Second-order group knockoffs with applications to GWAS.

Conditional testing via the knockoff framework allows one to identify -- among large number of possible explanatory variables -- those that carry unique information about an outcome of interest, and also provides a false discovery rate guarantee on the selection. This approach is particularly well suited to the analysis of genome wide association studies (GWAS), which have the goal of identifying genetic variants which influence traits of medical relevance. While conditional testing can be both more powerful and precise than traditional GWAS analysis methods, its vanilla implementation encounters a difficulty common to all multivariate analysis methods: it is challenging to distinguish among multiple, highly correlated regressors. This impasse can be overcome by shifting the object of inference from single variables to groups of correlated variables. To achieve this, it is necessary to construct "group knockoffs." While successful examples are already documented in the literature, this paper substantially expands the set of algorithms and software for group knockoffs. We focus in particular on second-order knockoffs, for which we describe correlation matrix approximations that are appropriate for GWAS data and that result in considerable computational savings. We illustrate the effectiveness of the proposed methods with simulations and with the analysis of albuminuria data from the UK Biobank. The described algorithms are implemented in an open-source Julia package Knockoffs.jl, for which both R and Python wrappers are available.

Controlled Variable Selection from Summary Statistics Only? A Solution via GhostKnockoffs and Penalized Regression.

Identifying which variables do influence a response while controlling false positives pervades statistics and data science. In this paper, we consider a scenario in which we only have access to summary statistics, such as the values of marginal empirical correlations between each dependent variable of potential interest and the response. This situation may arise due to privacy concerns, e.g., to avoid the release of sensitive genetic information. We extend GhostKnockoffs He et al. [2022] and introduce variable selection methods based on penalized regression achieving false discovery rate (FDR) control. We report empirical results in extensive simulation studies, demonstrating enhanced performance over previous work. We also apply our methods to genome-wide association studies of Alzheimer's disease, and evidence a significant improvement in power.

Beyond guilty by association at scale: searching for causal variants on the basis of genome-wide summary statistics.

Understanding the causal genetic architecture of complex phenotypes is essential for future research into disease mechanisms and potential therapies. Here, we present a novel framework for genome-wide detection of sets of variants that carry non-redundant information on the phenotypes and are therefore more likely to be causal in a biological sense. Crucially, our framework requires only summary statistics obtained from standard genome-wide marginal association testing. The described approach, implemented in open-source software, is also computationally efficient, requiring less than 15 minutes on a single CPU to perform genome-wide analysis. Through extensive genome-wide simulation studies, we show that the method can substantially outperform usual two-stage marginal association testing and fine-mapping procedures in precision and recall. In applications to a meta-analysis of ten large-scale genetic studies of Alzheimer's disease (AD), we identified 82 loci associated with AD, including 37 additional loci missed by conventional GWAS pipeline. The identified putative causal variants achieve state-of-the-art agreement with massively parallel reporter assays and CRISPR-Cas9 experiments. Additionally, we applied the method to a retrospective analysis of 67 large-scale GWAS summary statistics since 2013 for a variety of phenotypes. Results reveal the method's capacity to robustly discover additional loci for polygenic traits and pinpoint potential causal variants underpinning each locus beyond conventional GWAS pipeline, contributing to a deeper understanding of complex genetic architectures in post-GWAS analyses.

Ocular Surface Evaluation in Immunoglobulin G4-Related Ophthalmic Disease.

PURPOSE: To evaluate the functional and structural changes of the meibomian glands and ocular surface in immunoglobulin G4-related ophthalmic disease (IgG4-ROD) patients. DESIGN: Cross-sectional, matched case-control comparison study. METHODS: This study included 64 patients with biopsy-proven IgG4-ROD (aged 63.4 ± 12.2 years, 39 male) and 64 sex- and age-matched healthy controls. Patients were managed by hospitals covering the publicly funded ophthalmology service in Hong Kong. Outcome measures included anterior segment examination and keratographic and meibographic imagings. RESULTS: A total of 64 worst-affected eyes of the 64 IgG4-ROD patients were analyzed. Corneal fluorescein staining (P = .0187), lid margin telangiectasia (P = .0360), lid-parallel conjunctival folds (P = .0112), papillae (P = .0393), meibomian gland plugging (P = .0001), meibomian gland expressibility (P = .0001), and meibum quality (P = .0001) were more significant in IgG4-ROD patients compared with healthy controls. Both upper and lower meibomian gland dropouts (P = .001 and .0003), and tear meniscus height (P = .0001) were higher in IgG4-ROD patients. Non-invasive tear break-up time (NITBUT) (P = .0166) and Schirmer test results (P = .0243) were lower in IgG4-ROD patients. Upper (r = 0.336, P = .0140) meibomian gland dropouts and NITBUT (r = -0.293, P = .0497) were positively and negatively correlated with the IgG4-ROD onset age, respectively. The number of extraocular organ involvement was negatively correlated with the Schirmer test(r = -0.341, P = .0167). Lower NITBUT was found in IgG4-ROD eyes with lacrimal gland enlargement than in IgG4-ROD eyes without lacrimal gland enlargement radiologically (P < .0001). CONCLUSIONS: IgG4-ROD patients showed features of both aqueous tear deficiency and evaporative dry eye disease. We recommend ocular surface evaluation to all patients newly diagnosed with IgG4-ROD. Further studies are warranted to clarify the mechanism of IgG4-related dry eye disease.

Who did the ACA Medicaid expansion impact? Estimating the probability of being a complier.

Who enrolled in Medicaid as a consequence of the Affordable Care Act (ACA)? Using the 2010-2017 American Community Survey, I estimate how characteristics relating to work status and race/ethnicity affect the probability that an individual will be a complier, defined as those induced by the ACA Medicaid expansion to obtain Medicaid coverage. Across all states, I find that part-time workers, not non-workers, are the most likely to be compliers. This finding is not consistent with certain notions that Medicaid participants are the "undeserving poor" - a sentiment that may have hindered efforts to expand Medicaid in certain states. Additionally, I find that in non-expansion states, many of which have high Black populations, the probability of being a complier is higher for Blacks than for other racial/ethnic groups, suggesting that Black people in non-expansion states would be the largest beneficiaries of any new expansions. This paper not only identifies the types of individuals who were already impacted by the expansion but also identifies which populations would benefit the most from subsequent expansions.

Multivariate genome-wide association analysis by iterative hard thresholding.

MOTIVATION: In a genome-wide association study, analyzing multiple correlated traits simultaneously is potentially superior to analyzing the traits one by one. Standard methods for multivariate genome-wide association study operate marker-by-marker and are computationally intensive. RESULTS: We present a sparsity constrained regression algorithm for multivariate genome-wide association study based on iterative hard thresholding and implement it in a convenient Julia package MendelIHT.jl. In simulation studies with up to 100 quantitative traits, iterative hard thresholding exhibits similar true positive rates, smaller false positive rates, and faster execution times than GEMMA's linear mixed models and mv-PLINK's canonical correlation analysis. On UK Biobank data with 470 228 variants, MendelIHT completed a three-trait joint analysis (n=185 656) in 20 h and an 18-trait joint analysis (n=104 264) in 53 h with an 80 GB memory footprint. In short, MendelIHT enables geneticists to fit a single regression model that simultaneously considers the effect of all SNPs and dozens of traits. AVAILABILITY AND IMPLEMENTATION: Software, documentation, and scripts to reproduce our results are available from https://github.com/OpenMendel/MendelIHT.jl.

Unsupervised discovery of ancestry-informative markers and genetic admixture proportions in biobank-scale datasets.

Admixture estimation plays a crucial role in ancestry inference and genome-wide association studies (GWASs). Computer programs such as ADMIXTURE and STRUCTURE are commonly employed to estimate the admixture proportions of sample individuals. However, these programs can be overwhelmed by the computational burdens imposed by the 105 to 106 samples and millions of markers commonly found in modern biobanks. An attractive strategy is to run these programs on a set of ancestry-informative SNP markers (AIMs) that exhibit substantially different frequencies across populations. Unfortunately, existing methods for identifying AIMs require knowing ancestry labels for a subset of the sample. This supervised learning approach creates a chicken and the egg scenario. In this paper, we present an unsupervised, scalable framework that seamlessly carries out AIM selection and likelihood-based estimation of admixture proportions. Our simulated and real data examples show that this approach is scalable to modern biobank datasets. OpenADMIXTURE, our Julia implementation of the method, is open source and available for free.

Optimized Replacement T4 and T4+T3 Dosing in Male and Female Hypothyroid Patients With Different BMIs Using a Personalized Mechanistic Model of Thyroid Hormone Regulation Dynamics.

Objective: A personalized simulation tool, p-THYROSIM, was developed (1) to better optimize replacement LT4 and LT4+LT3 dosing for hypothyroid patients, based on individual hormone levels, BMIs, and gender; and (2) to better understand how gender and BMI impact thyroid dynamical regulation over time in these patients. Methods: p-THYROSIM was developed by (1) modifying and refining THYROSIM, an established physiologically based mechanistic model of the system regulating serum T3, T4, and TSH level dynamics; (2) incorporating sex and BMI of individual patients into the model; and (3) quantifying it with 3 experimental datasets and validating it with a fourth containing data from distinct male and female patients across a wide range of BMIs. For validation, we compared our optimized predictions with previously published results on optimized LT4 monotherapies. We also optimized combination T3+T4 dosing and computed unmeasured residual thyroid function (RTF) across a wide range of BMIs from male and female patient data. Results: Compared with 3 other dosing methods, the accuracy of p-THYROSIM optimized dosages for LT4 monotherapy was better overall (53% vs. 44%, 43%, and 38%) and for extreme BMI patients (63% vs. ~51% low BMI, 48% vs. ~36% and 22% for high BMI). Optimal dosing for combination LT4+LT3 therapy and unmeasured RTFs was predictively computed with p-THYROSIM for male and female patients in low, normal, and high BMI ranges, yielding daily T3 doses of 5 to 7.5 µg of LT3 combined with 62.5-100 µg of LT4 for women or 75-125 µg of LT4 for men. Also, graphs of steady-state serum T3, T4, and TSH concentrations vs. RTF (range 0%-50%) for untreated patients showed that neither BMI nor gender had any effect on RTF predictions for our patient cohort data. Notably, the graphs provide a means for estimating unmeasurable RTFs for individual patients from their hormone measurements before treatment. Conclusions: p-THYROSIM can provide accurate monotherapies for male and female hypothyroid patients, personalized with their BMIs. Where combination therapy is warranted, our results predict that not much LT3 is needed in addition to LT4 to restore euthyroid levels, suggesting opportunities for further research exploring combination therapy with lower T3 doses and slow-releasing T3 formulations.

A fast data-driven method for genotype imputation, phasing and local ancestry inference: MendelImpute.jl.

MOTIVATION: Current methods for genotype imputation and phasing exploit the volume of data in haplotype reference panels and rely on hidden Markov models (HMMs). Existing programs all have essentially the same imputation accuracy, are computationally intensive and generally require prephasing the typed markers. RESULTS: We introduce a novel data-mining method for genotype imputation and phasing that substitutes highly efficient linear algebra routines for HMM calculations. This strategy, embodied in our Julia program MendelImpute.jl, avoids explicit assumptions about recombination and population structure while delivering similar prediction accuracy, better memory usage and an order of magnitude or better run-times compared to the fastest competing method. MendelImpute operates on both dosage data and unphased genotype data and simultaneously imputes missing genotypes and phase at both the typed and untyped SNPs (single nucleotide polymorphisms). Finally, MendelImpute naturally extends to global and local ancestry estimation and lends itself to new strategies for data compression and hence faster data transport and sharing. AVAILABILITY AND IMPLEMENTATION: Software, documentation and scripts to reproduce our results are available from https://github.com/OpenMendel/MendelImpute.jl. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Iterative hard thresholding in genome-wide association studies: Generalized linear models, prior weights, and double sparsity.

BACKGROUND: Consecutive testing of single nucleotide polymorphisms (SNPs) is usually employed to identify genetic variants associated with complex traits. Ideally one should model all covariates in unison, but most existing analysis methods for genome-wide association studies (GWAS) perform only univariate regression. RESULTS: We extend and efficiently implement iterative hard thresholding (IHT) for multiple regression, treating all SNPs simultaneously. Our extensions accommodate generalized linear models, prior information on genetic variants, and grouping of variants. In our simulations, IHT recovers up to 30% more true predictors than SNP-by-SNP association testing and exhibits a 2-3 orders of magnitude decrease in false-positive rates compared with lasso regression. We also test IHT on the UK Biobank hypertension phenotypes and the Northern Finland Birth Cohort of 1966 cardiovascular phenotypes. We find that IHT scales to the large datasets of contemporary human genetics and recovers the plausible genetic variants identified by previous studies. CONCLUSIONS: Our real data analysis and simulation studies suggest that IHT can (i) recover highly correlated predictors, (ii) avoid over-fitting, (iii) deliver better true-positive and false-positive rates than either marginal testing or lasso regression, (iv) recover unbiased regression coefficients, (v) exploit prior information and group-sparsity, and (vi) be used with biobank-sized datasets. Although these advances are studied for genome-wide association studies inference, our extensions are pertinent to other regression problems with large numbers of predictors.

OPENMENDEL: a cooperative programming project for statistical genetics.

Statistical methods for genome-wide association studies (GWAS) continue to improve. However, the increasing volume and variety of genetic and genomic data make computational speed and ease of data manipulation mandatory in future software. In our view, a collaborative effort of statistical geneticists is required to develop open source software targeted to genetic epidemiology. Our attempt to meet this need is called the OPENMENDEL project (https://openmendel.github.io). It aims to (1) enable interactive and reproducible analyses with informative intermediate results, (2) scale to big data analytics, (3) embrace parallel and distributed computing, (4) adapt to rapid hardware evolution, (5) allow cloud computing, (6) allow integration of varied genetic data types, and (7) foster easy communication between clinicians, geneticists, statisticians, and computer scientists. This article reviews and makes recommendations to the genetic epidemiology community in the context of the OPENMENDEL project.

Comparison of Twitter Use of the American Society of Anesthesiologists and the American Association of Nurse Anesthetists.

Twitter has become a powerful tool for dissemination of information. The objective of this study was to evaluate Twitter usage of the American Society of Anesthesiologists (ASA) and the American Association of Nurse Anesthetists (AANA). All tweets from ASA ("@ASALifeline") and AANA ("@aanawebupdates") were collected over a 1-year time period. The content of each tweet was categorized using a rubric. ASA generated more original tweets than AANA. Twitter use was highest in October for ASA and September for AANA. Both societies are actively using Twitter. Future work should evaluate the impact of societal Twitter use.

An efficient protocol for computing the pKa of Zn-bound water.

At a given pH, whether a metal-bound water molecule is deprotonated or not can be determined if the pKa of the metal-bound water molecule (denoted pKw) is known. Although protocols/tools to predict the protonation states of titratable amino acid residues and small molecules have been developed, an efficient and accurate method to predict the absolute pKw values of metal complexes is lacking. Here, we present calibrated methods for optimizing the geometries and computing the absolute pKw values of a wide range of Zn2+-complexes containing protein-like ligating groups. We tested 18 different geometry-optimization methods on 19 ultra high-resolution structures of Zn2+ complexes of varying coordination numbers and ligating atoms and 98 methods in reproducing 36 experimental pKw values of diverse Zn2+ complexes in the absence and presence of explicit water molecules. The results underscore the importance of estimating the Zn2+-bound water/hydroxide solvation properly, whereas correcting for the basis set superposition error was not found to be important. The protocol presented can be used to (i) evaluate the geometries of the different Zn2+-sites found in proteins and (ii) to dissect the individual contributions of the various factors modulating the pKw in Zn2+-sites found in proteins. Predicting absolute pKw values in various environments with efficiency and accuracy will indicate when a Zn2+-bound water molecule is deprotonated, thus providing physical insight into the mechanisms of enzyme-catalyzed reactions and the design of drug candidates that can displace a metal-bound water molecule.

Expression Patterns, Prognostic Value, and Intratumoral Heterogeneity of PD-L1 and PD-1 in Thymoma and Thymic Carcinoma.

INTRODUCTION: Thymic epithelial tumors (TETs) including thymoma and thymic carcinoma are rare tumors with little data available to guide treatment. Immunotherapy with checkpoint blockade has shown promising activity, but data regarding the expression patterns and prognostic implications of programmed death 1 (PD-1) and its ligand (PD-L1) in TETs have yielded conflicting results. Intratumoral heterogeneity of PD-1/L1 expression has been shown in other cancers, but has not been described in the TET literature. METHODS: We performed a retrospective single-center review of 35 patients with resected TET. PD-1/L1 expression was assessed by immunohistochemistry using PD-1 clone: NAT105 and PD-L1 clone: 22C3. Tumor samples from 35 patients were evaluated including 32 patients with thymoma and 3 patients with thymic carcinoma. RESULTS: PD-L1 expression was detected in 83% (29 of 35) tumor samples, including 100% (3 of 3) of thymic carcinoma patients and 81% (26 of 32) of thymoma patients. PD-1 expression was detected in 77% (27 of 35), including 33% (1 of 3) of thymic carcinoma patients and 81% (26 of 32) thymoma patients. High PD-1 expression was associated with lower grade tumors. Unlike prior studies, PD-L1 expression was not associated with higher grade tumors or higher stage. Neither PD-L1 nor PD-1 expression was significantly associated with survival. Three patients with thymoma had multiple tumor sections evaluated for expression of PD-1/L1, with differing expression patterns of both PD-L1 and PD-1 observed in two patients. CONCLUSIONS: This study confirms high expression of PD-L1 and PD-1 in TET and shows for the first time intratumoral heterogeneity of PD-L1 and PD-1 in thymoma patients.

Adsorption dynamics of tannin on deacetylated electrospun Konjac glucomannan fabric.

A nonwoven fabric of Konjac glucomannan (KGM) for the adsorption of tannin was fabricated by using electrospinning and then followed by deacetylation with alkaline solution. To analyze the adsorption dynamics of tannin, the time course of the adsorption of tannin on the nonwoven KGM fabric was measured by immersing the fabric in tannin solution at different concentrations of tannin and amounts of the fabric. The initial and late stages of the adsorption behavior could be expressed, respectively, by using a diffusion-limited equation and a stoichiometric equation. A discussion on the dependence of the control parameters on the adsorption behavior is presented. The results represent the first step to provide an effective adsorption procedure for tannin in the use of modified KGM fabric.

Frequency, risk factors, and outcomes of central nervous system relapse in lymphoma patients treated with dose-adjusted EPOCH plus rituximab.

BACKGROUND: Central nervous system (CNS) relapse in non-Hodgkin lymphoma (NHL) is a rare but serious complication that carries a poor prognosis. The use of infusional etoposide, prednisone, vincristine, cyclophosphamide, doxorubicin, and rituximab (EPOCH-R) for frontline treatment of diffuse large B cell lymphoma (DLBCL) is increasing, though little is known about incidence of and risk factors for CNS relapse with this regimen PATIENTS AND METHODS: We completed a chart review of patients with NHL who received EPOCH-R as front line therapy. Data obtained included baseline and treatment characteristics including if patients received CNS directed therapy. We measured overall survival (OS), progression free survival (PFS), and progression to CNS involvement. RESULTS: We identified 223 patients who met the inclusion criteria, 72% had DLBCL. Of all the patients, 5.8% experienced CNS relapse, and 38.6% were treated with CNS prophylaxis. There was no difference in rate of CNS relapse, OS, or PFS between patients who had and had not received CNS prophylaxis. Patients whose serum lactate dehydrogenase was greater than twice the upper limit of normal at diagnosis and those with extranodal disease were significantly more likely to have CNS relapse (P = .0247 and 0.022, respectively) than their counterparts. CONCLUSIONS: The rate of CNS relapse in this patient population approaches 6%, not significantly different from reports on those receiving R-CHOP. The results of this study suggest that CNS prophylaxis might be more selectively used among patients treated with EPOCH-R with certain high-risk features.

Characterization of Nanocellulose Using Small-Angle Neutron, X-ray, and Dynamic Light Scattering Techniques.

Nanocellulose extracted from wood pulps using TEMPO (2,2,6,6-tetramethylpiperidine-1-oxyl radical)-mediated oxidation and sulfuric acid hydrolysis methods was characterized by small-angle neutron scattering (SANS), small-angle X-ray scattering (SAXS), and dynamic light scattering (DLS) techniques. The dimensions of this nanocellulose (TEMPO-oxidized cellulose nanofiber (TOCN) and sulfuric acid hydrolyzed cellulose nanocrystal (SACN)) revealed by the different scattering methods were compared with those characterized by transmission electron microscopy (TEM). The SANS and SAXS data were analyzed using a parallelepiped-based form factor. The width and thickness of the nanocellulose cross section were ∼8 and ∼2 nm for TOCN and ∼20 and ∼3 nm for SACN, respectively, where the fitting results from SANS and SAXS profiles were consistent with each other. DLS was carried out under both the VV mode with the polarizer and analyzer parallel to each other and the HV mode having them perpendicular to each other. Using rotational and translational diffusion coefficients obtained under the HV mode yielded a nanocellulose length qualitatively consistent with that observed by TEM, whereas the length derived by the translational diffusion coefficient under the VV mode appeared to be overestimated.

The supramolecular structure of bone: X-ray scattering analysis and lateral structure modeling.

The evolution of vertebrates required a key development in supramolecular evolution: internally mineralized collagen fibrils. In bone, collagen molecules and mineral crystals form a nanocomposite material comparable to cast iron in tensile strength, but several times lighter and more flexible. Current understanding of the internal nanoscale structure of collagen fibrils, derived from studies of rat tail tendon (RTT), does not explain how nucleation and growth of mineral crystals can occur inside a collagen fibril. Experimental obstacles encountered in studying bone have prevented a solution to this problem for several decades. This report presents a lateral packing model for collagen molecules in bone fibrils, based on the unprecedented observation of multiple resolved equatorial reflections for bone tissue using synchrotron small-angle X-ray scattering (SAXS; ∼1 nm resolution). The deduced structure for pre-mineralized bone fibrils includes features that are not present in RTT: spatially discrete microfibrils. The data are consistent with bone microfibrils similar to pentagonal Smith microfibrils, but are not consistent with the (nondiscrete) quasi-hexagonal microfibrils reported for RTT. These results indicate that collagen fibrils in bone and tendon differ in their internal structure in a manner that allows bone fibrils, but not tendon fibrils, to internally mineralize. In addition, the unique pattern of collagen cross-link types and quantities in mineralized tissues can be can be accounted for, in structural/functional terms, based on a discrete microfibril model.

Incorporation of Antiangiogenic Therapy Into the Non-Small-Cell Lung Cancer Paradigm.

Although molecular targeted agents have improved the treatment of lung cancer, their use has largely been restricted to limited subsets of the overall population that carry specific mutations. Angiogenesis, the formation of new blood vessels from existing networks, is an attractive, more general process for the development of targeted anticancer therapies, because it is critical for the growth of solid tumors, including non-small-cell lung cancer. Growing tissues require a vascular supply within a few millimeters. Therefore, solid tumors create a proangiogenic microenvironment to facilitate the development of new tumor-associated blood vessels, thus providing an adequate vascular supply for continued tumor growth. Antiangiogenic agents can specifically target the vascular endothelial growth factor (VEGF) signaling pathways, broadly inhibit multiple tyrosine kinases, or interfere with other angiogenic processes, such as disruption of existing tumor vasculature. The present report provides an overview of antiangiogenic therapy for non-small-cell lung cancer, including both currently approved antiangiogenic therapies (bevacizumab [anti-VEGF] and ramucirumab [anti-VEGF receptor 2] monoclonal antibodies), and a variety of promising novel agents in development. Although recent data have demonstrated promising efficacy for some novel agents, the overall development of antiangiogenic therapy has been hampered by redundancy in signaling pathways and the highly heterogeneous nature of tumors. An improved understanding of the molecular basis of angiogenesis will guide the development of new antiangiogenic therapies and the identification of biomarkers to predict which patients with lung cancer are most likely to benefit from antiangiogenic therapy.

Sole rearrangement but not amplification of MYC is associated with a poor prognosis in patients with diffuse large B cell lymphoma and B cell lymphoma unclassifiable.

Rearrangement of MYC is associated with a poor prognosis in patients with diffuse large B cell lymphoma (DLBCL) and B cell lymphoma unclassifiable (BCLU), particularly in the setting of double hit lymphoma (DHL). However, little is known about outcomes of patients who demonstrate MYC rearrangement without evidence of BCL2 or BCL6 rearrangement (single hit) or amplification (>4 copies) of MYC. We identified 87 patients with single hit lymphoma (SHL), 22 patients with MYC-amplified lymphoma (MYC amp) as well as 127 DLBCL patients without MYC rearrangement or amplification (MYC normal) and 45 patients with DHL, all treated with either R-CHOP (rituximab, cyclophosphamide, doxorubicin, vincristine, prednisone) or intensive induction therapy. For SHL and MYC amp patients, the 2-year progression-free survival rate (PFS) was 49% and 48% and 2-year overall survival rate (OS) was 59% and 71%, respectively. SHL patients receiving intensive induction experienced higher 2-year PFS (59% vs. 23%, P = 0·006) but similar 2-year OS as compared with SHL patients receiving R-CHOP. SHL DLBCL patients treated with R-CHOP, but not intensive induction, experienced significantly lower 2-year PFS and OS (P < 0·001 for both) when compared with MYC normal patients. SHL patients appear to have a poor prognosis, which may be improved with receipt of intensive induction.