The pan-ErbB negative regulator Lrig1 is an intestinal stem cell marker that functions as a tumor suppressor.

Lineage mapping has identified both proliferative and quiescent intestinal stem cells, but the molecular circuitry controlling stem cell quiescence is incompletely understood. By lineage mapping, we show Lrig1, a pan-ErbB inhibitor, marks predominately noncycling, long-lived stem cells that are located at the crypt base and that, upon injury, proliferate and divide to replenish damaged crypts. Transcriptome profiling of Lrig1(+) colonic stem cells differs markedly from the profiling of highly proliferative, Lgr5(+) colonic stem cells; genes upregulated in the Lrig1(+) population include those involved in cell cycle repression and response to oxidative damage. Loss of Apc in Lrig1(+) cells leads to intestinal adenomas, and genetic ablation of Lrig1 results in heightened ErbB1-3 expression and duodenal adenomas. These results shed light on the relationship between proliferative and quiescent intestinal stem cells and support a model in which intestinal stem cell quiescence is maintained by calibrated ErbB signaling with loss of a negative regulator predisposing to neoplasia.

Scalable and model-free detection of spatial patterns and colocalization.

The expeditious growth in spatial omics technologies enables the profiling of genome-wide molecular events at molecular and single-cell resolution, highlighting a need for fast and reliable methods to characterize spatial patterns. We developed SpaGene, a model-free method to discover spatial patterns rapidly in large-scale spatial omics studies. Analyzing simulation and a variety of spatially resolved transcriptomics data showed that SpaGene is more powerful and scalable than existing methods. Spatial expression patterns identified by SpaGene reconstruct unobserved tissue structures. SpaGene also successfully discovers ligand-receptor interactions through their colocalization.

scKWARN: Kernel-weighted-average robust normalization for single-cell RNA-seq data.

MOTIVATION: Single-cell RNA-seq normalization is an essential step to correct unwanted biases caused by sequencing depth, capture efficiency, dropout, and other technical factors. Existing normalization methods primarily reduce biases arising from sequencing depth by modeling count-depth relationship and/or assuming a specific distribution for read counts. However, these methods may lead to over or under-correction due to presence of technical biases beyond sequencing depth and the restrictive assumption on models and distributions. RESULTS: We present scKWARN, a Kernel Weighted Average Robust Normalization designed to correct known or hidden technical confounders without assuming specific data distributions or count-depth relationships. scKWARN generates a pseudo expression profile for each cell by borrowing information from its fuzzy technical neighbors through a kernel smoother. It then compares this profile against the reference derived from cells with the same bimodality patterns to determine the normalization factor. As demonstrated in both simulated and real datasets, scKWARN outperforms existing methods in removing a variety of technical biases while preserving true biological heterogeneity. AVAILABILITY AND IMPLEMENTATION: scKWARN is freely available at https://github.com/cyhsuTN/scKWARN.

FindAdapt: A python package for fast and accurate adapter detection in small RNA sequencing.

Adapter trimming is an essential step for analyzing small RNA sequencing data, where reads are generally longer than target RNAs ranging from 18 to 30 bp. Most adapter trimming tools require adapter information as input. However, adapter information is hard to access, specified incorrectly, or not provided with publicly available datasets, hampering their reproducibility and reusability. Manual identification of adapter patterns from raw reads is labor-intensive and error-prone. Moreover, the use of randomized adapters to reduce ligation biases during library preparation makes adapter detection even more challenging. Here, we present FindAdapt, a Python package for fast and accurate detection of adapter patterns without relying on prior information. We demonstrated that FindAdapt was far superior to existing approaches. It identified adapters successfully in 180 simulation datasets with diverse read structures and 3,184 real datasets covering a variety of commercial and customized small RNA library preparation kits. FindAdapt is stand-alone software that can be easily integrated into small RNA sequencing analysis pipelines.

NCI10066: a Phase 1/2 study of olaparib in combination with ramucirumab in previously treated metastatic gastric and gastroesophageal junction adenocarcinoma.

BACKGROUND: Our preclinical work revealed tumour hypoxia induces homologous recombination deficiency (HRD), increasing sensitivity to Poly (ADP-ribose) polymerase inhibitors. We aimed to induce tumour hypoxia with ramucirumab thereby sensitising tumours to olaparib. PATIENTS AND METHODS: This multi-institution single-arm Phase 1/2 trial enrolled patients with metastatic gastroesophageal adenocarcinoma refractory to ≥1 systemic treatment. In dose escalation, olaparib was evaluated at escalating dose levels with ramucirumab 8 mg/kg day 1 in 14-day cycles. The primary endpoint of Phase 1 was the recommended Phase 2 dose (RP2D), and in Phase 2 the primary endpoint was the overall response rate (ORR). RESULTS: Fifty-one patients received ramucirumab and olaparib. The RP2D was olaparib 300 mg twice daily with ramucirumab 8 mg/kg. In evaluable patients at the RP2D the ORR was 6/43 (14%) (95% CI 4.7-25.6). The median progression-free survival (PFS) was 2.8 months (95% CI 2.3-4.2) and median overall survival (OS) was 7.3 months (95% CI 5.7-13.0). Non-statistically significant improvements in PFS and OS were observed for patients with tumours with mutations in HRD genes. CONCLUSIONS: Olaparib and ramucirumab is well-tolerated with efficacy that exceeds historical controls with ramucirumab single agent for gastric cancer in a heavily pre-treated patient population.

Comprehensive evaluation of noise reduction methods for single-cell RNA sequencing data.

Normalization and batch correction are critical steps in processing single-cell RNA sequencing (scRNA-seq) data, which remove technical effects and systematic biases to unmask biological signals of interest. Although a number of computational methods have been developed, there is no guidance for choosing appropriate procedures in different scenarios. In this study, we assessed the performance of 28 scRNA-seq noise reduction procedures in 55 scenarios using simulated and real datasets. The scenarios accounted for multiple biological and technical factors that greatly affect the denoising performance, including relative magnitude of batch effects, the extent of cell population imbalance, the complexity of cell group structures, the proportion and the similarity of nonoverlapping cell populations, dropout rates and variable library sizes. We used multiple quantitative metrics and visualization of low-dimensional cell embeddings to evaluate the performance on batch mixing while preserving the original cell group and gene structures. Based on our results, we specified technical or biological factors affecting the performance of each method and recommended proper methods in different scenarios. In addition, we highlighted one challenging scenario where most methods failed and resulted in overcorrection. Our studies not only provided a comprehensive guideline for selecting suitable noise reduction procedures but also pointed out unsolved issues in the field, especially the urgent need of developing metrics for assessing batch correction on imperceptible cell-type mixing.

A Single Nucleotide Polymorphism in SH2B3/LNK Promotes Hypertension Development and Renal Damage.

BACKGROUND: SH2B3 (SH2B adaptor protein 3) is an adaptor protein that negatively regulates cytokine signaling and cell proliferation. A common missense single nucleotide polymorphism in SH2B3 (rs3184504) results in substitution of tryptophan (Trp) for arginine (Arg) at amino acid 262 and is a top association signal for hypertension in human genome-wide association studies. Whether this variant is causal for hypertension, and if so, the mechanism by which it impacts pathogenesis is unknown. METHODS: We used CRISPR-Cas9 technology to create mice homozygous for the major (Arg/Arg) and minor (Trp/Trp) alleles of this SH2B3 polymorphism. Mice underwent angiotensin II (Ang II) infusion to evaluate differences in blood pressure (BP) elevation and end-organ damage including albuminuria and renal fibrosis. Cytokine production and Stat4 phosphorylation was also assessed in Arg/Arg and Trp/Trp T cells. RESULTS: Trp/Trp mice exhibit 10 mmHg higher systolic BP during chronic Ang II infusion compared to Arg/Arg controls. Renal injury and perivascular fibrosis are exacerbated in Trp/Trp mice compared to Arg/Arg controls following Ang II infusion. Renal and ex vivo stimulated splenic CD8+ T cells from Ang II-infused Trp/Trp mice produce significantly more interferon gamma (IFNg) compared to Arg/Arg controls. Interleukin-12 (IL-12)-induced IFNg production is greater in Trp/Trp compared to Arg/Arg CD8+ T cells. In addition, IL-12 enhances Stat4 phosphorylation to a greater degree in Trp/Trp compared to Arg/Arg CD8+ T cells, suggesting that Trp-encoding SH2B3 exhibits less negative regulation of IL-12 signaling to promote IFNg production. Finally, we demonstrated that a multi-SNP model genetically predicting increased SH2B3 expression in lymphocytes is inversely associated with hypertension and hypertensive chronic kidney disease in humans.. CONCLUSIONS: Taken together, these results suggest that the Trp encoding allele of rs3184504 is causal for BP elevation and renal dysfunction, in part through loss of SH2B3-mediated repression of T cell IL-12 signaling leading to enhanced IFNg production.

The Genetic Landscape of Familial Pulmonary Fibrosis.

Rationale and Objectives: Up to 20% of idiopathic interstitial lung disease is familial, referred to as familial pulmonary fibrosis (FPF). An integrated analysis of FPF genetic risk was performed by comprehensively evaluating for genetic rare variants (RVs) in a large cohort of FPF kindreds. Methods: Whole-exome sequencing and/or candidate gene sequencing from affected individuals in 569 FPF kindreds was performed, followed by cosegregation analysis in large kindreds, gene burden analysis, gene-based risk scoring, cell-type enrichment analysis, and coexpression network construction. Measurements and Main Results: It was found that 14.9-23.4% of genetic risk in kindreds could be explained by RVs in genes previously linked to FPF, predominantly telomere-related genes. New candidate genes were identified in a small number of families-including SYDE1, SERPINB8, GPR87, and NETO1-and tools were developed for evaluation and prioritization of RV-containing genes across kindreds. Several pathways were enriched for RV-containing genes in FPF, including focal adhesion and mitochondrial complex I assembly. By combining single-cell transcriptomics with prioritized candidate genes, expression of RV-containing genes was discovered to be enriched in smooth muscle cells, type II alveolar epithelial cells, and endothelial cells. Conclusions: In the most comprehensive FPF genetic study to date, the prevalence of RVs in known FPF-related genes was defined, and new candidate genes and pathways relevant to FPF were identified. However, new RV-containing genes shared across multiple kindreds were not identified, thereby suggesting that heterogeneous genetic variants involving a variety of genes and pathways mediate genetic risk in most FPF kindreds.

scMRMA: single cell multiresolution marker-based annotation.

Single-cell RNA sequencing has become a powerful tool for identifying and characterizing cellular heterogeneity. One essential step to understanding cellular heterogeneity is determining cell identities. The widely used strategy predicts identities by projecting cells or cell clusters unidirectionally against a reference to find the best match. Here, we develop a bidirectional method, scMRMA, where a hierarchical reference guides iterative clustering and deep annotation with enhanced resolutions. Taking full advantage of the reference, scMRMA greatly improves the annotation accuracy. scMRMA achieved better performance than existing methods in four benchmark datasets and successfully revealed the expansion of CD8 T cell populations in squamous cell carcinoma after anti-PD-1 treatment.

Using the HER2/CEP17 FISH Ratio to Predict Pathologic Complete Response Following Neoadjuvant Anti-HER2 Doublet Therapy in HER2+ Breast Cancer.

BACKGROUND: Clinical trials of HER2-directed therapy that omit neoadjuvant conventional chemotherapy for HER+ breast cancer demonstrate that a subset of patients still obtains a pCR. Identifying tumor characteristics which predict pCR may help select patients for de-escalated neoadjuvant dual HER2-targeted treatment without chemotherapy. This is the first study evaluating the HER2/CEP17 ratio by FISH as a biomarker to predict pCR among patients who received neoadjuvant anti-HER2 regimens without chemotherapy. PATIENTS AND METHODS: Data from patients with locally advanced HER2+ breast cancer who received neoadjuvant dual HER2-targeted therapy without conventional chemotherapy from a single center was retrospectively reviewed. All patients were enrolled in one of 3 clinical trials evaluating chemotherapy de-escalation. Logistic regression modeling assessed for a relationship between the HER2/CEP17 FISH ratio obtained from baseline tissue biopsy and pCR based on pathology at the time of definitive breast surgery following neoadjuvant treatment. RESULTS: Following neoadjuvant treatment with dual HER2-targeted therapies in 56 patients, the probability of pCR was 73% among patients with a HER2 ratio of 13.1 compared to a probability of 38% among patients with HER2 ratio of 5.5 (OR 4.14, 95% CI 1.44-11.89; P = .012). This positive association persisted after controlling for different treatment regimens administered (OR 2.87, 95% CI 0.9-9.18, P = .020). CONCLUSIONS: These data found a positive association between the HER2/CEP17 FISH ratio and pCR following neoadjuvant dual HER2-targeted therapy without chemotherapy. Larger prospective studies are needed to validate the HER2 ratio as a biomarker to select patients for neoadjuvant dual anti-HER2 therapy without chemotherapy.

Dysregulated ligand-receptor interactions from single-cell transcriptomics.

MOTIVATION: Intracellular communication is crucial to many biological processes, such as differentiation, development, homeostasis and inflammation. Single-cell transcriptomics provides an unprecedented opportunity for studying cell-cell communications mediated by ligand-receptor interactions. Although computational methods have been developed to infer cell type-specific ligand-receptor interactions from one single-cell transcriptomics profile, there is lack of approaches considering ligand and receptor simultaneously to identifying dysregulated interactions across conditions from multiple single-cell profiles. RESULTS: We developed scLR, a statistical method for examining dysregulated ligand-receptor interactions between two conditions. scLR models the distribution of the product of ligands and receptors expressions and accounts for inter-sample variances and small sample sizes. scLR achieved high sensitivity and specificity in simulation studies. scLR revealed important cytokine signaling between macrophages and proliferating T cells during severe acute COVID-19 infection, and activated TGF-ß signaling from alveolar type II cells in the pathogenesis of pulmonary fibrosis. AVAILABILITY AND IMPLEMENTATION: scLR is freely available at https://github.com/cyhsuTN/scLR. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Identification and Characterization of Avoidable Hospital Admissions in Patients With Lung Cancer.

BACKGROUND: More than 50% of patients with lung cancer are admitted to the hospital while receiving treatment, which is a burden to patients and the healthcare system. This study characterizes the risk factors and outcomes of patients with lung cancer who were admitted to the hospital. METHODS: A multidisciplinary oncology care team conducted a retrospective medical record review of patients with lung cancer admitted in 2018. Demographics, disease and admission characteristics, and end-of-life care utilization were recorded. Following a multidisciplinary consensus review process, admissions were determined to be either "avoidable" or "unavoidable." Generalized estimating equation logistic regression models assessed risks and outcomes associated with avoidable admissions. RESULTS: In all, 319 admissions for 188 patients with a median age of 66 years (IQR, 59-74 years) were included. Cancer-related symptoms accounted for 65% of hospitalizations. Common causes of unavoidable hospitalizations were unexpected disease progression causing symptoms, chronic obstructive pulmonary disease exacerbation, and infection. Of the 47 hospitalizations identified as avoidable (15%), the median overall survival was 1.6 months compared with 9.7 months (hazard ratio, 2.07; 95% CI, 1.34-3.19; P<.001) for unavoidable hospitalizations. Significant reasons for avoidable admissions included cancer-related pain (P=.02), hypervolemia (P=.03), patient desire to initiate hospice services (P=.01), and errors in medication reconciliation or distribution (P<.001). Errors in medication management caused 26% of the avoidable hospitalizations. Of admissions in patients receiving immunotherapy (n=102) or targeted therapy (n=44), 9% were due to adverse effects of treatment. Patients receiving immunotherapy and targeted therapy were at similar risk of avoidable hospitalizations compared with patients not receiving treatment (P=.3 and P=.1, respectively). CONCLUSIONS: We found that 15% of hospitalizations among patients with lung cancer were potentially avoidable. Uncontrolled symptoms, delayed implementation of end-of-life care, and errors in medication reconciliation were associated with avoidable inpatient admissions. Symptom management tools, palliative care integration, and medication reconciliations may mitigate hospitalization risk.

Power and sample sizes estimation in clinical trials with treatment switching in intention-to-treat analysis: a simulation study.

BACKGROUND: Treatment switching, also called crossover, is common in clinical trials because of ethical concerns or other reasons. When it occurs and the primary objective is to identify treatment effects, the most widely used intention-to-treat analysis may lead to underpowered trials. Here, we presented an approach to preview power reductions and to estimate sample sizes required to achieve the desired power when treatment switching occurs in the intention-to-treat analysis. METHODS: We proposed a simulation-based approach and developed an R package to perform power and sample sizes estimation in clinical trials with treatment switching. RESULTS: We simulated a number of randomized trials incorporating treatment switching and investigated the impact of the relative effectiveness of the experimental treatment to the control, the switching probability, the switching time, and the deviation between the assumed and the real distributions for the survival time on power reductions and sample sizes estimation. The switching probability and the switching time are key determinants for significant power decreasing and thus sample sizes surging to maintain the desired power. The sample sizes required in randomized trials absence of treatment switching vary from around four-fifths to one-seventh of the sample sizes required in randomized trials allowing treatment switching as the switching probability increases. The power reductions and sample sizes increase with the decrease of switching time. CONCLUSIONS: The simulation-based approach not only provides a preview for power declining but also calculates the required sample size to achieve an expected power in the intention-to-treat analysis when treatment switching occurs. It will provide researchers and clinicians with useful information before randomized controlled trials are conducted.

Endogenous sex hormones, aromatase activity and lung cancer risk in postmenopausal never-smoking women.

Although reproductive factors have been repeatedly associated with lung cancer risk, no study to date has directly evaluated the relationship with endogenous sex hormones nor with aromatase activity in postmenopausal never-smoking women. A case-control study of 397 incident lung cancer cases and their individually matched controls, nested within the Shanghai Women's Health Study, was conducted among postmenopausal women who were lifetime never smokers. Prediagnostic concentrations of sex hormones was quantitated using LC-MS/MS assays in plasma. The product-substrate molar ratio of estrone to androstenedione was used as an index of aromatase activity (IAA). Multivariable conditional logistic regression models were used to calculate odds ratios (ORs) for lung cancer. Baseline concentrations of estradiol, free testosterone and IAA were inversely associated with subsequent risk of lung cancer in multivariable-adjusted models. When further adjusted for body mass index, the inverse association with estradiol was attenuated and no longer statistically significant, but the association with free testosterone and IAA remained. In analyses confined to participants having never used menopausal hormone therapy in 376 case-control pairs, the inverse association with free testosterone and IAA was slightly strengthened. OR for the highest vs the lowest quartile of free testosterone was 0.55 (95% CI = 0.34-0.90; Ptrend  = .03), and the corresponding OR for IAA was 0.57 (95% CI = 0.34-0.96; Ptrend  = .04). Our study, for the first time, suggests that higher levels of circulating free testosterone and estimated aromatase activity may be associated with lower lung cancer risk in postmenopausal never-smoking women.

COVID-19 vaccination and breakthrough infections in patients with cancer.

BACKGROUND: Vaccination is an important preventive health measure to protect against symptomatic and severe COVID-19. Impaired immunity secondary to an underlying malignancy or recent receipt of antineoplastic systemic therapies can result in less robust antibody titers following vaccination and possible risk of breakthrough infection. As clinical trials evaluating COVID-19 vaccines largely excluded patients with a history of cancer and those on active immunosuppression (including chemotherapy), limited evidence is available to inform the clinical efficacy of COVID-19 vaccination across the spectrum of patients with cancer. PATIENTS AND METHODS: We describe the clinical features of patients with cancer who developed symptomatic COVID-19 following vaccination and compare weighted outcomes with those of contemporary unvaccinated patients, after adjustment for confounders, using data from the multi-institutional COVID-19 and Cancer Consortium (CCC19). RESULTS: Patients with cancer who develop COVID-19 following vaccination have substantial comorbidities and can present with severe and even lethal infection. Patients harboring hematologic malignancies are over-represented among vaccinated patients with cancer who develop symptomatic COVID-19. CONCLUSIONS: Vaccination against COVID-19 remains an essential strategy in protecting vulnerable populations, including patients with cancer. Patients with cancer who develop breakthrough infection despite full vaccination, however, remain at risk of severe outcomes. A multilayered public health mitigation approach that includes vaccination of close contacts, boosters, social distancing, and mask-wearing should be continued for the foreseeable future.

PheWAS-ME: a web-app for interactive exploration of multimorbidity patterns in PheWAS.

SUMMARY: Electronic health records (EHRs) linked with a DNA biobank provide unprecedented opportunities for biomedical research in precision medicine. The Phenome-wide association study (PheWAS) is a widely used technique for the evaluation of relationships between genetic variants and a large collection of clinical phenotypes recorded in EHRs. PheWAS analyses are typically presented as static tables and charts of summary statistics obtained from statistical tests of association between a genetic variant and individual phenotypes. Comorbidities are common and typically lead to complex, multivariate gene-disease association signals that are challenging to interpret. Discovering and interrogating multimorbidity patterns and their influence in PheWAS is difficult and time-consuming. We present PheWAS-ME: an interactive dashboard to visualize individual-level genotype and phenotype data side-by-side with PheWAS analysis results, allowing researchers to explore multimorbidity patterns and their associations with a genetic variant of interest. We expect this application to enrich PheWAS analyses by illuminating clinical multimorbidity patterns present in the data. AVAILABILITY AND IMPLEMENTATION: A demo PheWAS-ME application is publicly available at https://prod.tbilab.org/phewas_me/. Sample datasets are provided for exploration with the option to upload custom PheWAS results and corresponding individual-level data. Online versions of the appendices are available at https://prod.tbilab.org/phewas_me_info/. The source code is available as an R package on GitHub (https://github.com/tbilab/multimorbidity_explorer). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MetaGSCA: A tool for meta-analysis of gene set differential coexpression.

Analyses of gene set differential coexpression may shed light on molecular mechanisms underlying phenotypes and diseases. However, differential coexpression analyses of conceptually similar individual studies are often inconsistent and underpowered to provide definitive results. Researchers can greatly benefit from an open-source application facilitating the aggregation of evidence of differential coexpression across studies and the estimation of more robust common effects. We developed Meta Gene Set Coexpression Analysis (MetaGSCA), an analytical tool to systematically assess differential coexpression of an a priori defined gene set by aggregating evidence across studies to provide a definitive result. In the kernel, a nonparametric approach that accounts for the gene-gene correlation structure is used to test whether the gene set is differentially coexpressed between two comparative conditions, from which a permutation test p-statistic is computed for each individual study. A meta-analysis is then performed to combine individual study results with one of two options: a random-intercept logistic regression model or the inverse variance method. We demonstrated MetaGSCA in case studies investigating two human diseases and identified pathways highly relevant to each disease across studies. We further applied MetaGSCA in a pan-cancer analysis with hundreds of major cellular pathways in 11 cancer types. The results indicated that a majority of the pathways identified were dysregulated in the pan-cancer scenario, many of which have been previously reported in the cancer literature. Our analysis with randomly generated gene sets showed excellent specificity, indicating that the significant pathways/gene sets identified by MetaGSCA are unlikely false positives. MetaGSCA is a user-friendly tool implemented in both forms of a Web-based application and an R package "MetaGSCA". It enables comprehensive meta-analyses of gene set differential coexpression data, with an optional module of post hoc pathway crosstalk network analysis to identify and visualize pathways having similar coexpression profiles.

Podocyte-Related Mechanisms Underlying Survival Benefit of Long-Term Angiotensin Receptor Blocker.

We previously found that short-term treatment (week 8 to 12 after injury) with high-dose angiotensin receptor blocker (ARB) induced the regression of existing glomerulosclerosis in 5/6 nephrectomy rats. We therefore assessed the effects of long-term intervention with ARB vs. nonspecific antihypertensives in this study. Adult rats underwent 5/6 nephrectomy and renal biopsy 8 weeks later. The rats were then divided into three groups with equivalent renal function and glomerular sclerosis and treated with high-dose losartan (ARB), nonspecific antihypertensive triple-therapy (TRX), or left untreated (Control) until week 30. We found that blood pressure, serum creatinine levels, and glomerulosclerosis were lower at sacrifice in ARB and TRX vs. Control. Only ARB reduced proteinuria and maintained the density of WT-1-positive podocytes. Glomerular tufts showed more double-positive cells for CD44, a marker of activated parietal epithelial cells, and synaptopodin after ARB vs. TRX or Control. ARB treatment reduced aldosterone levels. ARB-treated rats had significantly improved survival when compared with TRX or Control. We conclude that both long-term ARB and triple-therapy ameliorate progression, but do not sustain the regression of glomerulosclerosis. ARB resulted in the superior preservation of podocyte integrity and decreased proteinuria and aldosterone, linked to increased survival in the uremic environment.

A domain damage index to prioritizing the pathogenicity of missense variants.

Prioritizing causal variants is one major challenge for the clinical application of sequencing data. Prompted by the observation that 74.3% of missense pathogenic variants locate in protein domains, we developed an approach named domain damage index (DDI). DDI identifies protein domains depleted of rare missense variations in the general population, which can be further used as a metric to prioritize variants. DDI is significantly correlated with phylogenetic conservation, variant-level metrics, and reported pathogenicity. DDI achieved great performance for distinguishing pathogenic variants from benign ones in three benchmark datasets. The combination of DDI with the other two best approaches improved the performance of each individual method considerably, suggesting DDI provides a powerful and complementary way of variant prioritization.

Prediction models for breast cancer prognosis among Asian women.

BACKGROUND: Robust and reliable prognosis prediction models have not been developed and validated for Asian patients with breast cancer, a rapidly growing yet understudied population in the United States. METHODS: We used longitudinal data from the Shanghai Breast Cancer Survival Study, a population-based prospective cohort study (n = 5042), to develop prediction models for 5- and 10-year disease-free survival (DFS) and overall survival (OS). The initial models considered age at diagnosis, tumor grade, tumor size, number of positive nodes, TNM stage, chemotherapy, tamoxifen therapy, and estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) status. We then evaluated whether the addition of modifiable lifestyle factors (physical activity, soy isoflavones intake, and postdiagnostic weight change) improved the models. All final models have been validated internally and externally in the National Cancer Database when applicable. RESULTS: Our final models included age at diagnosis, tumor grade, tumor size, number of positive nodes, TNM stage, chemotherapy, tamoxifen therapy, ER status, PR status, 6-month postdiagnostic weight change, interaction between ER status and tamoxifen therapy, and interaction between age and TNM stage. The internal validation yielded C-statistics of 0.76, 0.74, 0.78, and 0.75 for 5-year DFS, 10-year DFS, 5-year OS, and 10-year OS, respectively. The external validation yielded C-statistics of 5- and 10-year OS both at 0.78 for Chinese ethnicity, 0.79 for East Asian ethnicity, and 0.75 and 0.76 for all ethnic groups combined. CONCLUSION: We developed prediction models for breast cancer prognosis from a large prospective study. Our prognostic models performed very well in women from the United States-particularly in Asian American women-and demonstrated high prediction accuracy and generalizability.