future aspirations and current limitations. A literature review was conducted on the topic using various databases (PubMed, Science Direct, and Wiley Online Library), searching in various reviews, and editorials on the topic, with overviewing the author's own published and unpublished work. This review discussed the steps of the validation process from in silico analysis to in vivo validation, to incorporation into clinical practice guidelines. In addition, reviewing the recent lines of research of bioinformatic studies related to HCC. In conclusion, the genetic, molecular and epigenetic markers discoveries are hot areas for HCC research. Bioinformatics will enhance our ability to accomplish this understanding in the near future. We face certain limitations that we need to overcome.

Comprehensive Isotope Ratio Metabolomics: Gas chromatography Isotope Ratio Mass Spectrometry of urinary metabolites and exhaled breath

We have developed an analytical procedure to measure the carbon isotopic composition of multiple compounds even when there is a partial overlap in the chromatographic profiles and applied this procedure to measure the carbon isotopic composition of different metabolites in human urine and exhaled breath. Method development and validation was performed with CRM IAEA-600 caffeine after calibration of the reference CO₂ gas using a mixture of certified undecane, pentadecane and eicosane δ(¹³C) standards. The alternative data treatment procedure included the correction of time-lag between Faraday cup amplifiers (44 ms at mass 45 and -160 ms at mass 46), the calculation and correction of chromatographic isotope effects on each peak (isotope shifts) and the calculation of the isotope ratio for each compound using the linear regression slope procedure with data only at the top of the chromatographic peak. In that way, partial chromatographic overlap between different metabolites can be tolerated (resolution equal or higher than 1). The reproducibility (SD) of the carbon isotope composition of 93 metabolites in human urine (n = 8) from one volunteer was typically better than 0.5 δ(¹³C) (range 0.1-2.0 δ(¹³C), median 0.4 δ(¹³C)). The method was applied to follow the carbon isotope composition of different metabolites in human urine and exhaled breath after the oral administration of 100 mg of universally labelled ¹³C-glucose to another human volunteer. It was demonstrated that isotopically labelled compounds could be detected in both samples even 2 h after administration. So, the developed methodology can be applied to multiple types of samples containing a large number of partially overlapping analytes including environmental applications, anti-doping control or metabolomics studies, including the use of enriched isotope tracers.

Molecular biomarkers and precision medicine in colorectal cancer: a systematic review of health economic analyses

An increased understanding of the biology of colorectal cancer (CRC) has fuelled identification of biomarkers with potential to drive a stratified precision medicine care approach in this common malignancy. We conducted a systematic review of health economic assessments of molecular biomarkers (MBMs) and their employment in patient stratification in CRC. Our analysis revealed scenarios where health economic analyses have been applied to evaluate the cost effectiveness of MBM-guided clinical interventions: (i) evaluation of Dihydropyrimidine dehydrogenase gene (DPYD) status to identify patients susceptible to 5-Fluouracil toxicity; (ii) determination of Uridine 5'-diphospho- glucuronosyltransferase family 1 member A1 gene (UGT1A1) polymorphism status to help guide irinotecan treatment; (iii) assessment of RAS/RAF mutational status to stratify patients for chemotherapy or Epidermal Growth Factor Receptor (EGFR) therapy and (iv) multigene expression analysis (Oncotype Dx) to identify and spare non-responders the debilitating effects of particular chemotherapy interventions. Our findings indicate that Oncotype Dx is cost-effective in high income settings within specific price points, by limiting treatment toxicity in CRC patients. DPYD status testing may also be cost effective in certain settings to avoid specific 5-FU toxicities post treatment. In contrast, current research does not support UGT1A1 polymorphism status as a cost-effective guide to irinotecan dosing, while the health economic evidence to support testing of KRAS/NRAS mutational status and chemo/EGFR therapy choice was inconclusive, despite its widespread adoption in CRC treatment management. However, we also show that there is a paucity of high-quality cost-effectiveness studies to support clinical application of precision medicine approaches in CRC.

Health economic impact of liquid biopsies in cancer management

INTRODUCTION

Liquid biopsies (LBs) are referred to as the sampling and analysis of non-solid tissue, primarily blood, as a diagnostic and monitoring tool for cancer. Because LBs are largely non-invasive, they are a less-costly alternative for serial analysis of tumor progression and heterogeneity to facilitate clinical management. Although a variety of tumor markers are proposed (e.g., free-circulating DNA), the clinical evidence for Circulating Tumor Cells (CTCs) is currently the most developed. Areas covered: This paper presents a health economic perspective of LBs in cancer management. We first briefly introduce the requirements in biomarker development and validation, illustrated for CTCs. Second, we discuss the state-of-art on the clinical utility of LBs in breast cancer in more detail. We conclude with a future perspective on the clinical use and reimbursement of LBs Expert commentary: A significant increase in clinical research on LBs can be observed and the results suggest a rapid change of cancer management. In addition to studies evaluating clinical utility of LBs, a smooth translation into clinical practice requires systematic assessment of the health economic benefits. This paper argues that (early stage) health economic research is required to facilitate its clinical use and to prioritize further evidence development.

Rethinking Causation in Cancer with Evolutionary Developmental Biology

Despite the productivity of basic cancer research, cancer continues to be a health burden to society because this research has not yielded corresponding clinical applications. Many proposed solutions to this dilemma have revolved around implementing organizational and policy changes related to cancer research. Here I argue for a different solution: a new conceptualization of causation in cancer. Neither the standard molecular biomarker approaches nor evolutionary biology approaches to cancer fully capture its complex causal dynamics, even when considered jointly. These approaches map on to Ernst Mayr's proximate-ultimate distinction, which is an inadequate conceptualization of causation in biological systems and makes it difficult to connect developmental and evolutionary viewpoints. I propose looking to evolutionary developmental biology (EvoDevo) to overcome the distinction and integrate the proximate and ultimate causal frameworks. I use the concepts of modularity and evolvability to show how an EvoDevo perspective can be manifested in cancer translational research. This perspective on causation in cancer is better suited for integrating the complexity of current empirical results and can facilitate novel developments in the investigation and clinical treatment of cancer.

Biomarkers: Delivering on the expectation of molecularly driven, quantitative health

Biomarkers are the pillars of precision medicine and are delivering on expectations of molecular, quantitative health. These features have made clinical decisions more precise and personalized, but require a high bar for validation. Biomarkers have improved health outcomes in a few areas such as cancer, pharmacogenetics, and safety. Burgeoning big data research infrastructure, the internet of things, and increased patient participation will accelerate discovery in the many areas that have not yet realized the full potential of biomarkers for precision health. Here we review themes of biomarker discovery, current implementations of biomarkers for precision health, and future opportunities and challenges for biomarker discovery. Impact statement Precision medicine evolved because of the understanding that human disease is molecularly driven and is highly variable across patients. This understanding has made biomarkers, a diverse class of biological measurements, more relevant for disease diagnosis, monitoring, and selection of treatment strategy. Biomarkers' impact on precision medicine can be seen in cancer, pharmacogenomics, and safety. The successes in these cases suggest many more applications for biomarkers and a greater impact for precision medicine across the spectrum of human disease. The authors assess the status of biomarker-guided medical practice by analyzing themes for biomarker discovery, reviewing the impact of these markers in the clinic, and highlight future and ongoing challenges for biomarker discovery. This work is timely and relevant, as the molecular, quantitative approach of precision medicine is spreading to many disease indications.

Cost Implications of Value-Based Pricing for Companion Diagnostic Tests in Precision Medicine

Many interpretations of personalized medicine, also referred to as precision medicine, include discussions of companion diagnostic tests that allow drugs to be targeted to those individuals who are most likely to benefit or that allow treatment to be designed in a way such that individuals who are unlikely to benefit do not receive treatment. Many authors have commented on the clinical and competitive implications of companion diagnostics, but there has been relatively little formal analysis of the cost implications of companion diagnostics, although cost reduction is often cited as a significant benefit of precision medicine. We investigate the potential impact on costs of precision medicine implemented through the use of companion diagnostics. We develop a framework in which the costs of companion diagnostic tests are determined by considerations of profit maximization and cost effectiveness. We analyze four scenarios that are defined by the incremental cost-effectiveness ratio of the new drug in the absence of a companion diagnostic test. We find that, in most scenarios, precision medicine strategies based on companion diagnostics should be expected to lead to increases in costs in the short term and that costs would fall only in a limited number of situations.

Precision Medicine and Non-Colorectal Cancer Liver Metastases: Fiction or Reality?

BACKGROUND

Non-colorectal liver metastases (nCRLM) constitute a variety of heterogeneous diseases and a considerable therapeutic challenge. Management is based on the primary tumor and the clinical course. In the era of precision medicine (PM) we know that cancer is heterogeneous within the tumor and across different sites.

METHODS

We give an overview of the path to PM through 'omics' beyond genomics. We refer to the experience gained to date from models such as colorectal cancer and we discuss the opportunity offered by PM for the management of nCRLM.

RESULTS

In order to best characterize and track tumor biological behaviors as well as to understand mechanisms of response to therapy and survival we suggest the application of novel clinical trial designs, a dynamic approach with serial monitoring involving evaluation of primary and metastatic sites. Quality and standardization of tissue acquisition and biobanking is a precondition for the reliability of this approach.

CONCLUSION

The application of PM is increasingly becoming a reality. Elucidating the mysteries of tumors in complex settings can only be achieved with the approach PM offers. nCRLM may serve as a model for the application of PM principles and techniques in understanding individual diseases and also cancer as an entity and therapeutic challenge.

Affinity biosensors for tumor-marker analysis

The use of cancer biomarkers is emerging as one of the most promising strategies for early detection and management of cancer. Biosensors can provide advanced platforms for biomarker analysis with the advantages of being easy to use, inexpensive, rapid and offering multi-analyte testing capability. The intention of this article is to discuss recent advances and trends in affinity biosensors for cancer diagnosis, prognosis and even theragnosis. The different types of affinity biosensors will be reviewed in terms of molecular recognition element. Current challenges and trends for this technology will be also discussed, with a particular emphasis on recent developments in miRNA detection.

Preliminary Development and Evaluation of an Algorithm to Identify Breast Cancer Chemotherapy Toxicities Using Electronic Medical Records and Administrative Data

PURPOSE

Breast cancer chemotherapy toxicity is not well documented outside of randomized trials. We developed and conducted preliminary evaluation of an algorithm to detect grade 3 and 4 toxicities using electronic data from a large integrated managed care organization.

METHODS

The algorithm used administrative, pharmacy, and electronic data from outpatient, emergency room, and inpatient records of 99 women diagnosed with breast cancer from 2006 to 2009 who underwent chemotherapy. Data were abstracted for 12 months post-treatment initiation (24 months for trastuzumab recipients). An oncology nurse independently blindly reviewed records; these results were the "gold standard." Sensitivity and specificity were calculated for overall toxicity, categories of toxicities, and toxicity by age or regimen. The algorithm was applied to an independent sample of 1,575 patients with breast cancer diagnosed during the study period to estimate prevalence rates.

RESULTS

The overall sensitivity for detecting chemotherapy-related toxicity was 89% (95% CI, 77% to 95%). The highest sensitivity was for identification of hematologic toxicities (97%; 95% CI, 84% to 99%). There were good sensitivities for infectious toxicity, but rates dropped for GI and neurological toxicities. Specificity was high within each category (89% to 99%), but when combined to measure any toxicity, it was lower (70%; 95% CI, 57% to 81%). When applied to an independent chemotherapy sample, the algorithm estimates a 26% rate of hematologic toxicity; rates were higher among patients age ≥ 65 years versus less than 65 years.

CONCLUSIONS

If validated in other samples and health care settings, algorithms to capture toxicity could be useful in comparative and cost-effectiveness evaluations of community practice-delivered treatment.

Issues surrounding the health economic evaluation of genomic technologies

AIM

Genomic interventions could enable improved disease stratification and individually tailored therapies. However, they have had a limited impact on clinical practice to date due to a lack of evidence, particularly economic evidence. This is partly because health economists are yet to reach consensus on whether existing methods are sufficient to evaluate genomic technologies. As different approaches may produce conflicting adoption decisions, clarification is urgently required. This article summarizes the methodological issues associated with conducting economic evaluations of genomic interventions.

MATERIALS & METHODS

A structured literature review was conducted to identify references that considered the methodological challenges faced when conducting economic evaluations of genomic interventions.

RESULTS

Methodological challenges related to the analytical approach included the choice of comparator, perspective and timeframe. Challenges in costing centered around the need to collect a broad range of costs, frequently, in a data-limited environment. Measuring outcomes is problematic as standard measures have limited applicability, however, alternative metrics (e.g., personal utility) are underdeveloped and alternative approaches (e.g., cost-benefit analysis) underused. Effectiveness data quality is weak and challenging to incorporate into standard economic analyses, while little is known about patient and clinician behavior in this context. Comprehensive value of information analyses are likely to be helpful.

CONCLUSION

Economic evaluations of genomic technologies present a particular challenge for health economists. New methods may be required to resolve these issues, but the evidence to justify alternative approaches is yet to be produced. This should be the focus of future work in this field.

Personalizing health care: feasibility and future implications

Considerable variety in how patients respond to treatments, driven by differences in their geno- and/ or phenotypes, calls for a more tailored approach. This is already happening, and will accelerate with developments in personalized medicine. However, its promise has not always translated into improvements in patient care due to the complexities involved. There are also concerns that advice for tests has been reversed, current tests can be costly, there is fragmentation of funding of care, and companies may seek high prices for new targeted drugs. There is a need to integrate current knowledge from a payer's perspective to provide future guidance. Multiple findings including general considerations; influence of pharmacogenomics on response and toxicity of drug therapies; value of biomarker tests; limitations and costs of tests; and potentially high acquisition costs of new targeted therapies help to give guidance on potential ways forward for all stakeholder groups. Overall, personalized medicine has the potential to revolutionize care. However, current challenges and concerns need to be addressed to enhance its uptake and funding to benefit patients.

Paying for personalized care: cancer biomarkers and comparative effectiveness

Genomic-based diagnostics can play a key role in creating a more efficient healthcare system by directing patients toward beneficial therapies and away from therapies that pose substantial risk or are unlikely to improve outcomes for the patient. We outline how the value provided by diagnostics is closely linked to a range of factors including magnitude of health outcome improvement, avoiding adverse effect, diagnostic parameters, process of care, resource utilization, and costs. Comparative effectiveness approaches to evidence generation, including health outcome measurements, quality of life, economic analyses, decision modeling, and pragmatic clinical trials, can be used to provide stakeholders with a range of information to inform treatment, guidelines, coverage, and reimbursement decisions. Evidence of comparative effectiveness can also help support value-based reimbursement of cancer biomarkers and treatment strategies as means of paying for personalized medicine.