Diffusion Tensor MRI is sensitive to fibrotic injury in a mouse model of oxalate induced chronic kidney disease.

Chronic kidney disease (CKD) is characterized by inflammation and fibrosis in the kidney. Renal biopsies and estimated glomerular filtration rate (eGFR) remain the standard of care, but these endpoints have limitations in detecting the stage, progression, and spatial distribution of fibrotic pathology in the kidney. MRI diffusion tensor imaging (DTI) has emerged as a promising non-invasive technology to evaluate renal fibrosis in vivo both in clinical and preclinical studies. However, these imaging studies have not systematically identified fibrosis particularly deeper in the kidney where biopsy sampling is limited, or completed an extensive analysis of whole organ histology, blood biomarkers, and gene expression to evaluate the relative strengths and weaknesses of MRI for evaluating renal fibrosis. In this study, we performed DTI in the sodium oxalate mouse model of CKD. The DTI parameters fractional anisotropy, apparent diffusion coefficient, and axial diffusivity were compared between the control and oxalate groups with region-of-interest (ROI) analysis to determine changes in the cortex and medulla. Additionally, voxel-based analysis (VBA) was implemented to systematically identify local regions of injury over the whole kidney. DTI parameters were found to be significantly different in the medulla by both ROI analysis and VBA, which also spatially matched with collagen III IHC. The DTI parameters in this medullary region exhibited moderate to strong correlations with histology, blood biomarkers, hydroxyproline and gene expression. Our results thus highlight the sensitivity of DTI to the heterogeneity of renal fibrosis and importance of whole kidney non-invasive imaging.

Unraveling Pre-filled Syringe Needle Clogging: Exploring a Fresh Outlook Through Innovative Techniques.

OBJECTIVE: This study aimed to investigate the movement of liquid in the needle region of staked-in-needle pre-filled syringes using neutron imaging and synchrotron X-ray tomography. The objective was to gain insights into the dynamics of liquid presence and understand the factors contributing to needle clogging. METHODS: Staked-in-needle pre-filled syringes were examined using neutron radiography and synchrotron X-ray phase-contrast computed tomography. Neutron radiography provided a 2D visualization of liquid presence in the needle, while synchrotron X-ray tomography offered high-resolution 3D imaging to study detailed morphological features of the liquid. RESULTS: Neutron radiography revealed liquid presence in the needle region for as-received samples and after temperature and pressure cycling. Pressure cycling had a more pronounced effect on liquid formation. Synchrotron X-ray tomography confirmed the presence of liquid and revealed various morphologies, including droplets of different sizes, liquid segments blocking sections of the needle, and a thin layer covering the needle wall. Liquid presence was also observed between the steel needle and the glass barrel. CONCLUSIONS: The combination of neutron imaging and synchrotron X-ray tomography provided valuable insights into the dynamics of liquid movement in staked-in-needle pre-filled syringes. Temperature and pressure cycling were found to contribute to additional liquid formation, with pressure changes playing a significant role. The detailed morphological analysis enhanced the understanding of microstructural arrangements within the needle. This research contributes to addressing the issue of needle clogging and can guide the development of strategies to improve pre-filled syringe performance.

Multi-ancestry GWAS analysis identifies two novel loci associated with diabetic eye disease and highlights APOL1 as a high risk locus in patients with diabetic macular edema.

Diabetic retinopathy (DR) is a common complication of diabetes. Approximately 20% of DR patients have diabetic macular edema (DME) characterized by fluid leakage into the retina. There is a genetic component to DR and DME risk, but few replicable loci. Because not all DR cases have DME, we focused on DME to increase power, and conducted a multi-ancestry GWAS to assess DME risk in a total of 1,502 DME patients and 5,603 non-DME controls in discovery and replication datasets. Two loci reached GWAS significance (p<5x10-8). The strongest association was rs2239785, (K150E) in APOL1. The second finding was rs10402468, which co-localized to PLVAP and ANKLE1 in vascular / endothelium tissues. We conducted multiple sensitivity analyses to establish that the associations were specific to DME status and did not reflect diabetes status or other diabetic complications. Here we report two novel loci for risk of DME which replicated in multiple clinical trial and biobank derived datasets. One of these loci, containing the gene APOL1, is a risk factor in African American DME and DKD patients, indicating that this locus plays a broader role in diabetic complications for multiple ancestries. Trial Registration: NCT00473330, NCT00473382, NCT03622580, NCT03622593, NCT04108156.

Quantifying KRAS G12C Covalent Drug Inhibitor Activity in Mouse Tumors Using Mass Spectrometry.

The growing opportunities recognized for covalent drug inhibitors, like KRAS G12C inhibitors, are driving the need for mass spectrometry methods that can quickly and robustly measure therapeutic drug activity in vivo for drug discovery research and development. Effective front-end sample preparation is critical for proteins extracted from tumors but is generally labor intensive and impractical for large sample numbers typical in pharmacodynamic (PD) studies. Herein, we describe an automated and integrated sample preparation method for the measurement of activity levels of KRAS G12C drug inhibitor alkylation from complex tumor samples involving high throughput detergent removal and preconcentration followed by quantitation using mass spectrometry. We introduce a robust assay with an average intra-assay coefficient of variation (CV) of 4% and an interassay CV of 6% obtained from seven studies, enabling us to understand the relationship between KRAS G12C target occupancy and the therapeutic PD effect from mouse tumor samples. Further, the data demonstrated that the drug candidate GDC-6036, a KRAS G12C covalent inhibitor, shows dose-dependent target inhibition (KRAS G12C alkylation) and MAPK pathway inhibition, which correlate with high antitumor potency in the MIA PaCa-2 pancreatic xenograft model.

NLoed: A Python Package for Nonlinear Optimal Experimental Design in Systems Biology.

Modeling in systems and synthetic biology relies on accurate parameter estimates and predictions. Accurate model calibration relies, in turn, on data and on how well suited the available data are to a particular modeling task. Optimal experimental design (OED) techniques can be used to identify experiments and data collection procedures that will most efficiently contribute to a given modeling objective. However, implementation of OED is limited by currently available software tools that are not well suited for the diversity of nonlinear models and non-normal data commonly encountered in biological research. Moreover, existing OED tools do not make use of the state-of-the-art numerical tools, resulting in inefficient computation. Here, we present the NLoed software package and demonstrate its use with in vivo data from an optogenetic system in Escherichia coli. NLoed is an open-source Python library providing convenient access to OED methods, with particular emphasis on experimental design for systems biology research. NLoed supports a wide variety of nonlinear, multi-input/output, and dynamic models and facilitates modeling and design of experiments over a wide variety of data types. To support OED investigations, the NLoed package implements maximum likelihood fitting and diagnostic tools, providing a comprehensive modeling workflow. NLoed offers an accessible, modular, and flexible OED tool set suited to the wide variety of experimental scenarios encountered in systems biology research. We demonstrate NLoed's capabilities by applying it to experimental design for characterization of a bacterial optogenetic system.

A whole genome sequencing study of moderate to severe asthma identifies a lung function locus associated with asthma risk.

Genome-wide association studies (GWAS) have identified many common variant loci associated with asthma susceptibility, but few studies investigate the genetics underlying moderate-to-severe asthma risk. Here, we present a whole-genome sequencing study comparing 3181 moderate-to-severe asthma patients to 3590 non-asthma controls. We demonstrate that asthma risk is genetically correlated with lung function measures and that this component of asthma risk is orthogonal to the eosinophil genetics that also contribute to disease susceptibility. We find that polygenic scores for reduced lung function are associated with younger asthma age of onset. Genome-wide, seven previously reported common asthma variant loci and one previously reported lung function locus, near THSD4, reach significance. We replicate association of the lung function locus in a recently published GWAS of moderate-to-severe asthma patients. We additionally replicate the association of a previously reported rare (minor allele frequency < 1%) coding variant in IL33 and show significant enrichment of rare variant burden in genes from common variant allergic disease loci. Our findings highlight the contribution of lung function genetics to moderate-to-severe asthma risk, and provide initial rare variant support for associations with moderate-to-severe asthma risk at several candidate genes from common variant loci.

RTK-Dependent Inducible Degradation of Mutant PI3Kα Drives GDC-0077 (Inavolisib) Efficacy.

PIK3CA is one of the most frequently mutated oncogenes; the p110a protein it encodes plays a central role in tumor cell proliferation. Small-molecule inhibitors targeting the PI3K p110a catalytic subunit have entered clinical trials, with early-phase GDC-0077 studies showing antitumor activity and a manageable safety profile in patients with PIK3CA-mutant breast cancer. However, preclinical studies have shown that PI3K pathway inhibition releases negative feedback and activates receptor tyrosine kinase signaling, reengaging the pathway and attenuating drug activity. Here we discover that GDC-0077 and taselisib more potently inhibit mutant PI3K pathway signaling and cell viability through unique HER2-dependent mutant p110a degradation. Both are more effective than other PI3K inhibitors at maintaining prolonged pathway suppression. This study establishes a new strategy for identifying inhibitors that specifically target mutant tumors by selective degradation of the mutant oncoprotein and provide a strong rationale for pursuing PI3Kα degraders in patients with HER2-positive breast cancer. SIGNIFICANCE: The PI3K inhibitors GDC-0077 and taselisib have a unique mechanism of action; both inhibitors lead to degradation of mutant p110a protein. The inhibitors that have the ability to trigger specific degradation of mutant p110a without significant change in wild-type p110a protein may result in improved therapeutic index in PIK3CA-mutant tumors.See related commentary by Vanhaesebroeck et al., p. 20.This article is highlighted in the In This Issue feature, p. 1.

MiDAS-Meaningful Immunogenetic Data at Scale.

Human immunogenetic variation in the form of HLA and KIR types has been shown to be strongly associated with a multitude of immune-related phenotypes. However, association studies involving immunogenetic loci most commonly involve simple analyses of classical HLA allelic diversity, resulting in limitations regarding the interpretability and reproducibility of results. We here present MiDAS, a comprehensive R package for immunogenetic data transformation and statistical analysis. MiDAS recodes input data in the form of HLA alleles and KIR types into biologically meaningful variables, allowing HLA amino acid fine mapping, analyses of HLA evolutionary divergence as well as experimentally validated HLA-KIR interactions. Further, MiDAS enables comprehensive statistical association analysis workflows with phenotypes of diverse measurement scales. MiDAS thus closes the gap between the inference of immunogenetic variation and its efficient utilization to make relevant discoveries related to immune and disease biology. It is freely available under a MIT license.

Characterization of Hepatic UDP-Glucuronosyltransferase Enzyme Abundance-Activity Correlations and Population Variability Using a Proteomics Approach and Comparison with Cytochrome P450 Enzymes.

The expression of ten major drug-metabolizing UDP-glucuronosyltransferase (UGT) enzymes in a panel of 130 human hepatic microsomal samples was measured using a liquid chromatography-tandem mass spectrometry-based approach. Simultaneously, ten cytochromes P450 and P450 reductase were also measured, and activity-expression relationships were assessed for comparison. The resulting data sets demonstrated that, with the exception of UGT2B17, 10th to 90th percentiles of UGT expression spanned 3- to 8-fold ranges. These ranges were small relative to ranges of reported mean UGT enzyme expression across different laboratories. We tested correlation of UGT expression with enzymatic activities using selective probe substrates. A high degree of abundance-activity correlation (Spearman's rank correlation coefficient > 0.6) was observed for UGT1As (1A1, 3, 4, 6) and cytochromes P450. In contrast, protein abundance and activity did not correlate strongly for UGT1A9 and UGT2B enzymes (2B4, 7, 10, 15, and 17). Protein abundance was strongly correlated for UGTs 2B7, 2B10, and 2B15. We suggest a number of factors may contribute to these differences including incomplete selectivity of probe substrates, correlated expression of these UGT2B isoforms, and the impact of splice and polymorphic variants on the peptides used in proteomics analysis, and exemplify this in the case of UGT2B10. Extensive correlation analyses identified important criteria for validating the fidelity of proteomics and enzymatic activity approaches for assessing UGT variability, population differences, and ontogenetic changes. SIGNIFICANCE STATEMENT: Protein expression data allow detailed assessment of interindividual variability and enzyme ontogeny. This study has observed that expression and enzyme activity are well correlated for hepatic UGT1A enzymes and cytochromes P450. However, for the UGT2B family, caution is advised when assuming correlation of expression and activity as is often done in physiologically based pharmacokinetic modeling. This can be due to incomplete probe substrate specificities, but may also be related to presence of inactive UGT protein materials and the effect of splicing variations.

Ocular phenotypes in a mouse model of impaired glucocerebrosidase activity.

Mutations in the GBA1 gene encoding glucocerebrosidase (GCase) are linked to Gaucher (GD) and Parkinson's Disease (PD). Since some GD and PD patients develop ocular phenotypes, we determined whether ocular phenotypes might result from impaired GCase activity and the corresponding accumulation of glucosylceramide (GluCer) and glucosylsphingosine (GluSph) in the Gba1D409V/D409V knock-in (Gba KI/KI; "KI") mouse. Gba KI mice developed age-dependent pupil dilation deficits to an anti-muscarinic agent; histologically, the iris covered the anterior part of the lens with adhesions between the iris and the anterior surface of the lens (posterior synechia). This may prevent pupil dilation in general, beyond an un-responsiveness of the iris to anti-muscarinics. Gba KI mice displayed atrophy and pigment dispersion of the iris, and occlusion of the iridocorneal angle by pigment-laden cells, reminiscent of secondary open angle glaucoma. Gba KI mice showed progressive thinning of the retina consistent with retinal degeneration. GluSph levels were increased in the anterior and posterior segments of the eye, suggesting that accumulation of lipids in the eye may contribute to degeneration in this compartment. We conclude that the Gba KI model provides robust and reproducible eye phenotypes which may be used to test for efficacy and establish biomarkers for GBA1-related therapies.

Generalized Additive Mixed Modeling of Longitudinal Tumor Growth Reduces Bias and Improves Decision Making in Translational Oncology.

Scientists working in translational oncology regularly conduct multigroup studies of mice with serially measured tumors. Longitudinal data collected can feature mid-study dropouts and complex nonlinear temporal response patterns. Parametric statistical models such as ones assuming exponential growth are useful for summarizing tumor volume over ranges for which the growth model holds, with the advantage that the model's parameter estimates can be used to summarize between-group differences in tumor volume growth with statistical measures of uncertainty. However, these same assumed growth models are too rigid to recapitulate patterns observed in many experiments, which in turn diminishes the effectiveness of their parameter estimates as summary statistics. To address this problem, we generalized such models by adopting a nonparametric approach in which group-level response trends for logarithmically scaled tumor volume are estimated as regression splines in a generalized additive mixed model. We also describe a novel summary statistic for group level splines over user-defined, experimentally relevant time ranges. This statistic reduces to the log-linear growth rate for data well described by exponential growth and also has a sampling distribution across groups that is well approximated by a multivariate Gaussian, thus facilitating downstream analysis. Real-data examples show that this nonparametric approach not only enhances fidelity in describing nonlinear growth scenarios but also improves statistical power to detect interregimen differences when compared with the simple exponential model so that it generalizes the linear mixed effects paradigm for analysis of log-linear growth to nonlinear scenarios in a useful way. SIGNIFICANCE: This work generalizes the statistical linear mixed modeling paradigm for summarizing longitudinally measured preclinical tumor volume studies to encompass studies with nonlinear and nonmonotonic group response patterns in a statistically rigorous manner.

Direct Drug Analysis in Polymeric Implants Using Desorption Electrospray Ionization - Mass Spectrometry Imaging (DESI-MSI).

PURPOSE: Desorption electrospray ionization mass spectrometry imaging (DESI-MSI) coupled with gas-phase ion mobility spectrometry was used to characterize the drug distribution in polymeric implants before and after exposure to accelerated in vitro release (IVR) media. DESI-MSI provides definitive chemical identification and localization of formulation components, including 2D chemical mapping of individual components with essentially no sample preparation. METHODS: Polymeric implants containing 40% (w/w) entecavir and poly(D,L-lactide) (PLA) were prepared and then exposed to either acidified PBS (pH 2.5) or MeOH:H2O (50:50, v/v) medias during a 7-day IVR test using continuous flow-through (CFT) cell dissolution. The amount of drug released from the polymer matrix during the 7-day IVR test was monitored by online-ultraviolet spectroscopy (UV) and HPLC-UV. After that period, intact implants and radial sections of implants were analyzed by DESI-MSI with ion mobility spectrometry. The active ingredient along with impurities and contaminants were used to generate chemical maps before and after exposure to the release medias. RESULTS: Bi-phasic release profiles were observed for implants during IVR release using both medias. During the second phase of release, implants exposed to PBS, pH 2.5, released the entecavir faster than the implants exposed to MeOH:H2O (50:50, v/v). Radial images of the polymer interior show that entecavir is localized along the central core of the implant after exposure to MeOH:H2O (50:50, v/v) and that the drug is more uniformly distributed throughout the implant after exposure to acidified PBS (pH 2.5). CONCLUSIONS: DESI-MSI coupled with ion mobility analysis produced chemical images of the drug distribution on the exterior and interior of cylindrical polymeric implants before and after exposure to various release medias. These results demonstrated the utility of this technique for rapid characterization of drug and impurity/degradant distribution within polymeric implants with direct implications for formulation development as well as analytical method development activities for various solid parenteral and oral dosage forms. These results are especially meaningful since samples were analyzed with essentially no preparative procedures.

Interpreting In Vitro Release Performance from Long-Acting Parenteral Nanosuspensions Using USP-4 Dissolution and Spectroscopic Techniques.

Injectable sustained release dosage forms have emerged as desirable therapeutic routes for patients that require life-long treatments. The prevalence of drug molecules with low aqueous solubility and bioavailability has added momentum toward the development of suspension-based long-acting parenteral (LAP) formulations; the previously undesirable physicochemical properties of Biopharmaceutics Classification System (BCS) Class II/IV compounds are best suited for extended release applications. Effective in vitro release (IVR) testing of crystalline suspensions affirms product quality during early-stage development and provides connections with in vivo performance. However, before in vitro-in vivo correlations (IVIVCs) can be established, it is necessary to evaluate formulation attributes that directly affect IVR properties. In this work, a series of crystalline LAP nanosuspensions were formulated with different stabilizing polymers and applied to a continuous flow-through (USP-4) dissolution method. This technique confirmed the role of salt effects on the stability of polymer-coated nanoparticles through the detection of disparate active pharmaceutical ingredient (API) release profiles. The polymer stabilizers with extended hydrophilic chains exhibited elevated intrapolymer activity from the loss of hydrogen-bond cushioning in dissolution media with heightened ionic strength, confirmed through one-dimensional (1D) 1H NMR and two-dimensional nuclear Overhauser effect spectroscopy (2D NOESY) experiments. Thus, steric repulsion within the affected nanosuspensions was limited and release rates decreased. Additionally, the strength of interaction between hydrophobic polymer components and the API crystalline surface contributed to suspension dissolution properties, confirmed through solution- and solid-state spectroscopic analyses. This study provides a unique perspective on the dynamic interface between the crystalline drug and aqueous microenvironment during dissolution.

Characterizing and Quantitating Therapeutic Tethered Multimeric Antibody Degradation Using Affinity Capture Mass Spectrometry.

There are many pharmacokinetic challenges associated with administering protein therapeutics, including biotransformation via clipping, deamidation, isomerization, oxidation, etc. In the case of engineered multivalent tethered antibody formats, proteolysis or deconjugation at the fusion or conjugation site present further issues. Unlike degradations associated with antibody drug conjugates, such biotransformations of tethered antibody formats usually result in degraded products with large mass differences. These large differences can result in processing or mass spectrometry response bias among the resulting product species that can lead to inaccurate stability quantitation. Herein, we describe an assay strategy for characterizing and quantitating degradations accurately for multivalent antibodies by incorporating response bias corrections. For the multivalent tethered antibody molecules selected, an ∼30-80% difference in response, compared to the cleaved product, was observed. To correct for the response bias, selected tethered multivalent antibodies and an IgG antibody (representing the stable intact and the degraded product species, respectively) were spiked in serum at known ratios for analysis. Following affinity capture, we generated calibration curves (five-parameter logistic fit p < 0.05) by plotting the measured ratios of the MS ion responses against the known spiked-in ratios (CVs < 8% for calibration standards). The qualified calibration curve (accuracy within 8% and 2% for measuring degradations of 5% and 15% product, respectively) was then used, through interpolation, to determine stability profiles for the same multivalent tethered antibody formats from both in vitro serum and pharmacokinetic study samples.

Probing in Vitro Release Kinetics of Long-Acting Injectable Nanosuspensions via Flow-NMR Spectroscopy.

Novel treatment routes are emerging for an array of diseases and afflictions. Complex dosage forms, based on active pharmaceutical ingredients (APIs) with previously undesirable physicochemical characteristics, are becoming mainstream and actively pursued in various pipeline initiatives. To fundamentally understand how constituents in these dosage forms interact on a molecular level, analytical methods need to be developed that encompass selectivity and sensitivity requirements previously reserved for a myriad of in vitro techniques. The knowledge of precise chemical interactions between drugs and excipients in a dosage form can streamline formulation development and process screening capabilities through the identification of properties that influence rates and mechanisms of drug release in a cost-effective manner, relative to long-term in vivo studies. Through this work, a noncompendial in vitro release (IVR) method was developed that distinguished the presence of individual components in a complex crystalline nanosuspension environment. Doravirine was formulated as a series of long-acting injectable nanosuspensions with assorted excipients, using low- and high-energy wet media milling methods. IVR behavior of all formulation components were monitored using a robust continuous flow-through (CFT) dissolution setup (USP-4 apparatus) with on-line 1H NMR end-analysis (flow-NMR). Results from this investigation led to a better understanding of formulation parameter influences on nanosuspension stability, surface chemistry, and dissolution behavior. Flow-NMR can be applied to a broad range of dosage forms in which specific molecular interactions from the solution microenvironment require further insight to enhance product development capabilities.

Characterization of the Ontogeny of Hepatic UDP-Glucuronosyltransferase Enzymes Based on Glucuronidation Activity Measured in Human Liver Microsomes.

An understanding of the postnatal development of hepatic UDP-glucuronosyltransferase (UGT) enzymes is required for accurate prediction of the age-dependent changes in pharmacokinetics of many drugs used in children. However, the maturation rate of hepatic UGT isoforms remains a major knowledge gap. This study aimed to establish the age-associated changes in glucuronidation activity of 10 major hepatic UGT isoforms in humans, namely, UGT1A1, UGT1A3, UGT1A4, UGT1A6, UGT1A9, UGT2B4, UGT2B7, UGT2B10, UGT2B15, and UGT2B17. Human liver microsomes from pediatric and adult donors were incubated under optimized incubation conditions to assess the activity rates of hepatic UGT isoforms using a panel of 19 in vitro UGT probe substrates and clinically used drugs. Statistically strong correlations of glucuronidation activities allowed the ontogeny of UGT1A1, UGT1A4, UGT2B7, UGT2B10, and UGT2B15 to be established using multiple selective UGT substrates and matched human liver microsome samples. The postnatal development of hepatic UGTs is isoform-dependent using either individual or cross-correlated selective isoform substrates. Maximal adult activity was reached at different times ranging from within a month (UGT1A1, UGT2B4, UGT2B7, UGT2B10, and UGT2B15), during infancy (UGT1A3, UGT1A4, and UGT1A9), to adolescence (UGT1A6 and UGT2B17). This study provides an extensive characterization of the postnatal ontogeny profiles of hepatic UGT enzymes that are instrumental for predicting drug disposition via in vitro-in vivo extrapolation algorithms and verifying pharmacokinetic predictions against in vivo observations via pediatric physiologically based pharmacokinetic modeling in pediatric patients.

Multiple-gene targeting and mismatch tolerance can confound analysis of genome-wide pooled CRISPR screens.

BACKGROUND: Genome-wide loss-of-function screens using the CRISPR/Cas9 system allow the efficient discovery of cancer cell vulnerabilities. While several studies have focused on correcting for DNA cleavage toxicity biases associated with copy number alterations, the effects of sgRNAs co-targeting multiple genomic loci in CRISPR screens have not been discussed. RESULTS: In this work, we analyze CRISPR essentiality screen data from 391 cancer cell lines to characterize biases induced by multi-target sgRNAs. We investigate two types of multi-targets: on-targets predicted through perfect sequence complementarity and off-targets predicted through sequence complementarity with up to two nucleotide mismatches. We find that the number of on-targets and off-targets both increase sgRNA activity in a cell line-specific manner and that existing additive models of gene knockout effects fail at capturing genetic interactions that may occur between co-targeted genes. We use synthetic lethality between paralog genes to show that genetic interactions can introduce biases in essentiality scores estimated from multi-target sgRNAs. We further show that single-mismatch tolerant sgRNAs can confound the analysis of gene essentiality and lead to incorrect co-essentiality functional networks. Lastly, we also find that single nucleotide polymorphisms located in protospacer regions can impair on-target activity as a result of mismatch tolerance. CONCLUSION: We show the impact of multi-target effects on estimating cancer cell dependencies and the impact of off-target effects caused by mismatch tolerance in sgRNA-DNA binding.

Previously reported placebo-response-associated variants do not predict patient outcomes in inflammatory disease Phase III trial placebo arms.

In clinical trials, a placebo response refers to improvement in disease symptoms arising from the psychological effect of receiving a treatment rather than the actual treatment under investigation. Previous research has reported genomic variation associated with the likelihood of observing a placebo response, but these studies have been limited in scope and have not been validated. Here, we analyzed whole-genome sequencing data from 784 patients undergoing placebo treatment in Phase III Asthma or Rheumatoid Arthritis trials to assess the impact of previously reported variation on patient outcomes in the placebo arms and to identify novel variants associated with the placebo response. Contrary to expectations based on previous reports, we did not observe any statistically significant associations between genomic variants and placebo treatment outcome. Our findings suggest that the biological origin of the placebo response is complex and likely to be variable between disease areas.

Enabling genome-wide association testing with multiple diseases and no healthy controls.

MOTIVATION: While large-scale whole genome sequencing is feasible the high costs compel investigators to focus on disease subjects. As a result large sequencing datasets of samples with different diseases are often readily available, but not healthy controls to contrast them with. While it is possible to perform an association study using only diseases, the associations could be driven by a disease acting as a control and not the focal disease. METHODS: We developed a genotype-on-phenotype reverse regression with a Bayesian spike and slab prior to enable association testing in datasets with multiple diseases. This method, referred to as revreg, flagged associations (both common and rare) that were driven by diseases that were not of primary interest. RESULTS: Based on simulations, revreg had 80% power to detect an odds ratio of 1.74 for common variants (3500 samples total) and 3.73 for rare variants (14,000 samples total), with minimal type I error. For common variants, we tested this method on 3657 whole genome sequenced samples aimed at discovering variants associated with disease risk of Chronic Obstructive Pulmonary Disease using three other diseases as controls. We demonstrated detection of six highly significant associations likely due to Age-Related Macular Degeneration. In an exome dataset of 8836 samples aimed at characterizing rare variants associated with disease risk of Asthma, using five other diseases as controls, we detected and removed genic regions due to AMD (C3, CFH, CFHR5, CFI, and DNMT3A) and RA (KRTAP13-4).

Germline genetic polymorphisms influence tumor gene expression and immune cell infiltration.

Cancer immunotherapy has emerged as an effective therapy in a variety of cancers. However, a key challenge in the field is that only a subset of patients who receive immunotherapy exhibit durable response. It has been hypothesized that host genetics influences the inherent immune profiles of patients and may underlie their differential response to immunotherapy. Herein, we systematically determined the association of common germline genetic variants with gene expression and immune cell infiltration of the tumor. We identified 64,094 expression quantitative trait loci (eQTLs) that associated with 18,210 genes (eGenes) across 24 human cancers. Overall, eGenes were enriched for their being involved in immune processes, suggesting that expression of immune genes can be shaped by hereditary genetic variants. We identified the endoplasmic reticulum aminopeptidase 2 (ERAP2) gene as a pan-cancer type eGene whose expression levels stratified overall survival in a subset of patients with bladder cancer receiving anti-PD-L1 (atezolizumab) therapy. Finally, we identified 103 gene signature QTLs (gsQTLs) that were associated with predicted immune cell abundance within the tumor microenvironment. Our findings highlight the impact of germline SNPs on cancer-immune phenotypes and response to therapy; and these analyses provide a resource for integration of germline genetics as a component of personalized cancer immunotherapy.