Real-World Effectiveness of BNT162b2 Against Infection and Severe Diseases in Children and Adolescents.

BACKGROUND: The efficacy of the BNT162b2 vaccine in pediatrics was assessed by randomized trials before the Omicron variant's emergence. The long-term durability of vaccine protection in this population during the Omicron period remains limited. OBJECTIVE: To assess the effectiveness of BNT162b2 in preventing infection and severe diseases with various strains of the SARS-CoV-2 virus in previously uninfected children and adolescents. DESIGN: Comparative effectiveness research accounting for underreported vaccination in 3 study cohorts: adolescents (12 to 20 years) during the Delta phase and children (5 to 11 years) and adolescents (12 to 20 years) during the Omicron phase. SETTING: A national collaboration of pediatric health systems (PEDSnet). PARTICIPANTS: 77 392 adolescents (45 007 vaccinated) during the Delta phase and 111 539 children (50 398 vaccinated) and 56 080 adolescents (21 180 vaccinated) during the Omicron phase. INTERVENTION: First dose of the BNT162b2 vaccine versus no receipt of COVID-19 vaccine. MEASUREMENTS: Outcomes of interest include documented infection, COVID-19 illness severity, admission to an intensive care unit (ICU), and cardiac complications. The effectiveness was reported as (1-relative risk)*100, with confounders balanced via propensity score stratification. RESULTS: During the Delta period, the estimated effectiveness of the BNT162b2 vaccine was 98.4% (95% CI, 98.1% to 98.7%) against documented infection among adolescents, with no statistically significant waning after receipt of the first dose. An analysis of cardiac complications did not suggest a statistically significant difference between vaccinated and unvaccinated groups. During the Omicron period, the effectiveness against documented infection among children was estimated to be 74.3% (CI, 72.2% to 76.2%). Higher levels of effectiveness were seen against moderate or severe COVID-19 (75.5% [CI, 69.0% to 81.0%]) and ICU admission with COVID-19 (84.9% [CI, 64.8% to 93.5%]). Among adolescents, the effectiveness against documented Omicron infection was 85.5% (CI, 83.8% to 87.1%), with 84.8% (CI, 77.3% to 89.9%) against moderate or severe COVID-19, and 91.5% (CI, 69.5% to 97.6%) against ICU admission with COVID-19. The effectiveness of the BNT162b2 vaccine against the Omicron variant declined 4 months after the first dose and then stabilized. The analysis showed a lower risk for cardiac complications in the vaccinated group during the Omicron variant period. LIMITATION: Observational study design and potentially undocumented infection. CONCLUSION: This study suggests that BNT162b2 was effective for various COVID-19-related outcomes in children and adolescents during the Delta and Omicron periods, and there is some evidence of waning effectiveness over time. PRIMARY FUNDING SOURCE: National Institutes of Health.

Clinical and Prognostic Biomarker Value of Blood-Circulating Inflammatory Cytokines in Hepatocellular Carcinoma.

INTRODUCTION: Circulating inflammatory cytokines play critical roles in tumor-associated inflammation and immune responses. Recent data have suggested that several interleukins (ILs) mediate carcinogenesis in hepatocellular carcinoma (HCC). However, the predictive and prognostic value of circulating ILs is yet to be validated. Our study aimed to evaluate the association of the serum ILs with overall survival (OS) and clinicopathologic features in a large cohort of HCC patients. METHODS: We prospectively collected data and serum samples from 767 HCC patients treated at the University of Texas MD Anderson Cancer Center between 2001 and 2014, with a median follow-up of 67.4 months (95% confidence interval [CI]: 52.5, 83.3). Biomarker association with OS was evaluated by the log-rank method. RESULTS: The median OS in this cohort was 14.2 months (95% CI: 12, 16.1 months). Clinicopathologic features were more advanced, and OS was significantly inferior in patients with high circulating levels of IL1-R1, IL-6, IL-8, IL-10, IL-15, IL-16, and IL-18. CONCLUSION: Our study shows that several serum IL levels are valid prognostic biomarker candidates and potential targets for therapy in HCC.

Subanalgesic morphine doses augment fentanyl analgesia by interacting with delta opioid receptors in male rats.

Opioids are commonly used for the treatment of postoperative and post-traumatic pain; however, their therapeutic effectiveness is limited by undesirable and life-threatening side effects. Researchers have long attempted to develop opioid co-administration therapies that enhance analgesia, but the complexity of opioid analgesia and our incomplete mechanistic understanding has made this a daunting task. We discovered that subanalgesic morphine doses (100 ng/kg-10 µg/kg) augmented the acute analgesic effect of fentanyl (20 µg/kg) following subcutaneous drug co-administration to male rats. In addition, administration of equivalent drug ratios to naïve rat spinal cord membranes induced a twofold increase in G protein activation. The rate of GTP hydrolysis remained unchanged. We demonstrated that these behavioral and biochemical effects were mediated by the delta opioid receptor (DOP). Subanalgesic doses of the DOP-selective agonist SNC80 also augmented the acute analgesic effect of fentanyl. Furthermore, co-administration of the DOP antagonist naltrindole with both fentanyl-morphine and fentanyl-SNC80 combinations prevented augmentation of both analgesia and G protein activation. The mu opioid receptor (MOP) antagonist cyprodime did not block augmentation. Confocal microscopy of the substantia gelatinosa of rats treated with fentanyl, subanalgesic morphine, or this combination showed that changes in MOP internalization did not account for augmentation effects. Together, these findings suggest that augmentation of fentanyl analgesia by subanalgesic morphine is mediated by increased G protein activation resulting from a synergistic interaction between or heterodimerization of MOPs and DOPs. This finding is of great therapeutic significance because it suggests a strategy for the development of DOP-selective ligands that can enhance the therapeutic index of clinically used MOP drugs.

Methylation-eQTL analysis in cancer research.

MOTIVATION: DNA methylation is a key epigenetic factor regulating gene expression. While promoter methylation has been well studied, recent publications have revealed that functionally important methylation also occurs in intergenic and distal regions, and varies across genes and tissue types. Given the growing importance of inter-platform integrative genomic analyses, there is an urgent need to develop methods to discover and characterize gene-level relationships between methylation and expression. RESULTS: We introduce a novel sequential penalized regression approach to identify methylation-expression quantitative trait loci (methyl-eQTLs), a term that we have coined to represent, for each gene and tissue type, a sparse set of CpG loci best explaining gene expression and accompanying weights indicating direction and strength of association. Using TCGA and MD Anderson colorectal cohorts to build and validate our models, we demonstrate our strategy better explains expression variability than current commonly used gene-level methylation summaries. The methyl-eQTLs identified by our approach can be used to construct gene-level methylation summaries that are maximally correlated with gene expression for use in integrative models, and produce a tissue-specific summary of which genes appear to be strongly regulated by methylation. Our results introduce an important resource to the biomedical community for integrative genomics analyses involving DNA methylation. AVAILABILITY AND IMPLEMENTATION: We produce an R Shiny app (https://rstudio-prd-c1.pmacs.upenn.edu/methyl-eQTL/) that interactively presents methyl-eQTL results for colorectal, breast and pancreatic cancer. The source R code for this work is provided in the Supplementary Material. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

HepatoScore-14: Measures of Biological Heterogeneity Significantly Improve Prediction of Hepatocellular Carcinoma Risk.

BACKGROUND AND AIMS: Therapeutic, clinical trial entry and stratification decisions for hepatocellular carcinoma (HCC) are made based on prognostic assessments, using clinical staging systems based on small numbers of empirically selected variables that insufficiently account for differences in biological characteristics of individual patients' disease. APPROACH AND RESULTS: We propose an approach for constructing risk scores from circulating biomarkers that produce a global biological characterization of individual patient's disease. Plasma samples were collected prospectively from 767 patients with HCC and 200 controls, and 317 proteins were quantified in a Clinical Laboratory Improvement Amendments-certified biomarker testing laboratory. We constructed a circulating biomarker aberration score for each patient, a score between 0 and 1 that measures the degree of aberration of his or her biomarker panel relative to normal, which we call HepatoScore. We used log-rank tests to assess its ability to substratify patients within existing staging systems/prognostic factors. To enhance clinical application, we constructed a single-sample score, HepatoScore-14, which requires only a subset of 14 representative proteins encompassing the global biological effects. Patients with HCC were split into three distinct groups (low, medium, and high HepatoScore) with vastly different prognoses (medial overall survival 38.2/18.3/7.1 months; P < 0.0001). Furthermore, HepatoScore accurately substratified patients within levels of existing prognostic factors and staging systems (P < 0.0001 for nearly all), providing substantial and sometimes dramatic refinement of expected patient outcomes with strong therapeutic implications. These results were recapitulated by HepatoScore-14, rigorously validated in repeated training/test splits, concordant across Myriad RBM (Austin, TX) and enzyme-linked immunosorbent assay kits, and established as an independent prognostic factor. CONCLUSIONS: HepatoScore-14 augments existing HCC staging systems, dramatically refining patient prognostic assessments and therapeutic decision making and enrollment in clinical trials. The underlying strategy provides a global biological characterization of disease, and can be applied broadly to other disease settings and biological media.

NetCellMatch: Multiscale Network-Based Matching of Cancer Cell Lines to Patients Using Graphical Wavelets.

Cancer cell lines serve as model in vitro systems for investigating therapeutic interventions. Recent advances in high-throughput genomic profiling have enabled the systematic comparison between cell lines and patient tumor samples. The highly interconnected nature of biological data, however, presents a challenge when mapping patient tumors to cell lines. Standard clustering methods can be particularly susceptible to the high level of noise present in these datasets and only output clusters at one unknown scale of the data. In light of these challenges, we present NetCellMatch, a robust framework for network-based matching of cell lines to patient tumors. NetCellMatch first constructs a global network across all cell line-patient samples using their genomic similarity. Then, a multi-scale community detection algorithm integrates information across topologically meaningful (clustering) scales to obtain Network-Based Matching Scores (NBMS). NBMS are measures of cluster robustness which map patient tumors to cell lines. We use NBMS to determine representative "avatar" cell lines for subgroups of patients. We apply NetCellMatch to reverse-phase protein array data obtained from The Cancer Genome Atlas for patients and the MD Anderson Cell Line Project for cell lines. Along with avatar cell line identification, we evaluate connectivity patterns for breast, lung, and colon cancer and explore the proteomic profiles of avatars and their corresponding top matching patients. Our results demonstrate our framework's ability to identify both patient-cell line matches and potential proteomic drivers of similarity. Our methods are general and can be easily adapted to other'omic datasets.

AIDS and COVID: A tale of two pandemics and the role of statisticians.

The world has experienced three global pandemics over the last half-century: HIV/AIDS, H1N1, and COVID-19. HIV/AIDS and COVID-19 are still with us and have wrought extensive havoc worldwide. There are many differences between these two infections and their global impacts, but one thing they have in common is the mobilization of scientific resources to both understand the infection and develop ways to combat it. As was the case with HIV, statisticians have been in the forefront of scientists working to understand transmission dynamics and the natural history of infection, determine prognostic factors for severe disease, and develop optimal study designs to assess therapeutics and vaccines.

Impact of Integrating Insulin-Like Growth Factor 1 Levels into Model for End-Stage Liver Disease Score for Survival Prediction in Hepatocellular Carcinoma Patients.

BACKGROUND: Liver reserve affects survival in hepatocellular carcinoma (HCC). Model for End-Stage Liver Disease (MELD) score is used to predict overall survival (OS) and to prioritize HCC patients on the transplantation waiting list, but more accurate models are needed. We hypothesized that integrating insulin-like growth factor 1 (IGF-1) levels into MELD score (MELD-IGF-1) improves OS prediction as compared to MELD. METHODS: We measured plasma IGF-1 levels in training (n = 310) and validation (n = 155) HCC cohorts and created MELD-IGF-1 score. Cox models were used to determine the association of MELD and MELD-IGF-1 with OS. Harrell's c-index was used to compare the predictive capacity. RESULTS: IGF-1 was significantly associated with OS in both cohorts. Patients with an IGF-1 level of ≤26 ng/mL in the training cohort and in the validation cohorts had significantly higher hazard ratios than patients with the same MELD but IGF-1 >26 ng/mL. In both cohorts, MELD-IGF-1 scores had higher c-indices (0.60 and 0.66) than MELD scores (0.58 and 0.60) (p < 0.001 in both cohorts). Overall, 26% of training and 52.9% of validation cohort patients were reclassified into different risk groups by MELD-IGF-1 (p < 0.001). CONCLUSIONS: After independent validation, the MELD-IGF-1 could be used to risk-stratify patients in clinical trials and for priority assignment for patients on liver transplantation waiting list.

Clinical and molecular characterization of early-onset colorectal cancer.

BACKGROUND: Colorectal cancer (CRC) incidence is increasing in adults younger than 50 years. This study evaluated clinical and molecular features to identify those features unique to early-onset CRC that differentiate these patients from patients 50 years old or older. METHODS: Baseline characteristics were evaluated according to the CRC onset age with 3 independent cohorts. A fourth cohort was used to describe the impact of age on the consensus molecular subtype (CMS) prevalence. RESULTS: This retrospective review of more than 36,000 patients with CRC showed that early-onset patients were more likely to have microsatellite instability (P = .038), synchronous metastatic disease (P = .009), primary tumors in the distal colon or rectum (P < .0001), and fewer BRAF V600 mutations (P < .001) in comparison with patients 50 years old or older. Patients aged 18 to 29 years had fewer adenomatous polyposis coli (APC) mutations (odds ratio [OR], 0.56; 95% confidence interval [CI], 0.35-0.90; P = .015) and an increased prevalence of signet ring histology (OR, 4.89; 95% CI, 3.23-7.39; P < .0001) in comparison with other patients younger than 50 years. In patients younger than 40 years, CMS1 was the most common subtype, whereas CMS3 and CMS4 were uncommon (P = .003). CMS2 was relatively stable across age groups. Early-onset patients with inflammatory bowel disease were more likely to have mucinous or signet ring histology (OR, 5.54; 95% CI, 2.24-13.74; P = .0004) and less likely to have APC mutations (OR, 0.24; 95% CI, 0.07-0.75; P = .019) in comparison with early-onset patients without predisposing conditions. CONCLUSIONS: Early-onset CRC is not only distinct from traditional CRC: special consideration should be given to and further investigations should be performed for both very young patients with CRC (18-29 years) and those with predisposing conditions. The etiology of the high rate of CMS1 in patients younger than 40 years deserves further exploration.

Robust and Gaussian spatial functional regression models for analysis of event-related potentials.

Event-related potentials (ERPs) summarize electrophysiological brain response to specific stimuli. They can be considered as correlated functions of time with both spatial correlation across electrodes and nested correlations within subjects. Commonly used analytical methods for ERPs often focus on pre-determined extracted components and/or ignore the correlation among electrodes or subjects, which can miss important insights, and tend to be sensitive to outlying subjects, time points or electrodes. Motivated by ERP data in a smoking cessation study, we introduce a Bayesian spatial functional regression framework that models the entire ERPs as spatially correlated functional responses and the stimulus types as covariates. This novel framework relies on mixed models to characterize the effects of stimuli while simultaneously accounting for the multilevel correlation structure. The spatial correlation among the ERP profiles is captured through basis-space Matérn assumptions that allow either separable or nonseparable spatial correlations over time. We induce both adaptive regularization over time and spatial smoothness across electrodes via a correlated normal-exponential-gamma (CNEG) prior on the fixed effect coefficient functions. Our proposed framework includes both Gaussian models as well as robust models using heavier-tailed distributions to make the regression automatically robust to outliers. We introduce predictive methods to select among Gaussian vs. robust models and models with separable vs. non-separable spatiotemporal correlation structures. Our proposed analysis produces global tests for stimuli effects across entire time (or time-frequency) and electrode domains, plus multiplicity-adjusted pointwise inference based on experiment-wise error rate or false discovery rate to flag spatiotemporal (or spatio-temporal-frequency) regions that characterize stimuli differences, and can also produce inference for any prespecified waveform components. Our analysis of the smoking cessation ERP data set reveals numerous effects across different types of visual stimuli.

Measles Serostatus Among Parturient Patients at 2 Philadelphia Hospitals in 2021.

This observational study explores whether rubella serostatus, which is routinely assessed during pregnancy, can serve as a proxy for measles serostatus in parturient persons.

A Randomized Controlled Trial of Postoperative Thoracic Epidural Analgesia Versus Intravenous Patient-controlled Analgesia After Major Hepatopancreatobiliary Surgery.

OBJECTIVES: The primary objective of this randomized trial was to compare thoracic epidural analgesia (TEA) to intravenous patient-controlled analgesia (IV-PCA) for pain control over the first 48 hours after hepatopancreatobiliary (HPB) surgery. Secondary endpoints were patient-reported outcomes, total narcotic utilization, and complications. BACKGROUND: Although adequate postoperative pain control is critical to patient and surgeon success, the optimal analgesia regimen in HPB surgery remains controversial. METHODS: Using a 2.5:1 randomization strategy, 140 patients were randomized to TEA (N = 106) or intravenous patient-controlled analgesia (N = 34). Patient-reported pain was measured on a Likert scale (0-10) at standard time intervals. Cumulative pain area under the curve was determined using the trapezoidal method. RESULTS: Between the study groups key demographic, comorbidity, clinical, and operative variables were equivalently distributed. The median area under the curve of the postoperative time 0- to 48-hour pain scores was lower in the TEA group (78.6 vs 105.2 pain-hours, P = 0.032) with a 35% reduction in patients experiencing ≥7/10 pain (43% vs 62%, P = 0.07). Patient-reported outcomes and total opiate use further supported the benefit of TEA on patient experience. Anesthesia-related events requiring change in analgesic therapy were comparable (12.2% vs 2.9%, respectively, P = 0.187). Grade 3 or higher surgical complications (6.6% vs 9.4%), median length of stay (6 days vs 6 days), readmission (1.9% vs 3.1%), and return to the operating room (0.9% vs 3.1%) were similar (all P > 0.05). There were no mortalities in either group. CONCLUSIONS: In major HPB surgery, TEA provides a superior patient experience through improved pain control and less narcotic use, without increased length of stay or complications.

Identification of differentially methylated loci using wavelet-based functional mixed models.

MOTIVATION: DNA methylation is a key epigenetic modification that can modulate gene expression. Over the past decade, a lot of studies have focused on profiling DNA methylation and investigating its alterations in complex diseases such as cancer. While early studies were mostly restricted to CpG islands or promoter regions, recent findings indicate that many of important DNA methylation changes can occur in other regions and DNA methylation needs to be examined on a genome-wide scale. In this article, we apply the wavelet-based functional mixed model methodology to analyze the high-throughput methylation data for identifying differentially methylated loci across the genome. Contrary to many commonly-used methods that model probes independently, this framework accommodates spatial correlations across the genome through basis function modeling as well as correlations between samples through functional random effects, which allows it to be applied to many different settings and potentially leads to more power in detection of differential methylation. RESULTS: We applied this framework to three different high-dimensional methylation data sets (CpG Shore data, THREE data and NIH Roadmap Epigenomics data), studied previously in other works. A simulation study based on CpG Shore data suggested that in terms of detection of differentially methylated loci, this modeling approach using wavelets outperforms analogous approaches modeling the loci as independent. For the THREE data, the method suggests newly detected regions of differential methylation, which were not reported in the original study. AVAILABILITY AND IMPLEMENTATION: Automated software called WFMM is available at https://biostatistics.mdanderson.org/SoftwareDownload CpG Shore data is available at http://rafalab.dfci.harvard.edu NIH Roadmap Epigenomics data is available at http://compbio.mit.edu/roadmap SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. CONTACT: jefmorris@mdanderson.org.

Refining prognosis in patients with hepatocellular carcinoma through incorporation of metabolic imaging biomarkers.

PURPOSE: 18F-fluorodeoxyglucose positron emission tomopraphy/computed tomography (FDGPET/CT) has been proven to be useful for imaging many types of cancer; however, its role is not well defined in hepatocellular carcinoma (HCC). We assessed the prognostic value of metabolic imaging biomarkers as established by baseline pretreatment FDG PET/CT in patients with HCC. METHODS: We retrospectively analyzed the records of patients with HCC who underwent FDG PET/CT before initial treatment from May 2013 through May 2014. Four PET/CT parameters were measured: maximum standardized uptake value (SUVmax), total lesion glycolysis (TLG), metabolic tumor volume (MTV), and tumor-to-normal-liver SUV ratio (TNR). Optimal cut-off values for the PET/CT parameters to stratify patients in terms of overall survival (OS) were determined. Multivariate analysis was performed to determine whether the PET/CT parameters could add to the prognostic value of the Cancer of the Liver Italian Program (CLIP) scoring system and the Barcelona-Clinic Liver Cancer (BCLC) staging system. RESULTS: The analysis included 56 patients. Univariate analysis of the association between OS and continuous variables, including the PET/CT parameters SUVmax, TLG, tumor size, total bilirubin level, and alkaline phosphatase level were significant predictors of OS. SUVmax ≥ 11.7, TLG ≥ 1,341, MTV ≥ 230 mL, and TNR ≥ 4.8 were identified as cut-off values. Multivariate analysis revealed that SUVmax ≥ 11.7 and TNR ≥ 4.8 were independent factors predicting a poor prognosis in both the CLIP scoring system and the BCLC staging system, as was TLG in the BCLC staging system. CONCLUSION: Pretreatment FDG PET/CT in patients with HCC can add to the prognostic value of standard clinical measures. Incorporation of imaging biomarkers derived from FDG PET/CT into HCC staging systems should be considered.

Multivariate functional response regression, with application to fluorescence spectroscopy in a cervical pre-cancer study.

Many scientific studies measure different types of high-dimensional signals or images from the same subject, producing multivariate functional data. These functional measurements carry different types of information about the scientific process, and a joint analysis that integrates information across them may provide new insights into the underlying mechanism for the phenomenon under study. Motivated by fluorescence spectroscopy data in a cervical pre-cancer study, a multivariate functional response regression model is proposed, which treats multivariate functional observations as responses and a common set of covariates as predictors. This novel modeling framework simultaneously accounts for correlations between functional variables and potential multi-level structures in data that are induced by experimental design. The model is fitted by performing a two-stage linear transformation-a basis expansion to each functional variable followed by principal component analysis for the concatenated basis coefficients. This transformation effectively reduces the intra-and inter-function correlations and facilitates fast and convenient calculation. A fully Bayesian approach is adopted to sample the model parameters in the transformed space, and posterior inference is performed after inverse-transforming the regression coefficients back to the original data domain. The proposed approach produces functional tests that flag local regions on the functional effects, while controlling the overall experiment-wise error rate or false discovery rate. It also enables functional discriminant analysis through posterior predictive calculation. Analysis of the fluorescence spectroscopy data reveals local regions with differential expressions across the pre-cancer and normal samples. These regions may serve as biomarkers for prognosis and disease assessment.

Comparison and Contrast of Two General Functional Regression Modeling Frameworks.

In this article, Greven and Scheipl describe an impressively general framework for performing functional regression that builds upon the generalized additive modeling framework. Over the past number of years, my collaborators and I have also been developing a general framework for functional regression, functional mixed models, which shares many similarities with this framework, but has many differences as well. In this discussion, I compare and contrast these two frameworks, to hopefully illuminate characteristics of each, highlighting their respecitve strengths and weaknesses, and providing recommendations regarding the settings in which each approach might be preferable.

Statistical Contributions to Bioinformatics: Design, Modeling, Structure Learning, and Integration.

The advent of high-throughput multi-platform genomics technologies providing whole-genome molecular summaries of biological samples has revolutionalized biomedical research. These technologiees yield highly structured big data, whose analysis poses significant quantitative challenges. The field of Bioinformatics has emerged to deal with these challenges, and is comprised of many quantitative and biological scientists working together to effectively process these data and extract the treasure trove of information they contain. Statisticians, with their deep understanding of variability and uncertainty quantification, play a key role in these efforts. In this article, we attempt to summarize some of the key contributions of statisticians to bioinformatics, focusing on four areas: (1) experimental design and reproducibility, (2) preprocessing and feature extraction, (3) unified modeling, and (4) structure learning and integration. In each of these areas, we highlight some key contributions and try to elucidate the key statistical principles underlying these methods and approaches. Our goals are to demonstrate major ways in which statisticians have contributed to bioinformatics, encourage statisticians to get involved early in methods development as new technologies emerge, and to stimulate future methodological work based on the statistical principles elucidated in this article and utilizing all availble information to uncover new biological insights.

Rejoinder to statistical contributions to bioinformatics: Design, modelling, structure learning and Integration.

We thank the discussants for their kind comments and their insightful analysis and discussion that has substantially added to the contribution of this issue. Overall, it seems the discussants have affirmed many of our primary points, and have also raised a number of other relevant and important issues that we did not emphasize in the paper. Several common threads emerged from these discussions, including the importance of software development, appropriate dissemination, and close collaboration with biomedical scientists and technology experts in order to ensure our work is relevant and impactful. Each discussant also mentioned other areas of bioinformatics that have been impacted by statistical researchers that we did not highlight in the original paper. In response, we will first summarize discuss these general themes, and then respond to specific comments of each discussant, and finally talk about the additional areas of bioinformatics impacted by statisticians that were mentioned by the reviewers.