Pro-survival signaling regulates lipophagy essential for multiple myeloma resistance to stress-induced death.

Pro-survival metabolic adaptations to stress in tumorigenesis remain less well defined. We find that multiple myeloma (MM) is unexpectedly dependent on beta-oxidation of long-chain fatty acids (FAs) for survival under both basal and stress conditions. However, under stress conditions, a second pro-survival signal is required to sustain FA oxidation (FAO). We previously found that CD28 is expressed on MM cells and transduces a significant pro-survival/chemotherapy resistance signal. We now find that CD28 signaling regulates autophagy/lipophagy that involves activation of the Ca2+→AMPK→ULK1 axis and regulates the translation of ATG5 through HuR, resulting in sustained lipophagy, increased FAO, and enhanced MM survival. Conversely, blocking autophagy/lipophagy sensitizes MM to chemotherapy in vivo. Our findings link a pro-survival signal to FA availability needed to sustain the FAO required for cancer cell survival under stress conditions and identify lipophagy as a therapeutic target to overcome treatment resistance in MM.

Analysis of Circulating Tumor DNA Predicts Outcomes of Short Course Consolidation Immunotherapy in Unresectable Stage III Non-Small Cell Lung Cancer.

INTRODUCTION: The current standard of care for patients with inoperable stage III non-small cell lung cancer (NSCLC) includes chemoradiotherapy (CRT) followed by one year of checkpoint inhibitor (CPI) therapy. However, the optimal duration of consolidation CPI remains unknown. Here, we characterized the relationship between circulating tumor DNA (ctDNA) minimal residual disease (MRD) and clinical outcomes of unresectable locally advanced NSCLC patients treated on a phase 2 trial of short course consolidation immunotherapy after CRT, with the goal of testing if ctDNA may be able to identify patients who do not require a full year of treatment. PATIENTS AND METHODS: Plasma samples for ctDNA analysis were collected from patients on the BTCRC LUN 16-081 trial after completion of CRT, prior to C2D1 of CPI (i.e. 1 month after treatment start), and at the end of up to 6 months of treatment. Tumor-informed ctDNA MRD analysis was performed using CAPP-Seq. Levels of ctDNA at each time point were correlated with clinical outcomes. RESULTS: Detection of ctDNA predicted significantly inferior progression-free survival (PFS) after completion of CRT (24-month 29% vs 65%, P = 0.0048), prior to C2D1 of CPI (24-month 0% vs 72%, P < 0.0001) and at the end of CPI (24-month 15% vs 67%, P = 0.0011). Additionally, patients with decreasing or undetectable ctDNA levels after one cycle of CPI had improved outcomes compared to patients with increasing ctDNA levels (24-month PFS 72% vs 0%, P < 0.0001). Progression of disease occurred within <12 months of starting CPI in all patients with increasing ctDNA levels at C2D1. CONCLUSION: Detection of ctDNA before, during, or after 6 months of consolidation CPI is strongly associated with inferior outcomes. Our findings suggest that analysis of ctDNA MRD may enable personalizing the duration of consolidation immunotherapy treatment.

Metabolic Insight into Glioma Heterogeneity: Mapping Whole Exome Sequencing to In Vivo Imaging with Stereotactic Localization and Deep Learning.

Intratumoral heterogeneity (ITH) complicates the diagnosis and treatment of glioma, partly due to the diverse metabolic profiles driven by underlying genomic alterations. While multiparametric imaging enhances the characterization of ITH by capturing both spatial and functional variations, it falls short in directly assessing the metabolic activities that underpin these phenotypic differences. This gap stems from the challenge of integrating easily accessible, colocated pathology and detailed genomic data with metabolic insights. This study presents a multifaceted approach combining stereotactic biopsy with standard clinical open-craniotomy for sample collection, voxel-wise analysis of MR images, regression-based GAM, and whole-exome sequencing. This work aims to demonstrate the potential of machine learning algorithms to predict variations in cellular and molecular tumor characteristics. This retrospective study enrolled ten treatment-naïve patients with radiologically confirmed glioma. Each patient underwent a multiparametric MR scan (T1W, T1W-CE, T2W, T2W-FLAIR, DWI) prior to surgery. During standard craniotomy, at least 1 stereotactic biopsy was collected from each patient, with screenshots of the sample locations saved for spatial registration to pre-surgical MR data. Whole-exome sequencing was performed on flash-frozen tumor samples, prioritizing the signatures of five glioma-related genes: IDH1, TP53, EGFR, PIK3CA, and NF1. Regression was implemented with a GAM using a univariate shape function for each predictor. Standard receiver operating characteristic (ROC) analyses were used to evaluate detection, with AUC (area under curve) calculated for each gene target and MR contrast combination. Mean AUC for five gene targets and 31 MR contrast combinations was 0.75 ± 0.11; individual AUCs were as high as 0.96 for both IDH1 and TP53 with T2W-FLAIR and ADC, and 0.99 for EGFR with T2W and ADC. These results suggest the possibility of predicting exome-wide mutation events from noninvasive, in vivo imaging by combining stereotactic localization of glioma samples and a semi-parametric deep learning method. The genomic alterations identified, particularly in IDH1, TP53, EGFR, PIK3CA, and NF1, are known to play pivotal roles in metabolic pathways driving glioma heterogeneity. Our methodology, therefore, indirectly sheds light on the metabolic landscape of glioma through the lens of these critical genomic markers, suggesting a complex interplay between tumor genomics and metabolism. This approach holds potential for refining targeted therapy by better addressing the genomic heterogeneity of glioma tumors.

A multisite study to develop and validate first trimester, circulating microparticle biomarkers for tiered risk stratification of spontaneous preterm birth in nulliparas.

BACKGROUND: Despite much research, advances in early prediction of spontaneous preterm birth (sPTB) has been slow. The evolving field of circulating microparticle (CMP) biology may identify novel blood-based, and clinically useful, biomarkers. OBJECTIVE: To test the ability of a previously identified, 7-marker set of CMP-derived proteins from the first trimester of pregnancy, in the form of an in vitro diagnostic multivariate index assay (IVDMIA), to stratify pregnant patients according to their risk for sPTB. STUDY DESIGN: We employed a previously validated set of CMP protein biomarkers, utilizing mass spectrometry assays and a nested case-control design in a subset of participants from the Nulliparous Pregnancy Outcomes Study: monitoring mothers-to-be (nuMoM2b). We evaluated these biomarkers in the form of an IVDMIA to predict risk for sPTB at different gestational ages. Plasma samples collected at 9- to 13-weeks' gestation were analyzed. The IVDMIA assigned subjects to 1 of 3 sPTB risk categories: low risk (LR), moderate risk (MR), or high risk (HR). Independent validation on a set-aside set confirmed the IVDMIA's performance in risk stratification. RESULTS: Samples from 400 participants from the nuMoM2b cohort were used for the study; of these, 160 delivered<37 weeks and 240 delivered at term. Through Monte Carlo simulation in which the validation results were adjusted based on actual weekly sPTB incidence rates in the nuMoM2b cohort, the IVDMIA stratifications demonstrated statistically significant differences among the risk groups in time-to-event (birth) analysis (P<.0001). The incidence-rate adjusted cumulative risks of sPTB at ≤32 weeks' gestation were 0.4%, 1.6%, and 7.5%, respectively for the LR, MR, and HR groups, respectively. Compared to the LR group, the corresponding risk ratios of the IVDMIA assigned MR and HR group were 4.25 (95% confidence interval [CI] 2.2-7.9) and 19.92 (95% CI 10.4-37.4), respectively. CONCLUSION: A first trimester CMP protein biomarker panel can be used to stratify risk for sPTB at different gestational ages. Such a multitiered stratification tool could be used to assess risk early in pregnancy to enable timely clinical management and interventions, and, ultimately, to enable the development of tailored care pathways for sPTB prevention.

Quantifying combined effects of colistin and ciprofloxacin against Escherichia coli in an in silico pharmacokinetic-pharmacodynamic model.

Co-administering a low dose of colistin (CST) with ciprofloxacin (CIP) may improve the antibacterial effect against resistant Escherichia coli, offering an acceptable benefit-risk balance. This study aimed to quantify the interaction between ciprofloxacin and colistin in an in silico pharmacokinetic-pharmacodynamic model from in vitro static time-kill experiments (using strains with minimum inhibitory concentrations, MICCIP 0.023-1 mg/L and MICCST 0.5-0.75 mg/L). It was also sought to demonstrate an approach of simulating concentrations at the site of infection with population pharmacokinetic and whole-body physiologically based pharmacokinetic models to explore the clinical value of the combination when facing more resistant strains (using extrapolated strains with lower susceptibility). The combined effect in the final model was described as the sum of individual drug effects with a change in drug potency: for ciprofloxacin, concentration at half maximum killing rate (EC50) in combination was 160% of the EC50 in monodrug experiments, while for colistin, the change in EC50 was strain-dependent from 54.1% to 119%. The benefit of co-administrating a lower-than-commonly-administrated colistin dose with ciprofloxacin in terms of drug effect in comparison to either monotherapy was predicted in simulated bloodstream infections and pyelonephritis. The study illustrates the value of pharmacokinetic-pharmacodynamic modelling and simulation in streamlining rational development of antibiotic combinations.

Antibiotic class with potent in vivo activity targeting lipopolysaccharide synthesis in Gram-negative bacteria.

Here, we describe the identification of an antibiotic class acting via LpxH, a clinically unexploited target in lipopolysaccharide synthesis. The lipopolysaccharide synthesis pathway is essential in most Gram-negative bacteria and there is no analogous pathway in humans. Based on a series of phenotypic screens, we identified a hit targeting this pathway that had activity on efflux-defective strains of Escherichia coli. We recognized common structural elements between this hit and a previously published inhibitor, also with activity against efflux-deficient bacteria. With the help of X-ray structures, this information was used to design inhibitors with activity on efflux-proficient, wild-type strains. Optimization of properties such as solubility, metabolic stability and serum protein binding resulted in compounds having potent in vivo efficacy against bloodstream infections caused by the critical Gram-negative pathogens E. coli and Klebsiella pneumoniae. Other favorable properties of the series include a lack of pre-existing resistance in clinical isolates, and no loss of activity against strains expressing extended-spectrum-ß-lactamase, metallo-ß-lactamase, or carbapenemase-resistance genes. Further development of this class of antibiotics could make an important contribution to the ongoing struggle against antibiotic resistance.

HSF1 Inhibits Antitumor Immune Activity in Breast Cancer by Suppressing CCL5 to Block CD8+ T-cell Recruitment.

Heat shock factor 1 (HSF1) is a stress-responsive transcription factor that promotes cancer cell malignancy. To provide a better understanding of the biological processes regulated by HSF1, here we developed an HSF1 activity signature (HAS) and found that it was negatively associated with antitumor immune cells in breast tumors. Knockdown of HSF1 decreased breast tumor size and caused an influx of several antitumor immune cells, most notably CD8+ T cells. Depletion of CD8+ T cells rescued the reduction in growth of HSF1-deficient tumors, suggesting HSF1 prevents CD8+ T-cell influx to avoid immune-mediated tumor killing. HSF1 suppressed expression of CCL5, a chemokine for CD8+ T cells, and upregulation of CCL5 upon HSF1 loss significantly contributed to the recruitment of CD8+ T cells. These findings indicate that HSF1 suppresses antitumor immune activity by reducing CCL5 to limit CD8+ T-cell homing to breast tumors and prevent immune-mediated destruction, which has implications for the lack of success of immune modulatory therapies in breast cancer. SIGNIFICANCE: The stress-responsive transcription factor HSF1 reduces CD8+ T-cell infiltration in breast tumors to prevent immune-mediated killing, indicating that cellular stress responses affect tumor-immune interactions and that targeting HSF1 could improve immunotherapies.

White matter integrity is associated with cognition and amyloid burden in older adult Koreans along the Alzheimer's disease continuum.

BACKGROUND: White matter (WM) microstructural changes in the hippocampal cingulum bundle (CBH) in Alzheimer's disease (AD) have been described in cohorts of largely European ancestry but are lacking in other populations. METHODS: We assessed the relationship between CBH WM integrity and cognition or amyloid burden in 505 Korean older adults aged ≥ 55 years, including 276 cognitively normal older adults (CN), 142 with mild cognitive impairment (MCI), and 87 AD patients, recruited as part of the Korean Brain Aging Study for the Early Diagnosis and Prediction of Alzheimer's disease (KBASE) at Seoul National University. RESULTS: Compared to CN, AD and MCI subjects showed significantly higher RD, MD, and AxD values (all p-values < 0.001) and significantly lower FA values (left p ≤ 0.002, right p ≤ 0.015) after Bonferroni adjustment for multiple comparisons. Most tests of cognition and mood (p < 0.001) as well as higher medial temporal amyloid burden (p < 0.001) were associated with poorer WM integrity in the CBH after Bonferroni adjustment. CONCLUSION: These findings are consistent with patterns of WM microstructural damage previously reported in non-Hispanic White (NHW) MCI/AD cohorts, reinforcing existing evidence from predominantly NHW cohort studies.

Distinct functions and transcriptional signatures in orally induced regulatory T cell populations.

Oral administration of antigen induces regulatory T cells (Treg) that can not only control local immune responses in the small intestine, but also traffic to the central immune system to deliver systemic suppression. Employing murine models of the inherited bleeding disorder hemophilia, we find that oral antigen administration induces three CD4+ Treg subsets, namely FoxP3+LAP-, FoxP3+LAP+, and FoxP3-LAP+. These T cells act in concert to suppress systemic antibody production induced by therapeutic protein administration. Whilst both FoxP3+LAP+ and FoxP3-LAP+ CD4+ T cells express membrane-bound TGF-ß (latency associated peptide, LAP), phenotypic, functional, and single cell transcriptomic analyses reveal distinct characteristics in the two subsets. As judged by an increase in IL-2Rα and TCR signaling, elevated expression of co-inhibitory receptor molecules and upregulation of the TGFß and IL-10 signaling pathways, FoxP3+LAP+ cells are an activated form of FoxP3+LAP- Treg. Whereas FoxP3-LAP+ cells express low levels of genes involved in TCR signaling or co-stimulation, engagement of the AP-1 complex members Jun/Fos and Atf3 is most prominent, consistent with potent IL-10 production. Single cell transcriptomic analysis further reveals that engagement of the Jun/Fos transcription factors is requisite for mediating TGFß expression. This can occur via an Il2ra dependent or independent process in FoxP3+LAP+ or FoxP3-LAP+ cells respectively. Surprisingly, both FoxP3+LAP+ and FoxP3-LAP+ cells potently suppress and induce FoxP3 expression in CD4+ conventional T cells. In this process, FoxP3-LAP+ cells may themselves convert to FoxP3+ Treg. We conclude that orally induced suppression is dependent on multiple regulatory cell types with complementary and interconnected roles.

ADAM8 is expressed widely in breast cancer and predicts poor outcome in hormone receptor positive, HER-2 negative patients.

BACKGROUND: Breast malignancies are the predominant cancer-related cause of death in women. New methods of diagnosis, prognosis and treatment are necessary. Previously, we identified the breast cancer cell surface protein ADAM8 as a marker of poor survival, and a driver of Triple-Negative Breast Cancer (TNBC) growth and spread. Immunohistochemistry (IHC) with a research-only anti-ADAM8 antibody revealed 34.0% of TNBCs (17/50) expressed ADAM8. To identify those patients who could benefit from future ADAM8-based interventions, new clinical tests are needed. Here, we report on the preclinical development of a highly specific IHC assay for detection of ADAM8-positive breast tumors. METHODS: Formalin-fixed paraffin-embedded sections of ADAM8-positive breast cell lines and patient-derived xenograft tumors were used in IHC to identify a lead antibody, appropriate staining conditions and controls. Patient breast cancer samples (n = 490) were used to validate the assay. Cox proportional hazards models assessed association between survival and ADAM8 expression. RESULTS: ADAM8 staining conditions were optimized, a lead anti-human ADAM8 monoclonal IHC antibody (ADP2) identified, and a breast staining/scoring control cell line microarray (CCM) generated expressing a range of ADAM8 levels. Assay specificity, reproducibility, and appropriateness of the CCM for scoring tumor samples were demonstrated. Consistent with earlier findings, 36.1% (22/61) of patient TNBCs expressed ADAM8. Overall, 33.9% (166/490) of the breast cancer population was ADAM8-positive, including Hormone Receptor (HR) and Human Epidermal Growth Factor Receptor-2 (HER2) positive cancers, which were tested for the first time. For the most prevalent HR-positive/HER2-negative subtype, high ADAM8 expression identified patients at risk of poor survival. CONCLUSIONS: Our studies show ADAM8 is widely expressed in breast cancer and provide support for both a diagnostic and prognostic value of the ADP2 IHC assay. As ADAM8 has been implicated in multiple solid malignancies, continued development of this assay may have broad impact on cancer management.

Flux estimation analysis systematically characterizes the metabolic shifts of the central metabolism pathway in human cancer.

Introduction: Glucose and glutamine are major carbon and energy sources that promote the rapid proliferation of cancer cells. Metabolic shifts observed on cell lines or mouse models may not reflect the general metabolic shifts in real human cancer tissue. Method: In this study, we conducted a computational characterization of the flux distribution and variations of the central energy metabolism and key branches in a pan-cancer analysis, including the glycolytic pathway, production of lactate, tricarboxylic acid (TCA) cycle, nucleic acid synthesis, glutaminolysis, glutamate, glutamine, and glutathione metabolism, and amino acid synthesis, in 11 cancer subtypes and nine matched adjacent normal tissue types using TCGA transcriptomics data. Result: Our analysis confirms the increased influx in glucose uptake and glycolysis and decreased upper part of the TCA cycle, i.e., the Warburg effect, in almost all the analyzed cancer. However, increased lactate production and the second half of the TCA cycle were only seen in certain cancer types. More interestingly, we failed to detect significantly altered glutaminolysis in cancer tissues compared to their adjacent normal tissues. A systems biology model of metabolic shifts through cancer and tissue types is further developed and analyzed. We observed that (1) normal tissues have distinct metabolic phenotypes; (2) cancer types have drastically different metabolic shifts compared to their adjacent normal controls; and (3) the different shifts in tissue-specific metabolic phenotypes result in a converged metabolic phenotype through cancer types and cancer progression. Discussion: This study strongly suggests the possibility of having a unified framework for studies of cancer-inducing stressors, adaptive metabolic reprogramming, and cancerous behaviors.

FLUXestimator: a webserver for predicting metabolic flux and variations using transcriptomics data.

Quantitative assessment of single cell fluxome is critical for understanding the metabolic heterogeneity in diseases. Unfortunately, laboratory-based single cell fluxomics is currently impractical, and the current computational tools for flux estimation are not designed for single cell-level prediction. Given the well-established link between transcriptomic and metabolomic profiles, leveraging single cell transcriptomics data to predict single cell fluxome is not only feasible but also an urgent task. In this study, we present FLUXestimator, an online platform for predicting metabolic fluxome and variations using single cell or general transcriptomics data of large sample-size. The FLUXestimator webserver implements a recently developed unsupervised approach called single cell flux estimation analysis (scFEA), which uses a new neural network architecture to estimate reaction rates from transcriptomics data. To the best of our knowledge, FLUXestimator is the first web-based tool dedicated to predicting cell-/sample-wise metabolic flux and metabolite variations using transcriptomics data of human, mouse and 15 other common experimental organisms. The FLUXestimator webserver is available at http://scFLUX.org/, and stand-alone tools for local use are available at https://github.com/changwn/scFEA. Our tool provides a new avenue for studying metabolic heterogeneity in diseases and has the potential to facilitate the development of new therapeutic strategies.

White matter integrity is associated with cognition and amyloid burden in older adult Koreans along the Alzheimer's disease continuum.

BACKGROUND: White matter (WM) microstructural changes in the hippocampal cingulum bundle (CBH) in Alzheimer's disease (AD) have been described in cohorts of largely European ancestry but are lacking in other populations. METHODS: We assessed the relationship between CBH WM integrity and cognition or amyloid burden in 505 Korean older adults aged ≥55 years, including 276 cognitively normal older adults (CN), 142 mild cognitive impairment (MCI), and 87 AD, recruited as part of the Korean Brain Aging Study for the Early Diagnosis and Prediction of Alzheimer's disease (KBASE) at Seoul National University. RESULTS: Compared to CN, AD and MCI subjects showed decreased WM integrity in the bilateral CBH. Cognition, mood, and higher amyloid burden were also associated with poorer WM integrity in the CBH. CONCLUSION: These findings are consistent with patterns of WM microstructural damage previously reported in non-Hispanic White (NHW) MCI/AD cohorts, reinforcing existing evidence from predominantly NHW cohort studies.

Asparagine starvation suppresses histone demethylation through iron depletion.

Intracellular α-ketoglutarate is an indispensable substrate for the Jumonji family of histone demethylases (JHDMs) mediating most of the histone demethylation reactions. Since α-ketoglutarate is an intermediate of the tricarboxylic acid cycle and a product of transamination, its availability is governed by the metabolism of several amino acids. Here, we show that asparagine starvation suppresses global histone demethylation. This process is neither due to the change of expression of histone-modifying enzymes nor due to the change of intracellular levels of α-ketoglutarate. Rather, asparagine starvation reduces the intracellular pool of labile iron, a key co-factor for the JHDMs to function. Mechanistically, asparagine starvation suppresses the expression of the transferrin receptor to limit iron uptake. Furthermore, iron supplementation to the culture medium restores histone demethylation and alters gene expression to accelerate cell death upon asparagine depletion. These results suggest that suppressing iron-dependent histone demethylation is part of the cellular adaptive response to asparagine starvation.

A Lesson of Immunosuppression in Renal Transplant: Retreat or Hold?

Background: Aiming at understanding whether there are cases of near-tolerance among long-term surviving kidney transplant recipients in our center, or even operant tolerance can be attempted based on their immune status, we analyzed changes of immune cell subsets and cytokines in various groups, and evaluated immune status of long-term survival recipients. Methods: A real-world, observational, retrospective cohort study was conducted in our hospital. Twenty-eight long-term recipients were selected as study subjects, 15 recent postoperative stable recipients, and 15 healthy subjects as controls. T and B lymphocyte subsets, MDSCs, and cytokines were detected and analyzed. Results: Treg/CD4 T cells, total B and B10 cells in long-term and recent renal recipients were lower than healthy controls (HC). The level of IFN-γ and IL-17A in long-term survival patients was obviously higher than that in recent postoperative stable recipients and HC, while TGF-ß1 level was significantly lower in long-term survival group than in short-term postoperative group and HC. Notably, compared with short-term recipients, it has been found that the IL-6 level in both positive and negative HLA groups were obviously lower (all P<0.05). In the long-term survival group, 43% of recipients were positive for urinary protein and 50% were positive for HLA antibody. Conclusion: This "real-world" study validates the findings of real status of long-term survival recipients observed in clinical trials. Contrary to a state of proper tolerance as expected, the group recipients in long-term survival were accompanied by the increased indicators of immune response, while those related to immune tolerance were not significantly increased. Long-term survival recipients with stable renal function may be in an immune equilibrium state where immunosuppression and rejection coexist under the action of low-intensity immune agents. If immunosuppressive agents are reduced or even removed, rejection may occur.

Pipeline for characterizing alternative mechanisms (PCAM) based on bi-clustering to study colorectal cancer heterogeneity.

The cells of colorectal cancer (CRC) in their microenvironment experience constant stress, leading to dysregulated activity in the tumor niche. As a result, cancer cells acquire alternative pathways in response to the changing microenvironment, posing significant challenges for the design of effective cancer treatment strategies. While computational studies on high-throughput omics data have advanced our understanding of CRC subtypes, characterizing the heterogeneity of this disease remains remarkably complex. Here, we present a novel computational Pipeline for Characterizing Alternative Mechanisms (PCAM) based on biclustering to gain a more detailed understanding of cancer heterogeneity. Our application of PCAM to large-scale CRC transcriptomics datasets suggests that PCAM can generate a wealth of information leading to new biological understanding and predictive markers of alternative mechanisms. Our key findings include: 1) A comprehensive collection of alternative pathways in CRC, associated with biological and clinical factors. 2) Full annotation of detected alternative mechanisms, including their enrichment in known pathways and associations with various clinical outcomes. 3) A mechanistic relationship between known clinical subtypes and outcomes on a consensus map, visualized by the presence of alternative mechanisms. 4) Several potential novel alternative drug resistance mechanisms for Oxaliplatin, 5-Fluorouracil, and FOLFOX, some of which were validated on independent datasets. We believe that gaining a deeper understanding of alternative mechanisms is a critical step towards characterizing the heterogeneity of CRC. The hypotheses generated by PCAM, along with the comprehensive collection of biologically and clinically associated alternative pathways in CRC, could provide valuable insights into the underlying mechanisms driving cancer progression and drug resistance, which could aid in the development of more effective cancer therapies and guide experimental design towards more targeted and personalized treatment strategies. The computational pipeline of PCAM is available in GitHub (https://github.com/changwn/BC-CRC).

New Dual Inhibitors of Bacterial Topoisomerases with Broad-Spectrum Antibacterial Activity and In Vivo Efficacy against Vancomycin-Intermediate Staphylococcus aureus.

A new series of dual low nanomolar benzothiazole inhibitors of bacterial DNA gyrase and topoisomerase IV were developed. The resulting compounds show excellent broad-spectrum antibacterial activities against Gram-positive Enterococcus faecalis, Enterococcus faecium and multidrug resistant (MDR) Staphylococcus aureus strains [best compound minimal inhibitory concentrations (MICs): range, <0.03125-0.25 µg/mL] and against the Gram-negatives Acinetobacter baumannii and Klebsiella pneumoniae (best compound MICs: range, 1-4 µg/mL). Lead compound 7a was identified with favorable solubility and plasma protein binding, good metabolic stability, selectivity for bacterial topoisomerases, and no toxicity issues. The crystal structure of 7a in complex with Pseudomonas aeruginosa GyrB24 revealed its binding mode at the ATP-binding site. Expanded profiling of 7a and 7h showed potent antibacterial activity against over 100 MDR and non-MDR strains of A. baumannii and several other Gram-positive and Gram-negative strains. Ultimately, in vivo efficacy of 7a in a mouse model of vancomycin-intermediate S. aureus thigh infection was also demonstrated.

Modified isotonic regression based phase I/II clinical trial design identifying optimal biological dose.

Conventional phase I/II clinical trial designs often use complicated parametric models to characterize the dose-response relationships and conduct the trials. However, the parametric models are hard to justify in practice, and the misspecification of parametric models can lead to substantially undesirable performances in phase I/II trials. Moreover, it is difficult for the physicians conducting phase I/II trials to clinically interpret the parameters of these complicated models, and such significant learning costs impede the translation of novel statistical designs into practical trial implementation. To solve these issues, we propose a transparent and efficient phase I/II clinical trial design, referred to as the modified isotonic regression-based design (mISO), to identify the optimal biological doses for molecularly targeted agents and immunotherapy. The mISO design makes no parametric model assumptions on the dose-response relationship and yields desirable performances under any clinically meaningful dose-response curves. The concise, clinically interpretable dose-response models and dose-finding algorithm make the proposed designs highly translational from the statistical community to the clinical community. We further extend the mISO design and develop the mISO-B design to handle the delayed outcomes. Our comprehensive simulation studies show that the mISO and mISO-B designs are highly efficient in optimal biological dose selection and patients allocation and outperform many existing phase I/II clinical trial designs. We also provide a trial example to illustrate the practical implementation of the proposed designs. The software for simulation and trial implementation are available for free download.

Clinically important alterations in pharmacogene expression in histologically severe nonalcoholic fatty liver disease.

Polypharmacy is common in patients with nonalcoholic fatty liver disease (NAFLD) and previous reports suggest that NAFLD is associated with altered drug disposition. This study aims to determine if patients with NAFLD are at risk for altered drug response by characterizing changes in hepatic mRNA expression of genes mediating drug disposition (pharmacogenes) across the histological NAFLD severity spectrum. We utilize RNA-seq for 93 liver biopsies with histologically staged NAFLD Activity Score (NAS), fibrosis stage, and steatohepatitis (NASH). We identify 37 significant pharmacogene-NAFLD severity associations including CYP2C19 downregulation. We chose to validate CYP2C19 due to its actionability in drug prescribing. Meta-analysis of 16 independent studies demonstrate that CYP2C19 is significantly downregulated to 46% in NASH, to 58% in high NAS, and to 43% in severe fibrosis. Our data demonstrate the downregulation of CYP2C19 in NAFLD which supports developing personalized medicine approaches for drugs sensitive to metabolism by the CYP2C19 enzyme.