Implementing personalized medicine in a cancer center

Biopsies: next-generation biospecimens for tailoring therapy

The majority of samples in existing tumour biobanks are surgical specimens of primary tumours. Insights into tumour biology, such as intratumoural heterogeneity, tumour-host crosstalk, and the evolution of the disease during therapy, require biospecimens from the primary tumour and those that reflect the patient's disease in specific contexts. Next-generation 'omics' technologies facilitate deep interrogation of tumours, but the characteristics of the samples can determine the ultimate accuracy of the results. The challenge is to biopsy tumours, in some cases serially over time, ensuring that the samples are representative, viable, and adequate both in quantity and quality for subsequent molecular applications. The collection of next-generation biospecimens, tumours, and blood samples at defined time points during the disease trajectory--either for discovery research or to guide clinical decisions--presents additional challenges and opportunities. From an organizational perspective, it also requires new additions to the multidisciplinary therapeutic team, notably interventional radiologists, molecular pathologists, and bioinformaticians. In this Review, we describe the existing procedures for sample procurement and processing of next-generation biospecimens, and highlight the issues involved in this endeavour, including the ethical, logistical, scientific, informational, and financial challenges accompanying next-generation biobanking.

Comparative effectiveness research in cancer genomics and precision medicine: current landscape and future prospects

A major promise of genomic research is information that can transform health care and public health through earlier diagnosis, more effective prevention and treatment of disease, and avoidance of drug side effects. Although there is interest in the early adoption of emerging genomic applications in cancer prevention and treatment, there are substantial evidence gaps that are further compounded by the difficulties of designing adequately powered studies to generate this evidence, thus limiting the uptake of these tools into clinical practice. Comparative effectiveness research (CER) is intended to generate evidence on the "real-world" effectiveness compared with existing standards of care so informed decisions can be made to improve health care. Capitalizing on funding opportunities from the American Recovery and Reinvestment Act of 2009, the National Cancer Institute funded seven research teams to conduct CER in genomic and precision medicine and sponsored a workshop on CER on May 30, 2012, in Bethesda, Maryland. This report highlights research findings from those research teams, challenges to conducting CER, the barriers to implementation in clinical practice, and research priorities and opportunities in CER in genomic and precision medicine. Workshop participants strongly emphasized the need for conducting CER for promising molecularly targeted therapies, developing and supporting an integrated clinical network for open-access resources, supporting bioinformatics and computer science research, providing training and education programs in CER, and conducting research in economic and decision modeling.

Iterative rank-order normalization of gene expression microarray data

Background

Many gene expression normalization algorithms exist for Affymetrix GeneChip microarrays. The most popular of these is RMA, primarily due to the precision and low noise produced during the process. A significant strength of this and similar approaches is the use of the entire set of arrays during both normalization and model-based estimation of signal. However, this leads to differing estimates of expression based on the starting set of arrays, and estimates can change when a single, additional chip is added to the set. Additionally, outlier chips can impact the signals of other arrays, and can themselves be skewed by the majority of the population.

Results

We developed an approach, termed IRON, which uses the best-performing techniques from each of several popular processing methods while retaining the ability to incrementally renormalize data without altering previously normalized expression. This combination of approaches results in a method that performs comparably to existing approaches on artificial benchmark datasets (i.e. spike-in) and demonstrates promising improvements in segregating true signals within biologically complex experiments.

Conclusions

By combining approaches from existing normalization techniques, the IRON method offers several advantages. First, IRON normalization occurs pair-wise, thereby avoiding the need for all chips to be normalized together, which can be important for large data analyses. Secondly, the technique does not require similarity in signal distribution across chips for normalization, which can be important for maintaining biologically relevant differences in a heterogeneous background. Lastly, IRON introduces fewer post-processing artifacts, particularly in data whose behavior violates common assumptions. Thus, the IRON method provides a practical solution to common needs of expression analysis. A software implementation of IRON is available at [http://gene.moffitt.org/libaffy/].

Plasma 25-hydroxyvitamin D3, folate and vitamin B12 biomarkers among international colorectal cancer patients: a pilot study

Vitamin D and folate are associated with decreased colorectal cancer risk and their association with colorectal cancer prognosis is under investigation. We assessed the levels of plasma 25-hydroxyvitamin D₃ (25(OH)D₃), folate and vitamin B₁₂ in an international pilot study in order to determine variability of these biomarkers based on geographical location. Plasma 25(OH)D₃, folate and vitamin B₁₂ concentrations were measured in 149 invasive, newly diagnosed colorectal cancer cases from Heidelberg (Germany), Seattle (WA, USA), and Tampa (FL, USA) and in ninety-one age- and sex-matched controls. Their associations with potential predictors were assessed using multivariate linear regression analyses. Plasma 25(OH)D₃, folate and vitamin B₁₂ concentrations differed by location. Other predictors were season for 25(OH)D₃ and tumour stage (vitamin B₁₂). Season-corrected average 25(OH)D₃ concentrations were higher in Heidelberg (31·7 ng/ml; range 11·0–83·0 ng/ml) than in Seattle (23·3 ng/ml; range 4·0–80·0 ng/ml) and Tampa (21·1 ng/ml; range 4·6–51·6 ng/ml). In Heidelberg, a strong seasonal variation was observed. Folate (11·1 ng/ml) and vitamin B₁₂ (395 pg/ml) concentrations in Heidelberg were lower than those in Seattle (25·3 ng/ml and 740 pg/ml, respectively) and Tampa (23·8 ng/ml and 522 pg/ml, respectively). Differences in plasma 25(OH)D₃ and folate concentrations between Heidelberg and the US sites were observed, probably reflecting variation in outdoor activities and sun-avoidance behaviour during summer as well as in folic acid fortification and supplement use. Intra-site differences at each study location were greater than between-location variability, suggesting that individual health behaviours play a significant role. Nevertheless, the intra-site differences we observed may be due to chance because of the limited sample size. Our pilot study illustrates the value of an international cohort in studying colorectal cancer prognosis to discern geographical differences in a broad range of exposures.

Transforming epidemiology for 21st century medicine and public health

In 2012, the National Cancer Institute (NCI) engaged the scientific community to provide a vision for cancer epidemiology in the 21st century. Eight overarching thematic recommendations, with proposed corresponding actions for consideration by funding agencies, professional societies, and the research community emerged from the collective intellectual discourse. The themes are (i) extending the reach of epidemiology beyond discovery and etiologic research to include multilevel analysis, intervention evaluation, implementation, and outcomes research; (ii) transforming the practice of epidemiology by moving toward more access and sharing of protocols, data, metadata, and specimens to foster collaboration, to ensure reproducibility and replication, and accelerate translation; (iii) expanding cohort studies to collect exposure, clinical, and other information across the life course and examining multiple health-related endpoints; (iv) developing and validating reliable methods and technologies to quantify exposures and outcomes on a massive scale, and to assess concomitantly the role of multiple factors in complex diseases; (v) integrating "big data" science into the practice of epidemiology; (vi) expanding knowledge integration to drive research, policy, and practice; (vii) transforming training of 21st century epidemiologists to address interdisciplinary and translational research; and (viii) optimizing the use of resources and infrastructure for epidemiologic studies. These recommendations can transform cancer epidemiology and the field of epidemiology, in general, by enhancing transparency, interdisciplinary collaboration, and strategic applications of new technologies. They should lay a strong scientific foundation for accelerated translation of scientific discoveries into individual and population health benefits.

Telomere length and risk of melanoma, squamous cell carcinoma, and basal cell carcinoma

BACKGROUND

Telomeres help maintain chromosomal structure and may influence tumorigenesis. We examined the association between telomere length and skin cancer in a clinic-based case-control study of 198 melanoma cases, 136 squamous cell carcinoma (SCC) cases, 185 basal cell carcinoma (BCC) cases, and 372 healthy controls.

METHODS

Cases were histologically confirmed patients treated at the Moffitt Cancer Center and University of South Florida Dermatology Clinic in Tampa, FL. Controls self-reported no history of cancer and underwent a skin cancer screening exam at study enrollment to rule out the presence of skin cancer. Quantitative real time PCR was used to measure telomere length in peripheral blood samples.

RESULTS

Melanoma patients had longer telomeres than controls (odds ratio (OR)=3.75; 95% confidence interval (CI): 2.02-6.94 for highest versus lowest tertile) (P for trend=<0.0001). In contrast, longer telomere length was significantly inversely associated with SCC (OR=0.01; 95% CI: 0.00-0.05 for highest versus lowest tertile) (P for trend=<0.0001) and BCC (OR=0.10; 95% CI: 0.06-0.19 for highest versus lowest tertile) (P for trend=<0.0001).

CONCLUSION

Telomere length may be involved in the development of skin cancer, although the effect on cancer risk differs for melanoma and non-melanoma carcinomas. Our findings suggest that long telomere length is positively associated with melanoma while inversely associated with SCC and BCC.

Importance of Health Information Technology, Electronic Health Records, and Continuously Aggregating Data to Comparative Effectiveness Research and Learning Health Care

Rapidly accumulating clinical information can support cancer care and discovery. Future success depends on information management, access, use, and reuse. Electronic health records (EHRs) are highlighted as a critical component of evidence development and implementation, but to fully harness the potential of EHRs, they need to be more than electronic renderings of the traditional paper medical chart. Clinical informatics and structured accessible secure data captured through EHR systems provide mechanisms through which EHRs can facilitate comparative effectiveness research (CER). Use of large linked administrative databases to answer comparative questions is an early version of informatics-enabled CER familiar to oncologists. An updated version of informatics-enabled CER relies on EHR-derived structured data linked with supplemental information to provide patient-level information that can be aggregated and analyzed to support hypothesis generation, comparative assessment, and personalized care. As implementation of EHRs continues to expand, electronic databases containing information collected via EHRs will continuously aggregate; aggregating data enhanced with real-time analytics can provide point-of-care evidence to oncologists, tailored to patient-level characteristics. The system learns when clinical care informs research, and insights derived from research are reinvested in care. Challenges must be overcome, including interoperability, standardization, access, and development of real-time analytics.

Radiomics: the process and the challenges

"Radiomics" refers to the extraction and analysis of large amounts of advanced quantitative imaging features with high throughput from medical images obtained with computed tomography, positron emission tomography or magnetic resonance imaging. Importantly, these data are designed to be extracted from standard-of-care images, leading to a very large potential subject pool. Radiomics data are in a mineable form that can be used to build descriptive and predictive models relating image features to phenotypes or gene-protein signatures. The core hypothesis of radiomics is that these models, which can include biological or medical data, can provide valuable diagnostic, prognostic or predictive information. The radiomics enterprise can be divided into distinct processes, each with its own challenges that need to be overcome: (a) image acquisition and reconstruction, (b) image segmentation and rendering, (c) feature extraction and feature qualification and (d) databases and data sharing for eventual (e) ad hoc informatics analyses. Each of these individual processes poses unique challenges. For example, optimum protocols for image acquisition and reconstruction have to be identified and harmonized. Also, segmentations have to be robust and involve minimal operator input. Features have to be generated that robustly reflect the complexity of the individual volumes, but cannot be overly complex or redundant. Furthermore, informatics databases that allow incorporation of image features and image annotations, along with medical and genetic data, have to be generated. Finally, the statistical approaches to analyze these data have to be optimized, as radiomics is not a mature field of study. Each of these processes will be discussed in turn, as well as some of their unique challenges and proposed approaches to solve them. The focus of this article will be on images of non-small-cell lung cancer.

The creation of an integrated health-information platform: building the framework to support personalized medicine

To advance medicine toward better evidence-based, cost-effective and individualized treatment, a new model of discovery, translation and delivery of information must be developed. This requires the collaboration of the major constituents in the areas of healthcare (i.e., patients, clinicians, administrators and researchers) and the partnership of disciplines (i.e., bioinformatics, epidemiologists, statisticians, information technologists, physicians and scientists) and organizations (i.e., drug and device companies, healthcare agencies, academic and community medical centers and information technology firms) to develop an integrative platform. The over-riding goal of this platform is to improve patient care, with the developed system enabling this by providing each of the major constituents with evidence-based and tailored information at the individual patient level.

Healthcare reform: it is getting personal

Healthcare reform must deal with the challenge of reducing the cost of care while embracing the opportunity to improve care delivery. Personalized medicine will be key to developing new care models. These models will provide coordinated and continuous care delivered by a team, with the individual patient as the central member of the team. Clinical effectiveness research will identify the best care models and reduce variation for populations of patients with a given disease. Personalized medicine will apply 'big science 'omics' with a systems biology approach to define disease networks, allowing us to add back appropriate variation in care for the individual. This effort will be enhanced by the electronic health record, which, combined with deep analytics, will capture detailed phenotypic data matched with the genotype. We are at the beginning of this journey and, despite a variety of technical, economic and societal hurdles, we cannot afford to fail.

Drug development and clinical trials--the path to an approved cancer drug

Advances in our understanding of cancer biology have led to the discovery of a spectrum of new therapeutic targets. However, despite remarkable progress in the identification and characterization of novel mechanisms of the oncogenic process, the success rate for approval of oncology drugs remains low relative to other therapeutic areas. Innovative preclinical and clinical approaches, such as the use of advanced genomic technologies, as well as branched adaptive clinical trial designs, have the potential to accelerate the development and approval of highly effective oncology drugs, along with a matching diagnostic test to identify those patients most likely to benefit from the new treatment. To maximize the effectiveness of these new strategies, close collaboration between academic, industry, and regulatory agencies will be required. In this Review, we highlight new approaches in preclinical and clinical drug development that will help accelerate approval of drugs, and aim to provide more-effective treatments alongside companion diagnostic tests to ensure the right treatment is given to the right patient.