Predictive genomic biomarkers of therapeutic effects in renal cell carcinoma

BACKGROUND

In recent years, there have been great improvements in the therapy of renal cell carcinoma. Nevertheless, the therapeutic effect varies significantly from person to person. To discern the effective treatment for different populations, predictive molecular biomarkers in response to target, immunological, and combined therapies are widely studied.

CONCLUSION

This review summarized those studies from three perspectives (SNPs, mutation, and expression level) and listed the relationship between biomarkers and therapeutic effect, highlighting the great potential of predictive molecular biomarkers in metastatic RCC therapy. However, due to a series of reasons, most of these findings require further validation.

Integration of pharmacogenomics and theranostics with nanotechnology as quality by design (QbD) approach for formulation development of novel dosage forms for effective drug therapy

To cater to medication needs in the future healthcare system, we need to shift from the conventional system of drug delivery to modern molecular signature-based drug delivery systems. The current drug therapies are either less effective, ineffective, or produce numerous adverse reactions. One scientific principle or discipline cannot adequately address all the problems, so we need an innovative application of the current scientific principles. Here we are proposing a novel concept of nanoformulation based on pharmacogenomics and theranostics for personalized error-free and targeted therapeutic agent delivery. The addition of more knowledge about the human genome opens the new way to study disease-gene, gene-drug, and drug-effect interactions, which is the basis of future medicines. Pharmacogenomics provides information about the disease etiology, role in genes in disease pathophysiology, disease biomarkers, drug targets, drug effects, and the fate of drugs inside the body. Theranostics approach utilizes the above information in diagnosis, treatment, and monitoring of the disease on a real-time basis. Personalized dosage forms can be formulated into a nanoformulation that provides a better therapeutic effect and minimizes adverse drug reactions. The therapeutic system needs to be shifted from the principle of one drug fits all to one drug unique population. In the present manuscript, we tried to conceptualize a modern therapeutic system by combining the three approaches viz. pharmacogenomics, theranostics, and nanotechnology applied in the area of formulation development to produce a multifunctional single tiny entity.

Genetic variants of the human host influencing the coronavirus-associated phenotypes (SARS, MERS and COVID-19): rapid systematic review and field synopsis

The COVID-19 pandemic has strengthened the interest in the biological mechanisms underlying the complex interplay between infectious agents and the human host. The spectrum of phenotypes associated with the SARS-CoV-2 infection, ranging from the absence of symptoms to severe systemic complications, raised the question as to what extent the variable response to coronaviruses (CoVs) is influenced by the variability of the hosts' genetic background.To explore the current knowledge about this question, we designed a systematic review encompassing the scientific literature published from Jan. 2003 to June 2020, to include studies on the contemporary outbreaks caused by SARS-CoV-1, MERS-CoV and SARS-CoV-2 (namely SARS, MERS and COVID-19 diseases). Studies were eligible if human genetic variants were tested as predictors of clinical phenotypes.An ad hoc protocol for the rapid review process was designed according to the PRISMA paradigm and registered at the PROSPERO database (ID: CRD42020180860). The systematic workflow provided 32 articles eligible for data abstraction (28 on SARS, 1 on MERS, 3 on COVID-19) reporting data on 26 discovery cohorts. Most studies considered the definite clinical diagnosis as the primary outcome, variably coupled with other outcomes (severity was the most frequently analysed). Ten studies analysed HLA haplotypes (1 in patients with COVID-19) and did not provide consistent signals of association with disease-associated phenotypes. Out of 22 eligible articles that investigated candidate genes (2 as associated with COVID-19), the top-ranked genes in the number of studies were ACE2, CLEC4M (L-SIGN), MBL, MxA (n = 3), ACE, CD209, FCER2, OAS-1, TLR4, TNF-α (n = 2). Only variants in MBL and MxA were found as possibly implicated in CoV-associated phenotypes in at least two studies. The number of studies for each predictor was insufficient to conduct meta-analyses.Studies collecting large cohorts from different ancestries are needed to further elucidate the role of host genetic variants in determining the response to CoVs infection. Rigorous design and robust statistical methods are warranted.

Clinical study designs and patient selection methods based on genomic biomarkers: Points-to-consider documents

Recently, genomic biomarkers have been widely used clinically for prediction of the efficacy and safety of pharmacotherapy and diagnosis and prognosis of pathological conditions. Therefore, genomic biomarkers are anticipated to accelerate not only precision medicine for pharmacotherapy but also development of molecularly targeted drugs. Because the design of clinical studies involving biomarkers may differ from conventional clinical study designs, a concept paper focused on clinical studies and patient selection methods based on genomic biomarkers is desired to prompt innovative drug development. Thus, this concept paper aimed to compile and present current scientific information from the related guidelines regarding application of genomic biomarkers to clinical trials and studies for drug development. We hope that this concept paper will prompt the development of guidelines for biomarker application to drug development by industry, regulatory authorities, the medical profession, and academia.

Balancing Confounding and Generalizability Using Observational, Real-world Data: 17-gene Genomic Prostate Score Assay Effect on Active Surveillance

Randomized, controlled trials can provide high-quality, unbiased evidence for therapeutic interventions but are not always a practical or viable study design for certain healthcare decisions, such as those involving prognostic or predictive testing. Studies using large, real-world databases may be more appropriate and more generalizable to the intended target population of physicians and patients to answer these questions but carry potential for hidden bias. We illustrate several emerging methods of analyzing observational studies using propensity score matching (PSM) and coarsened exact matching (CEM). These advanced statistical methods are intended to reveal a "hidden experiment" within an observational database, and so refute or confirm a potential causal effect of assignment to an intervention and study outcome. We applied these methods to the Optum™ Research Database (ORD; Eden Prairie, MN) of electronic health records and administrative claims data to assess the effect of the 17-gene Genomic Prostate Score® (GPS™; Genomic Health, Redwood City, CA) assay on use of active surveillance (AS). In a traditional multivariable logistic regression, the GPS assay increased the use of AS by 29% (95% CI, 24%-33%). Upon applying the matching methods, the effect of the GPS assay on AS use varied between 27% and 80% and the matched data were significant among all algorithms. All matching algorithms performed well in identifying matched data that improved the imbalance in baseline covariates. By using different matching methods to assess causal inference in an observational database, we provide further confidence that the effect of the GPS assay on AS use is statistically significant and unlikely to be a result of confounding due to differences in baseline characteristics of the patients or the settings in which they were seen.

Active Surveillance Use Among a Low-risk Prostate Cancer Population in a Large US Payer System: 17-Gene Genomic Prostate Score Versus Other Risk Stratification Methods

Many men with low-risk prostate cancer (PCa) receive definitive treatment despite recommendations that have been informed by two large, randomized trials encouraging active surveillance (AS). We conducted a retrospective cohort study using the Optum™ Research Database (Eden Prairie, MN) of electronic health records and administrative claims data to assess AS use for patients tested with a 17-gene Genomic Prostate Score™ (GPS; Genomic Health, Redwood City, CA) assay and/or prostate magnetic resonance imaging (MRI). De-identified records were extracted on health plan members enrolled from June 2013 to June 2016 who had ≥1 record of PCa (n 5 291,876). Inclusion criteria included age ≥18 years, new diagnosis, American Urological Association low-risk PCa (stage T1-T2a, prostate-specific antigen ≤10 ng/mL, Gleason score 5 6), and clinical activity for at least 12 months before and after diagnosis. Data included baseline characteristics, use of GPS testing and/or MRI, and definitive procedures. GPS or MRI testing was performed in 17% of men (GPS, n 5 375, 4%; MRI, n 5 1174, 13%). AS use varied from a low of 43% for men who only underwent MRI to 89% for GPStested men who did not undergo MRI (P <.001). At 6-month follow-up, AS use was 31.0% higher (95% CI, 27.6%-34.5%; P <.001) for men receiving the GPS test only versus men who did not undergo GPS testing or MRI; the difference was 30.5% at 12-month follow-up. In a large US payer system, the GPS assay was associated with significantly higher AS use at 6 and 12 months compared with men who had MRI only, or no GPS or MRI testing.

Genomic Biomarkers of a Randomized Trial Comparing First-line Everolimus and Sunitinib in Patients with Metastatic Renal Cell Carcinoma

BACKGROUND

Metastatic renal cell carcinoma (RCC) patients are commonly treated with vascular endothelial growth factor (VEGF) inhibitors or mammalian target of rapamycin inhibitors. Correlations between somatic mutations and first-line targeted therapy outcomes have not been reported on a randomized trial.

OBJECTIVE

To evaluate the relationship between tumor mutations and treatment outcomes in RECORD-3, a randomized trial comparing first-line everolimus (mTOR inhibitor) followed by sunitinib (VEGF inhibitor) at progression with the opposite sequence in 471 metastatic RCC patients.

DESIGN, SETTING, AND PARTICIPANTS

Targeted sequencing of 341 cancer genes at ∼540× coverage was performed on available tumor samples from 258 patients; 220 with clear cell histology (ccRCC).

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Associations between somatic mutations and median first-line progression free survival (PFS1L) and overall survival were determined in metastatic ccRCC using Cox proportional hazards models and log-rank tests.

RESULTS AND LIMITATIONS

Prevalent mutations (≥ 10%) were VHL (75%), PBRM1 (46%), SETD2 (30%), BAP1 (19%), KDM5C (15%), and PTEN (12%). With first-line everolimus, PBRM1 and BAP1 mutations were associated with longer (median [95% confidence interval {CI}] 12.8 [8.1, 18.4] vs 5.5 [3.1, 8.4] mo) and shorter (median [95% CI] 4.9 [2.9, 8.1] vs 10.5 [7.3, 12.9] mo) PFS1L, respectively. With first-line sunitinib, KDM5C mutations were associated with longer PFS1L (median [95% CI] of 20.6 [12.4, 27.3] vs 8.3 [7.8, 11.0] mo). Molecular subgroups of metastatic ccRCC based on PBRM1, BAP1, and KDM5C mutations could have predictive values for patients treated with VEGF or mTOR inhibitors. Most tumor DNA was obtained from primary nephrectomy samples (94%), which could impact correlation statistics.

CONCLUSIONS

PBRM1, BAP1, and KDM5C mutations impact outcomes of targeted therapies in metastatic ccRCC patients.

PATIENT SUMMARY

Large-scale genomic kidney cancer studies reported novel mutations and heterogeneous features among individual tumors, which could contribute to varied clinical outcomes. We demonstrated correlations between somatic mutations and treatment outcomes in clear cell renal cell carcinoma, supporting the value of genomic classification in prospective studies.

Genomic biomarkers of SUDEP in brain and heart

Sudden unexpected death in epilepsy (SUDEP) is the leading cause of epilepsy-related mortality, but how to predict which patients are at risk and how to prevent it remain uncertain. The underlying pathomechanisms of SUDEP are still largely unknown, but the general consensus is that seizures somehow disrupt normal cardiac or respiratory physiology leading to death. However, the proportion of SUDEP cases exhibiting cardiac or respiratory dysfunction as a critical factor in the terminal cascade of events remains unresolved. Although many general risk factors for SUDEP have been identified, the development of reliable patient-specific biomarkers for SUDEP is needed to provide more accurate risk prediction and personalized patient management strategies. Studies in animal models and patient groups have revealed at least nine different brain-heart genes that may contribute to a genetic susceptibility for SUDEP, making them potentially useful as genomic biomarkers. This review summarizes data on the relationship between these neurocardiac genes and SUDEP, discussing their brain-heart expression patterns and genotype-phenotype correlations in mouse models and people with epilepsy. These neurocardiac genes represent good first candidates for evaluation as genomic biomarkers of SUDEP in future studies. The development of validated reliable genomic biomarkers for SUDEP has the potential to transform the clinical treatment of epilepsy by pinpointing patients at risk of SUDEP and allowing optimized, genotype-guided therapeutic and prevention strategies.