Establishing Healthy Breath Baselines With Tin Oxide Sensors: Fundamental Building Blocks for Noninvasive Health Monitoring.

INTRODUCTION: Volatile organic compounds (VOCs) in breath serve as a source of biomarkers for medical conditions relevant to warfighter health including Corona Virus Disease and other potential biological threats. Electronic noses are integrated arrays of gas sensors that are cost-effective and miniaturized devices that rapidly respond to VOCs in exhaled breath. The current study seeks to qualify healthy breath baselines of exhaled VOC profiles through analysis using a commercialized array of metal oxide (MOX) sensors. MATERIALS AND METHODS: Subjects were recruited/consented through word of mouth and using posters. For each sample, breath was analyzed using an array of MOX sensors with parameters that were previously established. Data were also collected using a lifestyle questionnaire and from a blood test to assess markers of general health. Sensor data were processed using a feature extraction algorithm, which were analyzed through statistical approaches to identify correlations with confounding factors. Reproducibility was also assessed through relative standard deviation values of sensor features within a single subject and between different volunteers. RESULTS: A total of 164 breath samples were collected from different individuals, and 10 of these volunteers provided an additional 9 samples over 6 months for the longitudinal study. First, data from different subjects were analyzed, and the trends of the 17 extracted features were elucidated. This revealed not only a high degree of correlation between sensors within the array but also between some of the features extracted within a single sensor. This helped guide the removal of multicollinear features for multivariate statistical analyses. No correlations were identified between sensor features and confounding factors of interest (age, body mass index, smoking, and sex) after P-value adjustment, indicating that these variables have an insignificant impact on the observed sensor signal. Finally, the longitudinal replicates were analyzed, and reproducibility assessment showed that the variability between subjects was significantly higher than within replicates of a single volunteer (P-value = .002). Multivariate analyses within the longitudinal data displayed that subjects could not be distinguished from one another, indicating that there may be a universal healthy breath baseline that is not specific to particular individuals. CONCLUSIONS: The current study sought to qualify healthy baselines of VOCs in exhaled breath using a MOX sensor array that can be leveraged in the future to detect medical conditions relevant to warfighter health. For example, the results of the study will be useful, as the healthy breath VOC data from the sensor array can be cross-referenced in future studies aiming to use the device to distinguish disease states. Ultimately, the sensors may be integrated into a portable breathalyzer or current military gear to increase warfighter readiness through rapid and noninvasive health monitoring.

Analysis of Circulating Tumor DNA Predicts Outcomes of Short-Course Consolidation Immunotherapy in Unresectable Stage III NSCLC.

INTRODUCTION: The current standard of care for patients with inoperable stage III non-small cell lung cancer includes chemoradiotherapy (CRT) followed by 1 year of checkpoint inhibitor (CPI) therapy. Nevertheless, 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 patients with unresectable locally advanced non-small cell lung cancer treated on a phase 2 trial of short-course consolidation immunotherapy after CRT, with the goal of testing whether ctDNA may be able to identify patients who do not require a full year of treatment. METHODS: Plasma samples for ctDNA analysis were collected from patients on the Big Ten Cancer Research Consortium LUN 16-081 trial after completion of CRT, before day 1 of cycle 2 (C2D1) of CPI (i.e., 1 mo after treatment start), and at the end of up to 6 months of treatment. Tumor-informed ctDNA MRD analysis was performed using cancer personalized profiling by deep sequencing. Levels of ctDNA at each time point were correlated with clinical outcomes. RESULTS: Detection of ctDNA predicted significantly inferior progression-free survival after completion of CRT (24-mo 29% versus 65%, p = 0.0048), before C2D1 of CPI (24-mo 0% versus 72%, p < 0.0001) and at the end of CPI (24-mo 15% versus 67%, p = 0.0011). In addition, patients with decreasing or undetectable ctDNA levels after 1 cycle of CPI had improved outcomes compared with patients with increasing ctDNA levels (24-mo progression-free survival 72% versus 0%, p < 0.0001). Progression of disease occurred within less than 12 months of starting CPI in all patients with increasing ctDNA levels at C2D1. CONCLUSIONS: 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.

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.

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.

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.

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.

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.

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).

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.

Amplification-Free, High-Throughput Nanoplasmonic Quantification of Circulating MicroRNAs in Unprocessed Plasma Microsamples for Earlier Pancreatic Cancer Detection.

Pancreatic ductal adenocarcinoma (PDAC) is a deadly malignancy that is often detected at an advanced stage. Earlier diagnosis of PDAC is key to reducing mortality. Circulating biomarkers such as microRNAs are gaining interest, but existing technologies require large sample volumes, amplification steps, extensive biofluid processing, lack sensitivity, and are low-throughput. Here, we present an advanced nanoplasmonic sensor for the highly sensitive, amplification-free detection and quantification of microRNAs (microRNA-10b, microRNA-let7a) from unprocessed plasma microsamples. The sensor construct utilizes uniquely designed -ssDNA receptors attached to gold triangular nanoprisms, which display unique localized surface plasmon resonance (LSPR) properties, in a multiwell plate format. The formation of -ssDNA/microRNA duplex controls the nanostructure-biomolecule interfacial electronic interactions to promote the charge transfer/exciton delocalization processes and enhance the LSPR responses to achieve attomolar (10-18 M) limit of detection (LOD) in human plasma. This improve LOD allows the fabrication of a high-throughput assay in a 384-well plate format. The performance of nanoplasmonic sensors for microRNA detection was further assessed by comparing with the qRT-PCR assay of 15 PDAC patient plasma samples that shows a positive correlation between these two assays with the Pearson correlation coefficient value >0.86. Evaluation of >170 clinical samples reveals that oncogenic microRNA-10b and tumor suppressor microRNA-let7a levels can individually differentiate PDAC from chronic pancreatitis and normal controls with >94% sensitivity and >94% specificity at a 95% confidence interval (CI). Furthermore, combining both oncogenic and tumor suppressor microRNA levels significantly improves differentiation of PDAC stages I and II versus III and IV with >91% and 87% sensitivity and specificity, respectively, in comparison to the sensitivity and specificity values for individual microRNAs. Moreover, we show that the level of microRNAs varies substantially in pre- and post-surgery PDAC patients (n = 75). Taken together, this ultrasensitive nanoplasmonic sensor with excellent sensitivity and specificity is capable of assaying multiple biomarkers simultaneously and may facilitate early detection of PDAC to improve patient care.

Targeting intra-tumoral heterogeneity of human brain tumors with in vivo imaging: A roadmap for imaging genomics from multiparametric MR signals.

Resistance of high grade tumors to treatment involves cancer stem cell features, deregulated cell division, acceleration of genomic errors, and emergence of cellular variants that rely upon diverse signaling pathways. This heterogeneous tumor landscape limits the utility of the focal sampling provided by invasive biopsy when designing strategies for targeted therapies. In this roadmap review paper, we propose and develop methods for enabling mapping of cellular and molecular features in vivo to inform and optimize cancer treatment strategies in the brain. This approach leverages (1) the spatial and temporal advantages of in vivo imaging compared with surgical biopsy, (2) the rapid expansion of meaningful anatomical and functional magnetic resonance signals, (3) widespread access to cellular and molecular information enabled by next-generation sequencing, and (4) the enhanced accuracy and computational efficiency of deep learning techniques. As multiple cellular variants may be present within volumes below the resolution of imaging, we describe a mapping process to decode micro- and even nano-scale properties from the macro-scale data by simultaneously utilizing complimentary multiparametric image signals acquired in routine clinical practice. We outline design protocols for future research efforts that marry revolutionary bioinformation technologies, growing access to increased computational capability, and powerful statistical classification techniques to guide rational treatment selection.

Tumor Infiltrating Lymphocytes in Multi-National Cohorts of Ductal Carcinoma In Situ (DCIS) of Breast.

Tumor-infiltrating lymphocytes (TILs) are prognostic in invasive breast cancer. However, their prognostic significance in ductal carcinoma in situ (DCIS) has been controversial. To investigate the prognostic role of TILs in DCIS outcome, we used different scoring methods for TILs in multi-national cohorts from Asian and European women. Self-described race was genetically confirmed using QC Infinium array combined with radmixture software. Stromal TILs, touching TILs, circumferential TILs, and hotspots were quantified on H&E-stained slides and correlated with the development of second breast cancer events (BCE) and other clinico-pathological variables. In univariate survival analysis, age older than 50 years, hormone receptor positivity and the presence of circumferential TILs were weakly associated with the absence of BCE at the 5-year follow-up in all cohorts (p < 0.03; p < 0.02; and p < 0.02, respectively, adjusted p = 0.11). In the multivariable analysis, circumferential TILs were an independent predictor of a better outcome (Wald test p = 0.01), whereas younger age was associated with BCE. Asian patients were younger with larger, higher grade, HR negative DCIS lesions, and higher TIL variables. The spatial arrangement of TILs may serve as a better prognostic indicator in DCIS cases than stromal TILs alone and may be added in guidelines for TILs evaluation in DCIS.

PLUS: Predicting cancer metastasis potential based on positive and unlabeled learning.

Metastatic cancer accounts for over 90% of all cancer deaths, and evaluations of metastasis potential are vital for minimizing the metastasis-associated mortality and achieving optimal clinical decision-making. Computational assessment of metastasis potential based on large-scale transcriptomic cancer data is challenging because metastasis events are not always clinically detectable. The under-diagnosis of metastasis events results in biased classification labels, and classification tools using biased labels may lead to inaccurate estimations of metastasis potential. This issue is further complicated by the unknown metastasis prevalence at the population level, the small number of confirmed metastasis cases, and the high dimensionality of the candidate molecular features. Our proposed algorithm, called Positive and unlabeled Learning from Unbalanced cases and Sparse structures (PLUS), is the first to use a positive and unlabeled learning framework to account for the under-detection of metastasis events in building a classifier. PLUS is specifically tailored for studying metastasis that deals with the unbalanced instance allocation as well as unknown metastasis prevalence, which are not considered by other methods. PLUS achieves superior performance on synthetic datasets compared with other state-of-the-art methods. Application of PLUS to The Cancer Genome Atlas Pan-Cancer gene expression data generated metastasis potential predictions that show good agreement with the clinical follow-up data, in addition to predictive genes that have been validated by independent single-cell RNA-sequencing datasets.

Aberrant epigenetic and transcriptional events associated with breast cancer risk.

BACKGROUND: Genome-wide association studies have identified several breast cancer susceptibility loci. However, biomarkers for risk assessment are still missing. Here, we investigated cancer-related molecular changes detected in tissues from women at high risk for breast cancer prior to disease manifestation. Disease-free breast tissue cores donated by healthy women (N = 146, median age = 39 years) were processed for both methylome (MethylCap) and transcriptome (Illumina's HiSeq4000) sequencing. Analysis of tissue microarray and primary breast epithelial cells was used to confirm gene expression dysregulation. RESULTS: Transcriptomic analysis identified 69 differentially expressed genes between women at high and those at average risk of breast cancer (Tyrer-Cuzick model) at FDR < 0.05 and fold change ≥ 2. Majority of the identified genes were involved in DNA damage checkpoint, cell cycle, and cell adhesion. Two genes, FAM83A and NEK2, were overexpressed in tissue sections (FDR < 0.01) and primary epithelial cells (p < 0.05) from high-risk breasts. Moreover, 1698 DNA methylation changes were identified in high-risk breast tissues (FDR < 0.05), partially overlapped with cancer-related signatures, and correlated with transcriptional changes (p < 0.05, r ≤ 0.5). Finally, among the participants, 35 women donated breast biopsies at two time points, and age-related molecular alterations enhanced in high-risk subjects were identified. CONCLUSIONS: Normal breast tissue from women at high risk of breast cancer bears molecular aberrations that may contribute to breast cancer susceptibility. This study is the first molecular characterization of the true normal breast tissues, and provides an opportunity to investigate molecular markers of breast cancer risk, which may lead to new preventive approaches.

Establishment and external verification of an oxidative stress-related gene signature to predict clinical outcomes and therapeutic responses of colorectal cancer.

Objective: Accumulated evidence highlights the biological significance of oxidative stress in tumorigenicity and progression of colorectal cancer (CRC). Our study aimed to establish a reliable oxidative stress-related signature to predict patients' clinical outcomes and therapeutic responses. Methods: Transcriptome profiles and clinical features of CRC patients were retrospectively analyzed from public datasets. LASSO analysis was used to construct an oxidative stress-related signature to predict overall survival, disease-free survival, disease-specific survival, and progression-free survival. Additionally, antitumor immunity, drug sensitivity, signaling pathways, and molecular subtypes were analyzed between different risk subsets through TIP, CIBERSORT, oncoPredict, etc. approaches. The genes in the signature were experimentally verified in the human colorectal mucosal cell line (FHC) along with CRC cell lines (SW-480 and HCT-116) through RT-qPCR or Western blot. Results: An oxidative stress-related signature was established, composed of ACOX1, CPT2, NAT2, NRG1, PPARGC1A, CDKN2A, CRYAB, NGFR, and UCN. The signature displayed an excellent capacity for survival prediction and was linked to worse clinicopathological features. Moreover, the signature correlated with antitumor immunity, drug sensitivity, and CRC-related pathways. Among molecular subtypes, the CSC subtype had the highest risk score. Experiments demonstrated that CDKN2A and UCN were up-regulated and ACOX1, CPT2, NAT2, NRG1, PPARGC1A, CRYAB, and NGFR were down-regulated in CRC than normal cells. In H2O2-induced CRC cells, their expression was notably altered. Conclusion: Altogether, our findings constructed an oxidative stress-related signature that can predict survival outcomes and therapeutic response in CRC patients, thus potentially assisting prognosis prediction and adjuvant therapy decisions.

Antibacterial sulfonimidamide-based oligopeptides as type I signal peptidase inhibitors: Synthesis and biological evaluation.

Oligopeptide boronates with a lipophilic tail are known to inhibit the type I signal peptidase in E. coli, which is a promising drug target for developing novel antibiotics. Antibacterial activity depends on these oligopeptides having a cationic modification to increase their permeation. Unfortunately, this modification is associated with cytotoxicity, motivating the need for novel approaches. The sulfonimidamide functionality has recently gained much interest in drug design and discovery, as a means of introducing chirality and an imine-handle, thus allowing for the incorporation of additional substituents. This in turn can tune the chemical and biological properties, which are here explored. We show that introducing the sulfonimidamide between the lipophilic tail and the peptide in a series of signal peptidase inhibitors resulted in antibacterial activity, while the sulfonamide isostere and previously known non-cationic analogs were inactive. Additionally, we show that replacing the sulfonamide with a sulfonimidamide resulted in decreased cytotoxicity, and similar results were seen by adding a cationic sidechain to the sulfonimidamide motif. This is the first report of incorporation of the sulfonimidamide functional group into bioactive peptides, more specifically into antibacterial oligopeptides, and evaluation of its biological effects.

Tumor-derived IL-6 and trans-signaling among tumor, fat, and muscle mediate pancreatic cancer cachexia.

Most patients with pancreatic adenocarcinoma (PDAC) suffer cachexia; some do not. To model heterogeneity, we used patient-derived orthotopic xenografts. These phenocopied donor weight loss. Furthermore, muscle wasting correlated with mortality and murine IL-6, and human IL-6 associated with the greatest murine cachexia. In cell culture and mice, PDAC cells elicited adipocyte IL-6 expression and IL-6 plus IL-6 receptor (IL6R) in myocytes and blood. PDAC induced adipocyte lipolysis and muscle steatosis, dysmetabolism, and wasting. Depletion of IL-6 from malignant cells halved adipose wasting and abolished myosteatosis, dysmetabolism, and atrophy. In culture, adipocyte lipolysis required soluble (s)IL6R, while IL-6, sIL6R, or palmitate induced myotube atrophy. PDAC cells activated adipocytes to induce myotube wasting and activated myotubes to induce adipocyte lipolysis. Thus, PDAC cachexia results from tissue crosstalk via a feed-forward, IL-6 trans-signaling loop. Malignant cells signal via IL-6 to muscle and fat, muscle to fat via sIL6R, and fat to muscle via lipids and IL-6, all targetable mechanisms for treatment of cachexia.