Proteogenomic analysis of chemo-refractory high-grade serous ovarian cancer.

To improve the understanding of chemo-refractory high-grade serous ovarian cancers (HGSOCs), we characterized the proteogenomic landscape of 242 (refractory and sensitive) HGSOCs, representing one discovery and two validation cohorts across two biospecimen types (formalin-fixed paraffin-embedded and frozen). We identified a 64-protein signature that predicts with high specificity a subset of HGSOCs refractory to initial platinum-based therapy and is validated in two independent patient cohorts. We detected significant association between lack of Ch17 loss of heterozygosity (LOH) and chemo-refractoriness. Based on pathway protein expression, we identified 5 clusters of HGSOC, which validated across two independent patient cohorts and patient-derived xenograft (PDX) models. These clusters may represent different mechanisms of refractoriness and implicate putative therapeutic vulnerabilities.

A proteogenomic portrait of lung squamous cell carcinoma.

Lung squamous cell carcinoma (LSCC) remains a leading cause of cancer death with few therapeutic options. We characterized the proteogenomic landscape of LSCC, providing a deeper exposition of LSCC biology with potential therapeutic implications. We identify NSD3 as an alternative driver in FGFR1-amplified tumors and low-p63 tumors overexpressing the therapeutic target survivin. SOX2 is considered undruggable, but our analyses provide rationale for exploring chromatin modifiers such as LSD1 and EZH2 to target SOX2-overexpressing tumors. Our data support complex regulation of metabolic pathways by crosstalk between post-translational modifications including ubiquitylation. Numerous immune-related proteogenomic observations suggest directions for further investigation. Proteogenomic dissection of CDKN2A mutations argue for more nuanced assessment of RB1 protein expression and phosphorylation before declaring CDK4/6 inhibition unsuccessful. Finally, triangulation between LSCC, LUAD, and HNSCC identified both unique and common therapeutic vulnerabilities. These observations and proteogenomics data resources may guide research into the biology and treatment of LSCC.

Proteogenomic Characterization Reveals Therapeutic Vulnerabilities in Lung Adenocarcinoma.

To explore the biology of lung adenocarcinoma (LUAD) and identify new therapeutic opportunities, we performed comprehensive proteogenomic characterization of 110 tumors and 101 matched normal adjacent tissues (NATs) incorporating genomics, epigenomics, deep-scale proteomics, phosphoproteomics, and acetylproteomics. Multi-omics clustering revealed four subgroups defined by key driver mutations, country, and gender. Proteomic and phosphoproteomic data illuminated biology downstream of copy number aberrations, somatic mutations, and fusions and identified therapeutic vulnerabilities associated with driver events involving KRAS, EGFR, and ALK. Immune subtyping revealed a complex landscape, reinforced the association of STK11 with immune-cold behavior, and underscored a potential immunosuppressive role of neutrophil degranulation. Smoking-associated LUADs showed correlation with other environmental exposure signatures and a field effect in NATs. Matched NATs allowed identification of differentially expressed proteins with potential diagnostic and therapeutic utility. This proteogenomics dataset represents a unique public resource for researchers and clinicians seeking to better understand and treat lung adenocarcinomas.

Integrated Proteogenomic Characterization across Major Histological Types of Pediatric Brain Cancer.

We report a comprehensive proteogenomics analysis, including whole-genome sequencing, RNA sequencing, and proteomics and phosphoproteomics profiling, of 218 tumors across 7 histological types of childhood brain cancer: low-grade glioma (n = 93), ependymoma (32), high-grade glioma (25), medulloblastoma (22), ganglioglioma (18), craniopharyngioma (16), and atypical teratoid rhabdoid tumor (12). Proteomics data identify common biological themes that span histological boundaries, suggesting that treatments used for one histological type may be applied effectively to other tumors sharing similar proteomics features. Immune landscape characterization reveals diverse tumor microenvironments across and within diagnoses. Proteomics data further reveal functional effects of somatic mutations and copy number variations (CNVs) not evident in transcriptomics data. Kinase-substrate association and co-expression network analysis identify important biological mechanisms of tumorigenesis. This is the first large-scale proteogenomics analysis across traditional histological boundaries to uncover foundational pediatric brain tumor biology and inform rational treatment selection.

Integrated Proteogenomic Characterization of Clear Cell Renal Cell Carcinoma.

To elucidate the deregulated functional modules that drive clear cell renal cell carcinoma (ccRCC), we performed comprehensive genomic, epigenomic, transcriptomic, proteomic, and phosphoproteomic characterization of treatment-naive ccRCC and paired normal adjacent tissue samples. Genomic analyses identified a distinct molecular subgroup associated with genomic instability. Integration of proteogenomic measurements uniquely identified protein dysregulation of cellular mechanisms impacted by genomic alterations, including oxidative phosphorylation-related metabolism, protein translation processes, and phospho-signaling modules. To assess the degree of immune infiltration in individual tumors, we identified microenvironment cell signatures that delineated four immune-based ccRCC subtypes characterized by distinct cellular pathways. This study reports a large-scale proteogenomic analysis of ccRCC to discern the functional impact of genomic alterations and provides evidence for rational treatment selection stemming from ccRCC pathobiology.

Power of Bayesian and Heuristic Tests to Detect Cross-Species Introgression with Reference to Gene Flow in the Tamias quadrivittatus Group of North American Chipmunks.

In the past two decades, genomic data have been widely used to detect historical gene flow between species in a variety of plants and animals. The Tamias quadrivittatus group of North America chipmunks, which originated through a series of rapid speciation events, are known to undergo massive amounts of mitochondrial introgression. Yet in a recent analysis of targeted nuclear loci from the group, no evidence for cross-species introgression was detected, indicating widespread cytonuclear discordance. The study used the heuristic method HYDE to detect gene flow, which may suffer from low power. Here we use the Bayesian method implemented in the program BPP to re-analyze these data. We develop a Bayesian test of introgression, calculating the Bayes factor via the Savage-Dickey density ratio using the Markov chain Monte Carlo (MCMC) sample under the model of introgression. We take a stepwise approach to constructing an introgression model by adding introgression events onto a well-supported binary species tree. The analysis detected robust evidence for multiple ancient introgression events affecting the nuclear genome, with introgression probabilities reaching 63%. We estimate population parameters and highlight the fact that species divergence times may be seriously underestimated if ancient cross-species gene flow is ignored in the analysis. We examine the assumptions and performance of HYDE and demonstrate that it lacks power if gene flow occurs between sister lineages or if the mode of gene flow does not match the assumed hybrid-speciation model with symmetrical population sizes. Our analyses highlight the power of likelihood-based inference of cross-species gene flow using genomic sequence data. [Bayesian test; BPP; chipmunks; introgression; MSci; multispecies coalescent; Savage-Dickey density ratio.].

Prospective Cohort Study to Investigate the Safety of Preoperative Tumor Necrosis Factor Inhibitor Exposure in Patients With Inflammatory Bowel Disease Undergoing Intra-abdominal Surgery.

BACKGROUND & AIMS: Whether preoperative treatment of inflammatory bowel disease (IBD) with tumor necrosis factor inhibitors (TNFis) increases the risk of postoperative infectious complications remains controversial. The primary aim of this study was to determine whether preoperative exposure to TNFis is an independent risk factor for postoperative infectious complications within 30 days of surgery. METHODS: We conducted a multicenter prospective observational study of patients with IBD undergoing intra-abdominal surgery across 17 sites from the Crohn's & Colitis Foundation Clinical Research Alliance. Infectious complications were categorized as surgical site infections (SSIs) or non-SSIs. Current TNFi exposure was defined as use within 12 weeks of surgery, and serum was collected for drug-level analyses. Multivariable models for occurrence of the primary outcome, any infection, or SSI were adjusted by predefined covariates (age, sex, preoperative steroid use, and disease type), baseline variables significantly associated (P < .05) with any infection or SSI separately, and TNFi exposure status. Exploratory models used TNFi exposure based on serum drug concentration. RESULTS: A total of 947 patients were enrolled from September 2014 through June 2017. Current TNFi exposure was reported by 382 patients. Any infection (18.1% vs 20.2%, P = .469) and SSI (12.0% vs 12.6%, P = .889) rates were similar in patients currently exposed to TNFis and those unexposed. In multivariable analysis, current TNFi exposure was not associated with any infection (odds ratio, 1.050; 95% confidence interval, 0.716-1.535) or SSI (odds ratio, 1.249; 95% confidence interval, 0.793-1.960). Detectable TNFi drug concentration was not associated with any infection or SSI. CONCLUSIONS: Preoperative TNFi exposure was not associated with postoperative infectious complications in a large prospective multicenter cohort.

Metabolic engineering and mechanical investigation of enhanced plant autoluminescence.

The fungal bioluminescence pathway (FBP) was identified from glowing fungi, which releases self-sustained visible green luminescence. However, weak bioluminescence limits the potential application of the bioluminescence system. Here, we screened and characterized a C3'H1 (4-coumaroyl shikimate/quinate 3'-hydroxylase) gene from Brassica napus, which efficiently converts p-coumaroyl shikimate to caffeic acid and hispidin. Simultaneous expression of BnC3'H1 and NPGA (null-pigment mutant in A. nidulans) produces more caffeic acid and hispidin as the natural precursor of luciferin and significantly intensifies the original fungal bioluminescence pathway (oFBP). Thus, we successfully created enhanced FBP (eFBP) plants emitting 3 × 1011 photons/min/cm2 , sufficient to illuminate its surroundings and visualize words clearly in the dark. The glowing plants provide sustainable and bio-renewable illumination for the naked eyes, and manifest distinct responses to diverse environmental conditions via caffeic acid biosynthesis pathway. Importantly, we revealed that the biosynthesis of caffeic acid and hispidin in eFBP plants derived from the sugar pathway, and the inhibitors of the energy production system significantly reduced the luminescence signal rapidly from eFBP plants, suggesting that the FBP system coupled with the luciferin metabolic flux functions in an energy-driven way. These findings lay the groundwork for genetically creating stronger eFBP plants and developing more powerful biological tools with the FBP system.

Homogeneous Lateral Lithium Intercalation into Transition Metal Dichalcogenides via Ion Backgating.

Lithium intercalation has become a versatile tool for realizing emergent quantum phenomena in two-dimensional (2D) materials. However, the insertion of lithium ions may be accompanied by the creation of wrinkles and cracks, which prevents the material from manifesting its intrinsic properties under substantial charge injection. By using the recently developed ion backgating technique, we successfully realize lateral intercalation in 1T-TiSe2 and 2H-NbSe2, which shows substantially improved sample homogeneity. The homogeneity at high lithium doping is not only demonstrated via low-temperature transport measurements but also directly visualized by topographical imaging through in situ atomic force microscopy (AFM). The application of lateral intercalation to a broad spectrum of 2D materials can greatly facilitate the search for exotic quantum phenomena.

A flexible approach for causal inference with multiple treatments and clustered survival outcomes.

When drawing causal inferences about the effects of multiple treatments on clustered survival outcomes using observational data, we need to address implications of the multilevel data structure, multiple treatments, censoring, and unmeasured confounding for causal analyses. Few off-the-shelf causal inference tools are available to simultaneously tackle these issues. We develop a flexible random-intercept accelerated failure time model, in which we use Bayesian additive regression trees to capture arbitrarily complex relationships between censored survival times and pre-treatment covariates and use the random intercepts to capture cluster-specific main effects. We develop an efficient Markov chain Monte Carlo algorithm to draw posterior inferences about the population survival effects of multiple treatments and examine the variability in cluster-level effects. We further propose an interpretable sensitivity analysis approach to evaluate the sensitivity of drawn causal inferences about treatment effect to the potential magnitude of departure from the causal assumption of no unmeasured confounding. Expansive simulations empirically validate and demonstrate good practical operating characteristics of our proposed methods. Applying the proposed methods to a dataset on older high-risk localized prostate cancer patients drawn from the National Cancer Database, we evaluate the comparative effects of three treatment approaches on patient survival, and assess the ramifications of potential unmeasured confounding. The methods developed in this work are readily available in the R $$ \mathsf{R}\kern.15em $$ package riAFTBART $$ \mathsf{riAFTBART} $$ .

Prior Immunogenicity to Anti-TNF Biologics Is Not Associated with Increased Anti-drug Antibodies to Vedolizumab or Ustekinumab.

INTRODUCTION: Patients with inflammatory bowel disease (IBD) on biologic therapy may lose response to anti-tumor necrosis factor agents (anti-TNFs) due to the development of anti-drug antibodies (ADAs). A history of anti-TNF ADA increases the risk of developing ADA to subsequent anti-TNFs; however, it is not known whether ADA to anti-TNFs increases the risk of ADA development to vedolizumab (VDZ) or ustekinumab (UST). We aimed to investigate whether prior history of ADA to anti-TNF increases the risk of ADA to VDZ and UST. METHODS: We conducted a retrospective cohort study of patients at a tertiary care IBD center over the course of four years who had previous anti-TNF drug and ADA level data during maintenance treatment and subsequent VDZ or UST drug and antibody levels, all collected as standard of care. The primary outcome was the rate of ADA development to VDZ and UST in patients with and without prior anti-TNF immunogenicity. Descriptive statistics summarized the data and univariate tested associations. RESULTS: Of the 152 IBD patients analyzed, 41 (27%) had a history of previous anti-TNF ADA with 22 (53.7%) having simultaneously undetectable anti-TNF drug levels. There was no significant difference in the rates of ustekinumab and vedolizumab ADA development between patients with prior ADA and patients without prior ADA (1/41 [2.7%] vs 1/111 [0.9%]; p = 0.54). There was also no difference in concomitant immunomodulator use with ustekinumab or vedolizumab initiation in patients with or without prior ADA (13/41 [31.7%] vs 31/111 [27.9%], p = 0.84). Neither patient who developed ADA to VDZ or UST was on concomitant immunomodulator at drug initiation, and both patients had detectable drug levels at the time of antibody detection. CONCLUSIONS: We observed that prior immunogenicity to anti-TNF agents does not confer an increased risk of immunogenicity to ustekinumab or vedolizumab. Our data support the use of vedolizumab or ustekinumab as monotherapy for the treatment of IBD.

De Novo Ostomy Placement Is Associated with Increased Outpatient Opioid Use In Patients with Inflammatory Bowel Disease.

BACKGROUND/AIMS: Opioid use is associated with poor outcomes in patients with inflammatory bowel disease (IBD). We aimed to identify novel factors associated with increased outpatient opioid (OPRx) use following IBD-related hospitalization. METHODS: This was a retrospective cohort study of IBD patients ≥ 18 years old, hospitalized during 2018. The primary outcome was receiving ≥ 1(OPRx) in the year following index hospitalization (IH), excluding prescriptions written within 2 weeks of discharge. Secondary outcomes included having 1-2 vs ≥ 3 OPRx and rates of healthcare utilization. Univariate and multivariate analyses tested associations with OPRx. RESULTS: Of 526 patients analyzed, 209 (40%) received at least 1 OPRx; with a median of 2 [1-3] OPRx. Presence or placement of ostomy at IH, exposure to opioids during IH, ulcerative colitis (UC), mental health comorbidities, admission for surgery and managed on the surgical service, and IBD surgery within 1 year prior to IH were associated with ≥ 1 OPRx on univariate analysis. On multivariable analysis, UC, ostomy placement during IH, anxiety, and inpatient opioid exposure were independently associated with ≥ 1 OPRx. A majority (> 70%) of both inpatient and outpatient opioid prescriptions were written by surgeons. Patients requiring ≥ 3 OPRx had the highest rates of unplanned IBD surgery (56% p = 0.04), all-cause repeat hospitalization (81%, p = 0.003), and IBD-related repeat hospitalization (77%, p = 0.007) in the year following IH. CONCLUSIONS: A multimodal approach to pain management for IBD patients, as well as increased recognition that any patient with a de novo ostomy is at particular risk of opioid use, is needed.

Gestational Diabetes, Subsequent Type 2 Diabetes, and Food Security Status: National Health and Nutrition Examination Survey, 2007-2018.

INTRODUCTION: Despite many studies linking various risk factors to the association between gestational diabetes and subsequent type 2 diabetes, little is known about how food insecurity affects their association. We aimed to assess how the association between gestational diabetes and subsequent type 2 diabetes varies by food security status among women in the US. METHODS: This study is a secondary data analysis of 9,505 US women aged 20 years or older who had at least 1 live birth; we used cross-sectional data from the National Health and Nutrition Examination Survey (NHANES) from 2007 through 2018. The main outcome was a diagnosis of type 2 diabetes in the subsequent years after the first live birth. We used multivariable survey-weighted negative binomial regressions to examine whether the association between gestational diabetes and subsequent type 2 diabetes differed by food security status, with and without adjusting for health behavior factors. RESULTS: Gestational diabetes was significantly associated with subsequent type 2 diabetes (incidence rate ratio [IRR], 2.57; 95% CI, 2.45-2.69). The association between gestational diabetes and subsequent type 2 diabetes was significantly different by food security status (IRR, 2.34; 95% CI, 2.23-2.45 among food-secure women; IRR, 2.99; 95% CI, 2.70-3.28 among food-insecure women). CONCLUSION: The association between gestational diabetes and subsequent type 2 diabetes differs significantly by food security status. Public health and health care practitioners should consider food security status when designing and implementing diabetes prevention interventions for women with a history of gestational diabetes.

Evaluating the Impact of New York's Executive Order on Face Mask Use on COVID-19 Cases and Mortality: a Comparative Interrupted Times Series Study.

BACKGROUND: On April 17, 2020, the State of New York (NY) implemented an Executive Order that requires all people in NY to wear a face mask or covering in public settings where social distancing cannot be maintained. Although the Centers for Disease Control and Prevention recommended face mask use by the general public, there is a lack of evidence on the effect of face mask policies on the spread of COVID-19 at the state level. OBJECTIVE: To assess the impact of the Executive Order on face mask use on COVID-19 cases and mortality in NY. DESIGN: A comparative interrupted time series analysis was used to assess the impact of the Executive Order in NY with Massachusetts (MA) as a comparison state. PARTICIPANTS: We analyzed data on COVID-19 in NY and MA from March 25 to May 6, 2020. INTERVENTION: The Executive Order on face mask use in NY. MAIN MEASURES: Daily numbers of COVID-19 confirmed cases and deaths. KEY RESULTS: The average daily number of confirmed cases in NY decreased from 8549 to 5085 after the Executive Order took effect, with a trend change of 341 (95% CI, 187-496) cases per day. The average daily number of deaths decreased from 521 to 384 during the same two time periods, with a trend change of 52 (95% CI, 44-60) deaths per day. Compared to MA, the decreasing trend in NY was significantly greater for both daily numbers of confirmed cases (P = 0.003) and deaths (P < 0.001). CONCLUSIONS: The Executive Order on face mask use in NY led to a significant decrease in both daily numbers of COVID-19 confirmed cases and deaths. Findings from this study provide important evidence to support state-level policies that require face mask use by the general public.

New frontiers against sorafenib resistance in renal cell carcinoma: From molecular mechanisms to predictive biomarkers.

Renal cell carcinoma (RCC) is a highly vascularized tumor and prone to distant metastasis. Sorafenib is the first targeted multikinase inhibitor and first-line chemical drug approved for RCC therapy. In fact, only a small number of RCC patients benefit significantly from sorafenib treatment, while the growing prevalence of sorafenib resistance has become a major obstacle for drug therapy effectivity of sorafenib. The molecular mechanisms of sorafenib resistance in RCC are not completely understood by now. Herein, we comprehensively summarize the underlying mechanisms of sorafenib resistance and molecular biomarkers for predicting sorafenib responsiveness. Moreover, we outline strategies suitable for overcoming sorafenib resistance and prospect potential approaches for identifying biomarkers associated with sorafenib resistance in RCC, which contributes to guide individualized and precision drug therapy.

Estimating heterogeneous survival treatment effect in observational data using machine learning.

Methods for estimating heterogeneous treatment effect in observational data have largely focused on continuous or binary outcomes, and have been relatively less vetted with survival outcomes. Using flexible machine learning methods in the counterfactual framework is a promising approach to address challenges due to complex individual characteristics, to which treatments need to be tailored. To evaluate the operating characteristics of recent survival machine learning methods for the estimation of treatment effect heterogeneity and inform better practice, we carry out a comprehensive simulation study presenting a wide range of settings describing confounded heterogeneous survival treatment effects and varying degrees of covariate overlap. Our results suggest that the nonparametric Bayesian Additive Regression Trees within the framework of accelerated failure time model (AFT-BART-NP) consistently yields the best performance, in terms of bias, precision, and expected regret. Moreover, the credible interval estimators from AFT-BART-NP provide close to nominal frequentist coverage for the individual survival treatment effect when the covariate overlap is at least moderate. Including a nonparametrically estimated propensity score as an additional fixed covariate in the AFT-BART-NP model formulation can further improve its efficiency and frequentist coverage. Finally, we demonstrate the application of flexible causal machine learning estimators through a comprehensive case study examining the heterogeneous survival effects of two radiotherapy approaches for localized high-risk prostate cancer.

Quantile Regression Forests to Identify Determinants of Neighborhood Stroke Prevalence in 500 Cities in the USA: Implications for Neighborhoods with High Prevalence.

Stroke exerts a massive burden on the US health and economy. Place-based evidence is increasingly recognized as a critical part of stroke management, but identifying the key determinants of neighborhood stroke prevalence and the underlying effect mechanisms is a topic that has been treated sparingly in the literature. We aim to fill in the research gaps with a study focusing on urban health. We develop and apply analytical approaches to address two challenges. First, domain expertise on drivers of neighborhood-level stroke outcomes is limited. Second, commonly used linear regression methods may provide incomplete and biased conclusions. We created a new neighborhood health data set at census tract level by pooling information from multiple sources. We developed and applied a machine learning-based quantile regression method to uncover crucial neighborhood characteristics for neighborhood stroke outcomes among vulnerable neighborhoods burdened with high prevalence of stroke. Neighborhoods with a larger share of non-Hispanic blacks, older adults, or people with insufficient sleep tended to have a higher prevalence of stroke, whereas neighborhoods with a higher socio-economic status in terms of income and education had a lower prevalence of stroke. The effects of five major determinants varied geographically and were significantly stronger among neighborhoods with high prevalence of stroke. Highly flexible machine learning identifies true drivers of neighborhood cardiovascular health outcomes from wide-ranging information in an agnostic and reproducible way. The identified major determinants and the effect mechanisms can provide important avenues for prioritizing and allocating resources to develop optimal community-level interventions for stroke prevention.

Insights into Impact of DNA Copy Number Alteration and Methylation on the Proteogenomic Landscape of Human Ovarian Cancer via a Multi-omics Integrative Analysis.

In this work, we propose iProFun, an integrative analysis tool to screen for proteogenomic functional traits perturbed by DNA copy number alterations (CNAs) and DNA methylations. The goal is to characterize functional consequences of DNA copy number and methylation alterations in tumors and to facilitate screening for cancer drivers contributing to tumor initiation and progression. Specifically, we consider three functional molecular quantitative traits: mRNA expression levels, global protein abundances, and phosphoprotein abundances. We aim to identify those genes whose CNAs and/or DNA methylations have cis-associations with either some or all three types of molecular traits. Compared with analyzing each molecular trait separately, the joint modeling of multi-omics data enjoys several benefits: iProFun experienced enhanced power for detecting significant cis-associations shared across different omics data types, and it also achieved better accuracy in inferring cis-associations unique to certain type(s) of molecular trait(s). For example, unique associations of CNAs/methylations to global/phospho protein abundances may imply posttranslational regulations.We applied iProFun to ovarian high-grade serous carcinoma tumor data from The Cancer Genome Atlas and Clinical Proteomic Tumor Analysis Consortium and identified CNAs and methylations of 500 and 121 genes, respectively, affecting the cis-functional molecular quantitative traits of the corresponding genes. We observed substantial power gain via the joint analysis of iProFun. For example, iProFun identified 117 genes whose CNAs were associated with phosphoprotein abundances by leveraging mRNA expression levels and global protein abundances. By comparison, analyses based on phosphoprotein data alone identified none. A network analysis of these 117 genes revealed the known oncogene AKT1 as a key hub node interacting with many of the rest. In addition, iProFun identified one gene, BIN2, whose DNA methylation has cis-associations with its mRNA expression, global protein, and phosphoprotein abundances. These and other genes identified by iProFun could serve as potential drug targets for ovarian cancer.