Polymyalgia rheumatica during combination therapy with atezolizumab plus bevacizumab for advanced hepatocellular carcinoma.

The combination therapy of atezolizumab, an anti-programmed cell death ligand-1 antibody, plus bevacizumab (Atz/Bev) is widely used to treat patients with advanced hepatocellular carcinoma (HCC). The development of polymyalgia rheumatica (PMR) during immune checkpoint inhibitor therapy for patients with HCC has not been reported to date. Two patients who developed PMR during Atz/Bev therapy for advanced HCC are reported. Both patients developed fever, bilateral symmetrical shoulder pain, morning stiffness, and an elevated C-reactive protein level. Their symptoms improved rapidly with prednisolone (PSL) 15-20 mg/d, and their C-reactive protein levels decreased. In PMR, long-term low-dose PSL should be administered. In the present patients who developed PMR as immune-related adverse events, starting with a small dose of PSL resulted in rapid improvement of symptoms.

Immune subtypes and neoantigen-related immune evasion in advanced colorectal cancer.

Elimination of cancerous cells by the immune system is an important mechanism of protection from cancer, however, its effectiveness can be reduced owing to development of resistance and evasion. To understand the systemic immune response in advanced untreated primary colorectal cancer, we analyze immune subtypes and immune evasion via neoantigen-related mechanisms. We identify a distinctive cancer subtype characterized by immune evasion and very poor overall survival. This subtype has less clonal highly expressed neoantigens and high chromosomal instability, resulting in adaptive immune resistance mediated by the immune checkpoint molecules and neoantigen presentation disorders. We also observe that neoantigen depletion caused by immunoediting and high clonal neoantigen load are correlated with a good overall survival. Our results indicate that the status of the tumor microenvironment and neoantigen composition are promising new prognostic biomarkers with potential relevance for treatment plan decisions in advanced CRC.

Prediction of early recurrence of pancreatic ductal adenocarcinoma after resection.

BACKGROUND: Even after curative resection, pancreatic ductal adenocarcinoma (PDAC) patients suffer a high rate of recurrence. There is an unmet need to predict which patients will experience early recurrence after resection in order to adjust treatment strategies. METHODS: Data of patients with resectable PDAC undergoing surgical resection between January 2005 and September 2018 were reviewed to stratify for early recurrence defined as occurring within 6 months of resection. Preoperative data including demographics, tumor markers, blood immune-inflammatory factors and clinicopathological data were examined. We employed Elastic Net, a sparse modeling method, to construct models predicting early recurrence using these multiple preoperative factors. As a result, seven preoperative factors were selected: age, duke pancreatic monoclonal antigen type 2 value, neutrophil:lymphocyte ratio, systemic immune-inflammation index, tumor size, lymph node metastasis and is peripancreatic invasion. Repeated 10-fold cross-validations were performed, and area under the receiver operating characteristic curve (AUC) and decision curve analysis were used to evaluate the usefulness of the models. RESULTS: A total of 136 patients was included in the final analysis, of which 35 (34%) experienced early recurrence. Using Elastic Net, we found that 7 of 14 preoperative factors were useful for the predictive model. The mean AUC of all models constructed in the repeated validation was superior to the standard marker CA 19-9 (0.718 vs 0.657), whereas the AUC of the model constructed from the entire patient cohort was 0.767. Decision curve analysis showed that the models had a higher mean net benefit across the majority of the range of reasonable threshold probabilities. CONCLUSION: A model using multiple preoperative factors can improve prediction of early resectable PDAC recurrence.

Dataset of tugHall simulations of cell evolution for colorectal cancer.

Dataset contains results of multiple parallel calculations using the tugHall simulator. Output data of simulations are variant allele frequencies for four genes (APC, KRAS, TP53, and PIK3CA) related to colorectal cancer. During each simulation tugHall stochastically reproduces Darwinian evolution for cancer cells and calculates clonal heterogeneity. The probabilities of stochastic processes depend on a correspondence matrix between genome information and cancer hallmarks. As a result, tugHall records variant allele frequencies for the final stage of evolution. The number of trials is several million to get rich statistics of stochastic processes. These data can be used for approximate Bayesian computation and other statistical methods to get personalized coefficients for patients with colorectal cancer. The procedure of usage data is explained in our paper [Bioinformatics, 36, 11 (2020) 3597] in which the part of these data was used.

Semi-parametric empirical Bayes factor for genome-wide association studies.

Bayes factor analysis has the attractive property of accommodating the risks of both false negatives and false positives when identifying susceptibility gene variants in genome-wide association studies (GWASs). For a particular SNP, the critical aspect of this analysis is that it incorporates the probability of obtaining the observed value of a statistic on disease association under the alternative hypotheses of non-null association. An approximate Bayes factor (ABF) was proposed by Wakefield (Genetic Epidemiology 2009;33:79-86) based on a normal prior for the underlying effect-size distribution. However, misspecification of the prior can lead to failure in incorporating the probability under the alternative hypothesis. In this paper, we propose a semi-parametric, empirical Bayes factor (SP-EBF) based on a nonparametric effect-size distribution estimated from the data. Analysis of several GWAS datasets revealed the presence of substantial numbers of SNPs with small effect sizes, and the SP-EBF attributed much greater significance to such SNPs than the ABF. Overall, the SP-EBF incorporates an effect-size distribution that is estimated from the data, and it has the potential to improve the accuracy of Bayes factor analysis in GWASs.

A hypomorphic variant in EYS detected by genome-wide association study contributes toward retinitis pigmentosa.

The genetic basis of Japanese autosomal recessive retinitis pigmentosa (ARRP) remains largely unknown. Herein, we applied a 2-step genome-wide association study (GWAS) in 640 Japanese patients. Meta-GWAS identified three independent peaks at P < 5.0 × 10-8, all within the major ARRP gene EYS. Two of the three were each in linkage disequilibrium with a different low frequency variant (allele frequency < 0.05); a known founder Mendelian mutation (c.4957dupA, p.S1653Kfs*2) and a non-synonymous variant (c.2528 G > A, p.G843E) of unknown significance. mRNA harboring c.2528 G > A failed to restore rhodopsin mislocalization induced by morpholino-mediated knockdown of eys in zebrafish, consistent with the variant being pathogenic. c.2528 G > A solved an additional 7.0% of Japanese ARRP cases. The third peak was in linkage disequilibrium with a common non-synonymous variant (c.7666 A > T, p.S2556C), possibly representing an unreported disease-susceptibility signal. GWAS successfully unraveled genetic causes of a rare monogenic disorder and identified a high frequency variant potentially linked to development of local genome therapeutics.

Quantification of multicellular colonization in tumor metastasis using exome-sequencing data.

Metastasis is a major cause of cancer-related mortality, and it is essential to understand how metastasis occurs in order to overcome it. One relevant question is the origin of a metastatic tumor cell population. Although the hypothesis of a single-cell origin for metastasis from a primary tumor has long been prevalent, several recent studies using mouse models have supported a multicellular origin of metastasis. Human bulk whole-exome sequencing (WES) studies also have demonstrated a multiple "clonal" origin of metastasis, with different mutational compositions. Specifically, there has not yet been strong research to determine how many founder cells colonize a metastatic tumor. To address this question, under the metastatic model of "single bottleneck followed by rapid growth," we developed a method to quantify the "founder cell population size" in a metastasis using paired WES data from primary and metachronous metastatic tumors. Simulation studies demonstrated the proposed method gives unbiased results with sufficient accuracy in the range of realistic settings. Applying the proposed method to real WES data from four colorectal cancer patients, all samples supported a multicellular origin of metastasis and the founder size was quantified, ranging from 3 to 17 cells. Such a wide-range of founder sizes estimated by the proposed method suggests that there are large variations in genetic similarity between primary and metastatic tumors in the same subjects, which may explain the observed (dis)similarity of drug responses between tumors.

Two-stage analysis for selecting fixed numbers of features in omics association studies.

One of main roles of omics-based association studies with high-throughput technologies is to screen out relevant molecular features, such as genetic variants, genes, and proteins, from a large pool of such candidate features based on their associations with the phenotype of interest. Typically, screened features are subject to validation studies using more established or conventional assays, where the number of evaluable features is relatively limited, so that there may exist a fixed number of features measurable by these assays. Such a limitation necessitates narrowing a feature set down to a fixed size, following an initial screening analysis via multiple testing where adjustment for multiplicity is made. We propose a two-stage screening approach to control the false discovery rate (FDR) for a feature set with fixed size that is subject to validation studies, rather than for a feature set from the initial screening analysis. Out of the feature set selected in the first stage with a relaxed FDR level, a fraction of features with most statistical significance is firstly selected. For the remaining feature set, features are selected based on biological consideration only, without regard to any statistical information, which allows evaluating the FDR level for the finally selected feature set with fixed size. Improvement of the power is discussed in the proposed two-stage screening approach. Simulation experiments based on parametric models and real microarray datasets demonstrated substantial increment in the number of screened features for biological consideration compared with the standard screening approach, allowing for more extensive and in-depth biological investigations in omics association studies.

How many patients are required to provide a high level of reliability in the Japanese version of the CARE Measure? A secondary analysis.

BACKGROUND: Empathy is widely regarded as being key to effective consultation in general practice. The Consultation and Relational Empathy (CARE) Measure is a widely used and well-validated patient-rated measure in English. A Japanese version of the CARE Measure has undergone preliminary validation, but its ability to differentiate between individual doctors has not been established. The current study sought to investigate the reliability of the Japanese version of the CARE Measure in terms of discrimination between doctors. METHODS: We conducted secondary analysis of a dataset involving 252 patients assessed by nine attending General Practitioners. The intra-cluster correlation coefficient was evaluated as an index of the reliability of the Japanese version of the CARE Measure for discriminating between doctors. With a criterion of intra-cluster correlation coefficient = 0.8, we conducted a decision (D) study using generalizability theory to determine the required number of patients for reliable CARE Measure estimates. RESULTS: The ability of the CARE Measure to discriminate between doctors increased with the number of patients assessed per doctor. A sample size of 38 or more patients provided an average intra-cluster correlation coefficient of 0.8. CONCLUSIONS: The Japanese CARE Measure appears to reliably discriminate between doctors with a feasible number of patient-ratings per doctor. Further studies involving larger numbers of doctors with a multicenter analysis are required to confirm the results of the current study, which was conducted at a single institution.

Sample Size for Successful Genome-Wide Association Study of Major Depressive Disorder.

Major depressive disorder (MDD) is a complex, heritable psychiatric disorder. Advanced statistical genetics for genome-wide association studies (GWASs) have suggested that the heritability of MDD is largely explained by common single nucleotide polymorphisms (SNPs). However, until recently, there has been little success in identifying MDD-associated SNPs. Here, based on an empirical Bayes estimation of a semi-parametric hierarchical mixture model using summary statistics from GWASs, we show that MDD has a distinctive polygenic architecture consisting of a relatively small number of risk variants (~17%), e.g., compared to schizophrenia (~42%). In addition, these risk variants were estimated to have very small effects (genotypic odds ratio ≤ 1.04 under the additive model). Based on the estimated architecture, the required sample size for detecting significant SNPs in a future GWAS was predicted to be exceptionally large. It is noteworthy that the number of genome-wide significant MDD-associated SNPs would rapidly increase when collecting 50,000 or more MDD-cases (and the same number of controls); it can reach as much as 100 SNPs out of nearly independent (linkage disequilibrium pruned) 100,000 SNPs for ~120,000 MDD-cases.

Empirical Bayes Estimation of Semi-parametric Hierarchical Mixture Models for Unbiased Characterization of Polygenic Disease Architectures.

Genome-wide association studies (GWAS) suggest that the genetic architecture of complex diseases consists of unexpectedly numerous variants with small effect sizes. However, the polygenic architectures of many diseases have not been well characterized due to lack of simple and fast methods for unbiased estimation of the underlying proportion of disease-associated variants and their effect-size distribution. Applying empirical Bayes estimation of semi-parametric hierarchical mixture models to GWAS summary statistics, we confirmed that schizophrenia was extremely polygenic [~40% of independent genome-wide SNPs are risk variants, most within odds ratio (OR = 1.03)], whereas rheumatoid arthritis was less polygenic (~4 to 8% risk variants, significant portion reaching OR = 1.05 to 1.1). For rheumatoid arthritis, stratified estimations revealed that expression quantitative loci in blood explained large genetic variance, and low- and high-frequency derived alleles were prone to be risk and protective, respectively, suggesting a predominance of deleterious-risk and advantageous-protective mutations. Despite genetic correlation, effect-size distributions for schizophrenia and bipolar disorder differed across allele frequency. These analyses distinguished disease polygenic architectures and provided clues for etiological differences in complex diseases.

Re-assessment of multiple testing strategies for more efficient genome-wide association studies.

Although enormous costs have been dedicated to discovering relevant disease-related genetic variants, especially in genome-wide association studies (GWASs), only a small fraction of estimated heritability can be explained by these results. This is the so-called missing heritability problem. The conventional use of overly conservative multiple testing strategies based on controlling the familywise error rate (FWER), in particular with a genome-wide significance threshold of P <5 × 10-8, is one of the most important issues from a statistical perspective. To help resolve this problem, we performed comprehensive re-assessments of currently available strategies using recently published, extremely large-scale GWAS data sets of rheumatoid arthritis and schizophrenia (>50,000 subjects). The estimates of statistical power averaged for all disease-related genetic variants of the standard FWER-based strategy were only 0.09% for the rheumatoid arthritis data and 0.04% for the schizophrenia data. To design more efficient strategies, we also conducted an extensive comparison of multiple testing strategies by applying false discovery rate (FDR)-controlling procedures to these data sets and simulations, and found that the FDR-based procedures achieved higher power than the FWER-based strategy, even at a strict FDR level (e.g., FDR = 1%). We also discuss a useful alternative measure, namely "partial power," which is an averaged power for detecting the clinically and biologically meaningful genetic factors with the largest effects. Simulation results suggest that the FDR-based procedures can achieve sufficient partial power (>80%) for detecting these factors (odds ratios of >1.05) with 80,000 subjects, and thus this may be a useful measure for defining realistic objectives of future GWASs.

Selective breeding and selection mapping using a novel wild-derived heterogeneous stock of mice revealed two closely-linked loci for tameness.

Tameness is a major behavioral factor for domestication, and can be divided into two potential components: motivation to approach humans (active tameness) and reluctance to avoid humans (passive tameness). We identified genetic loci for active tameness through selective breeding, selection mapping, and association analysis. In previous work using laboratory and wild mouse strains, we found that laboratory strains were predominantly selected for passive tameness but not active tameness during their domestication. To identify genetic regions associated with active tameness, we applied selective breeding over 9 generations for contacting, a behavioural parameter strongly associated with active tameness. The prerequisite for successful selective breeding is high genetic variation in the target population, so we established and used a novel resource, wild-derived heterogeneous stock (WHS) mice from eight wild strains. The mice had genetic variations not present in other outbred mouse populations. Selective breeding of the WHS mice increased the contacting level through the generations. Selection mapping was applied to the selected population using a simulation based on a non-selection model and inferred haplotype data derived from single-nucleotide polymorphisms. We found a genomic signature for selection on chromosome 11 containing two closely linked loci.

An efficient and flexible test for rare variant effects.

Since it has been claimed that rare variants with extremely small allele frequency play a crucial role in complex traits, there is great demand for the development of a powerful test for detecting these variants. However, due to the extremely low frequencies of rare variants, common statistical testing methods do not work well, which has motivated recent extensive research on developing an efficient testing procedure for rare variant effects. Many studies have suggested effective testing procedures with reasonably high power under some presumed assumptions of parametric statistical models. However, if the parametric assumptions are violated, these tests are possibly under-powered. In this paper, we develop an optimal, powerful statistical test called the aggregated conditional score test (ACST) for simultaneously testing M rare variant effects without restrictive parametric assumptions. The proposed test uses a test statistic aggregating the conditional score statistics of effect sizes of M rare variants. In simulation studies, ACST generally performed well compared with the two most commonly used tests, the optimal sequence kernel association test (SKAT-O) and Kullback-Leibler distance test. Finally, we demonstrate the performance and practical utility of ACST using the Dallas Heart Study data.

Germline mutation rates and the long-term phenotypic effects of mutation accumulation in wild-type laboratory mice and mutator mice.

The germline mutation rate is an important parameter that affects the amount of genetic variation and the rate of evolution. However, neither the rate of germline mutations in laboratory mice nor the biological significance of the mutation rate in mammalian populations is clear. Here we studied genome-wide mutation rates and the long-term effects of mutation accumulation on phenotype in more than 20 generations of wild-type C57BL/6 mice and mutator mice, which have high DNA replication error rates. We estimated the base-substitution mutation rate to be 5.4 × 10(-9) (95% confidence interval = 4.6 × 10(-9)-6.5 × 10(-9)) per nucleotide per generation in C57BL/6 laboratory mice, about half the rate reported in humans. The mutation rate in mutator mice was 17 times that in wild-type mice. Abnormal phenotypes were 4.1-fold more frequent in the mutator lines than in the wild-type lines. After several generations, the mutator mice reproduced at substantially lower rates than the controls, exhibiting low pregnancy rates, lower survival rates, and smaller litter sizes, and many of the breeding lines died out. These results provide fundamental information about mouse genetics and reveal the impact of germline mutation rates on phenotypes in a mammalian population.

Congenital Achromatopsia and Macular Atrophy Caused by a Novel Recessive PDE6C Mutation (p.E591K).

PURPOSE: We have previously reported clinical features of two siblings, a sister with complete achromatopsia (ACHM) and a brother with incomplete ACHM, in a consanguineous Japanese family. With the current study, we intended to identify a disease-causing mutation in the siblings and to investigate why the phenotypes of the siblings differed. METHODS: We performed a comprehensive ophthalmic examination for each sibling and parent. Whole-exome and Sanger sequencing were performed on genomic DNA. Molecular modeling was analyzed in an in silico study. RESULTS: The ophthalmic examination revealed severe macular atrophy in the older female sibling at 30 years of age and mild macular atrophy in the brother at 26 years of age. The genetic analysis identified a novel homozygous PDE6C mutation (p.E591K) as the disease-causing allele in the siblings. Each parent was heterozygous for the mutation. Molecular modeling showed that the mutation could cause a conformational change in the PDE6C protein and result in reduced phosphodiesterase activity. We also identified an OPN1SW mutation (p.G79R), which is associated with congenital tritan deficiencies, in the sister and the father but not in the brother. CONCLUSIONS: A novel homozygous PDE6C mutation was identified as the cause of ACHM. In addition, we identified an OPN1SW mutation in the sibling with complete ACHM, which might explain the difference in phenotype (complete versus incomplete ACHM) between the siblings.

RHO Mutations (p.W126L and p.A346P) in Two Japanese Families with Autosomal Dominant Retinitis Pigmentosa.

Purpose. To investigate genetic and clinical features of patients with rhodopsin (RHO) mutations in two Japanese families with autosomal dominant retinitis pigmentosa (adRP). Methods. Whole-exome sequence analysis was performed in ten adRP families. Identified RHO mutations for the cosegregation analysis were confirmed by Sanger sequencing. Ophthalmic examinations were performed to evaluate the RP phenotypes. The impact of the RHO mutation on the rhodopsin conformation was examined by molecular modeling analysis. Results. In two adRP families, we identified two RHO mutations (c.377G>T (p.W126L) and c.1036G>C (p.A346P)), one of which was novel. Complete cosegregation was confirmed for each mutation exhibiting the RP phenotype in both families. Molecular modeling predicted that the novel mutation (p.W126L) might impair rhodopsin function by affecting its conformational transition in the light-adapted form. Clinical phenotypes showed that patients with p.W126L exhibited sector RP, whereas patients with p.A346P exhibited classic RP. Conclusions. Our findings demonstrated that the novel mutation (p.W126L) may be associated with the phenotype of sector RP. Identification of RHO mutations is a very useful tool for predicting disease severity and providing precise genetic counseling.

The interaction of a single-nucleotide polymorphism with age on response to interferon-α and ribavirin therapy in female patients with hepatitis C infection.

Older female patients exhibit a poor response to the current standard treatment for hepatitis C, interferon-α, and ribavirin (PEG-IFN-α/RBV). In this study, we reported that the combination of age and the genotype of a novel SNP can predict response to standard treatment (P = 7.31 × 10(-8)). The model incorporating genotype of the novel SNP, rs1287948, predicts response more accurately (AUC = 0.934; 95% CI = 0.881-0.988) in women as compared with the model using age and the previously identified SNP, rs8099917.

Whole-exome sequencing identifies a novel ALMS1 mutation (p.Q2051X) in two Japanese brothers with Alström syndrome.

PURPOSE: No mutations associated with Alström syndrome (AS), a rare autosomal recessive disease, have been reported in the Japanese population. The purpose of this study was to investigate the genetic and clinical features of two brothers with AS in a consanguineous Japanese family. METHODS: Whole-exome sequencing analysis was performed on two brothers with AS and their unaffected parents. We performed a complete ophthalmic examination, including decimal best-corrected visual acuity, slit-lamp and funduscopic examination, visual-field and color-vision testing, full-field electroretinography, and optical coherence tomography. Fasting blood tests and systemic examinations were also performed. RESULTS: A novel mutation (c.6151C>T in exon 8) in the Alström syndrome 1 (ALMS1) gene that causes a premature termination codon at amino acid 2051 (p.Q2051X), was identified in the homozygous state in the affected brothers and in the heterozygous state in the parents. The ophthalmologic findings for both brothers revealed infantile-onset severe retinal degeneration and visual impairment, marked macular thinning, and severe cataracts. Systemic findings showed hepatic dysfunction, hyperlipidemia, hypogonadism, short stature, and wide feet in both brothers, whereas hearing loss, renal failure, abnormal digits, history of developmental delay, scoliosis, hypertension, and alopecia were not observed in either brother. The older brother exhibited type 2 diabetic mellitus and obesity, whereas the younger brother had hyperinsulinemia and subclinical hypothyroidism. CONCLUSIONS: A novel ALMS1 mutation was identified by using whole-exome sequencing analysis, which is useful not only to identify a disease causing mutation but also to exclude other gene mutations. Although characteristic ophthalmologic findings and most systemic findings were similar between the brothers, the brothers differed slightly in terms of glucose tolerance and thyroid function.

Detecting selection using time-series data of allele frequencies with multiple independent reference Loci.

Recently, in 2013 Feder et al. proposed the frequency increment test (FIT), which evaluates natural selection at a single diallelic locus by the use of time-series data of allele frequencies. This test is unbiased under conditions of constant population size and no sampling noise. Here, we expand upon the FIT by introducing a test that explicitly allows for changes in population size by using information from independent reference loci. Various demographic models suggest that our proposed test is unbiased irrespective of fluctuations in population size when sampling noise can be ignored and that it has greater power to detect selection than the FIT if sufficient reference loci are used.