Integrating precision medicine in the study and clinical treatment of a severely mentally ill person

Power and Design Issues in Crossover-Based N-Of-1 Clinical Trials with Fixed Data Collection Periods

“N-of-1,” or single subject, clinical trials seek to determine if an intervention strategy is more efficacious for an individual than an alternative based on an objective, empirical, and controlled study. The design of such trials is typically rooted in a simple crossover strategy with multiple intervention response evaluation periods. The effect of serial correlation between measurements, the number of evaluation periods, the use of washout periods, heteroscedasticity (i.e., unequal variances among responses to the interventions) and intervention-associated carry-over phenomena on the power of such studies is crucially important for putting the yield and feasibility of N-of-1 trial designs into context. We evaluated the effect of these phenomena on the power of different designs for N-of-1 trials using analytical theory based on standard likelihood principles assuming an autoregressive lag 1, i.e., AR(1), serial correlation structure among the measurements as well as simulation studies. By evaluating the power to detect effects in many different settings, we show that the influence of serial correlation and heteroscedasticity on power can be substantial, but can also be mitigated to some degree through the use of appropriate multiple evaluation periods. We also show that the detection of certain types of carry-over effects can be heavily influenced by design considerations as well.

Personalized medicine: motivation, challenges, and progress

There is a great deal of hype surrounding the concept of personalized medicine. Personalized medicine is rooted in the belief that since individuals possess nuanced and unique characteristics at the molecular, physiological, environmental exposure, and behavioral levels, they may need to have interventions provided to them for diseases they possess that are tailored to these nuanced and unique characteristics. This belief has been verified to some degree through the application of emerging technologies such as DNA sequencing, proteomics, imaging protocols, and wireless health monitoring devices, which have revealed great inter-individual variation in disease processes. In this review, we consider the motivation for personalized medicine, its historical precedents, the emerging technologies that are enabling it, some recent experiences including successes and setbacks, ways of vetting and deploying personalized medicines, and future directions, including potential ways of treating individuals with fertility and sterility issues. We also consider current limitations of personalized medicine. We ultimately argue that since aspects of personalized medicine are rooted in biological realities, personalized medicine practices in certain contexts are likely to be inevitable, especially as relevant assays and deployment strategies become more efficient and cost-effective.

Artificial Intelligence and Personalized Medicine

The development of high-throughput, data-intensive biomedical research assays and technologies has created a need for researchers to develop strategies for analyzing, integrating, and interpreting the massive amounts of data they generate. Although a wide variety of statistical methods have been designed to accommodate 'big data,'  experiences with the use of artificial intelligence (AI) techniques suggest that they might be particularly appropriate. In addition,  the results of the application of these assays reveal a great heterogeneity in the pathophysiologic factors and processes that contribute to disease, suggesting that there is a need to tailor, or 'personalize,' medicines to the nuanced and often unique features possessed by individual patients. Given how important data-intensive assays are to revealing appropriate intervention targets and strategies for  treating an individual with a disease, AI can play an important role in the development of personalized medicines. We describe many areas where AI can play such a role and argue that AI's ability to advance personalized medicine will depend critically on not only the refinement of relevant assays, but also on ways of storing, aggregating, accessing, and ultimately integrating, the data they produce. We also point out the limitations of many AI techniques in developing personalized medicines as well as consider areas for further research.

Rechargeable Stimulators in Deep Brain Stimulation for Obsessive-Compulsive Disorder: A Prospective Interventional Cohort Study

BACKGROUND

From 1999 onwards, deep brain stimulation (DBS) has been proposed as an alternative to capsulotomy in refractory cases of obsessive-compulsive disorder (OCD). Although rechargeable implantable pulse generators (rIPGs) have been used extensively in DBS for movement disorders, there are no reports on rIPGs in patients with a psychiatric DBS indication, and even possible objections to their use.

OBJECTIVE

We aim to evaluate rIPGs in OCD in terms of effectiveness, applicability, safety, and need for IPG replacement.

METHODS

In this prospective before-after study recruiting from 2007 until 2012, OCD patients requiring at least one IPG replacement per 18 months were proposed to have a rIPG implanted at the next IPG depletion. OCD severity was the primary outcome. Ten patients were analyzed.

RESULTS

Psychiatric symptoms and global functioning remained stable in the two years after as compared to the two years before rIPG implantation. Over the same period, the prescribed OCD medication doses did not increase and the DBS stimulation parameters were largely unaltered. Until the end of the follow-up (mean 4¾ years; maximum seven years), the DBS-related surgery frequency decreased and there were no rIPG replacements. During the first few weeks after implantation, two patients obsessively checked the rIPG, but afterwards there were no signs of compulsively checking or recharging the rIPG. Two patients experienced rIPG overdischarges (five occurrences in total).

CONCLUSIONS

This is the first report on rIPGs in DBS for OCD patients. The use of rIPGs in this population appears to be effective, applicable, and safe and diminishes the need for IPG replacements.

Reducing INDEL calling errors in whole genome and exome sequencing data

Background

INDELs, especially those disrupting protein-coding regions of the genome, have been strongly associated with human diseases. However, there are still many errors with INDEL variant calling, driven by library preparation, sequencing biases, and algorithm artifacts.

Methods

We characterized whole genome sequencing (WGS), whole exome sequencing (WES), and PCR-free sequencing data from the same samples to investigate the sources of INDEL errors. We also developed a classification scheme based on the coverage and composition to rank high and low quality INDEL calls. We performed a large-scale validation experiment on 600 loci, and find high-quality INDELs to have a substantially lower error rate than low-quality INDELs (7% vs. 51%).

Results

Simulation and experimental data show that assembly based callers are significantly more sensitive and robust for detecting large INDELs (>5 bp) than alignment based callers, consistent with published data. The concordance of INDEL detection between WGS and WES is low (53%), and WGS data uniquely identifies 10.8-fold more high-quality INDELs. The validation rate for WGS-specific INDELs is also much higher than that for WES-specific INDELs (84% vs. 57%), and WES misses many large INDELs. In addition, the concordance for INDEL detection between standard WGS and PCR-free sequencing is 71%, and standard WGS data uniquely identifies 6.3-fold more low-quality INDELs. Furthermore, accurate detection with Scalpel of heterozygous INDELs requires 1.2-fold higher coverage than that for homozygous INDELs. Lastly, homopolymer A/T INDELs are a major source of low-quality INDEL calls, and they are highly enriched in the WES data.

Conclusions

Overall, we show that accuracy of INDEL detection with WGS is much greater than WES even in the targeted region. We calculated that 60X WGS depth of coverage from the HiSeq platform is needed to recover 95% of INDELs detected by Scalpel. While this is higher than current sequencing practice, the deeper coverage may save total project costs because of the greater accuracy and sensitivity. Finally, we investigate sources of INDEL errors (for example, capture deficiency, PCR amplification, homopolymers) with various data that will serve as a guideline to effectively reduce INDEL errors in genome sequencing.

Electronic supplementary material

The online version of this article (doi:10.1186/s13073-014-0089-z) contains supplementary material, which is available to authorized users.