CD4+ T cell immunity is dependent on an intrinsic stem-like program.

CD4+ T cells are central to various immune responses, but the molecular programs that drive and maintain CD4+ T cell immunity are not entirely clear. Here we identify a stem-like program that governs the CD4+ T cell response in transplantation models. Single-cell-transcriptomic analysis revealed that naive alloantigen-specific CD4+ T cells develop into TCF1hi effector precursor (TEP) cells and TCF1-CXCR6+ effectors in transplant recipients. The TCF1-CXCR6+CD4+ effectors lose proliferation capacity and do not reject allografts upon adoptive transfer into secondary hosts. By contrast, the TCF1hiCD4+ TEP cells have dual features of self-renewal and effector differentiation potential, and allograft rejection depends on continuous replenishment of TCF1-CXCR6+ effectors from TCF1hiCD4+ TEP cells. Mechanistically, TCF1 sustains the CD4+ TEP cell population, whereas the transcription factor IRF4 and the glycolytic enzyme LDHA govern the effector differentiation potential of CD4+ TEP cells. Deletion of IRF4 or LDHA in T cells induces transplant acceptance. These findings unravel a stem-like program that controls the self-renewal capacity and effector differentiation potential of CD4+ TEP cells and have implications for T cell-related immunotherapies.

The bone microenvironment invigorates metastatic seeds for further dissemination.

Metastasis has been considered as the terminal step of tumor progression. However, recent genomic studies suggest that many metastases are initiated by further spread of other metastases. Nevertheless, the corresponding pre-clinical models are lacking, and underlying mechanisms are elusive. Using several approaches, including parabiosis and an evolving barcode system, we demonstrated that the bone microenvironment facilitates breast and prostate cancer cells to further metastasize and establish multi-organ secondary metastases. We uncovered that this metastasis-promoting effect is driven by epigenetic reprogramming that confers stem cell-like properties on cancer cells disseminated from bone lesions. Furthermore, we discovered that enhanced EZH2 activity mediates the increased stemness and metastasis capacity. The same findings also apply to single cell-derived populations, indicating mechanisms distinct from clonal selection. Taken together, our work revealed an unappreciated role of the bone microenvironment in metastasis evolution and elucidated an epigenomic reprogramming process driving terminal-stage, multi-organ metastases.

hDirect-MAP: projection-free single-cell modeling of response to checkpoint immunotherapy.

There is a lack of robust generalizable predictive biomarkers of response to immune checkpoint blockade in multiple types of cancer. We develop hDirect-MAP, an algorithm that maps T cells into a shared high-dimensional (HD) expression space of diverse T cell functional signatures in which cells group by the common T cell phenotypes rather than dimensional reduced features or a distorted view of these features. Using projection-free single-cell modeling, hDirect-MAP first removed a large group of cells that did not contribute to response and then clearly distinguished T cells into response-specific subpopulations that were defined by critical T cell functional markers of strong differential expression patterns. We found that these grouped cells cannot be distinguished by dimensional-reduction algorithms but are blended by diluted expression patterns. Moreover, these identified response-specific T cell subpopulations enabled a generalizable prediction by their HD metrics. Tested using five single-cell RNA-seq or mass cytometry datasets from basal cell carcinoma, squamous cell carcinoma and melanoma, hDirect-MAP demonstrated common response-specific T cell phenotypes that defined a generalizable and accurate predictive biomarker.

Life-Course Brain Health as a Determinant of Late-Life Mental Health: American Association for Geriatric Psychiatry Expert Panel Recommendations.

This position statement of the Expert Panel on Brain Health of the American Association for Geriatric Psychiatry (AAGP) emphasizes the critical role of life course brain health in shaping mental well-being during the later stages of life. Evidence posits that maintaining optimal brain health earlier in life is crucial for preventing and managing brain aging-related disorders such as dementia/cognitive decline, depression, stroke, and anxiety. We advocate for a holistic approach that integrates medical, psychological, and social frameworks with culturally tailored interventions across the lifespan to promote brain health and overall mental well-being in aging adults across all communities. Furthermore, our statement underscores the significance of prevention, early detection, and intervention in identifying cognitive decline, mood changes, and related mental illness. Action should also be taken to understand and address the needs of communities that traditionally have unequal access to preventive health information and services. By implementing culturally relevant and tailored evidence-based practices and advancing research in geriatric psychiatry, behavioral neurology, and geroscience, we can enhance the quality of life for older adults facing the unique challenges of aging. This position statement emphasizes the intrinsic link between brain health and mental health in aging, urging healthcare professionals, policymakers, and a broader society to prioritize comprehensive strategies that safeguard and promote brain health from birth through later years across all communities. The AAGP Expert Panel has the goal of launching further activities in the coming months and years.

Automatic Segmentation in Acute Ischemic Stroke: Prognostic Significance of Topological Stroke Volumes on Stroke Outcome.

BACKGROUND: Stroke infarct volume predicts patient disability and has utility for clinical trial outcomes. Accurate infarct volume measurement requires manual segmentation of stroke boundaries in diffusion-weighted magnetic resonance imaging scans which is time-consuming and subject to variability. Automatic infarct segmentation should be robust to rotation and reflection; however, prior work has not encoded this property into deep learning architecture. Here, we use rotation-reflection equivariance and train a deep learning model to segment stroke volumes in a large cohort of well-characterized patients with acute ischemic stroke in different vascular territories. METHODS: In this retrospective study, patients were selected from a stroke registry at Houston Methodist Hospital. Eight hundred seventy-five patients with acute ischemic stroke in any brain area who had magnetic resonance imaging with diffusion-weighted imaging were included for analysis and split 80/20 for training/testing. Infarct volumes were manually segmented by consensus of 3 independent clinical experts and cross-referenced against radiology reports. A rotation-reflection equivariant model was developed based on U-Net and grouped convolutions. Segmentation performance was evaluated using Dice score, precision, and recall. Ninety-day modified Rankin Scale outcome prediction was also evaluated using clinical variables and segmented stroke volumes in different brain regions. RESULTS: Segmentation model Dice scores are 0.88 (95% CI, 0.87-0.89; training) and 0.85 (0.82-0.88; testing). The modified Rankin Scale outcome prediction AUC using stroke volume in 30 refined brain regions based upon modified Rankin Scale-relevance areas adjusted for clinical variables was 0.80 (0.76-0.83) with an accuracy of 0.75 (0.72-0.78). CONCLUSIONS: We trained a deep learning model with encoded rotation-reflection equivariance to segment acute ischemic stroke lesions in diffusion- weighted imaging using a large data set from the Houston Methodist stroke center. The model achieved competitive performance in 175 well-balanced hold-out testing cases that include strokes from different vascular territories. Furthermore, the location specific stroke volume segmentations from the deep learning model combined with clinical factors demonstrated high AUC and accuracy for 90-day modified Rankin Scale in an outcome prediction model.

Early-Onset Micromorphological Changes of Neuronal Fiber Bundles During Radiotherapy.

BACKGROUND: Patients receiving cranial radiation face the risk of delayed brain dysfunction. However, an early medical imaging marker is not available until irreversible morphological changes emerge. PURPOSE: To explore the micromorphological white matter changes during the radiotherapy session by utilizing an along-tract analysis framework. STUDY TYPE: Prospective. POPULATION: Eighteen nasopharyngeal carcinoma (two female) patients receiving cranial radiation. FIELD STRENGTH/SEQUENCE: 3.0 T; Diffusion tensor imaging (DTI) and T1- and T2-weighted images (T1W, T2W); computed tomography (CT). ASSESSMENT: Patients received three DTI imaging scans during the radiotherapy (RT), namely the baseline scan (1-2 days before RT began), the middle scan (the middle of the RT session), and the end scan (1-2 days after RT ended). Twelve fibers were segmented after whole-brain tractography. Then, the fractional anisotropy (FA) values and the cumulative radiation dose received for each fiber streamline were resampled and projected into their center fiber. STATISTICAL TESTS: The contrast among the three scans (P1: middle scan-baseline scan; P2: end scan-middle scan; P3: end scan-baseline scan) were compared using the linear mixed model for each of the 12 center fibers. Then, a dose-responsiveness relationship was performed using Pearson correlation. P < 0.05 was considered statistically significant. RESULTS: Six of the 12 center fibers showed significant changes of FA values during the RT but with heterogeneous patterns. The significant changes along a specific center fiber were associated with their cumulative dose received (Genu: P1 r = -0.6182, P2 r = -0.5907; Splenium: P1 r = 0.4055, P = 0.1063, P2 r = 0.6742; right uncinate fasciculus: P1 r = -0.3865, P2 r = -0.4912, P = 0.0533; right corticospinal tract: P1 r = 0.4273, P = 0.1122, P2 r = -0.6885). DATA CONCLUSION: The along-tract analysis might provide sensitive measures on the early-onset micromorphological changes. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY: Stage 3.

The radiomic-clinical model using the SHAP method for assessing the treatment response of whole-brain radiotherapy: a multicentric study.

OBJECTIVE: To develop and validate a pretreatment magnetic resonance imaging (MRI)-based radiomic-clinical model to assess the treatment response of whole-brain radiotherapy (WBRT) by using SHapley Additive exPlanations (SHAP), which is derived from game theory, and can explain the output of different machine learning models. METHODS: We retrospectively enrolled 228 patients with brain metastases from two medical centers (184 in the training cohort and 44 in the validation cohort). Treatment responses of patients were categorized as a non-responding group vs. a responding group according to the Response Assessment in Neuro-Oncology Brain Metastases (RANO-BM) criteria. For each tumor, 960 features were extracted from the MRI sequence. The least absolute shrinkage and selection operator (LASSO) was used for feature selection. A support vector machine (SVM) model incorporating clinical factors and radiomic features wase used to construct the radiomic-clinical model. SHAP method explained the SVM model by prioritizing the importance of features, in terms of assessment contribution. RESULTS: Three radiomic features and three clinical factors were identified to build the model. Radiomic-clinical model yielded AUCs of 0.928 (95%CI 0.901-0.949) and 0.851 (95%CI 0.816-0.886) for assessing the treatment response in the training cohort and validation cohort, respectively. SHAP summary plot illustrated the feature's value affected the feature's impact attributed to model, and SHAP force plot showed the integration of features' impact attributed to individual response. CONCLUSION: The radiomic-clinical model with the SHAP method can be useful for assessing the treatment response of WBRT and may assist clinicians in directing personalized WBRT strategies in an understandable manner. KEY POINTS: • Radiomic-clinical model can be useful for assessing the treatment response of WBRT. • SHAP could explain and visualize radiomic-clinical machine learning model in a clinician-friendly way.

Novel STAT3 small-molecule inhibitors identified by structure-based virtual ligand screening incorporating SH2 domain flexibility.

Efforts to develop STAT3 inhibitors have focused on its SH2 domain starting with short phosphotyrosylated peptides based on STAT3 binding motifs, e.g. pY905LPQTV within gp130. Despite binding to STAT3 with high affinity, issues regarding stability, bioavailability, and membrane permeability of these peptides, as well as peptidomimetics such as CJ-887, have limited their further clinical development and led to interest in small-molecule inhibitors. Some small molecule STAT3 inhibitors, identified using structure-based virtual ligand screening (SB-VLS); while having favorable drug-like properties, suffer from weak binding affinities, possibly due to the high flexibility of the target domain. We conducted molecular dynamic (MD) simulations of the SH2 domain in complex with CJ-887, and used an averaged structure from this MD trajectory as an "induced-active site" receptor model for SB-VLS of 110,000 compounds within the SPEC database. Screening was followed by re-docking and re-scoring of the top 30% of hits, selection for hit compounds that directly interact with pY + 0 binding pocket residues R609 and S613, and testing for STAT3 targeting in vitro, which identified two lead hits with good activity and favorable drug-like properties. Unlike most small-molecule STAT3 inhibitors previously identified, which contain negatively-charged moieties that mediate binding to the pY + 0 binding pocket, these compounds are uncharged and likely will serve as better candidates for anti-STAT3 drug development. IMPLICATIONS: SB-VLS, using an averaged structure from molecular dynamics (MD) simulations of STAT3 SH2 domain in a complex with CJ-887, a known peptidomimetic binder, identify two highly potent, neutral, low-molecular weight STAT3-inhibitors with favorable drug-like properties.

Epithelial-to-mesenchymal transition is not required for lung metastasis but contributes to chemoresistance.

The role of epithelial-to-mesenchymal transition (EMT) in metastasis is a longstanding source of debate, largely owing to an inability to monitor transient and reversible EMT phenotypes in vivo. Here we establish an EMT lineage-tracing system to monitor this process in mice, using a mesenchymal-specific Cre-mediated fluorescent marker switch system in spontaneous breast-to-lung metastasis models. We show that within a predominantly epithelial primary tumour, a small proportion of tumour cells undergo EMT. Notably, lung metastases mainly consist of non-EMT tumour cells that maintain their epithelial phenotype. Inhibiting EMT by overexpressing the microRNA miR-200 does not affect lung metastasis development. However, EMT cells significantly contribute to recurrent lung metastasis formation after chemotherapy. These cells survived cyclophosphamide treatment owing to reduced proliferation, apoptotic tolerance and increased expression of chemoresistance-related genes. Overexpression of miR-200 abrogated this resistance. This study suggests the potential of an EMT-targeting strategy, in conjunction with conventional chemotherapies, for breast cancer treatment.

Driver network as a biomarker: systematic integration and network modeling of multi-omics data to derive driver signaling pathways for drug combination prediction.

MOTIVATION: Drug combinations that simultaneously suppress multiple cancer driver signaling pathways increase therapeutic options and may reduce drug resistance. We have developed a computational systems biology tool, DrugComboExplorer, to identify driver signaling pathways and predict synergistic drug combinations by integrating the knowledge embedded in vast amounts of available pharmacogenomics and omics data. RESULTS: This tool generates driver signaling networks by processing DNA sequencing, gene copy number, DNA methylation and RNA-seq data from individual cancer patients using an integrated pipeline of algorithms, including bootstrap aggregating-based Markov random field, weighted co-expression network analysis and supervised regulatory network learning. It uses a systems pharmacology approach to infer the combinatorial drug efficacies and synergy mechanisms through drug functional module-induced regulation of target expression analysis. Application of our tool on diffuse large B-cell lymphoma and prostate cancer demonstrated how synergistic drug combinations can be discovered to inhibit multiple driver signaling pathways. Compared with existing computational approaches, DrugComboExplorer had higher prediction accuracy based on in vitro experimental validation and probability concordance index. These results demonstrate that our network-based drug efficacy screening approach can reliably prioritize synergistic drug combinations for cancer and uncover potential mechanisms of drug synergy, warranting further studies in individual cancer patients to derive personalized treatment plans. AVAILABILITY AND IMPLEMENTATION: DrugComboExplorer is available at https://github.com/Roosevelt-PKU/drugcombinationprediction. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Sepsis outcomes in solid organ transplant recipients.

BACKGROUND: We present data on a cohort of patients diagnosed with sepsis over a 10-year period comparing outcomes in solid organ transplant (SOT) and non-solid organ transplant (non-SOT) recipients. METHODS: This is a retrospective single-center study of patients with diagnosis of sepsis from 1/1/06 to 6/30/16. Cases and controls were matched by year of sepsis diagnosis with propensity score matching. Conditional logistic regression and repeated measurement models were performed for binary outcomes. Trends over time for in-hospital mortality were determined using the Cochran-Armitage test. A gamma-distributed model was performed on the continuous variables. RESULTS: Overall, there were 18 632 admission encounters with a discharge diagnosis of sepsis in 14 780 unique patients. Of those admissions, 1689 were SOT recipients. After 1:1 matching by year, there were three thousand three hundred and forty patients (1670 cases; 1670 controls) diagnosed with sepsis. There was a decreasing trend for in-hospital mortality for sepsis over time in SOT patients and non-SOT patients (P < .05) due to early sepsis recognition and improved standard of care. Despite higher comorbidities in the SOT group, conditional logistic regression showed that in-hospital mortality for sepsis in SOT patients was similar compared with non-SOT patients (odds ratio [OR] =1.14 [95% confidence interval {CI}, 0.95-1.37], P = .161). However, heart and lung SOT subgroups had higher odds of dying compared with the non-SOT group (OR = 1.83 [95% CI, 1.30-2.57], P < .001 and OR = 1.77 [95% CI, 1.34-2.34], P < .001). On average, SOT patients had 2 days longer hospital length of stay compared with non-SOT admissions (17.00 ± 19.54 vs 15.23 ± 17.07, P < .05). Additionally, SOT patients had higher odds of hospital readmission within 30 days (OR = 1.25 [95% CI, 1.06-1.51], P = .020), and higher odds for DIC compared with non-SOT patients (OR = 1.76 [95% CI, 1.10-2.86], P = .021). CONCLUSION: Sepsis in solid organ transplants and non-solid organ transplant patients have similar mortality; however, the subset of heart and lung transplant recipients with sepsis has a higher rate of mortality compared with the non-solid organ transplant recipients. SOT with sepsis as a group has a higher hospital readmission rate compared with non-transplant sepsis patients.

Aurora-B mediated ATM serine 1403 phosphorylation is required for mitotic ATM activation and the spindle checkpoint.

The ATM kinase plays a critical role in the maintenance of genetic stability. ATM is activated in response to DNA damage and is essential for cell-cycle checkpoints. Here, we report that ATM is activated in mitosis in the absence of DNA damage. We demonstrate that mitotic ATM activation is dependent on the Aurora-B kinase and that Aurora-B phosphorylates ATM on serine 1403. This phosphorylation event is required for mitotic ATM activation. Further, we show that loss of ATM function results in shortened mitotic timing and a defective spindle checkpoint, and that abrogation of ATM Ser1403 phosphorylation leads to this spindle checkpoint defect. We also demonstrate that mitotically activated ATM phosphorylates Bub1, a critical kinetochore protein, on Ser314. ATM-mediated Bub1 Ser314 phosphorylation is required for Bub1 activity and is essential for the activation of the spindle checkpoint. Collectively, our data highlight mechanisms of a critical function of ATM in mitosis.

Correlating mammographic and pathologic findings in clinical decision support using natural language processing and data mining methods.

BACKGROUND: A key challenge to mining electronic health records for mammography research is the preponderance of unstructured narrative text, which strikingly limits usable output. The imaging characteristics of breast cancer subtypes have been described previously, but without standardization of parameters for data mining. METHODS: The authors searched the enterprise-wide data warehouse at the Houston Methodist Hospital, the Methodist Environment for Translational Enhancement and Outcomes Research (METEOR), for patients with Breast Imaging Reporting and Data System (BI-RADS) category 5 mammogram readings performed between January 2006 and May 2015 and an available pathology report. The authors developed natural language processing (NLP) software algorithms to automatically extract mammographic and pathologic findings from free text mammogram and pathology reports. The correlation between mammographic imaging features and breast cancer subtype was analyzed using one-way analysis of variance and the Fisher exact test. RESULTS: The NLP algorithm was able to obtain key characteristics for 543 patients who met the inclusion criteria. Patients with estrogen receptor-positive tumors were more likely to have spiculated margins (P = .0008), and those with tumors that overexpressed human epidermal growth factor receptor 2 (HER2) were more likely to have heterogeneous and pleomorphic calcifications (P = .0078 and P = .0002, respectively). CONCLUSIONS: Mammographic imaging characteristics, obtained from an automated text search and the extraction of mammogram reports using NLP techniques, correlated with pathologic breast cancer subtype. The results of the current study validate previously reported trends assessed by manual data collection. Furthermore, NLP provides an automated means with which to scale up data extraction and analysis for clinical decision support. Cancer 2017;114-121. © 2016 American Cancer Society.

Early clearance vs persistence of de novo donor-specific antibodies following lung transplantation.

BACKGROUND: The natural history of de novo donor-specific antibodies (dnDSA) after lung transplantation is not well-described. We sought to determine the incidence and risk factors associated with dnDSA and compare outcomes between recipients with transient (or isolated) vs persistent dnDSA after transplantation. METHODS: A single-center review of all lung transplants from 1/2009-7/2013. DSAs were tested eight times in the first year and every 4 months thereafter. Outcomes examined included acute rejection and graft failure. RESULTS: Median follow-up was 18 months (range: 1-61 months), and 24.6% of 333 first-time lung-only transplant recipients developed a dnDSA. Ethnicity, HLA-DQ mismatches, post-transplantation platelet transfusion and Lung Allocation Score >60 were associated with dnDSA (P<.05). Overall graft survival was worse for dnDSA-positive vs negative recipients (P=.025). Of 323 recipients with 1-year follow-up, 72 (22.2%) developed dnDSA, and in 25 (34.7%), the dnDSA was transient and cleared. Recipients with transient dnDSA were less likely to develop acute rejection than those with persistent dnDSA (P=.007). CONCLUSIONS: Early post-lung transplantation, dnDSA occurred in 1/4 of recipients, was associated with peri-transplant risk factors and resulted in decreased survival. Spontaneous clearance of dnDSA, seen in one-third of recipients, was associated with a lower risk of acute rejection.

Targeting RPL39 and MLF2 reduces tumor initiation and metastasis in breast cancer by inhibiting nitric oxide synthase signaling.

We previously described a gene signature for breast cancer stem cells (BCSCs) derived from patient biopsies. Selective shRNA knockdown identified ribosomal protein L39 (RPL39) and myeloid leukemia factor 2 (MLF2) as the top candidates that affect BCSC self-renewal. Knockdown of RPL39 and MLF2 by specific siRNA nanoparticles in patient-derived and human cancer xenografts reduced tumor volume and lung metastases with a concomitant decrease in BCSCs. RNA deep sequencing identified damaging mutations in both genes. These mutations were confirmed in patient lung metastases (n = 53) and were statistically associated with shorter median time to pulmonary metastasis. Both genes affect the nitric oxide synthase pathway and are altered by hypoxia. These findings support that extensive tumor heterogeneity exists within primary cancers; distinct subpopulations associated with stem-like properties have increased metastatic potential.

How cells explore shape space: a quantitative statistical perspective of cellular morphogenesis.

Through statistical analysis of datasets describing single cell shape following systematic gene depletion, we have found that the morphological landscapes explored by cells are composed of a small number of attractor states. We propose that the topology of these landscapes is in large part determined by cell-intrinsic factors, such as biophysical constraints on cytoskeletal organization, and reflects different stable signaling and/or transcriptional states. Cell-extrinsic factors act to determine how cells explore these landscapes, and the topology of the landscapes themselves. Informational stimuli primarily drive transitions between stable states by engaging signaling networks, while mechanical stimuli tune, or even radically alter, the topology of these landscapes. As environments fluctuate, the topology of morphological landscapes explored by cells dynamically adapts to these fluctuations. Finally we hypothesize how complex cellular and tissue morphologies can be generated from a limited number of simple cell shapes.

Cellular and molecular processes in ovarian cancer metastasis. A Review in the Theme: Cell and Molecular Processes in Cancer Metastasis.

Ovarian cancer is the most lethal gynecological malignancy. It is usually diagnosed at a late stage, with a 5-yr survival rate of <30%. The majority of ovarian cancer cases are diagnosed after tumors have widely spread within the peritoneal cavity, limiting the effectiveness of debulking surgery and chemotherapy. Owing to a substantially lower survival rate at late stages of disease than at earlier stages, the major cause of ovarian cancer deaths is believed to be therapy-resistant metastasis. Although metastasis plays a crucial role in promoting ovarian tumor progression and decreasing patient survival rates, the underlying mechanisms of ovarian cancer spread have yet to be thoroughly explored. For many years, researchers have believed that ovarian cancer metastasizes via a passive mechanism by which ovarian cancer cells are shed from the primary tumor and carried by the physiological movement of peritoneal fluid to the peritoneum and omentum. However, the recent discovery of hematogenous metastasis of ovarian cancer to the omentum via circulating tumor cells instigated rethinking of the mode of ovarian cancer metastasis and the importance of the "seed-and-soil" hypothesis for ovarian cancer metastasis. In this review we discuss the possible mechanisms by which ovarian cancer cells metastasize from the primary tumor to the omentum, the cross-talk signaling events between ovarian cancer cells and various stromal cells that play crucial roles in ovarian cancer metastasis, and the possible clinical implications of these findings in the management of this deadly, highly metastatic disease.

Molecular dynamics simulation study reveals potential substrate entry path into γ-secretase/presenilin-1.

Presenilin 1 (PS1) is the catalytic unit of γ-secretase which cleaves more than one hundred substrates. Among them, amyloid precursor protein (APP) and Notch are notable for their pivotal role in the pathogenesis of Alzheimer's disease (AD) and certain types of cancer. The hydrolysis process occurring inside the hydrophobic lipid bilayer remains unclear. With the aim to understand the mechanism of intramembrane proteolysis by γ-secretase, we constructed a homology model of human PS1 and performed molecular dynamics simulation in explicit membrane phospholipids with different components. During the simulation, TM9 was found to exhibit a high level of flexibility that involved in "gate-open" movement of TM2 and TM6, and thus partially exposed the catalytic residues. The highly conserved PALP motif acts as an anchor to mediate the conformation changes of TM6 induced by TM9. Moreover, direct interactions were observed between 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphoethanolamine (POPE) and the active site of γ-secretase, indicating that the lipid molecules have the potential to modulate γ-secretase by contacting with the catalytic residues, i.e., ASP 257 and ASP 385 of PS1. The intermediate states indicate a potential substrate penetration pathway through the interface of TM2 and TM6, which may be induced by changes of TM9. To our knowledge, this is the first molecular simulation study that reveals dynamic behavior of the human PS1 structure in the lipid bilayer and provides insight into the substrate entry path for subsequent intramembrane hydrolysis, which is critical information required for new strategy development of γ-secretase modulators to alleviate devastating AD.

A computational method for clinically relevant cancer stratification and driver mutation module discovery using personal genomics profiles.

BACKGROUND: Personalized genomics instability, e.g., somatic mutations, is believed to contribute to the heterogeneous drug responses in patient cohorts. However, it is difficult to discover personalized driver mutations that are predictive of drug sensitivity owing to diverse and complex mutations of individual patients. To circumvent this problem, a novel computational method is presented to discover potential drug sensitivity relevant cancer subtypes and identify driver mutation modules of individual subtypes by coupling differentially expressed genes (DEGs) based subtyping analysis with the driver mutation network analysis. RESULTS: The proposed method was applied to breast cancer and lung cancer samples available from The Cancer Genome Atlas (TCGA). Cancer subtypes were uncovered with significantly different survival rates, and more interestingly, distinct driver mutation modules were also discovered among different subtypes, indicating the potential mechanism of heterogeneous drug sensitivity. CONCLUSIONS: The research findings can be used to help guide the repurposing of known drugs and their combinations in order to target these dysfunctional modules and their downstream signaling effectively for achieving personalized or precision medicine treatment.

DrugComboRanker: drug combination discovery based on target network analysis.

MOTIVATION: Currently there are no curative anticancer drugs, and drug resistance is often acquired after drug treatment. One of the reasons is that cancers are complex diseases, regulated by multiple signaling pathways and cross talks among the pathways. It is expected that drug combinations can reduce drug resistance and improve patients' outcomes. In clinical practice, the ideal and feasible drug combinations are combinations of existing Food and Drug Administration-approved drugs or bioactive compounds that are already used on patients or have entered clinical trials and passed safety tests. These drug combinations could directly be used on patients with less concern of toxic effects. However, there is so far no effective computational approach to search effective drug combinations from the enormous number of possibilities. RESULTS: In this study, we propose a novel systematic computational tool DRUGCOMBORANKER: to prioritize synergistic drug combinations and uncover their mechanisms of action. We first build a drug functional network based on their genomic profiles, and partition the network into numerous drug network communities by using a Bayesian non-negative matrix factorization approach. As drugs within overlapping community share common mechanisms of action, we next uncover potential targets of drugs by applying a recommendation system on drug communities. We meanwhile build disease-specific signaling networks based on patients' genomic profiles and interactome data. We then identify drug combinations by searching drugs whose targets are enriched in the complementary signaling modules of the disease signaling network. The novel method was evaluated on lung adenocarcinoma and endocrine receptor positive breast cancer, and compared with other drug combination approaches. These case studies discovered a set of effective drug combinations top ranked in our prediction list, and mapped the drug targets on the disease signaling network to highlight the mechanisms of action of the drug combinations. AVAILABILITY AND IMPLEMENTATION: The program is available on request.