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.

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.

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.

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.

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.

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.

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.

CombPDX: a unified statistical framework for evaluating drug synergism in patient-derived xenografts.

Anticancer combination therapy has been developed to increase efficacy by enhancing synergy. Patient-derived xenografts (PDXs) have emerged as reliable preclinical models to develop effective treatments in translational cancer research. However, most PDX combination study designs focus on single dose levels, and dose-response surface models are not appropriate for testing synergism. We propose a comprehensive statistical framework to assess joint action of drug combinations from PDX tumor growth curve data. We provide various metrics and robust statistical inference procedures that locally (at a fixed time) and globally (across time) access combination effects under classical drug interaction models. Integrating genomic and pharmacological profiles in non-small-cell lung cancer (NSCLC), we have shown the utilities of combPDX in discovering effective therapeutic combinations and relevant biological mechanisms. We provide an interactive web server, combPDX ( https://licaih.shinyapps.io/CombPDX/ ), to analyze PDX tumor growth curve data and perform power analyses.

Sample Selection Bias in Evaluation of Prediction Performance of Causal Models.

Causal models are notoriously difficult to validate because they make untestable assumptions regarding confounding. New scientific experiments offer the possibility of evaluating causal models using prediction performance. Prediction performance measures are typically robust to violations in causal assumptions. However prediction performance does depend on the selection of training and test sets. In particular biased training sets can lead to optimistic assessments of model performance. In this work, we revisit the prediction performance of several recently proposed causal models tested on a genetic perturbation data set of Kemmeren [5]. We find that sample selection bias is likely a key driver of model performance. We propose using a less-biased evaluation set for assessing prediction performance and compare models on this new set. In this setting, the causal models have similar or worse performance compared to standard association based estimators such as Lasso. Finally we compare the performance of causal estimators in simulation studies which reproduce the Kemmeren structure of genetic knockout experiments but without any sample selection bias. These results provide an improved understanding of the performance of several causal models and offer guidance on how future studies should use Kemmeren.

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.

TUSC2 immunogene enhances efficacy of chemo-immuno combination on KRAS/LKB1 mutant NSCLC in humanized mouse model.

KRAS/LKB1 (STK11) NSCLC metastatic tumors are intrinsically resistant to anti-PD-1 or PD-L1 immunotherapy. In this study, we use a humanized mouse model to show that while carboplatin plus pembrolizumab reduce tumor growth moderately and transiently, the addition of the tumor suppressor gene TUSC2, delivered systemically in nanovesicles, to this combination, eradicates tumors in the majority of animals. Immunoprofiling of the tumor microenvironment shows the addition of TUSC2 mediates: (a) significant infiltration of reconstituted human functional cytotoxic T cells, natural killer cells, and dendritic cells; (b) induction of antigen-specific T cell responses; (c) enrichment of functional central and memory effector T cells; and (d) decreased levels of PD-1+ T cells, myeloid-derived suppressor cells, Tregs, and M2 tumor associated macrophages. Depletion studies show the presence of functional central and memory effector T cells are required for the efficacy. TUSC2 sensitizes KRAS/LKB1 tumors to carboplatin plus pembrolizumab through modulation of the immune contexture towards a pro-immune tumor microenvironment.

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.

Combined MEK/MDM2 inhibition demonstrates antitumor efficacy in TP53 wild-type thyroid and colorectal cancers with MAPK alterations.

Most tumors with activating MAPK (mitogen-activated protein kinase) pathway alterations respond poorly to MEK inhibitors alone. Here, we evaluated combination therapy with MEK inhibitor selumetinib and MDM2 inhibitor KRT-232 in TP53 wild-type and MAPK altered colon and thyroid cancer models. In vitro, we showed synergy between selumetinib and KRT-232 on cell proliferation and colony formation assays. Immunoblotting confirmed p53 upregulation and MEK pathway inhibition. The combination was tested in vivo in seven patient-derived xenograft (PDX) models (five colorectal carcinoma and two papillary thyroid carcinoma models) with different KRAS, BRAF, and NRAS mutations. Combination therapy significantly prolonged event-free survival compared with monotherapy in six of seven models tested. Reverse-phase protein arrays and immunohistochemistry, respectively, demonstrated upregulation of the p53 pathway and in two models cleaved caspase 3 with combination therapy. In summary, combined inhibition of MEK and MDM2 upregulated p53 expression, inhibited MAPK signaling and demonstrated greater antitumor efficacy than single drug therapy in both in vitro and in vivo settings. These findings support further clinical testing of the MEK/MDM2 inhibitor combination in tumors of epithelial origin with MAPK pathway alterations.

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.

Bayesian Structure Learning in Multi-layered Genomic Networks.

Integrative network modeling of data arising from multiple genomic platforms provides insight into the holistic picture of the interactive system, as well as the flow of information across many disease domains including cancer. The basic data structure consists of a sequence of hierarchically ordered datasets for each individual subject, which facilitates integration of diverse inputs, such as genomic, transcriptomic, and proteomic data. A primary analytical task in such contexts is to model the layered architecture of networks where the vertices can be naturally partitioned into ordered layers, dictated by multiple platforms, and exhibit both undirected and directed relationships. We propose a multi-layered Gaussian graphical model (mlGGM) to investigate conditional independence structures in such multi-level genomic networks in human cancers. We implement a Bayesian node-wise selection (BANS) approach based on variable selection techniques that coherently accounts for the multiple types of dependencies in mlGGM; this flexible strategy exploits edge-specific prior knowledge and selects sparse and interpretable models. Through simulated data generated under various scenarios, we demonstrate that BANS outperforms other existing multivariate regression-based methodologies. Our integrative genomic network analysis for key signaling pathways across multiple cancer types highlights commonalities and differences of p53 integrative networks and epigenetic effects of BRCA2 on p53 and its interaction with T68 phosphorylated CHK2, that may have translational utilities of finding biomarkers and therapeutic targets.

LMW cyclin E and its novel catalytic partner CDK5 are therapeutic targets and prognostic biomarkers in salivary gland cancers.

Salivary gland cancers (SGCs) are rare yet aggressive malignancies with significant histological heterogeneity, which has made prediction of prognosis and development of targeted therapies challenging. In majority of patients, local recurrence and/or distant metastasis are common and systemic treatments have minimal impact on survival. Therefore, identification of novel targets for treatment that can also be used as predictors of recurrence for multiple histological subtypes of SGCs is an area of unmet need. In this study, we developed a novel transgenic mouse model of SGC, efficiently recapitulating the major histological subtype (adenocarcinomas of the parotid gland) of human SGC. CDK2 knock out (KO) mice crossed with MMTV-low molecular weight forms of cyclin E (LMW-E) mice generated the transgenic mouse models of SGC, which arise in the parotid region of the salivary gland, similar to the common site of origin seen in human SGCs. To identify the CDK2 independent catalytic partner(s) of LMW-E, we used LMW-E expressing cell lines in mass spectrometric analysis and subsequent biochemical validation in pull down assays. These studies revealed that in the absence of CDK2, LMW-E preferentially binds to CDK5. Molecular targeting of CDK5, using siRNA, resulted in inhibition of cell proliferation of human SGCs overexpressing LMW-E. We also provide clinical evidence of significant association of LMW-E/CDK5 co-expression and decreased recurrence free survival in human SGC. Immunohistochemical analysis of LMW-E and CDK5 in 424 patients representing each of the four major histological subtypes of human salivary cancers (Aci, AdCC, MEC, and SDC) revealed that LMW-E and CDK5 are concordantly (positive/positive or negative/negative) expressed in 70% of these patients. The co-expression of LMW-E/CDK5 (both positive) robustly predicts the likelihood of recurrence, regardless of the histological classification of these tumors. Collectively, our results suggest that CDK5 is a novel and targetable biomarker for the treatment of patients with SGC presenting with LMW-E overexpressing tumors.

Targeting Replicative Stress and DNA Repair by Combining PARP and Wee1 Kinase Inhibitors Is Synergistic in Triple Negative Breast Cancers with Cyclin E or BRCA1 Alteration.

The identification of biomarker-driven targeted therapies for patients with triple negative breast cancer (TNBC) remains a major clinical challenge, due to a lack of specific targets. Here, we show that cyclin E, a major regulator of G1 to S transition, is deregulated in TNBC and is associated with mutations in DNA repair genes (e.g., BRCA1/2). Breast cancers with high levels of cyclin E not only have a higher prevalence of BRCA1/2 mutations, but also are associated with the worst outcomes. Using several in vitro and in vivo model systems, we show that TNBCs that harbor either mutations in BRCA1/2 or overexpression of cyclin E are very sensitive to the growth inhibitory effects of AZD-1775 (Wee 1 kinase inhibitor) when used in combination with MK-4837 (PARP inhibitor). Combination treatment of TNBC cell lines with these two agents results in synergistic cell killing due to induction of replicative stress, downregulation of DNA repair and cytokinesis failure that results in increased apoptosis. These findings highlight the potential clinical application of using cyclin E and BRCA mutations as biomarkers to select only those patients with the highest replicative stress properties that may benefit from combination treatment with Wee 1 kinase and PARP inhibitors.

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.