Precision medicine in the clouds

Precision medicine and the problem of structural injustice

Many countries currently invest in technologies and data infrastructures to foster precision medicine (PM), which is hoped to better tailor disease treatment and prevention to individual patients. But who can expect to benefit from PM? The answer depends not only on scientific developments but also on the willingness to address the problem of structural injustice. One important step is to confront the problem of underrepresentation of certain populations in PM cohorts via improved research inclusivity. Yet, we argue that the perspective needs to be broadened because the (in)equitable effects of PM are also strongly contingent on wider structural factors and prioritization of healthcare strategies and resources. When (and before) implementing PM, it is crucial to attend to how the organisation of healthcare systems influences who will benefit, as well as whether PM may present challenges for a solidaristic sharing of costs and risks. We discuss these issues through a comparative lens of healthcare models and PM-initiatives in the United States, Austria, and Denmark. The analysis draws attention to how PM hinges on-and simultaneously affects-access to healthcare services, public trust in data handling, and prioritization of healthcare resources. Finally, we provide suggestions for how to mitigate foreseeable negative effects.

Personal Dense Dynamic Data Clouds Connect Systems Biomedicine to Scientific Wellness

The dramatic convergence of molecular biology, genomics, proteomics, metabolomics, bioinformatics, and artificial intelligence has provided a substrate for deep understanding of the biological basis of health and disease. Systems biology is a holistic, dynamic, integrative, cross-disciplinary approach to biological complexity that embraces experimentation, technology, computation, and clinical translation. Systems Medicine integrates genome analyses and longitudinal deep phenotyping with biological pathways and networks to understand mechanisms of disease, identify relevant blood biomarkers, define druggable molecular targets, and enhance the maintenance or restoration of wellness. Two programs initiated our understanding of data-driven population-based wellness. The Pioneer 100 Study of Scientific Wellness and the much larger Arivale commercial program that followed had two spectacular results: demonstrating the feasibility and utility of collecting longitudinal multiomic data, and then generating dense, dynamic data clouds for each individual to utilize actionable metrics for promoting health and preventing disease when combined with personalized coaching. Future developments in these domains will enable better population health and personal, preventive, predictive, participatory (P4) health care.

Bioinformatic Challenges Detecting Genetic Variation in Precision Medicine Programs

Precision medicine programs to identify clinically relevant genetic variation have been revolutionized by access to increasingly affordable high-throughput sequencing technologies. A decade of continual drops in per-base sequencing costs means it is now feasible to sequence an individual patient genome and interrogate all classes of genetic variation for < $1,000 USD. However, while advances in these technologies have greatly simplified the ability to obtain patient sequence information, the timely analysis and interpretation of variant information remains a challenge for the rollout of large-scale precision medicine programs. This review will examine the challenges and potential solutions that exist in identifying predictive genetic biomarkers and pharmacogenetic variants in a patient and discuss the larger bioinformatic challenges likely to emerge in the future. It will examine how both software and hardware development are aiming to overcome issues in short read mapping, variant detection and variant interpretation. It will discuss the current state of the art for genetic disease and the remaining challenges to overcome for complex disease. Success across all types of disease will require novel statistical models and software in order to ensure precision medicine programs realize their full potential now and into the future.

Applications and Biocompatibility of Mesoporous Silica Nanocarriers in the Field of Medicine

Mesoporous silica nanocarrier (MSN) preparations have a wide range of medical applications. Studying the biocompatibility of MSN is an important part of clinical transformation. Scientists have developed different types of mesoporous silica nanocarriers (MSNs) for different applications to realize the great potential of MSNs in the field of biomedicine, especially in tumor treatment. MSNs have achieved good results in diagnostic bioimaging, tissue engineering, cancer treatment, vaccine development, biomaterial application and diagnostics. MSNs can improve the therapeutic efficiency of drugs, introduce new drug delivery strategies, and provide advantages that traditional drugs lack. It is necessary not only to innovate MSNs but also to comprehensively understand their biological distribution. In this review, we summarize the various medical uses of MSN preparations and explore the factors that affect their distribution and biocompatibility in the body based on metabolism. Designing more reasonable therapeutic nanomedicine is an important task for the further development of the potential clinical applications of MSNs.

The funhouse mirror: the I in personalised healthcare

Precision Medicine is driven by the idea that the rapidly increasing range of relatively cheap and efficient self-tracking devices make it feasible to collect multiple kinds of phenotypic data. Advocates of N = 1 research emphasize the countless opportunities personal data provide for optimizing individual health. At the same time, using biomarker data for lifestyle interventions has shown to entail complex challenges. In this paper, we argue that researchers in the field of precision medicine need to address the performative dimension of collecting data. We propose the fun-house mirror as a metaphor for the use of personal health data; each health data source yields a particular type of image that can be regarded as a 'data mirror' that is by definition specific and skewed. This requires competence on the part of individuals to adequately interpret the images thus provided.

The Outcomes of Scientific Debates Should Be Published: The Arivale Story

There is an ongoing scientific debate regarding the merits and shortcomings of P4 Medicine (predictive, preventive, personalized, and participatory) and O4 Medicine (overtesting, overdiagnosis, overtreatment, and overcharging). P4 Medicine promises to revolutionize scientific wellness through longitudinal big data collection, denoted as "dense phenotyping," which could uncover early, actionable signs of disease, thus allowing earlier interventions and possible disease reversal. On the other hand, O4 Medicine draws attention to the potential side effects of P4 Medicine: overtesting, overdiagnosis, overtreatment, and overcharging fees. Preliminary data from the P4 Medicine concept have been recently published. A novel biotechnology company, Arivale, provided customers with services based on P4 Medicine principles; however it could not sustain its operations and closed its doors in April 2019. In this report, we provide our own insights as to why Arivale failed. While we do not discount that in the future, improved testing strategies may provide a path to better health, we suggest that until the evidence is provided, selling of such products to the public, especially through the "direct to consumer" approach, should be discouraged. We hope that our analysis will provide useful information for the burgeoning fields of personalized medicine, preventive medicine, and direct to consumer health testing.

Molecular screening program to select molecular-based recommended therapies for metastatic cancer patients: analysis from the ProfiLER trial

BACKGROUND

Antitumor activity of molecular-targeted agents is guided by the presence of documented genomic alteration in specific histological subtypes. We aim to explore the feasibility, efficacy and therapeutic impact of molecular profiling in routine setting.

PATIENTS AND METHODS

This multicentric prospective study enrolled adult or pediatric patients with solid or hematological advanced cancer previously treated in advanced/metastatic setting and noneligible to curative treatment. Each molecular profile was established on tumor, relapse or biopsies, and reviewed by a molecular tumor board (MTB) to identify molecular-based recommended therapies (MBRT). The main outcome was to assess the incidence rate of genomic mutations in routine setting, across specific histological types. Secondary objectives included a description of patients with actionable alterations and for whom MBRT was initiated, and overall response rate.

RESULTS

Four centers included 2579 patients from February 2013 to February 2017, and the MTB reviewed the molecular profiles achieved for 1980 (76.8%) patients. The most frequently altered genes were CDKN2A (N = 181, 7%), KRAS (N = 177, 7%), PIK3CA (N = 185, 7%), and CCND1 (N = 104, 4%). An MBRT was recommended for 699/2579 patients (27%), and only 163/2579 patients (6%) received at least one MBRT. Out of the 182 lines of MBRT initiated, 23 (13%) partial responses were observed. However, only 0.9% of the whole cohort experienced an objective response.

CONCLUSION

An MBRT was provided for 27% of patients in our study, but only 6% of patients actually received matched therapy with an overall response rate of 0.9%. Molecular screening should not be used at present to guide decision-making in routine clinical practice outside of clinical trials.This trial is registered with ClinicalTrials.gov, number NCT01774409.