Structure-based classification predicts drug response in EGFR-mutant NSCLC.

Epidermal growth factor receptor (EGFR) mutations typically occur in exons 18-21 and are established driver mutations in non-small cell lung cancer (NSCLC)1-3. Targeted therapies are approved for patients with 'classical' mutations and a small number of other mutations4-6. However, effective therapies have not been identified for additional EGFR mutations. Furthermore, the frequency and effects of atypical EGFR mutations on drug sensitivity are unknown1,3,7-10. Here we characterize the mutational landscape in 16,715 patients with EGFR-mutant NSCLC, and establish the structure-function relationship of EGFR mutations on drug sensitivity. We found that EGFR mutations can be separated into four distinct subgroups on the basis of sensitivity and structural changes that retrospectively predict patient outcomes following treatment with EGFR inhibitors better than traditional exon-based groups. Together, these data delineate a structure-based approach for defining functional groups of EGFR mutations that can effectively guide treatment and clinical trial choices for patients with EGFR-mutant NSCLC and suggest that a structure-function-based approach may improve the prediction of drug sensitivity to targeted therapies in oncogenes with diverse mutations.

A unified mediation analysis framework for integrative cancer proteogenomics with clinical outcomes.

MOTIVATION: Multilevel molecular profiling of tumors and the integrative analysis with clinical outcomes have enabled a deeper characterization of cancer treatment. Mediation analysis has emerged as a promising statistical tool to identify and quantify the intermediate mechanisms by which a gene affects an outcome. However, existing methods lack a unified approach to handle various types of outcome variables, making them unsuitable for high-throughput molecular profiling data with highly interconnected variables. RESULTS: We develop a general mediation analysis framework for proteogenomic data that include multiple exposures, multivariate mediators on various scales of effects as appropriate for continuous, binary and survival outcomes. Our estimation method avoids imposing constraints on model parameters such as the rare disease assumption, while accommodating multiple exposures and high-dimensional mediators. We compare our approach to other methods in extensive simulation studies at a range of sample sizes, disease prevalence and number of false mediators. Using kidney renal clear cell carcinoma proteogenomic data, we identify genes that are mediated by proteins and the underlying mechanisms on various survival outcomes that capture short- and long-term disease-specific clinical characteristics. AVAILABILITY AND IMPLEMENTATION: Software is made available in an R package (https://github.com/longjp/mediateR). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Consensus subtypes of hepatocellular carcinoma associated with clinical outcomes and genomic phenotypes.

BACKGROUND AND AIMS: Although many studies revealed transcriptomic subtypes of HCC, concordance of the subtypes are not fully examined. We aim to examine a consensus of transcriptomic subtypes and correlate them with clinical outcomes. APPROACH AND RESULTS: By integrating 16 previously established genomic signatures for HCC subtypes, we identified five clinically and molecularly distinct consensus subtypes. STM (STeM) is characterized by high stem cell features, vascular invasion, and poor prognosis. CIN (Chromosomal INstability) has moderate stem cell features, but high genomic instability and low immune activity. IMH (IMmune High) is characterized by high immune activity. BCM (Beta-Catenin with high Male predominance) is characterized by prominent ß-catenin activation, low miRNA expression, hypomethylation, and high sensitivity to sorafenib. DLP (Differentiated and Low Proliferation) is differentiated with high hepatocyte nuclear factor 4A activity. We also developed and validated a robust predictor of consensus subtype with 100 genes and demonstrated that five subtypes were well conserved in patient-derived xenograft models and cell lines. By analyzing serum proteomic data from the same patients, we further identified potential serum biomarkers that can stratify patients into subtypes. CONCLUSIONS: Five HCC subtypes are correlated with genomic phenotypes and clinical outcomes and highly conserved in preclinical models, providing a framework for selecting the most appropriate models for preclinical studies.

Palbociclib plus endocrine therapy significantly enhances overall survival of HR+/HER2- metastatic breast cancer patients compared to endocrine therapy alone in the second-line setting: A large institutional study.

Cyclin-dependent-kinase-4/6 inhibitor (CDK4/6i) plus endocrine therapy (ET) is standard of care for patients with advanced hormone receptor (HR)-positive, HER2-negative breast cancer (BC). The Breast Medical Oncology database at MD Anderson Cancer Center (MDACC) was analyzed to assess effectiveness of the CDK4/6i palbociclib plus ET compared to ET alone. From a total of 5402 advanced HR+ HER2- BC patients referred to MDACC between 1997 and 2020, we identified eligible patients who received palbociclib in combination with first-line (n = 778) and second-line (n = 410) ET. We further identified "control" patients who received ET alone in the first-line (n = 2452) and second-line (n = 1183) settings. Propensity score matching analysis was conducted to balance baseline demographic and clinical characteristics between palbociclib and control cohorts to assess the effect of palbociclib treatment on progression-free survival (PFS) and overall survival (OS). For propensity-matched-cohort in the first-line setting (n = 708), palbociclib group had significantly longer median PFS (17.4 vs 11.1 months; P < .0001) compared to controls. Median OS (44.3 vs 40.2 months) did not show a statistically significant benefit in the first line setting. However, in the second-line setting, with 380 propensity-matched-cohort, the palbociclib group had significantly longer PFS (10 vs 5 months, P < .0001) as well as OS (33 vs 24 months; P < .022), compared to controls. We conclude that in this single center analysis of a large cohort of metastatic HR+ HER2- BC patients, palbociclib in combination with ET was associated with improved PFS in both first-line and second-line settings and OS in the second-line setting compared to ET alone cohort.

Cytoplasmic Cyclin E Expression Predicts for Response to Neoadjuvant Chemotherapy in Breast Cancer.

BACKGROUND: Pathologic complete response (pCR) has been shown to be associated with favorable outcomes in breast cancer. Predictors of pCR could be useful in guiding treatment decisions regarding neoadjuvant therapy. The objective of this study was to evaluate cyclin E as a predictor of response to neoadjuvant chemotherapy in breast cancer. METHODS: Patients (n = 285) with stage II-III breast cancer were enrolled in a prospective study and received neoadjuvant chemotherapy with anthracyclines, taxanes, or combination of the two. Pretreatment biopsies from 190 patients and surgical specimens following chemotherapy from 192 patients were available for immunohistochemical analysis. Clinical and pathologic responses were recorded and associated with presence of tumor infiltrating lymphocytes, cyclin E, adipophilin, programmed cell death-ligand 1, and elastase staining and other patient, tumor and treatment characteristics. RESULTS: The pCR rate was significantly lower in patients with cytoplasmic cyclin E staining compared with those who had no cyclin E expression (16.1% vs 38.9%, P = 0.0005). In multivariable logistic regression analysis, the odds of pCR for patients who had cytoplasmic negative tumors was 9.35 times (P value < 0.0001) that compared with patients with cytoplasmic positive tumors after adjusting for ER, PR, and HER2 status. Cytoplasmic cyclin E expression also predicts long-term outcome and is associated with reduced disease free, recurrence free, and overall survival rates, independent of increased pretreatment tumor infiltrating lymphocytes. CONCLUSIONS: Cyclin E independently predicted response to neoadjuvant chemotherapy. Hence, its routine immunohistochemical analysis could be used clinically to identify those breast cancer patients expected to have a poor response to anthracycline/taxane-based chemotherapy.

Estimation of high-dimensional directed acyclic graphs with surrogate intervention.

Directed acyclic graphs (DAGs) have been used to describe causal relationships between variables. The standard method for determining such relations uses interventional data. For complex systems with high-dimensional data, however, such interventional data are often not available. Therefore, it is desirable to estimate causal structure from observational data without subjecting variables to interventions. Observational data can be used to estimate the skeleton of a DAG and the directions of a limited number of edges. We develop a Bayesian framework to estimate a DAG using surrogate interventional data, where the interventions are applied to a set of external variables, and thus such interventions are considered to be surrogate interventions on the variables of interest. Our work is motivated by expression quantitative trait locus (eQTL) studies, where the variables of interest are the expression of genes, the external variables are DNA variations, and interventions are applied to DNA variants during the process of a randomly selected DNA allele being passed to a child from either parent. Our method, surrogate intervention recovery of a DAG ($\texttt{sirDAG}$), first constructs a DAG skeleton using penalized regressions and the subsequent partial correlation tests, and then estimates the posterior probabilities of all the edge directions after incorporating DNA variant data. We demonstrate the utilities of $\texttt{sirDAG}$ by simulation and an application to an eQTL study for 550 breast cancer patients.

Compositional zero-inflated network estimation for microbiome data.

BACKGROUND: The estimation of microbial networks can provide important insight into the ecological relationships among the organisms that comprise the microbiome. However, there are a number of critical statistical challenges in the inference of such networks from high-throughput data. Since the abundances in each sample are constrained to have a fixed sum and there is incomplete overlap in microbial populations across subjects, the data are both compositional and zero-inflated. RESULTS: We propose the COmpositional Zero-Inflated Network Estimation (COZINE) method for inference of microbial networks which addresses these critical aspects of the data while maintaining computational scalability. COZINE relies on the multivariate Hurdle model to infer a sparse set of conditional dependencies which reflect not only relationships among the continuous values, but also among binary indicators of presence or absence and between the binary and continuous representations of the data. Our simulation results show that the proposed method is better able to capture various types of microbial relationships than existing approaches. We demonstrate the utility of the method with an application to understanding the oral microbiome network in a cohort of leukemic patients. CONCLUSIONS: Our proposed method addresses important challenges in microbiome network estimation, and can be effectively applied to discover various types of dependence relationships in microbial communities. The procedure we have developed, which we refer to as COZINE, is available online at https://github.com/MinJinHa/COZINE .

iDINGO-integrative differential network analysis in genomics with Shiny application.

Motivation: Differential network analysis is an important way to understand network rewiring involved in disease progression and development. Building differential networks from multiple 'omics data provides insight into the holistic differences of the interactive system under different patient-specific groups. DINGO was developed to infer group-specific dependencies and build differential networks. However, DINGO and other existing tools are limited to analyze data arising from a single platform, and modeling each of the multiple 'omics data independently does not account for the hierarchical structure of the data. Results: We developed the iDINGO R package to estimate group-specific dependencies and make inferences on the integrative differential networks, considering the biological hierarchy among the platforms. A Shiny application has also been developed to facilitate easier analysis and visualization of results, including integrative differential networks and hub gene identification across platforms. Availability and implementation: R package is available on CRAN (https://cran.r-project.org/web/packages/iDINGO) and Shiny application at https://github.com/MinJinHa/iDINGO. Contact: mjha@mdanderson.org. Supplementary information: Supplementary data are available at Bioinformatics online.

Bayesian inference of hub nodes across multiple networks.

Hub nodes within biological networks play a pivotal role in determining phenotypes and disease outcomes. In the multiple network setting, we are interested in understanding network similarities and differences across different experimental conditions or subtypes of disease. The majority of proposed approaches for joint modeling of multiple networks focus on the sharing of edges across graphs. Rather than assuming the network similarities are driven by individual edges, we instead focus on the presence of common hub nodes, which are more likely to be preserved across settings. Specifically, we formulate a Bayesian approach to the problem of multiple network inference which allows direct inference on shared and differential hub nodes. The proposed method not only allows a more intuitive interpretation of the resulting networks and clearer guidance on potential targets for treatment, but also improves power for identifying the edges of highly connected nodes. Through simulations, we demonstrate the utility of our method and compare its performance to current popular methods that do not borrow information regarding hub nodes across networks. We illustrate the applicability of our method to inference of co-expression networks from The Cancer Genome Atlas ovarian carcinoma dataset.

DINGO: differential network analysis in genomics.

MOTIVATION: Cancer progression and development are initiated by aberrations in various molecular networks through coordinated changes across multiple genes and pathways. It is important to understand how these networks change under different stress conditions and/or patient-specific groups to infer differential patterns of activation and inhibition. Existing methods are limited to correlation networks that are independently estimated from separate group-specific data and without due consideration of relationships that are conserved across multiple groups. METHOD: We propose a pathway-based differential network analysis in genomics (DINGO) model for estimating group-specific networks and making inference on the differential networks. DINGO jointly estimates the group-specific conditional dependencies by decomposing them into global and group-specific components. The delineation of these components allows for a more refined picture of the major driver and passenger events in the elucidation of cancer progression and development. RESULTS: Simulation studies demonstrate that DINGO provides more accurate group-specific conditional dependencies than achieved by using separate estimation approaches. We apply DINGO to key signaling pathways in glioblastoma to build differential networks for long-term survivors and short-term survivors in The Cancer Genome Atlas. The hub genes found by mRNA expression, DNA copy number, methylation and microRNA expression reveal several important roles in glioblastoma progression. AVAILABILITY AND IMPLEMENTATION: R Package at: odin.mdacc.tmc.edu/∼vbaladan. CONTACT: veera@mdanderson.org SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PenPC: A two-step approach to estimate the skeletons of high-dimensional directed acyclic graphs.

Estimation of the skeleton of a directed acyclic graph (DAG) is of great importance for understanding the underlying DAG and causal effects can be assessed from the skeleton when the DAG is not identifiable. We propose a novel method named PenPC to estimate the skeleton of a high-dimensional DAG by a two-step approach. We first estimate the nonzero entries of a concentration matrix using penalized regression, and then fix the difference between the concentration matrix and the skeleton by evaluating a set of conditional independence hypotheses. For high-dimensional problems where the number of vertices p is in polynomial or exponential scale of sample size n, we study the asymptotic property of PenPC on two types of graphs: traditional random graphs where all the vertices have the same expected number of neighbors, and scale-free graphs where a few vertices may have a large number of neighbors. As illustrated by extensive simulations and applications on gene expression data of cancer patients, PenPC has higher sensitivity and specificity than the state-of-the-art method, the PC-stable algorithm.

Beneficial effects of voglibose administration on body weight and lipid metabolism via gastrointestinal bile acid modification.

This study was designed with the goal of examining the effects of voglibose administration on body weight and lipid metabolism and underlying mechanism high fat diet-induced obese mice. Male C57BL/6 mice were randomly assigned to one of four groups: a control diet (CTL), high-fat diet (HF), high-fat diet supplemented with voglibose (VO), and high fat diet pair-fed group (PF). After 12 weeks, the following characteristics were investigated: serum lipid and glucose levels, serum polar metabolite profiles, and expression levels of genes involved in lipid and bile acid metabolism. In addition, pyrosequencing was used to analyze the composition of gut microbiota found in feces. Total body weight gain was significantly lower in the VO group than in the CTL, HF, and PF groups. The VO group exhibited improved metabolic profiles including those of blood glucose, triglyceride, and total cholesterol levels. The 12-week voglibose administration decreased the ratio of Firmicutes to Bacteroidetes found in feces. Circulating levels of taurocholic and cholic acid were significantly higher in the VO group than in the HF and CTL groups. Deoxycholic acid levels tended to be higher in the VO group than in the HF group. Voglibose administration downregulated expression levels of CYP8B1 and HNF4α genes and upregulated those of PGC1α, whereas FXRα was not affected. Voglibose administration elicits changes in the composition of the intestinal microbiota and circulating metabolites, which ultimately has systemic effects on body weight and lipid metabolism in mice.

Partial correlation matrix estimation using ridge penalty followed by thresholding and re-estimation.

Motivated by the problem of construction of gene co-expression network, we propose a statistical framework for estimating high-dimensional partial correlation matrix by a three-step approach. We first obtain a penalized estimate of a partial correlation matrix using ridge penalty. Next we select the non-zero entries of the partial correlation matrix by hypothesis testing. Finally we re-estimate the partial correlation coefficients at these non-zero entries. In the second step, the null distribution of the test statistics derived from penalized partial correlation estimates has not been established. We address this challenge by estimating the null distribution from the empirical distribution of the test statistics of all the penalized partial correlation estimates. Extensive simulation studies demonstrate the good performance of our method. Application on a yeast cell cycle gene expression data shows that our method delivers better predictions of the protein-protein interactions than the Graphic Lasso.

Effect of the peripartum depressive symptoms on the Internet use disorder of their offspring in late childhood: retrospective longitudinal study.

Internet use disorder (IUD) is an emerging social and mental health concern. This study aimed to analyze the relative risk of IUD in late childhood among children whose mothers experienced peripartum depressive symptoms. This study included 762 participants (397 boys and 365 girls) and was conducted in 2017 (aged 9) and 2019 (aged 11). We analyzed the adjusted relative risk of being at high risk for IUD based on whether the mother experienced depressive symptoms during pregnancy or one month after delivery. We also considered the persistence of depressed mood for 4 months after delivery and the severity of peripartum depressive symptoms. From 2017, 20.7% of boys and 14.0% of girls were at high risk of developing IUD. Compared to the non-peripartum depressive group, girls whose mothers experienced peripartum depressive symptoms and those that persisted for 4 months were 1.084 and 1.124 times more likely to be at high risk of IUD (95% confidence interval = 1.005-1.170 and 1.013-1.248), respectively. There were no statistically significant differences among boys. Peripartum depressed mood could be one of risk factors of IUD. IUD needs to be monitored in children whose mothers experienced peripartum depressive symptoms.

Association between the utilization of senior centers and participation in health check-ups.

The global older adult population is increasing. Early detection and intervention through health check-ups are crucial for successful aging, as they play a significant role in identifying and addressing diseases. This study explored the relationship between the utilization of senior centers and the promotion of health check-ups. It utilized data from 10,097 individuals aged 65 years and above, sourced from the 2020 Elderly Survey in South Korea. The primary variable of interest was classified into two groups: those who utilized senior centers and those who did not. Subgroups were further categorized based on the frequency of usage and the presence of family members among senior centers users. Logistic regression analyses were conducted to assess the association between the utilization of senior centers and participation in health check-ups. Both men and women utilizing senior centers demonstrated a higher likelihood of participating in health check-ups compared with those who did not use senior centers. Participants visiting senior centers in a week exhibited a progressively higher likelihood of engaging in health check-ups compared with those who visited such senior centers zero times a week. Senior centers can serve as effective intervention methods to enhance health check-ups among older adults. Furthermore, this can contribute to fostering successful aging among older adults.

Targeting DNA2 overcomes metabolic reprogramming in multiple myeloma.

DNA damage resistance is a major barrier to effective DNA-damaging therapy in multiple myeloma (MM). To discover mechanisms through which MM cells overcome DNA damage, we investigate how MM cells become resistant to antisense oligonucleotide (ASO) therapy targeting Interleukin enhancer binding factor 2 (ILF2), a DNA damage regulator that is overexpressed in 70% of MM patients whose disease has progressed after standard therapies have failed. Here, we show that MM cells undergo adaptive metabolic rewiring to restore energy balance and promote survival in response to DNA damage activation. Using a CRISPR/Cas9 screening strategy, we identify the mitochondrial DNA repair protein DNA2, whose loss of function suppresses MM cells' ability to overcome ILF2 ASO-induced DNA damage, as being essential to counteracting oxidative DNA damage. Our study reveals a mechanism of vulnerability of MM cells that have an increased demand for mitochondrial metabolism upon DNA damage activation.

Estimating causal effects with hidden confounding using instrumental variables and environments.

Recent works have proposed regression models which are invariant across data collection environments [24, 20, 11, 16, 8]. These estimators often have a causal interpretation under conditions on the environments and type of invariance imposed. One recent example, the Causal Dantzig (CD), is consistent under hidden confounding and represents an alternative to classical instrumental variable estimators such as Two Stage Least Squares (TSLS). In this work we derive the CD as a generalized method of moments (GMM) estimator. The GMM representation leads to several practical results, including 1) creation of the Generalized Causal Dantzig (GCD) estimator which can be applied to problems with continuous environments where the CD cannot be fit 2) a Hybrid (GCD-TSLS combination) estimator which has properties superior to GCD or TSLS alone 3) straightforward asymptotic results for all methods using GMM theory. We compare the CD, GCD, TSLS, and Hybrid estimators in simulations and an application to a Flow Cytometry data set. The newly proposed GCD and Hybrid estimators have superior performance to existing methods in many settings.

Association between changes in having of cancer patients in the family and depression: A longitudinal panel study.

BACKGROUND: Cancer diagnosis can cause considerable stress among patients and their families. Both may experience clinical depression and severe anxiety. Therefore, this study investigated the association between the occurrence of cancer patients in the family and the depression among family members. METHODS: Data from the Korean Longitudinal Study of Aging (2006-2020) were used. A total of 6251 participants who completed the short-form Center for Epidemiologic Studies Depression Scale (CESD-10-D) questionnaire were included. General estimating equations were used to assess the temporal effects of changes on depression in the presence of cancer patients in the family. RESULTS: Having cancer patients in the family was associated with a high risk of depression among both men and women (men, Odds Ratio (OR):1.78, 95 % Confidence Intervals (CI) 1.13-2.79; women, OR:1.53, 95 % CI 1.06-2.22). Depressive symptoms were particularly high in women, especially when cancer symptoms were more severe than previous surveys (OR: 2.48, 95 % CI 1.18-5.20). LIMITATIONS: First, non-responders were excluded but this could be affected by underestimation bias. Second, depression was defined as the CESD-10-D score, and the biological risk factors of depression could not be identified because of survey-based database. Third, due to the retrospective design study, confirming the causal relationship clearly is difficult. Finally, residual scheming effects of unmeasured variables could not be eliminated. CONCLUSION: Our findings support efforts to diagnose and manage depression in the families of cancer patients. Accordingly, healthcare services and supportive interventions to reduce the psychological factors of cancer patients' families are needed.

3-Phosphoinositide-dependent kinase 1 drives acquired resistance to osimertinib.

Osimertinib sensitive and resistant NSCLC NCI-H1975 clones are used to model osimertinib acquired resistance in humanized and non-humanized mice and delineate potential resistance mechanisms. No new EGFR mutations or loss of the EGFR T790M mutation are found in resistant clones. Resistant tumors grown under continuous osimertinib pressure both in humanized and non-humanized mice show aggressive tumor regrowth which is significantly less sensitive to osimertinib as compared with parental tumors. 3-phosphoinositide-dependent kinase 1 (PDK1) is identified as a potential driver of osimertinib acquired resistance, and its selective inhibition by BX795 and CRISPR gene knock out, sensitizes resistant clones. In-vivo inhibition of PDK1 enhances the osimertinib sensitivity against osimertinib resistant xenograft and a patient derived xenograft (PDX) tumors. PDK1 knock-out dysregulates PI3K/Akt/mTOR signaling, promotes cell cycle arrest at the G1 phase. Yes-associated protein (YAP) and active-YAP are upregulated in resistant tumors, and PDK1 knock-out inhibits nuclear translocation of YAP. Higher expression of PDK1 and an association between PDK1 and YAP are found in patients with progressive disease following osimertinib treatment. PDK1 is a central upstream regulator of two critical drug resistance pathways: PI3K/AKT/mTOR and YAP.

Efficacy of futibatinib, an irreversible fibroblast growth factor receptor inhibitor, in FGFR-altered breast cancer.

Several alterations in fibroblast growth factor receptor (FGFR) genes have been found in breast cancer; however, they have not been well characterized as therapeutic targets. Futibatinib (TAS-120; Taiho) is a novel, selective, pan-FGFR inhibitor that inhibits FGFR1-4 at nanomolar concentrations. We sought to determine futibatinib's efficacy in breast cancer models. Nine breast cancer patient-derived xenografts (PDXs) with various FGFR1-4 alterations and expression levels were treated with futibatinib. Antitumor efficacy was evaluated by change in tumor volume and time to tumor doubling. Alterations indicating sensitization to futibatinib in vivo were further characterized in vitro. FGFR gene expression between patient tumors and matching PDXs was significantly correlated; however, overall PDXs had higher FGFR3-4 expression. Futibatinib inhibited tumor growth in 3 of 9 PDXs, with tumor stabilization in an FGFR2-amplified model and prolonged regression (> 110 days) in an FGFR2 Y375C mutant/amplified model. FGFR2 overexpression and, to a greater extent, FGFR2 Y375C expression in MCF10A cells enhanced cell growth and sensitivity to futibatinib. Per institutional and public databases, FGFR2 mutations and amplifications had a population frequency of 1.1%-2.6% and 1.5%-2.5%, respectively, in breast cancer patients. FGFR2 alterations in breast cancer may represent infrequent but highly promising targets for futibatinib.