Towards precision medicine

In-depth analysis of data from the RAS-ALS study reveals new insights in rasagiline treatment for amyotrophic lateral sclerosis

BACKGROUND AND PURPOSE

In 2016, we concluded a randomized controlled trial testing 1 mg rasagiline per day add-on to standard therapy in 252 amyotrophic lateral sclerosis (ALS) patients. This article aims at better characterizing ALS patients who could possibly benefit from rasagiline by reporting new subgroup analysis and genetic data.

METHODS

We performed further exploratory in-depth analyses of the study population and investigated the relevance of single nucleotide polymorphisms (SNPs) related to the dopaminergic system.

RESULTS

Placebo-treated patients with very slow disease progression (loss of Amyotrophic Lateral Sclerosis Functional Rating Scale-Revised [ALSFRS-R] per month before randomization of ≤0.328 points) showed a per se survival probability after 24 months of 0.85 (95% confidence interval = 0.65-0.94). The large group of intermediate to fast progressing ALS patients showed a prolonged survival in the rasagiline group compared to placebo after 6 and 12 months (p = 0.02, p = 0.04), and a reduced decline of ALSFRS-R after 18 months (p = 0.049). SNP genotypes in the MAOB gene and DRD2 gene did not show clear associations with rasagiline treatment effects.

CONCLUSIONS

These results underline the need to consider individual disease progression at baseline in future ALS studies. Very slow disease progressors compromise the statistical power of studies with treatment durations of 12-18 months using clinical endpoints. Analysis of MAOB and DRD2 SNPs revealed no clear relationship to any outcome parameter. More insights are expected from future studies elucidating whether patients with DRD2CC genotype (Rs2283265) show a pronounced benefit from treatment with rasagiline, pointing to the opportunities precision medicine could open up for ALS patients in the future.

Pharmacogenomics of chloroquine and hydroxychloroquine: current evidence and future implications

As substrates of CYP2C8, CYP3A4/5 and CYP2D6, chloroquine's (CQ) and hydroxychloroquine's (HCQ) efficacy and safety may be affected by variants in the genes encoding these enzymes. This paper aims to assimilate the current evidence on the pharmacogenomics of CQ/HCQ and to identify risk phenotypes affecting the safety or efficacy of these drugs. It has been found that some CYP3A5, CYP2D6 and CYP2C8 genetic variants may affect the safety or effectiveness of CQ/HCQ. The phenotypes predictively representing ultra-rapid and poor metabolizers have been considered high-risk phenotypes. After considering these high-risk phenotypes in different ethnic groups, it is predicted that a considerable proportion of patients taking CQ/HCQ may be at risk of either therapeutic failure or severe toxicities.

The evolution of comprehensive genetic analysis in neurology: Implications for precision medicine

Technological advancements have facilitated the availability of reliable and thorough genetic analysis in many medical fields, including neurology. In this review, we focus on the importance of selecting the appropriate genetic test to aid in the accurate identification of disease utilizing currently employed technologies for analyzing monogenic neurological disorders. Moreover, the applicability of comprehensive analysis via NGS for various genetically heterogeneous neurological disorders is reviewed, revealing its efficiency in clarifying a frequently cloudy diagnostic picture and delivering a conclusive and solid diagnosis that is essential for the proper management of the patient. The feasibility and effectiveness of medical genetics in neurology require interdisciplinary cooperation among several medical specialties and geneticists, to select and perform the most relevant test according to each patient's medical history, using the most appropriate technological tools. The prerequisites for a comprehensive genetic analysis are discussed, highlighting the utility of appropriate gene selection, variant annotation, and classification. Moreover, genetic counseling and interdisciplinary collaboration could improve diagnostic yield further. Additionally, a sub-analysis is conducted on the 1,502,769 variation records with submitted interpretations in the Clinical Variation (ClinVar) database, with a focus on neurology-related genes, to clarify the value of suitable variant categorization. Finally, we review the current applications of genetic analysis in the diagnosis and personalized management of neurological patients and the advances in the research and scientific knowledge of hereditary neurological disorders that are evolving the utility of genetic analysis towards the individualization of the treatment strategy.

Predictors of response to anti-CGRP monoclonal antibodies: a 24-week, multicenter, prospective study on 864 migraine patients

Background and objectives

The identification of predictors of response to antiCGRP mAbs could favor tailored therapies and personalized treatment plans. This study is aimed at investigating predictors of ≥ 50%, ≥ 75% and 100% response at 24 weeks in patients with high-frequency episodic (HFEM: 8–14 days/month) or chronic migraine (CM).

Methods

This is a large, multicenter, cohort, real-life study. We considered all consecutive adult patients affected by HFEM or CM who were prescribed antiCGRP mAbs for ≥ 24 weeks in 20 headache centers. Patients were interviewed face-to-face using a shared semi-structured questionnaire carefully exploring socio-demographic and clinical characteristics. Patients received subcutaneous erenumab (70 mg or140 mg, monthly), galcanezumab (120 mg monthly, following a 240 mg loading dose), or fremanezumab (225 mg, monthly or 675 mg, quarterly) according to drug market availability, physician’s choice, or patient’s preference. The primary endpoint of the study was the assessment of ≥ 50% response predictors at 24 weeks. Secondary endpoints included ≥ 75% and 100% response predictors at 24 weeks.

Results

Eight hundred sixty-four migraine patients had been treated with antiCGRP mAbs for ≥ 24 weeks (erenumab: 639 pts; galcanezumab: 173 pts; fremanezumab: 55 pts). The ≥50% response (primary endpoint) in HFEM was positively associated with unilateral pain (UP) + unilateral cranial autonomic symptoms (UAs) (OR:4.23, 95%CI:1.57–11.4; p = 0.004), while in CM was positively associated with UAs (OR:1.49, 95%CI:1.05–2.11; p = 0.026), UP + UAs (OR:1.90, 95%CI:1.15–3.16; p = 0.012), UP + allodynia (OR:1.71, 95%CI:1.04–2.83; p = 0.034), and negatively associated with obesity (OR:0.21, 95%CI:0.07–0.64; p = 0.006). The 75% response (secondary endpoint) was positively associated with UP + UAs in HFEM (OR:3.44, 95%CI:1.42–8.31; p = 0.006) and with UP + UAs (OR:1.78, 95%CI:1.14–2.80; p = 0.012) and UP + allodynia (OR:1.92, 95%CI:1.22–3.06; p = 0.005) in CM. No predictor of 100% response emerged in patients with HFEM or CM.

Conclusions

A critical evaluation of headache characteristics indicating peripheral or central sensitization may help in predicting responsiveness to antiCGRP mAbs in HFEM and CM. A more precise pain profiling may represent a steppingstone for a mechanism-based approach and personalized treatment of migraine with compounds targeting specific molecular mechanisms.

iCEMIGE: Integration of CEll-morphometrics, MIcrobiome, and GEne biomarker signatures for risk stratification in breast cancers

BACKGROUND

The development of precision medicine is essential for personalized treatment and improved clinical outcome, whereas biomarkers are critical for the success of precision therapies.

AIM

To investigate whether iCEMIGE (integration of CEll-morphometrics, MIcro biome, and GEne biomarker signatures) improves risk stratification of breast cancer (BC) patients.

METHODS

We used our recently developed machine learning technique to identify cellular morphometric biomarkers (CMBs) from the whole histological slide images in The Cancer Genome Atlas (TCGA) breast cancer (TCGA-BRCA) cohort. Multivariate Cox regression was used to assess whether cell-morphometrics prognosis score (CMPS) and our previously reported 12-gene expression prognosis score (GEPS) and 15-microbe abundance prognosis score (MAPS) were independent prognostic factors. iCEMIGE was built upon the sparse representation learning technique. The iCEMIGE scoring model performance was measured by the area under the receiver operating characteristic curve compared to CMPS, GEPS, or MAPS alone. Nomogram models were created to predict overall survival (OS) and progress-free survival (PFS) rates at 5- and 10-year in the TCGA-BRCA cohort.

RESULTS

We identified 39 CMBs that were used to create a CMPS system in BCs. CMPS, GEPS, and MAPS were found to be significantly independently associated with OS. We then established an iCEMIGE scoring system for risk stratification of BC patients. The iGEMIGE score has a significant prognostic value for OS and PFS independent of clinical factors (age, stage, and estrogen and progesterone receptor status) and PAM50-based molecular subtype. Importantly, the iCEMIGE score significantly increased the power to predict OS and PFS compared to CMPS, GEPS, or MAPS alone.

CONCLUSION

Our study demonstrates a novel and generic artificial intelligence framework for multimodal data integration toward improving prognosis risk stratification of BC patients, which can be extended to other types of cancer.

Regional Radiomics Similarity Networks Reveal Distinct Subtypes and Abnormality Patterns in Mild Cognitive Impairment

Individuals with mild cognitive impairment (MCI) of different subtypes show distinct alterations in network patterns. The first aim of this study is to identify the subtypes of MCI by employing a regional radiomics similarity network (R2SN). The second aim is to characterize the abnormality patterns associated with the clinical manifestations of each subtype. An individual-level R2SN is constructed for N = 605 normal controls (NCs), N = 766 MCI patients, and N = 283 Alzheimer's disease (AD) patients. MCI patients' R2SN profiles are clustered into two subtypes using nonnegative matrix factorization. The patterns of brain alterations, gene expression, and the risk of cognitive decline in each subtype are evaluated. MCI patients are clustered into "similar to the pattern of NCs" (N-CI, N = 252) and "similar to the pattern of AD" (A-CI, N = 514) subgroups. Significant differences are observed between the subtypes with respect to the following: 1) clinical measures; 2) multimodal neuroimaging; 3) the proportion of progression to dementia (61.54% for A-CI and 21.77% for N-CI) within three years; 4) enriched genes for potassium-ion transport and synaptic transmission. Stratification into the two subtypes provides new insight for risk assessment and precise early intervention for MCI patients.

Machine learning as the new approach in understanding biomarkers of suicidal behavior

In psychiatry, compared to other medical fields, the identification of biological markers that would complement current clinical interview, and enable more objective and faster clinical diagnosis, implement accurate monitoring of treatment response and remission, is grave. Current technological development enables analyses of various biological marks in high throughput scale at reasonable costs, and therefore 'omic' studies are entering the psychiatry research. However, big data demands a whole new plethora of skills in data processing, before clinically useful information can be extracted. So far the classical approach to data analysis did not really contribute to identification of biomarkers in psychiatry, but the extensive amounts of data might get to a higher level, if artificial intelligence in the shape of machine learning algorithms would be applied. Not many studies on machine learning in psychiatry have been published, but we can already see from that handful of studies that the potential to build a screening portfolio of biomarkers for different psychopathologies, including suicide, exists.

Convergence of Precision Medicine and Public Health Into Precision Public Health: Toward a Big Data Perspective

The field of precision medicine explores disease treatments by looking at genetic, socio-environmental, and clinical factors, thus trying to provide a holistic view of a person's health. Public health, on the other hand, is focused on improving the health of populations through preventive strategies and timely interventions. With recent advances in technology, we are able to collect, analyze and store for the first-time large volumes of real-time, diverse and continuous health data. Typically, the field of precision medicine deals with a huge amount of data from few individuals; public health, on the other hand, deals with limited data from a population. With the coming of Big Data, the fields of precision medicine and public health are converging into precision public health, the study of biological and genetic factors supported by large amounts of population data. In this paper, we explore through a comprehensive review the data types and use cases found in precision medicine and public health. We also discuss how these data types and use cases can converge toward precision public health, as well as challenges and opportunities provided by research and analyses of health data.

The Future of Clinical Trial Design: The Transition from Hard Endpoints to Value-Based Endpoints

Clinical trials have been conducted since 500 BC. Currently, the methodological gold standard is the randomized controlled clinical trial, introduced by Austin Bradford Hill. This standard has produced enormous amounts of high-quality evidence, resulting in evidence-based clinical guidelines for physicians. However, the current trial paradigm needs to evolve because of the ongoing decrease of the incidence of hard endpoints and spiraling trial costs. While new trial designs, such as adaptive clinical trials, may lead to an increase in efficiency and decrease in costs, we propose a shift towards value-based trial design: a paradigm that mirrors value-based thinking in business and health care. Value-based clinical trials will use technology to focus more on symptoms and endpoints that patients care about, will incorporate fewer research centers, and will measure a state or consequence of disease at home or at work. Furthermore, they will measure the subjective experience of subjects in relation to other objective measurements. Ideally, the endpoints are suitable for individual assessment of the effect of an intervention. The value-based clinical trial of the future will have a low burden for participants, allowing for the inclusion of neglected populations such as children and the elderly, will be data-rich due to a high frequency of measurements, and can be conducted with technology that is already available.