Converging and emerging threats to health security.

Advances in biological sciences have outpaced regulatory and legal frameworks for biosecurity. Simultaneously, there has been a convergence of scientific disciplines such as synthetic biology, data science, advanced computing and many other technologies, which all have applications in health. For example, advances in cybercrime methods have created ransomware attacks on hospitals, which can cripple health systems and threaten human life. New kinds of biological weapons which fall outside of traditional Cold War era thinking can be created synthetically using genetic code. These convergent trajectories are dramatically expanding the repertoire of methods which can be used for benefit or harm. We describe a new risk landscape for which there are few precedents, and where regulation and mitigation are a challenge. Rapidly evolving patterns of technology convergence and proliferation of dual-use risks expose inadequate societal preparedness. We outline examples in the areas of biological weapons, antimicrobial resistance, laboratory security and cybersecurity in health care. New challenges in health security such as precision harm in medicine can no longer be addressed within the isolated vertical silo of health, but require cross-disciplinary solutions from other fields. Nor can they cannot be managed effectively by individual countries. We outline the case for new cross-disciplinary approaches in risk analysis to an altered risk landscape.

Adaptive Global Innovative Learning Environment for Glioblastoma: GBM AGILE.

Glioblastoma (GBM) is a deadly disease with few effective therapies. Although much has been learned about the molecular characteristics of the disease, this knowledge has not been translated into clinical improvements for patients. At the same time, many new therapies are being developed. Many of these therapies have potential biomarkers to identify responders. The result is an enormous amount of testable clinical questions that must be answered efficiently. The GBM Adaptive Global Innovative Learning Environment (GBM AGILE) is a novel, multi-arm, platform trial designed to address these challenges. It is the result of the collective work of over 130 oncologists, statisticians, pathologists, neurosurgeons, imagers, and translational and basic scientists from around the world. GBM AGILE is composed of two stages. The first stage is a Bayesian adaptively randomized screening stage to identify effective therapies based on impact on overall survival compared with a common control. This stage also finds the population in which the therapy shows the most promise based on clinical indication and biomarker status. Highly effective therapies transition in an inferentially seamless manner in the identified population to a second confirmatory stage. The second stage uses fixed randomization to confirm the findings from the first stage to support registration. Therapeutic arms with biomarkers may be added to the trial over time, while others complete testing. The design of GBM AGILE enables rapid clinical testing of new therapies and biomarkers to speed highly effective therapies to clinical practice. Clin Cancer Res; 24(4); 737-43. ©2017 AACR.

Poly-ligand profiling differentiates trastuzumab-treated breast cancer patients according to their outcomes.

Assessing the phenotypic diversity underlying tumour progression requires the identification of variations in the respective molecular interaction networks. Here we report proof-of-concept for a platform called poly-ligand profiling (PLP) that surveys these system states and distinguishes breast cancer patients who did or did not derive benefit from trastuzumab. We perform tissue-SELEX on breast cancer specimens to enrich single-stranded DNA (ssDNA) libraries that preferentially interact with molecular components associated with the two clinical phenotypes. Testing of independent sample sets verifies the ability of PLP to classify trastuzumab-treated patients according to their clinical outcomes with ROC-AUC of 0.78. Standard HER2 testing of the same patients gives a ROC-AUC of 0.47. Kaplan-Meier analysis reveals a median increase in benefit from trastuzumab-containing treatments of 300 days for PLP-positive compared to PLP-negative patients. If prospectively validated, PLP may increase success rates in precision oncology and clinical trials, thus improving both patient care and drug development.

Plasma Exosome Profiling of Cancer Patients by a Next Generation Systems Biology Approach.

Technologies capable of characterizing the full breadth of cellular systems need to be able to measure millions of proteins, isoforms, and complexes simultaneously. We describe an approach that fulfils this criterion: Adaptive Dynamic Artificial Poly-ligand Targeting (ADAPT). ADAPT employs an enriched library of single-stranded oligodeoxynucleotides (ssODNs) to profile complex biological samples, thus achieving an unprecedented coverage of system-wide, native biomolecules. We used ADAPT as a highly specific profiling tool that distinguishes women with or without breast cancer based on circulating exosomes in their blood. To develop ADAPT, we enriched a library of ~1011 ssODNs for those associating with exosomes from breast cancer patients or controls. The resulting 106 enriched ssODNs were then profiled against plasma from independent groups of healthy and breast cancer-positive women. ssODN-mediated affinity purification and mass spectrometry identified low-abundance exosome-associated proteins and protein complexes, some with known significance in both normal homeostasis and disease. Sequencing of the recovered ssODNs provided quantitative measures that were used to build highly accurate multi-analyte signatures for patient classification. Probing plasma from 500 subjects with a smaller subset of 2000 resynthesized ssODNs stratified healthy, breast biopsy-negative, and -positive women. An AUC of 0.73 was obtained when comparing healthy donors with biopsy-positive patients.

Prioritization schema for immunotherapy clinical trials in glioblastoma.

BACKGROUND: Emerging immunotherapeutic strategies for the treatment of glioblastoma (GBM) such as dendritic cell (DC) vaccines, heat shock proteins, peptide vaccines, and adoptive T-cell therapeutics, to name a few, have transitioned from the bench to clinical trials. With upcoming strategies and developing therapeutics, it is challenging to critically evaluate the practical, clinical potential of individual approaches and to advise patients on the most promising clinical trials. METHODS: The authors propose a system to prioritize such therapies in an organized and data-driven fashion. This schema is based on four categories of factors: antigenic target robustness, immune-activation and -effector responses, preclinical vetting, and early evidence of clinical response. Each of these categories is subdivided to focus on the most salient elements for developing a successful immunotherapeutic approach for GBM, and a numerical score is generated. RESULTS: The Score Card reveals therapeutics that have the most robust data to support their use, provides a reference prioritization score, and can be applied in a reiterative fashion with emerging data. CONCLUSIONS: The authors hope that this schema will give physicians an evidence-based and rational framework to make the best referral decisions to better guide and serve this patient population.

The national biomarker development alliance: confronting the poor productivity of biomarker research and development.

Making precision (personalized) medicine a routine clinical reality will require a comprehensive inventory of validated biomarkers and molecular diagnostic tests to stratify disease subtypes and improve accuracy in diagnosis and treatment selection. Realization of this promise has been hindered by the poor productivity of biomarker identification and validation. This situation reflects deficiencies that are pervasive across the entire spectrum of biomarker R&D, from discovery to clinical validation and in the failure of regulatory and reimbursement policies to accommodate new classes of biomarkers. The launch of the National Biomarker Development Alliance is the culmination of a 2-year review and consultation process involving diverse stakeholders to advance standards, best practices and guidelines to enhance biomarker discovery and validation by adoption of systems-based approaches and trans-sector collaboration between academia, clinical medicine and relevant private and public sector stakeholders.

Regulatory and reimbursement innovation.

Coverage with evidence development and parallel review for molecular diagnostics aid regulation and reimbursement.

Biospecimens, biomarkers, and burgeoning data: the imperative for more rigorous research standards.

Knowledge of the altered molecular landscapes in disease offers great promise for developing biomarker-based tests to improve diagnosis and optimize treatment. Progress in biomarker research has been frustratingly slow due to the poor clinical trial design and the lack of standards for specimen collection, biomarker analysis, and data reporting. The ability of high throughput genomics, proteomics, and other 'omics' platforms to profile a large number of analytes in a single assay, together with the pending prospect of rapid expansion of whole exome and whole genome sequencing for clinical use, is increasing the technical and logistical complexity of biomarker validation. Harnessing these new technologies and improved productivity in biomarker validation will depend on adopting systems-based approaches and require major changes in the organization and funding strategies for biomarker research. A systems approach will require new multi-institution collaborations, the integration of diverse technical and clinical activities, greater engagement of industry, and education of regulators, clinicians, and payers about how to use biomarkers for improved patient management and clinical outcomes.

Leveling the playing field: bringing development of biomarkers and molecular diagnostics up to the standards for drug development.

Molecular diagnostics are becoming increasingly important in clinical research to stratify or identify molecularly profiled patient cohorts for targeted therapies, to modify the dose of a therapeutic, and to assess early response to therapy or monitor patients. Molecular diagnostics can also be used to identify the pharmacogenetic risk of adverse drug reactions. The articles in this CCR Focus section on molecular diagnosis describe the development and use of markers to guide medical decisions regarding cancer patients. They define sources of preanalytic variability that need to be minimized, as well as the regulatory and financial challenges involved in developing diagnostics and integrating them into clinical practice. They also outline a National Cancer Institute program to assist diagnostic development. Molecular diagnostic clinical tests require rigor in their development and clinical validation, with sensitivity, specificity, and validity comparable to those required for the development of therapeutics. These diagnostics must be offered at a realistic cost that reflects both their clinical value and the costs associated with their development. When genome-sequencing technologies move into the clinic, they must be integrated with and traceable to current technology because they may identify more efficient and accurate approaches to drug development. In addition, regulators may define progressive drug approval for companion diagnostics that requires further evidence regarding efficacy and safety before full approval can be achieved. One way to accomplish this is to emphasize phase IV postmarketing, hypothesis-driven clinical trials with biological characterization that would permit an accurate definition of the association of low-prevalence gene alterations with toxicity or response in large cohorts.