Immune dysregulation and autoreactivity correlate with disease severity in SARS-CoV-2-associated multisystem inflammatory syndrome in children.

Multisystem inflammatory syndrome in children (MIS-C) is a life-threatening post-infectious complication occurring unpredictably weeks after mild or asymptomatic SARS-CoV-2 infection. We profiled MIS-C, adult COVID-19, and healthy pediatric and adult individuals using single-cell RNA sequencing, flow cytometry, antigen receptor repertoire analysis, and unbiased serum proteomics, which collectively identified a signature in MIS-C patients that correlated with disease severity. Despite having no evidence of active infection, MIS-C patients had elevated S100A-family alarmins and decreased antigen presentation signatures, indicative of myeloid dysfunction. MIS-C patients showed elevated expression of cytotoxicity genes in NK and CD8+ T cells and expansion of specific IgG-expressing plasmablasts. Clinically severe MIS-C patients displayed skewed memory T cell TCR repertoires and autoimmunity characterized by endothelium-reactive IgG. The alarmin, cytotoxicity, TCR repertoire, and plasmablast signatures we defined have potential for application in the clinic to better diagnose and potentially predict disease severity early in the course of MIS-C.

Computationally inferred cell-type specific epigenome-wide DNA methylation analysis unveils distinct methylation patterns among immune cells for HIV infection in three cohorts.

BACKGROUND: Epigenome-wide association studies (EWAS) have identified CpG sites associated with HIV infection in blood cells in bulk, which offer limited knowledge of cell-type specific methylation patterns associated with HIV infection. In this study, we aim to identify differentially methylated CpG sites for HIV infection in immune cell types: CD4+ T-cells, CD8+ T-cells, B cells, Natural Killer (NK) cells, and monocytes. METHODS: Applying a computational deconvolution method, we performed a cell-type based EWAS for HIV infection in three independent cohorts (Ntotal = 1,382). DNA methylation in blood or in peripheral blood mononuclear cells (PBMCs) was profiled by an array-based method and then deconvoluted by Tensor Composition Analysis (TCA). The TCA-computed CpG methylation in each cell type was first benchmarked by bisulfite DNA methylation capture sequencing in a subset of the samples. Cell-type EWAS of HIV infection was performed in each cohort separately and a meta-EWAS was conducted followed by gene set enrichment analysis. RESULTS: The meta-analysis unveiled a total of 2,021 cell-type unique significant CpG sites for five inferred cell types. Among these inferred cell-type unique CpG sites, the concordance rate in the three cohorts ranged from 96% to 100% in each cell type. Cell-type level meta-EWAS unveiled distinct patterns of HIV-associated differential CpG methylation, where 74% of CpG sites were unique to individual cell types (false discovery rate, FDR <0.05). CD4+ T-cells had the largest number of unique HIV-associated CpG sites (N = 1,624) compared to any other cell type. Genes harboring significant CpG sites are involved in immunity and HIV pathogenesis (e.g. CD4+ T-cells: NLRC5, CX3CR1, B cells: IFI44L, NK cells: IL12R, monocytes: IRF7), and in oncogenesis (e.g. CD4+ T-cells: BCL family, PRDM16, monocytes: PRDM16, PDCD1LG2). HIV-associated CpG sites were enriched among genes involved in HIV pathogenesis and oncogenesis that were enriched among interferon-α and -γ, TNF-α, inflammatory response, and apoptotic pathways. CONCLUSION: Our findings uncovered computationally inferred cell-type specific modifications in the host epigenome for people with HIV that contribute to the growing body of evidence regarding HIV pathogenesis.

tRFtarget 2.0: expanding the targetome landscape of transfer RNA-derived fragments.

tRFtarget 1.0 (http://trftarget.net/) is a platform consolidating both computationally predicted and experimentally validated binding sites between transfer RNA-derived fragments (tRFs) and target genes (or transcripts) across multiple organisms. Here, we introduce a newly released version of tRFtarget 2.0, in which we integrated 6 additional tRF sources, resulting in a comprehensive collection of 2614 high-quality tRF sequences spanning across 9 species, including 1944 Homo sapiens tRFs and one newly incorporated species Rattus norvegicus. We also expanded target genes by including ribosomal RNAs, long non-coding RNAs, and coding genes >50 kb in length. The predicted binding sites have surged up to approximately 6 billion, a 20.5-fold increase than that in tRFtarget 1.0. The manually curated publications relevant to tRF targets have increased to 400 and the gene-level experimental evidence has risen to 232. tRFtarget 2.0 introduces several new features, including a web-based tool that identifies potential binding sites of tRFs in user's own datasets, integration of standardized tRF IDs, and inclusion of external links to contents within the database. Additionally, we enhanced website framework and user interface. With these improvements, tRFtarget 2.0 is more user-friendly, providing researchers a streamlined and comprehensive platform to accelerate their research progress.

iDESC: identifying differential expression in single-cell RNA sequencing data with multiple subjects.

BACKGROUND: Single-cell RNA sequencing (scRNA-seq) technology has enabled assessment of transcriptome-wide changes at single-cell resolution. Due to the heterogeneity in environmental exposure and genetic background across subjects, subject effect contributes to the major source of variation in scRNA-seq data with multiple subjects, which severely confounds cell type specific differential expression (DE) analysis. Moreover, dropout events are prevalent in scRNA-seq data, leading to excessive number of zeroes in the data, which further aggravates the challenge in DE analysis. RESULTS: We developed iDESC to detect cell type specific DE genes between two groups of subjects in scRNA-seq data. iDESC uses a zero-inflated negative binomial mixed model to consider both subject effect and dropouts. The prevalence of dropout events (dropout rate) was demonstrated to be dependent on gene expression level, which is modeled by pooling information across genes. Subject effect is modeled as a random effect in the log-mean of the negative binomial component. We evaluated and compared the performance of iDESC with eleven existing DE analysis methods. Using simulated data, we demonstrated that iDESC had well-controlled type I error and higher power compared to the existing methods. Applications of those methods with well-controlled type I error to three real scRNA-seq datasets from the same tissue and disease showed that the results of iDESC achieved the best consistency between datasets and the best disease relevance. CONCLUSIONS: iDESC was able to achieve more accurate and robust DE analysis results by separating subject effect from disease effect with consideration of dropouts to identify DE genes, suggesting the importance of considering subject effect and dropouts in the DE analysis of scRNA-seq data with multiple subjects.

Cis-meQTL for cocaine use-associated DNA methylation in an HIV-positive cohort show pleiotropic effects on multiple traits.

BACKGROUND: Cocaine use (CU) is associated with psychiatric and medical diseases. Little is known about the mechanisms of CU-related comorbidities. Findings from preclinical and clinical studies have suggested that CU is associated with aberrant DNA methylation (DNAm) that may be influenced by genetic variants [i.e., methylation quantitative trait loci (meQTLs)]. In this study, we mapped cis-meQTLs for CU-associated DNAm sites (CpGs) in an HIV-positive cohort (Ntotal = 811) and extended the meQTLs to multiple traits. RESULTS: We conducted cis-meQTL analysis for 224 candidate CpGs selected for their association with CU in blood. We identified 7,101 significant meQTLs [false discovery rate (FDR) < 0.05], which mostly mapped to genes involved in immunological functions and were enriched in immune pathways. We followed up the meQTLs using phenome-wide association study and trait enrichment analyses, which revealed 9 significant traits. We tested for causal effects of CU on these 9 traits using Mendelian Randomization and found evidence that CU plays a causal role in increasing hypertension (p-value = 2.35E-08) and decreasing heel bone mineral density (p-value = 1.92E-19). CONCLUSIONS: These findings suggest that genetic variants for CU-associated DNAm have pleiotropic effects on other relevant traits and provide new insights into the causal relationships between cocaine use and these complex traits.

Systematically identifying genetic signatures including novel SNP-clusters, nonsense variants, frame-shift INDELs, and long STR expansions that potentially link to unknown phenotypes existing in dog breeds.

BACKGROUND: In light of previous studies that profiled breed-specific traits or used genome-wide association studies to refine loci associated with characteristic morphological features in dogs, the field has gained tremendous genetic insights for known dog traits observed among breeds. Here we aim to address the question from a reserve perspective: whether there are breed-specific genotypes that may underlie currently unknown phenotypes. This study provides a complete set of breed-specific genetic signatures (BSGS). Several novel BSGS with significant protein-altering effects were highlighted and validated. RESULTS: Using the next generation whole-genome sequencing technology coupled with unsupervised machine learning for pattern recognitions, we constructed and analyzed a high-resolution sequence map for 76 breeds of 412 dogs. Genomic structures including novel single nucleotide polymorphisms (SNPs), SNP clusters, insertions, deletions (INDELs) and short tandem repeats (STRs) were uncovered mutually exclusively among breeds. We also partially validated some novel nonsense variants by Sanger sequencing with additional dogs. Four novel nonsense BSGS were found in the Bernese Mountain Dog, Samoyed, Bull Terrier, and Basset Hound, respectively. Four INDELs resulting in either frame-shift or codon disruptions were found in the Norwich Terrier, Airedale Terrier, Chow Chow and Bernese Mountain Dog, respectively. A total of 15 genomic regions containing three types of BSGS (SNP-clusters, INDELs and STRs) were identified in the Akita, Alaskan Malamute, Chow Chow, Field Spaniel, Keeshond, Shetland Sheepdog and Sussex Spaniel, in which Keeshond and Sussex Spaniel each carried one amino-acid changing BSGS in such regions. CONCLUSION: Given the strong relationship between human and dog breed-specific traits, this study might be of considerable interest to researchers and all. Novel genetic signatures that can differentiate dog breeds were uncovered. Several functional genetic signatures might indicate potentially breed-specific unknown phenotypic traits or disease predispositions. These results open the door for further investigations. Importantly, the computational tools we developed can be applied to any dog breeds as well as other species. This study will stimulate new thinking, as the results of breed-specific genetic signatures may offer an overarching relevance of the animal models to human health and disease.

tRFtarget: a database for transfer RNA-derived fragment targets.

Transfer RNA-derived fragments (tRFs) are a new class of small non-coding RNAs and play important roles in biological and physiological processes. Prediction of tRF target genes and binding sites is crucial in understanding the biological functions of tRFs in the molecular mechanisms of human diseases. We developed a publicly accessible web-based database, tRFtarget (http://trftarget.net), for tRF target prediction. It contains the computationally predicted interactions between tRFs and mRNA transcripts using the two state-of-the-art prediction tools RNAhybrid and IntaRNA, including location of the binding sites on the target, the binding region, and free energy of the binding stability with graphic illustration. tRFtarget covers 936 tRFs and 135 thousand predicted targets in eight species. It allows researchers to search either target genes by tRF IDs or tRFs by gene symbols/transcript names. We also integrated the manually curated experimental evidence of the predicted interactions into the database. Furthermore, we provided a convenient link to the DAVID® web server to perform downstream functional pathway analysis and gene ontology annotation on the predicted target genes. This database provides useful information for the scientific community to experimentally validate tRF target genes and facilitate the investigation of the molecular functions and mechanisms of tRFs.

A novel transcriptional risk score for risk prediction of complex human diseases.

Recently polygenetic risk score (PRS) has been successfully used in the risk prediction of complex human diseases. Many studies incorporated internal information, such as effect size distribution, or external information, such as linkage disequilibrium, functional annotation, and pleiotropy among multiple diseases, to optimize the performance of PRS. To leverage on multiomics datasets, we developed a novel flexible transcriptional risk score (TRS), in which messenger RNA expression levels were imputed and weighted for risk prediction. In simulation studies, we demonstrated that single-tissue TRS has greater prediction power than LDpred, especially when there is a large effect of gene expression on the phenotype. Multitissue TRS improves prediction accuracy when there are multiple tissues with independent contributions to disease risk. We applied our method to complex traits, including Crohn's disease, type 2 diabetes, and so on. The single-tissue TRS method outperformed LDpred and AnnoPred across the tested traits. The performance of multitissue TRS is trait-dependent. Moreover, our method can easily incorporate information from epigenomic and proteomic data upon the availability of reference datasets.

G2S3: A gene graph-based imputation method for single-cell RNA sequencing data.

Single-cell RNA sequencing technology provides an opportunity to study gene expression at single-cell resolution. However, prevalent dropout events result in high data sparsity and noise that may obscure downstream analyses in single-cell transcriptomic studies. We propose a new method, G2S3, that imputes dropouts by borrowing information from adjacent genes in a sparse gene graph learned from gene expression profiles across cells. We applied G2S3 and ten existing imputation methods to eight single-cell transcriptomic datasets and compared their performance. Our results demonstrated that G2S3 has superior overall performance in recovering gene expression, identifying cell subtypes, reconstructing cell trajectories, identifying differentially expressed genes, and recovering gene regulatory and correlation relationships. Moreover, G2S3 is computationally efficient for imputation in large-scale single-cell transcriptomic datasets.

Discovery and Mediation Analysis of Cross-Phenotype Associations Between Asthma and Body Mass Index in 12q13.2.

Twin studies suggest that shared genetics contributes to the comorbidity of asthma and obesity, but candidate-gene studies provide limited evidence of pleiotropy. We conducted genome-wide association analyses of asthma and body mass index (BMI; weight (kg)/height (m)2)) among 305,945 White British subjects recruited into the UK Biobank in 2006-2010. We searched for overlapping signals and conducted mediation analyses on genome-wide-significant cross-phenotype associations, assessing moderation by sex and age at asthma diagnosis, and adjusting for confounders of the asthma-BMI relationship. We identified a genome-wide-significant cross-phenotype association at rs705708 (asthma odds ratio = 1.05, 95% confidence interval: 1.03, 1.07; P = 7.20 × 10-9; and BMI ß = -0.065, 95% confidence interval: -0.087, -0.042; P = 1.30 × 10-8). rs705708 resides on 12q13.2, which harbors 9 other asthma- and BMI-associated variants (all P < 5 × 10-5 for asthma; all but one P < 5 × 10-5 for BMI). Follow-up analyses of rs705708 show that most of the BMI association occurred independently of asthma, with consistent magnitude between men and women and persons with and without asthma, irrespective of age at diagnosis; the asthma association was stronger for childhood versus adult asthma; and both associations remained after confounder adjustment. This suggests that 12q13.2 displays pleiotropy for asthma and BMI. Upon further characterization, 12q13.2 might provide a target for interventions that simultaneously prevent or treat asthma and obesity.

Identification of trans-eQTLs using mediation analysis with multiple mediators.

BACKGROUND: Mapping expression quantitative trait loci (eQTLs) has provided insight into gene regulation. Compared to cis-eQTLs, the regulatory mechanisms of trans-eQTLs are less known. Previous studies suggest that trans-eQTLs may regulate expression of remote genes by altering the expression of nearby genes. Trans-association has been studied in the mediation analysis with a single mediator. However, prior applications with one mediator are prone to model misspecification due to correlations between genes. Motivated from the observation that trans-eQTLs are more likely to associate with more than one cis-gene than randomly selected SNPs in the GTEx dataset, we developed a computational method to identify trans-eQTLs that are mediated by multiple mediators. RESULTS: We proposed two hypothesis tests for testing the total mediation effect (TME) and the component-wise mediation effects (CME), respectively. We demonstrated in simulation studies that the type I error rates were controlled in both tests despite model misspecification. The TME test was more powerful than the CME test when the two mediation effects are in the same direction, while the CME test was more powerful than the TME test when the two mediation effects are in opposite direction. Multiple mediator analysis had increased power to detect mediated trans-eQTLs, especially in large samples. In the HapMap3 data, we identified 11 mediated trans-eQTLs that were not detected by the single mediator analysis in the combined samples of African populations. Moreover, the mediated trans-eQTLs in the HapMap3 samples are more likely to be trait-associated SNPs. In terms of computation, although there is no limit in the number of mediators in our model, analysis takes more time when adding additional mediators. In the analysis of the HapMap3 samples, we included at most 5 cis-gene mediators. Majority of the trios we considered have one or two mediators. CONCLUSIONS: Trans-eQTLs are more likely to associate with multiple cis-genes than randomly selected SNPs. Mediation analysis with multiple mediators improves power of identification of mediated trans-eQTLs, especially in large samples.

BRCA1 mRNA expression modifies the effect of T cell activation score on patient survival in breast cancer.

BACKGROUND: Effector CD8+ T cell activation and its cytotoxic function to eradicate tumor cells depend on the T cell recognition of tumor neoantigens, and are positively associated with improved survival in breast cancer. Tumor suppressor BRCA1 and cell cycle regulator CCND1 play a critical role in maintaining genome integrity and tumorigenesis, respectively. However, it is still unclear how BRCA1 and CCND1 expression levels affect the effect of T cell activation on breast cancer patient survival. METHODS: The interactions between T cell activation status and either BRCA1 or CCND1 expression were evaluated using Kaplan-Meier survival curves and multivariate Cox regression models in a public dataset with 1088 breast cancer patients. RESULTS: Among the patients with low BRCA1 or CCND1 expression, the Activation group showed better overall survival than the Exhaustion group. Adjusted hazards ratios were 0.43 (95% CI: 0.20-0.93) in patients with a low BRCA1 level, and 0.39 (95% CI: 0.19-0.81) in patients with a low CCND1 level, respectively. There was a significant trend in both subgroups (p-trend = 0.011 in the low BRCA1 group, and p-trend = 0.009 in the low CCND1 group). In contrast, there is no significant association in patients with either high BRCA1 or high CCND1 levels. There is a significant interaction between T cell activation status and BRCA1 level (p = 0.009), but not between T cell activation status and CCND1 level (p = 0.135). CONCLUSIONS: BRCA1 expression modified the effect of T cell activation status on patient survival in breast cancer, suggesting that the existence of neoantigens and the enhancement of neoantigen presentation in combination with immune checkpoint blockade may have synergistic effects on patient outcome.

Longitudinal SNP-set association analysis of quantitative phenotypes.

Many genetic epidemiological studies collect repeated measurements over time. This design not only provides a more accurate assessment of disease condition, but allows us to explore the genetic influence on disease development and progression. Thus, it is of great interest to study the longitudinal contribution of genes to disease susceptibility. Most association testing methods for longitudinal phenotypes are developed for single variant, and may have limited power to detect association, especially for variants with low minor allele frequency. We propose Longitudinal SNP-set/sequence kernel association test (LSKAT), a robust, mixed-effects method for association testing of rare and common variants with longitudinal quantitative phenotypes. LSKAT uses several random effects to account for the within-subject correlation in longitudinal data, and allows for adjustment for both static and time-varying covariates. We also present a longitudinal trait burden test (LBT), where we test association between the trait and the burden score in linear mixed models. In simulation studies, we demonstrate that LBT achieves high power when variants are almost all deleterious or all protective, while LSKAT performs well in a wide range of genetic models. By making full use of trait values from repeated measures, LSKAT is more powerful than several tests applied to a single measurement or average over all time points. Moreover, LSKAT is robust to misspecification of the covariance structure. We apply the LSKAT and LBT methods to detect association with longitudinally measured body mass index in the Framingham Heart Study, where we are able to replicate association with a circadian gene NR1D2.

Statistical Analysis of Multiple Phenotypes in Genetic Epidemiologic Studies: From Cross-Phenotype Associations to Pleiotropy.

In the context of genetics, pleiotropy refers to the phenomenon in which a single genetic locus affects more than 1 trait or disease. Genetic epidemiologic studies have identified loci associated with multiple phenotypes, and these cross-phenotype associations are often incorrectly interpreted as examples of pleiotropy. Pleiotropy is only one possible explanation for cross-phenotype associations. Cross-phenotype associations may also arise due to issues related to study design, confounder bias, or nongenetic causal links between the phenotypes under analysis. Therefore, it is necessary to dissect cross-phenotype associations carefully to uncover true pleiotropic loci. In this review, we describe statistical methods that can be used to identify robust statistical evidence of pleiotropy. First, we provide an overview of univariate and multivariate methods for discovery of cross-phenotype associations and highlight important considerations for choosing among available methods. Then, we describe how to dissect cross-phenotype associations by using mediation analysis. Pleiotropic loci provide insights into the mechanistic underpinnings of disease comorbidity, and they may serve as novel targets for interventions that simultaneously treat multiple diseases. Discerning between different types of cross-phenotype associations is necessary to realize the public health potential of pleiotropic loci.

Elevated T cell activation score is associated with improved survival of breast cancer.

PURPOSE: Immune checkpoints cytotoxic T lymphocyte antigen 4 (CTLA4) and programmed cell death 1 receptor (PD-1) negatively regulate CD8+ T cell functions, impeding the capacity of effector T cells to kill tumors. Here, we study the prognostic significance of CTLA4, PD-1 and T cell activation status in breast cancer. METHODS: Using a publicly accessed RNA-seq dataset including 1087 breast cancer patients, we performed Kaplan-Meier survival curves and multivariate Cox regression models to evaluate the associations of CTLA4, PD-1, and weighted T cell activation score with patients' overall survival. RESULTS: Survival analyses showed that high CTLA4 but low PD-1 expression was associated with a poor overall survival, and that high T cell activation score was associated with an improved survival. The median survival was 216.6 months (95% CI 114.1-244.9) for the T activation group, 127.0 months (95% CI 112.3-212.1) for the intermediate, and 120.5 months (95% CI 93.8 to ∞) for the exhaustion (Log-rank p = 0.084). This association was verified in multivariate Cox regression analysis. The hazard ratios were 0.81 (95% CI 0.56-1.19) for the intermediate group, and 0.48 (95% CI 0.26-0.86) for the activation group, respectively, in comparison to the exhaustion group (p value for trend = 0.016). CONCLUSIONS: T cell activation score has significantly positive relationship with patients' overall survival, and may serve as a marker of personalized immunotherapy in breast cancer patients. Cocktail rather than single immune checkpoint blockade may yield more benefit for breast cancer patients.

A case-control design for testing and estimating epigenetic effects on complex diseases.

Epigenetic modifications may play an important role in the formation and progression of complex diseases through the regulation of gene expression. The systematic identification of epigenetic variants that contribute to human diseases can be made possible using genome-wide association studies (GWAS), although epigenetic effects are currently not included in commonly used case-control designs for GWAS. Here, we show that epigenetic modifications can be integrated into a case-control setting by dissolving the overall genetic effect into its different components, additive, dominant and epigenetic. We describe a general procedure for testing and estimating the significance of each component based on a conventional chi-squared test approach. Simulation studies were performed to investigate the power and false-positive rate of this procedure, providing recommendations for its practical use. The integration of epigenetic variants into GWAS can potentially improve our understanding of how genetic, environmental and stochastic factors interact with epialleles to construct the genetic architecture of complex diseases.

Towards a comprehensive picture of the genetic landscape of complex traits.

The formation of phenotypic traits, such as biomass production, tumor volume and viral abundance, undergoes a complex process in which interactions between genes and developmental stimuli take place at each level of biological organization from cells to organisms. Traditional studies emphasize the impact of genes by directly linking DNA-based markers with static phenotypic values. Functional mapping, derived to detect genes that control developmental processes using growth equations, has proven powerful for addressing questions about the roles of genes in development. By treating phenotypic formation as a cohesive system using differential equations, a different approach-systems mapping-dissects the system into interconnected elements and then map genes that determine a web of interactions among these elements, facilitating our understanding of the genetic machineries for phenotypic development. Here, we argue that genetic mapping can play a more important role in studying the genotype-phenotype relationship by filling the gaps in the biochemical and regulatory process from DNA to end-point phenotype. We describe a new framework, named network mapping, to study the genetic architecture of complex traits by integrating the regulatory networks that cause a high-order phenotype. Network mapping makes use of a system of differential equations to quantify the rule by which transcriptional, proteomic and metabolomic components interact with each other to organize into a functional whole. The synthesis of functional mapping, systems mapping and network mapping provides a novel avenue to decipher a comprehensive picture of the genetic landscape of complex phenotypes that underlie economically and biomedically important traits.

A model for family-based case-control studies of genetic imprinting and epistasis.

Genetic imprinting, or called the parent-of-origin effect, has been recognized to play an important role in the formation and pathogenesis of human diseases. Although the epigenetic mechanisms that establish genetic imprinting have been a focus of many genetic studies, our knowledge about the number of imprinting genes and their chromosomal locations and interactions with other genes is still scarce, limiting precise inference of the genetic architecture of complex diseases. In this article, we present a statistical model for testing and estimating the effects of genetic imprinting on complex diseases using a commonly used case-control design with family structure. For each subject sampled from a case and control population, we not only genotype its own single nucleotide polymorphisms (SNPs) but also collect its parents' genotypes. By tracing the transmission pattern of SNP alleles from parental to offspring generation, the model allows the characterization of genetic imprinting effects based on Pearson tests of a 2 × 2 contingency table. The model is expanded to test the interactions between imprinting effects and additive, dominant and epistatic effects in a complex web of genetic interactions. Statistical properties of the model are investigated, and its practical usefulness is validated by a real data analysis. The model will provide a useful tool for genome-wide association studies aimed to elucidate the picture of genetic control over complex human diseases.

Gender, Age, and Comorbidity Status Predict Improved Survival with Adjuvant Chemotherapy Following Lobectomy for Non-small Cell Lung Cancers Larger than 4 cm.

PURPOSE: To identify pretreatment prognostic factors associated with improved outcomes in patients with non-small cell lung cancer (NSCLC) tumors larger than 4 cm receiving adjuvant chemotherapy following surgery versus surgery alone. METHODS: Information was collected from the National Cancer Data Base on adults diagnosed with NSCLC who underwent lobectomy with or without adjuvant chemotherapy for pathologic T2 tumors measuring at least 4 cm, with no lymph node involvement or distant metastasis. The data were analyzed using model-based recursive partitioning for survival. RESULTS: Patients who underwent chemotherapy following surgery experienced a survival benefit compared with patients who were treated with surgery alone (5-year survival of 66 vs. 49 %, p<0.0001). Overall, women had improved 5-year survival relative to men (60 vs. 47 %, p<0.0001). Despite this observation, three groups of women experienced no benefit from adjuvant chemotherapy:women 65-72 years with a Charlson-Deyo (CD) score ≥1 (5-year survival: 51 vs. 58 %, p = 0.29), women >72 with a CD score = 0 (5-year survival: 53 vs. 56 %, p = 0.57), and women >72 with a CD score ≥1 (5-year survival: 40 vs. 56 %, p = 0.04). By contrast, all groups of men identified by recursive partitioning analysis demonstrated improved survivals with adjuvant chemotherapy. CONCLUSIONS: Adjuvant chemotherapy appears to increase survival for patients with resected NSCLC tumors larger than 4 cm. Women with NSCLC experience improved survival relative to men regardless of treatment. However,there are certain groups of women over 65 years old who do not benefit from adjuvant chemotherapy.