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Motivation Differential expression analysis in single-cell transcriptomics unveils cell type-specific responses to various treatments or biological conditions. To ensure the robustness and reliability of the analysis, it is essential to have a solid experimental design with ample statistical power and sample size. However, existing methods for power and sample size calculation often assume a specific distribution for single-cell transcriptomics data, potentially deviating from the true data distribution. Moreover, they commonly overlook cell–cell correlations within individual samples, posing challenges in accurately representing biological phenomena. Additionally, due to the complexity of deriving an analytic formula, most methods employ time-consuming simulation-based strategies. Results We propose an analytic-based method named scPS for calculating power and sample sizes based on generalized estimating equations. scPS stands out by making no assumptions about the data distribution and considering cell–cell correlations within individual samples. scPS is a rapid and powerful approach for designing experiments in single-cell differential expression analysis. Availability and implementation scPS is freely available at https://github.com/cyhsuTN/scPS and Zenodo https://zenodo.org/records/13375996. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 K ernel W eighted A verage R obust N ormalization 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Simple Summary: Small RNA sequencing has been widely used for characterizing the landscape of small non-coding RNAs—the most abundant cargo in extracellular vesicles (EVs). Here, we performed a systematic assessment of the quality, technical, and potential biological biases introduced by different EV isolation methods and the enrichment of specific, small RNA biotypes in EVs. The findings in this study guide the quality control of EV small RNA-seq and the selection of EV isolation techniques and enhance the interpretation of small RNA contents and the preferential loading of specific RNA biotypes into EVs. Motivation: Extracellular vesicles (EVs) are produced and released by most cells and are now recognized to play a role in intercellular communication through the delivery of molecular cargo, including proteins, lipids, and RNA. Small RNA sequencing (small RNA-seq) has been widely used to characterize the small RNA content in EVs. However, there is a lack of a systematic assessment of the quality, technical biases, RNA composition, and RNA biotypes enrichment for small RNA profiling of EVs across cell types, biofluids, and conditions. Methods: We collected and reanalyzed small RNA-seq datasets for 2756 samples from 83 studies involving 55 with EVs only and 28 with both EVs and matched donor cells. We assessed their quality by the total number of reads after adapter trimming, the overall alignment rate to the host and non-host genomes, and the proportional abundance of total small RNA and specific biotypes, such as miRNA, tRNA, rRNA, and Y RNA. Results: We found that EV extraction methods varied in their reproducibility in isolating small RNAs, with effects on small RNA composition. Comparing proportional abundances of RNA biotypes between EVs and matched donor cells, we discovered that rRNA and tRNA fragments were relatively enriched, but miRNAs and snoRNA were depleted in EVs. Except for the export of eight miRNAs being context-independent, the selective release of most miRNAs into EVs was study-specific. Conclusion: This work guides quality control and the selection of EV isolation methods and enhances the interpretation of small RNA contents and preferential loading in EVs. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. This novel exploratory study is the first to examine whether pathologic complete response following various neoadjuvant anti-HER2 doublet therapies without conventional chemotherapy is dependent on the level of HER2 amplification. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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-β 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background Since several lines of evidence suggest that estrogens may be involved in lung carcinogenesis, it has been hypothesized that intake of phytoestrogens, similar in molecular structure to mammalian estrogens, may be associated with lung cancer development. Objective The aim was to prospectively evaluate the association between phytoestrogen exposure and lung cancer risk in never-smoking women. Methods We conducted a nested case-control study within a population-based prospective cohort study of women. A total of 478 incident lung cancer cases and their individually matched controls were identified among never-smoking women after a mean follow-up of 15.6 years. Habitual intake of and internal exposure to phytoestrogens were assessed by repeated dietary surveys and urinary biomarkers, respectively. ORs and 95% CIs for lung cancer were estimated in conditional logistic regression models. Results After adjustment for potential confounders, a moderate intake of dietary isoflavones was inversely associated with lung cancer risk in never-smoking women, with the OR for the second quartile vs. the lowest quartile of intake being 0.52 (95% CI: 0.35, 0.76). Further increasing intake did not convey additional benefits, with ORs (95% CI) for the third and fourth quartiles of 0.53 (0.36, 0.78) and 0.47 (0.31, 0.72), respectively (P -overall < 0.001 and P -nonlinearity = 0.006). A similar association was seen when exposure to isoflavones was assessed by urinary biomarkers. ORs (95% CI) for the second, third, and fourth quartiles compared with the lowest quartile of urinary isoflavone excretion were 0.57 (0.39, 0.83), 0.64 (0.44, 0.92), and 0.60 (0.41, 0.86), respectively. The inverse association reached a plateau beyond the second quartile, with P -overall = 0.04 and P -nonlinearity = 0.15. Urinary excretion of gut-microbiota–derived metabolites of lignans was not related to lung cancer risk. Conclusions This study suggests that moderately increasing intake of isoflavone-rich foods is associated with lower risk of lung cancer in never-smoking women. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Lessons Learned: Cediranib and olaparib combination did not result in clinically meaningful activity in patients with metastatic pancreatic ductal adenocarcinoma without known BRCA mutation. Background: Cediranib, a vascular endothelial growth factor receptor inhibitor, suppresses expression of BRCA1/2 and RAD51 inducing homologous recombination DNA repair deficiency (HRD) in several cancer cell lines and xenograft models [1]. Olaparib provides a clinical benefit in patients with metastatic pancreatic adenocarcinoma (mPDAC) with germline BRCA mutation (gBRCAmt) [2]. We hypothesized that cediranib induces HRD in the absence of gBRCAmt and synergizes with olaparib, resulting in an objective response in patients with mPDAC. Methods: Patients with mPDAC with at least one prior systemic chemotherapy were enrolled. Patients with known gBRCAmt were excluded. Patients took cediranib 30 mg daily and olaparib 200 mg twice daily, orally. The primary endpoint was objective response (OR) rate. Results: Nineteen patients received the study drugs. Seven patients came off treatment before the first restaging scan: six because of clinical progression and one because of an adverse event. No OR was observed. Six patients had stable disease (SD) as a best overall response. The median duration of SD was 3.1 months. The median overall survival was 3.4 months. Common treatment‐related adverse events were fatigue, hypertension, and diarrhea. Conclusion: Cediranib and olaparib combination did not result in clinically meaningful activity in patients with mPDAC without gBRCAmt. [ABSTRACT FROM AUTHOR]

Background: In 2013, the American Society of Clinical Oncology (ASCO)'s Continuing Education Committee recommended establishing an interprofessional, longitudinal cohort pilot project. The main goals of the cohort were to gain feedback from oncology providers on how they use resources to address their learning needs and gain insights into the utility of different ASCO educational activities.Methods: The ASCO Learning Cohort Pilot Project included 49 ASCO members that were representative of the overall Society membership demographics and ran from November 2015 through August 2016. Participants documented monthly learning needs and completed monthly feedback activities focused on specific ASCO educational resources.Results: The Learning Cohort Pilot Project proved a viable and innovative cohort model for analyzing the learning process for oncology healthcare professionals. The development, operations, and compliance required unique infrastructure to accomplish this project. Relationships between participant demographic variables and learning preferences are reported elsewhere.Conclusion: The ASCO Learning Cohort Project is a unique educational project that demonstrated feasibility and has met its goals. This paper outlines the processes of establishing a learning cohort, including participant selection, project design, and participant feedback. We evaluate the project model as a means to better understand the learning needs and behaviors of oncology healthcare professionals. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. Author summary: Analyses of gene set differential coexpression often shed light on molecular mechanisms underlying phenotypes and diseases. However, results from conceptually similar individual studies are often inconsistent and underpowered to reach definitive conclusions. We provide an open-source application facilitating the aggregation of evidence of differential coexpression across studies and the estimation of more robust common effects, with an optional module of post hoc pathway crosstalk network analysis to identify and visualize pathways having similar coexpression profiles. We established the usefulness of MetaGSCA via case studies of chronic kidney disease and non-small cell lung cancer, and applied it to a pan-cancer analysis of 11 cancer types. We further demonstrated the tool with 100 randomly generated gene sets and showed excellent specificity, indicating low false positive rates. [ABSTRACT FROM AUTHOR]

Ductal carcinoma in situ (DCIS) is the most common type of pre-invasive breast cancer diagnosed in women. Because the majority of DCIS cases are unlikely to progress to invasive breast cancer, many women are over-treated for DCIS. By understanding the molecular basis of early stage breast cancer progression, we may identify better prognostic factors and design treatments tailored specifically to the predicted outcome of DCIS. Chemokines are small soluble molecules with complex roles in inflammation and cancer progression. Previously, we demonstrated that CCL2/CCR2 chemokine signaling in breast cancer cell lines regulated growth and invasion through p42/44MAPK and SMAD3 dependent mechanisms. Here, we sought to determine the clinical and functional relevance of CCL2/CCR2 signaling proteins to DCIS progression. Through immunostaining analysis of DCIS and IDC tissues, we show that expression of CCL2, CCR2, phospho-SMAD3 and phospho-p42/44MAPK correlate with IDC. Using PDX models and an immortalized hDCIS.01 breast epithelial cell line, we show that breast epithelial cells with high CCR2 and high CCL2 levels form invasive breast lesions that express phospho-SMAD3 and phospho-p42/44MAPK. These studies demonstrate that increased CCL2/CCR2 signaling in breast tissues is associated with DCIS progression, and could be a signature to predict the likelihood of DCIS progression to IDC. [ABSTRACT FROM AUTHOR]

How health care providers select topics and activities for learning is key to meeting their needs. The goal of this study was to investigate how oncology providers identify knowledge gaps and choose learning activities. An online focus group within a larger longitudinal study was conducted between November 2015 and August 2016. Participants were chosen by convenience and stratified random sampling of diverse types of oncology providers. Providers were asked monthly to identify learning needs, explain how they identified those needs, and describe the learning activity they chose to meet those needs. Thirty-two oncology providers recorded 201 learning needs via online journal entries (mean 6 entries per person). Needs were associated with practice setting and professional role (p < .05). Colleague recommendation predicted learning needs for advanced practice providers (APPs) (p = .003). Patient cases drove > 50% of identified learning needs across groups. Learning activity preferences were associated with practice setting (p < .05). Choice of learning activity was associated with practice setting, professional role, and geographic location. Colleague recommendation was important for APPs (p = .025). Over 75% of learner responses identify convenience and content quality as important factors in choosing an activity. This study represents a quantitative assessment of learning behaviors for oncology providers and shows that identification of learning needs and activity selection differ by provider demographics. Limitations include small size and underrepresentation of some groups. Our findings should be confirmed with larger samples. Future research should focus on assessment of cohort versus individual needs and learning priorities. [ABSTRACT FROM AUTHOR]

Background: Tocilizumab, an IL-6 receptor antagonist, can be used to treat cytokine release syndrome (CRS), with observed improvements in a coronavirus disease 2019 (COVID-19) case series.Research Question: The goal of this study was to determine if tocilizumab benefits patients hospitalized with COVID-19.Study Design and Methods: This observational study of consecutive COVID-19 patients hospitalized between March 10, 2020, and March 31, 2020, and followed up through April 21, 2020, was conducted by chart review. Patients were treated with tocilizumab using an algorithm that targeted CRS. Survival and mechanical ventilation (MV) outcomes were reported for 14 days and stratified according to disease severity designated at admission (severe, ≥ 3 L supplemental oxygen to maintain oxygen saturation > 93%). For tocilizumab-treated patients, pre/post analyses of clinical response, biomarkers, and safety outcomes were assessed. Post hoc survival analyses were conducted for race/ethnicity.Results: Among the 239 patients, median age was 64 years; 36% and 19% were black and Hispanic, respectively. Hospital census increased exponentially, yet MV census did not. Severe disease was associated with lower survival (78% vs 93%; P < .001), greater proportion requiring MV (44% vs 5%; P < .001), and longer median MV days (5.5 vs 1.0; P = .003). Tocilizumab-treated patients (n = 153 [64%]) comprised 90% of those with severe disease; 44% of patients with nonsevere disease received tocilizumab for evolving CRS. Tocilizumab-treated patients with severe disease had higher admission levels of high-sensitivity C-reactive protein (120 vs 71 mg/L; P < .001) and received tocilizumab sooner (2 vs 3 days; P < .001), but their survival was similar to that of patients with nonsevere disease (83% vs 91%; P = .11). For tocilizumab-treated patients requiring MV, survival was 75% (95% CI, 64-89). Following tocilizumab treatment, few adverse events occurred, and oxygenation and inflammatory biomarkers (eg, high-sensitivity C-reactive protein, IL-6) improved; however, D-dimer and soluble IL-2 receptor (also termed CD25) levels increased significantly. Survival in black and Hispanic patients, after controlling for age, was significantly higher than in white patients (log-rank test, P = .002).Interpretation: A treatment algorithm that included tocilizumab to target CRS may influence MV and survival outcomes. In tocilizumab-treated patients, oxygenation and inflammatory biomarkers improved, with higher than expected survival. Randomized trials must confirm these findings. [ABSTRACT FROM AUTHOR]

Background: Acute exacerbation (AE) of COPD may be accompanied by the deterioration of cardiovascular comorbidities, as evidenced by the increased incidence of acute cardiovascular events.Research Question: The goal of this study was to determine whether preceding AE might be associated with mortality of cardiovascular events.Study Design and Methods: Using a health insurance research database in Taiwan, patients with COPD were identified who experienced first-time acute myocardial infarction (AMI; n = 26,442), ischemic stroke (n = 54,959), and intracranial hemorrhage (ICH; n = 14,893) over a 13-year period. In each cohort, 4,356, 6,655, and 1,727 patients, respectively, had been hospitalized for AE within the previous year prior to the index cardiovascular events, and patients with COPD but without hospitalization for AEs constituted the control subjects. ORs of 90-day mortality and hazard ratios (HRs) of overall mortality during follow-up in relation to hospitalization for an AE and the frequency of hospitalization for AEs (ie, 1 and ≥ 2 hospitalizations for AEs) were estimated with adjustment for potential confounders.Results: Hospitalization for an AE was independently associated with 90-day mortality of AMI (OR, 1.33; 95% CI, 1.24-1.43), ischemic stroke (OR, 1.46; 95% CI, 1.36-1.56), and ICH (OR, 1.19; 95% CI, 1.06-1.32). Hospitalization for an AE was associated with overall mortality of AMI (HR, 1.23; 95% CI, 1.19-1.27), ischemic stroke (HR, 1.29; 95% CI, 1.26-1.33), and ICH (HR, 1.19; 95% CI, 1.13-1.26). In addition, compared with control subjects, patients with more frequent hospitalizations for AEs exhibited significant trends at higher risk of 90-day and overall mortality of AMI, ischemic stroke, and ICH. Finally, these results were consistent with propensity score matching-based estimates.Interpretation: Preceding hospitalization for AEs is associated with 90-day and overall mortality of cardiovascular events in COPD. [ABSTRACT FROM AUTHOR]

Introduction: Immune‐related adverse event (IRAE) onset may represent a clinical biomarker for anti‐programmed cell death protein 1 (PD‐1) antibody response based on emerging evidence from patients with various advanced malignancies. This phenomenon has not been previously reported in a multidisease cohort of patients with gastrointestinal (GI) cancer with Food and Drug Administration (FDA)‐approved indications to receive immune checkpoint inhibitor therapy. Materials and Methods: The study was a multicenter retrospective cohort analysis of 76 patients with GI cancer who had received anti‐PD‐1 antibodies for FDA‐approved indications. The primary and secondary outcomes of the study were progression‐free survival (PFS) and overall survival (OS) in patients based upon IRAE presence, respectively. PFS and OS were estimated by the Kaplan‐Meier method; a Cox proportional‐hazards model adjusted for IRAE onset, patient age, and enrolling institution was used to analyze outcomes. Results: Median PFS and OS were prolonged in patients who experienced IRAEs compared with those who did not experience them (PFS: not reached [NR] vs. 3.9 months [hazard ratio (HR) 0.13, 95% confidence interval (CI) 0.05–0.3, p <.001]; OS: NR vs. 7.4 months [HR 0.11, 95% CI 0.03–0.36, p <.001]). Among patients who experienced IRAEs, there were no significant differences in PFS and OS by either initial IRAE severity, management, or time to onset. Conclusion: Patients with gastrointestinal cancer who experienced IRAEs while on anti‐PD‐1 antibodies demonstrated significant improvements in PFS and OS compared with their counterparts who did not develop IRAEs. Although these findings add to results from studies in other tumor types, larger prospective studies are needed prior to clinical adoption of IRAE onset as a biomarker for immune checkpoint inhibitor response. Implications for Practice: Predictive clinical biomarkers for immune checkpoint inhibitor response have been understudied in the field of immuno‐oncology. Immune‐related adverse event onset appears to be one such biomarker. Across tumor types, immune‐related adverse event onset has been associated with response to anti‐programmed cell death protein 1 (PD‐1) antibodies. The results of this study demonstrate this for the first time in patients with gastrointestinal cancer receiving anti‐PD‐1 antibodies. Before immune‐related adverse event onset can be adopted clinically as a predictive biomarker for immune checkpoint inhibitor response, however, larger prospective studies are needed to better understand the nuances between immune‐related adverse event characteristics (severity, site, management, timing of onset) and immune checkpoint inhibitor effectiveness. Despite the potential of immune checkpoint inhibitors, few patients with gastrointestinal cancer are eligible to receive these therapeutic agents. The search for biomarkers to better select patients who might respond is ongoing. This article assesses immune‐related adverse events as a possible biomarker in gastrointestinal cancer. [ABSTRACT FROM AUTHOR]

Objectives Patients with RA and SLE have an excess cardiovascular risk. We aimed to evaluate outcomes of acute cardiovascular events in these patients. Methods Using a nationwide database of Taiwan, we identified adult patients who experienced first-time acute myocardial infarction (n = 191 008), intracranial haemorrhage (n = 169 923) and ischaemic stroke (n = 486 890) over a 13-year period. Odds ratios (ORs) of in-hospital mortality and hazard ratios (HRs) of overall mortality and adverse outcomes during long-term follow-up in relation to RA and SLE were estimated with adjustment for potential confounders. Results In each cohort, 748, 410 and 1419 patients had established RA; 256, 292 and 622 patients had SLE. Among acute myocardial infarction patients, RA and SLE were associated with in-hospital mortality (RA: OR 1.61, 95% CI 1.33, 1.95; SLE: OR 2.31, 95% CI 1.62, 3.28) and overall mortality. Additionally, RA (HR 1.28, 95% CI 1.18, 1.38) and SLE (HR 1.46, 95% CI 1.27, 1.69) increased the risk of major adverse cardiac events. After intracranial haemorrhage, patients with RA and SLE had higher risks of in-hospital mortality (RA: OR 1.61, 95% CI 1.26, 2.06; SLE: OR 3.00, 95% CI 2.33, 3.86) and overall mortality. After ischaemic stroke, RA and SLE increased in-hospital mortality (RA: OR 1.45, 95% CI 1.15, 1.83; SLE: OR 2.18, 95% CI 1.57, 3.02), overall mortality and recurrent cerebrovascular events (RA: HR 1.10, 95% CI 1.002, 1.21; SLE: HR 1.31, 95% CI 1.14, 1.51), among which ischaemic stroke (HR 1.39, 95% CI 1.19, 1.62) was more likely to recur in SLE patients. Conclusion Both RA and SLE are consistently associated with adverse outcomes following acute cardiovascular events, highlighting the necessity of integrated care for affected patients. [ABSTRACT FROM AUTHOR]

BETting on combination treatment: MYCN is a well-known oncogene that plays a role in cancer aggressiveness, but it is typically associated with neuroendocrine cancers. Schafer et al. found a role for MYCN in triple-negative breast cancer, a particularly aggressive form of the disease, and identified a potential intervention. Although there is no standard way to target MYCN directly, the authors determined that a group of experimental drugs called BET inhibitors are effective against triple-negative breast cancers that overexpress MYCN, especially when coupled with the inhibition of MEK, another known oncogene. Triple-negative breast cancer (TNBC) is an aggressive form of breast cancer that does not respond to endocrine therapy or human epidermal growth factor receptor 2 (HER2)–targeted therapies. Individuals with TNBC experience higher rates of relapse and shorter overall survival compared to patients with receptor-positive breast cancer subtypes. Preclinical discoveries are needed to identify, develop, and advance new drug targets to improve outcomes for patients with TNBC. Here, we report that MYCN, an oncogene typically overexpressed in tumors of the nervous system or with neuroendocrine features, is heterogeneously expressed within a substantial fraction of primary and recurrent TNBC and is expressed in an even higher fraction of TNBCs that do not display a pathological complete response after neoadjuvant chemotherapy. We performed high-throughput chemical screens on TNBC cell lines with varying amounts of MYCN expression and determined that cells with higher expression of MYCN were more sensitive to bromodomain and extraterminal motif (BET) inhibitors. Combined BET and MEK inhibition resulted in a synergistic decrease in viability, both in vitro and in vivo, using cell lines and patient-derived xenograft (PDX) models. Our preclinical data provide a rationale to advance a combination of BET and MEK inhibitors to clinical investigation for patients with advanced MYCN-expressing TNBC. [ABSTRACT FROM AUTHOR]

PURPOSE: ASCO is the premier and largest global professional society for oncology care professionals. In 2015, ASCO launched a longitudinal Learning Cohort Pilot Project to catalog and better understand the learning behaviors and preferences of oncology health care providers. A secondary goal was to assess learner preferences and utilization related to ASCO's portfolio of educational resources. METHODS: The Learning Cohort Pilot Project was conducted between November 2015 and August 2016 with 49 ASCO members. Participants were selected via convenience sampling and stratified random sampling to generate a cohort that mirrored the demographic distribution of overall ASCO membership. Participants completed a different ASCO resource-specific feedback activity each month, which measured professional educational needs, sources sought, and preferences for educational resources. Responses were organized by demographic variables in our participant pool to identify trends in provider learning preferences. Fisher's exact test was used to assess the association between participant demographics and practice setting and responses. Holm's procedure was used to adjust for multiple testing. RESULTS: The Learning Cohort Pilot Project revealed statistically significant relationships between participant demographic variables and learning preferences. Age and practice setting were the demographic variables most consistently associated with the different preferences explored throughout the targeted activities. CONCLUSION: The results of this pilot cohort reinforced the hypothesis that oncology care providers have different professional educational needs and preferences that can be potentially anticipated and met with tailored resources. Delivering solutions to meet these needs represents an opportunity for further research and resource development. [ABSTRACT FROM AUTHOR]

Objective: We evaluated the prognostic value of lymph node ratio (LNR) for the survival of breast cancer patients using Bayesian inference. Methods: Data on 5,279 women with infiltrating duct and lobular carcinoma breast cancer, diagnosed from 2006–2010, was obtained from the NCI SEER Cancer Registry. A prognostic modeling framework was proposed using Bayesian inference to estimate the impact of LNR in breast cancer survival. Based on the proposed model, we then developed a web application for estimating LNR and predicting overall survival. Results: The final survival model with LNR outperformed the other models considered (C-statistic 0.71). Compared to directly measured LNR, estimated LNR slightly increased the accuracy of the prognostic model. Model diagnostics and predictive performance confirmed the effectiveness of Bayesian modeling and the prognostic value of the LNR in predicting breast cancer survival. Conclusion: The estimated LNR was found to have a significant predictive value for the overall survival of breast cancer patients.Significance: We used Bayesian inference to estimate LNR which was then used to predict overall survival. The models were developed from a large population-based cancer registry. We also built a user-friendly web application for individual patient survival prognosis. The diagnostic value of the LNR and the effectiveness of the proposed model were evaluated by comparisons with existing prediction models. [ABSTRACT FROM AUTHOR]

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%5fme/. 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%5fme%5finfo/. The source code is available as an R package on GitHub (https://github.com/tbilab/multimorbidity%5fexplorer). Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

MicroRNAs (miRNAs) are small endogenous non-coding functional RNAs that post-transcriptionally regulate gene expression. They play essential roles in nearly all biological processes including cell development and differentiation, DNA damage repair, cell death as well as intercellular communication. They are highly involved in cancer, acting as tumor suppressors and/or promoters to modulate cell proliferation, epithelial-mesenchymal transition and tumor invasion and metastasis. Recent studies have shown that more than half of miRNAs are located within protein-coding or non-coding genes. Intragenic miRNAs and their host genes either share the promoter or have independent transcription. Meanwhile, miRNAs work as partners or antagonists of their host genes by fine-tuning their target genes functionally associated with host genes. This review outlined the complicated relationship between intragenic miRNAs and host genes. Focusing on miRNAs known as oncogenes or tumor suppressors in specific cancer types, it studied co-expression relationships between these miRNAs and host genes in the cancer types using TCGA data sets, which validated previous findings and revealed common, tumor-specific and even subtype-specific patterns. These observations will help understand the function of intragenic miRNAs and further develop miRNA therapeutics in cancer. [ABSTRACT FROM AUTHOR]

The article reports factors including income, costs and healthcare utilization that influence surgical choices in patients with early stage breast cancer. Topics discussed include mastectomy and breast conservation surgery (BCS) with radiation being a common treatment, and only mastectomy being more common in women with lower income levels.

Power/sample size (power) analysis estimates the likelihood of successfully finding the statistical significance in a data set. There has been a growing recognition of the importance of power analysis in the proper design of experiments. Power analysis is complex, yet necessary for the success of large studies. It is important to design a study that produces statistically accurate and reliable results. Power computation methods have been well established for both microarray-based gene expression studies and genotyping microarray-based genome-wide association studies. High-throughput sequencing (HTS) has greatly enhanced our ability to conduct biomedical studies at the highest possible resolution (per nucleotide). However, the complexity of power computations is much greater for sequencing data than for the simpler genotyping array data. Research on methods of power computations for HTS-based studies has been recently conducted but is not yet well known or widely used. In this article, we describe the power computation methods that are currently available for a range of HTS-based studies, including DNA sequencing, RNA-sequencing, microbiome sequencing and chromatin immunoprecipitation sequencing. Most importantly, we review the methods of power analysis for several types of sequencing data and guide the reader to the relevant methods for each data type. [ABSTRACT FROM AUTHOR]

Background The first-line treatment for metastatic clear cell renal cell carcinoma (mccRCC) includes immune oncology (IO) based combination therapy. The current standard includes a PD-1 inhibitor plus either an anti-CTLA-4 inhibitor (IO/IO) or an anti-vascular endothelial growth factor receptor tyrosine kinase inhibitor (VEGF TKI) (IO/TKI). Currently, there is no evidence to guide a physician's choice between an IO/IO versus an IO/TKI combination. The phase III IMmotion 151 trial performed RNA-seq from 823 ccRCC tumors and established seven biologically distinct gene expression clusters of ccRCC (Motzer and Rini et al., Cancer Cell 2020). The seven clusters showed differential responses to immune checkpoint inhibitor and may serve as a predictive biomarker to select clusters to assign patients with mccRCC to either an IO/IO (ipilimumab/ nivolumab) or an IO/TKI (nivolumab/cabozantinib) regimen. Methods Patients diagnosed with mccRCC without prior systemic therapy (including in the neoadjuvant or adjuvant setting) and at least one measurable lesion as defined by RECIST 1.1 are eligible for enrollment. RNA-seq will be performed on metastatic tumor specimens and used to assign tumor clusters. Patients with cluster 1/2 tumors will be assigned to the nivolumab/cabozantinib arm. Patients with cluster 4/5 tumors will be assigned to the ipilimumab/nivolumab arm. Cluster 3/6/7 will be excluded. The primary endpoint is overall response rate (ORR) per RECIST 1.1. Key secondary endpoints include progression-free survival, depth of response >80%, and rate of immune-related adverse events (irAEs). The hypothesis is that use of tumor clusters to assign front-line therapy to either nivolumab/cabozantinib or ipilimumab/nivolumab will lead to a 20% greater ORR compared to unselected historical controls in CheckMate 9ER (ORR 55%) or CheckMate 214 (ORR: 40%). This trial adopts Simon's MiniMax two-stage design (power: 80%, one-sided alpha: 0.1). For the nivolumab/cabozantinib arm, stage I will enroll 12 eligible patients. If there are 7 or more responders in the first 12 patients, stage II will enroll an additional 14 patients (n=26). For the ipilimumab/ nivolumab arm, stage I will enroll 16 eligible patients. If there are 7 or more responders in the first 16, stage II will enroll an additional 12 patients (n=28). The primary endpoint will be met if there are 15 or more responders (ORR>60%). This trial is open and enrolling with 12 patients on study at Vanderbilt University Medical Center and additional sites to open including UT Southwestern, City of Hope, University Hospitals Seidman Cancer Center, and Cleveland Clinic. This trial is funded by the Department of Defense Kidney Cancer Research Program Clinical Trial Award (W81XWH-22-1-1033) (NCT05361720). CDMRP DOD Funding: yes [ABSTRACT FROM AUTHOR]

Summary Single cell RNA sequencing is a revolutionary technique to characterize inter-cellular transcriptomics heterogeneity. However, the data are noise-prone because gene expression is often driven by both technical artifacts and genuine biological variations. Proper disentanglement of these two effects is critical to prevent spurious results. While several tools exist to detect and remove low-quality cells in one single cell RNA-seq dataset, there is lack of approach to examining consistency between sample sets and detecting systematic biases, batch effects and outliers. We present scRNABatchQC, an R package to compare multiple sample sets simultaneously over numerous technical and biological features, which gives valuable hints to distinguish technical artifact from biological variations. scRNABatchQC helps identify and systematically characterize sources of variability in single cell transcriptome data. The examination of consistency across datasets allows visual detection of biases and outliers. Availability and implementation scRNABatchQC is freely available at https://github.com/liuqivandy/scRNABatchQC as an R package. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Single-cell RNA sequencing (scRNA-seq) has become a powerful tool for the systematic investigation of cellular diversity. As a number of computational tools have been developed to identify and visualize cell populations within a single scRNA-seq dataset, there is a need for methods to quantitatively and statistically define proportional shifts in cell population structures across datasets, such as expansion or shrinkage or emergence or disappearance of cell populations. Here we present sc-UniFrac, a framework to statistically quantify compositional diversity in cell populations between single-cell transcriptome landscapes. sc-UniFrac enables sensitive and robust quantification in simulated and experimental datasets in terms of both population identity and quantity. We have demonstrated the utility of sc-UniFrac in multiple applications, including assessment of biological and technical replicates, classification of tissue phenotypes and regional specification, identification and definition of altered cell infiltrates in tumorigenesis, and benchmarking batch-correction tools. sc-UniFrac provides a framework for quantifying diversity or alterations in cell populations across conditions and has broad utility for gaining insight into tissue-level perturbations at the single-cell resolution. [ABSTRACT FROM AUTHOR]

Illumina genotyping arrays have powered thousands of large-scale genome-wide association studies over the past decade. Yet, because of the tremendous volume and complicated genetic assumptions of Illumina genotyping data, processing and quality control (QC) of these data remain a challenge. Thorough QC ensures the accurate identification of single-nucleotide polymorphisms and is required for the correct interpretation of genetic association results. By processing genotyping data on > 100 000 subjects from >10 major Illumina genotyping arrays, we have accumulated extensive experience in handling some of the most peculiar scenarios related to the processing and QC of Illumina genotyping data. Here, we describe strategies for processing Illumina genotyping data from the raw data to an analysis ready format, and we elaborate on the necessary QC procedures required at each processing step. High-quality Illumina genotyping data sets can be obtained by following our detailed QC strategies. [ABSTRACT FROM AUTHOR]

Background: Enhancers are distal cis-regulatory elements that control gene expression. Despite an increasing appreciation of the importance of enhancers in cellular function and disease, our knowledge of enhancer-regulated transcription is very limited. Nascent RNA sequencing technologies, such as global nuclear run-on sequencing (GRO-seq) and precision run-on sequencing (PRO-seq), not only provide a direct and reliable measurement of enhancer activity, but also allow for quantifying transcription of enhancers and target genes simultaneously, making these technologies extremely useful for exploring enhancer-mediated regulation. Results: Nascent RNA sequencing analysis (NRSA) provides a comprehensive view of enhancer-mediated gene regulation. NRSA not only outperforms existing methods for enhancer identification, but also enables annotation and quantification of active enhancers, and prediction of their target genes. Furthermore, NRSA identifies functionally important enhancers by integrating 1) nascent transcriptional changes in enhancers and their target genes and 2) binding profiles from regulator(s) of interest. Applied to wildtype and histone deacetylase 3 (Hdac3) knockout mouse livers, NRSA showed that HDAC3 regulates RNA polymerase recruitment through both proximal (promoter) and distal (enhancer) regulatory elements. Integrating ChIP-seq with PRO-seq data, NRSA prioritized enhancers based on their potential contribution to mediating HDAC3 regulation. Conclusions: NRSA will greatly facilitate the usage of nascent RNA sequencing techniques and accelerate the study of enhancer-mediated regulation. [ABSTRACT FROM AUTHOR]

Background: One of the most important and often neglected components of a successful RNA sequencing (RNA-Seq) experiment is sample size estimation. A few negative binomial model-based methods have been developed to estimate sample size based on the parameters of a single gene. However, thousands of genes are quantified and tested for differential expression simultaneously in RNA-Seq experiments. Thus, additional issues should be carefully addressed, including the false discovery rate for multiple statistic tests, widely distributed read counts and dispersions for different genes. Results: To solve these issues, we developed a sample size and power estimation method named RnaSeqSampleSize, based on the distributions of gene average read counts and dispersions estimated from real RNA-seq data. Datasets from previous, similar experiments such as the Cancer Genome Atlas (TCGA) can be used as a point of reference. Read counts and their dispersions were estimated from the reference's distribution; using that information, we estimated and summarized the power and sample size. RnaSeqSampleSize is implemented in R language and can be installed from Bioconductor website. A user friendly web graphic interface is provided at http://cqs.mc.vanderbilt.edu/shiny/RnaSeqSampleSize/. Conclusions: RnaSeqSampleSize provides a convenient and powerful way for power and sample size estimation for an RNAseq experiment. It is also equipped with several unique features, including estimation for interested genes or pathway, power curve visualization, and parameter optimization. [ABSTRACT FROM AUTHOR]

Purpose The promise of precision oncology is that identification of genomic alterations will direct the rational use of molecularly targeted therapy. This approach is particularly applicable to neoplasms that are resistant to standard cytotoxic chemotherapy, like T-cell leukemias and lymphomas. In this study, we tested the feasibility of targeted next-generation sequencing in profiles of diverse T-cell neoplasms and focused on the therapeutic utility of targeting activated JAK1 and JAK3 in an index case. Patients and Methods Using Foundation One and Foundation One Heme assays, we performed genomic profiling on 91 consecutive T-cell neoplasms for alterations in 405 genes. The samples were sequenced to high uniform coverage with an Illumina HiSeq and averaged a coverage depth of greater than 500× for DNA and more than 8M total pairs for RNA. An index case of T-cell prolymphocytic leukemia (T-PLL), which was analyzed by targeted next-generation sequencing, is presented. T-PLL cells were analyzed by RNA-seq, in vitro drug testing, mass cytometry, and phospho-flow. Results One third of the samples had genomic aberrations in the JAK-STAT pathway, most often composed of JAK1 and JAK3 gain-of-function mutations. We present an index case of a patient with T-PLL with a clonal JAK1 V658F mutation that responded to ruxolitinib therapy. After relapse developed, an expanded clone that harbored mutant JAK3 M511I and downregulation of the phosphatase, CD45, was identified. We demonstrate that the JAK missense mutations were activating, caused pathway hyperactivation, and conferred cytokine hypersensitivity. Conclusion These results underscore the utility of profiling occurrences of resistance to standard regimens and support JAK enzymes as rational therapeutic targets for T-cell leukemias and lymphomas. [ABSTRACT FROM AUTHOR]