Showing papers by "Gong Tang published in 2022"

Elevated serum globulin fraction as a biomarker of multiyear disease severity in inflammatory bowel disease

Abstract: Background Serum protein reflects albumin and globulin levels, both of which can be altered in inflammatory bowel disease (IBD). The implications of a high globulin fraction in IBD are unknown. We hypothesized that a high globulin fraction may function independently of albumin as a biomarker of disease severity in IBD patients over a multiyear period. Methods This was an observational study from a prospective IBD registry of a tertiary care center. High globulin fraction was defined as an elevated globulin level >4 g/dL. Data collected included patient demographics, medication exposures, quality-of-life scores, disease activity, emergency department visits, telephone calls, hospitalizations, and IBD-related surgeries over a 4-year period. Comparisons between patients with a high globulin fraction and those without were performed using Pearson’s chi-squared, Student’s and Mann-Whitney tests. Multivariate analyses were used to assess the relationship between high globulin fraction and healthcare utilization. Results A total of 1767 IBD patients with a 4-year follow up were included: 53.5% female, mean age 48.4±15.1 years, and 65.4% with Crohn’s disease. Of these patients, 446 (25.2%) presented with elevated globulin fraction. Patients with a high globulin fraction were more likely to be hospitalized during the study period. This result remained significant after multivariate analysis for both Crohn’s disease patients and those with ulcerative colitis. Conclusion A high globulin fraction is independently associated with greater disease severity and healthcare utilization in IBD patients, and may function as a routinely available biomarker of a more severe future disease trajectory.

Prognostic and predictive implications of the intrinsic subtypes and gene expression signatures in early-stage HER2+ breast cancer: A pooled analysis of CALGB 40601, NeoALTTO, and NSABP B-41 trials.

Abstract: 509 Background: Several biologic features are implicated in the differences in response and survival to dual (trastuzumab and lapatinib [HL]) vs. single (trastuzumab [H]) HER2-blockade across neoadjuvant trials in early-stage HER2+ breast cancer. We evaluated the association of intrinsic subtypes and gene expression signatures with pathologic complete response (pCR) and event-free survival (EFS) in a pooled analysis of three independent phase III neoadjuvant studies with similar designs: CALGB 40601 (Alliance), NeoALTTO, and NSABP B-41. Methods: Gene expression profiling by RNA sequencing was assessed on 761 pre-treatment samples (264 from CALGB 40601, 249 from NeoALTTO, 248 from NSABP B-41). Intrinsic subtypes and 759 gene expression signatures were calculated. We studied the association of pCR and the benefit of dual (HL) vs. single (H) HER2-blockade by tumor intrinsic subtype in the pooled set. The ability of multiple gene expression signatures to predict pCR and EFS across the three studies was also tested by logistic and Cox regression analyses. Results: pCR status was associated with EFS only in HER2-Enriched (HR 0.45, 95% CI 0.29-0.71, p-value < 0.001) and Basal-like (HR 0.19, 95% CI 0.04-0.86, p-value 0.031) intrinsic subtypes, but not in Luminal and/or ER+ tumors. The EFS benefit of dual vs. single HER2-blockade was limited to HER2-Enriched tumors (HR 0.47, 95% CI 0.27-0.81, p-value 0.007). When evaluating the three clinical trials separately, we found 89/759 (11.7%) gene expression signatures in common for the prediction of pCR across the three clinical trials, including HER2-amplicon and immune activation signatures. Luminal-related signatures were associated with lower pCR rates but better EFS outcomes, especially in patients with residual disease. Stratified Cox regression models by study showed a significant and strong association of NK, B and plasma cells, as well as Ig-related signatures with a better EFS outcome, while vascular, proliferation, and metastasis signatures were associated with poor EFS. Conclusions: In early-stage HER2+ breast cancer, the relationship between pCR and EFS differs by tumor intrinsic subtype, and the benefit of dual vs. single HER2-blockade seems to be limited to HER2-Enriched subtype tumors. Immune signatures were associated with higher pCR rates and better EFS, luminal signatures were associated with lower pCR rates but good EFS outcomes, and vascular/proliferation/metastasis signatures were associated with poor EFS across the three clinical trials. Clinical trial identification: CALGB 40601: NCT00770809. (CALGB is part of the Alliance for Clinical Trials in Oncology). NeoALTTO: NCT00553358 NSABP B-41: NCT00486668

An integral genomic signature approach for tailored cancer therapy using genome-wide sequencing data

Abstract: Abstract Low-cost multi-omics sequencing is expected to become clinical routine and transform precision oncology. Viable computational methods that can facilitate tailored intervention while tolerating sequencing biases are in high demand. Here we propose a class of transparent and interpretable computational methods called integral genomic signature (iGenSig) analyses, that address the challenges of cross-dataset modeling through leveraging information redundancies within high-dimensional genomic features, averaging feature weights to prevent overweighing, and extracting unbiased genomic information from large tumor cohorts. Using genomic dataset of chemical perturbations, we develop a battery of iGenSig models for predicting cancer drug responses, and validate the models using independent cell-line and clinical datasets. The iGenSig models for five drugs demonstrate predictive values in six clinical studies, among which the Erlotinib and 5-FU models significantly predict therapeutic responses in three studies, offering clinically relevant insights into their inverse predictive signature pathways. Together, iGenSig provides a computational framework to facilitate tailored cancer therapy based on multi-omics data.

Standard Operating Procedures for Biospecimen Collection, Processing, and Storage: From the Type 1 Diabetes in Acute Pancreatitis Consortium.

Abstract: ABSTRACT Differences in methods for biospecimen collection, processing, and storage can yield considerable variability and error. Therefore, best practices for standard operating procedures are critical for successful discovery, development, and validation of disease biomarkers. Here, we describe standard operating procedures developed for biospecimen collection during the DREAM (Diabetes RElated to Acute pancreatitis and its Mechanisms) Study within the Type 1 Diabetes in Acute Pancreatitis Consortium. Notably, these protocols were developed using an integrative process based on prior consortium experience and with input from working groups with expertise in immunology, pancreatitis, and diabetes. Publication and adoption consistent biospecimen protocols will inform future studies and allow for better comparisons across different metabolic research efforts.

Impact of prepregnancy body mass index on adverse pregnancy outcomes: analysis from the Longitudinal Indian Family hEalth cohort study

Abstract: Both high and low maternal prepregnancy body mass index can lead to suboptimal fetal growth and risk of pregnancy complications. In developed countries, nearly half of all women of childbearing age are either overweight or obese, and most data linking maternal body mass index and adverse pregnancy complications are limited to these populations.This study aimed to prospectively evaluate the relationships between prepregnancy body mass index and adverse pregnancy outcomes using the Longitudinal Indian Family hEalth (LIFE) study.We modeled the relationships between prepregnancy body mass index and adverse pregnancy outcomes such as low birthweight, preterm birth, cesarean delivery, intrauterine growth restriction, miscarriage, and fetal death among 675 women aged 15 to 35 years with singleton pregnancies in the Longitudinal Indian Family hEalth study, a population-based prospective pregnancy cohort study conducted in Telangana, India. Prepregnancy body mass index was calculated as weight in kilograms divided by height in meters squared and was classified into 4 categories using the World Health Organization recommendations for Asian adults. Prepregnancy body mass index was assessed at a mean of 12.3 months before pregnancy. Odds ratios and 95% confidence intervals of adverse pregnancy outcomes were modeled and adjusted for confounders.Obese women had a 3-fold increased risk of cesarean delivery (odds ratio, 3.13; 95% confidence interval, 1.56-6.29) compared with normal-weight women. Those who were overweight also had a marginally increased risk of cesarean delivery, albeit not statistically significant (odds ratio, 1.17; 95% confidence interval, 0.61-2.24). Underweight women had a modestly increased risk of low birthweight, compared with normal-weight women (odds ratio, 1.12; 95% confidence interval, 0.71-1.77), although results were not significant. Conversely, obese (odds ratio, 0.71; 95% confidence interval, 0.28-1.77) and overweight (odds ratio, 0.61; 95% confidence interval, 0.24-1.51) women had a marginally decreased risk of low birthweight.Our data suggest that women with elevated prepregnancy body mass index may have a higher risk of adverse pregnancy outcomes, especially cesarean delivery. Although this study has limited generalizability, our findings are generalizable to rural to periurban regions of India. Further studies exploring the translatability of these findings to other populations are needed. In addition, targeted prepregnancy intervention studies and programs that include counseling on optimization of preconception health and lifestyle modification for improvement of subsequent pregnancy outcomes among overweight and obese women are needed.

Classification Models for Predicting Inflammatory Bowel Disease Healthcare Utilization

Abstract: Objective. Inflammatory Bowel Disorders (IBD) is a group of gastric disorders that include well-known maladies such as Crohn’s disease and Ulcerative Colitis (UC), as well as a number of other gastric disorders that are not well classified. Subgroups of patients contribute disproportionately to treatment costs. This work aims to create and evaluate machine learning models designed to use demographic and clinical predictors of IBD to predict which patients would fall into the “high healthcare utilization” category. Materials and Methods. A series of machine learning models were trained on a dataset extracted from a prospective natural history registry from a tertiary IBD center and associated healthcare charges. The models were trained to predict which patients are likely to have the highest healthcare utilization charges (top 15%). Results. A gradient-boosted trees classification model (accuracy 0.898, AUC 0.748) performed best out of the 12 evaluated modeling approaches. Conclusion. Classification models such as the ones evaluated in this work provide a reasonable basis for a clinical decision support system designed to predict which IBD patients are likely to become high expenditure.