Predicting response to neoadjuvant chemotherapy for colorectal liver metastasis using deep learning on prechemotherapy cross-sectional imaging.

BACKGROUND AND OBJECTIVES: Deep learning models (DLMs) are applied across domains of health sciences to generate meaningful predictions. DLMs make use of neural networks to generate predictions from discrete data inputs. This study employs DLM on prechemotherapy cross-sectional imaging to predict patients' response to neoadjuvant chemotherapy. METHODS: Adult patients with colorectal liver metastasis who underwent surgery after neoadjuvant chemotherapy were included. A DLM was trained on computed tomography images using attention-based multiple-instance learning. A logistic regression model incorporating clinical parameters of the Fong clinical risk score was used for comparison. Both model performances were benchmarked against the Response Evaluation Criteria in Solid Tumors criteria. A receiver operating curve was created and resulting area under the curve (AUC) was determined. RESULTS: Ninety-five patients were included, with 33,619 images available for study inclusion. Ninety-five percent of patients underwent 5-fluorouracil-based chemotherapy with oxaliplatin and/or irinotecan. Sixty percent of the patients were categorized as chemotherapy responders (30% reduction in tumor diameter). The DLM had an AUC of 0.77. The AUC for the clinical model was 0.41. CONCLUSIONS: Image-based DLM for prediction of response to neoadjuvant chemotherapy in patients with colorectal cancer liver metastases was superior to a clinical-based model. These results demonstrate potential to identify nonresponders to chemotherapy and guide select patients toward earlier curative resection.

Outcomes after Surgical Microwave Ablation for the Treatment of Colorectal Liver Metastasis.

BACKGROUND: Colorectal cancer (CRC) is the third most common cause of cancer mortality worldwide. Of these, approximately 25% will have liver metastasis. We performed 394 microwave ablations (MWA) and analyzed outcomes for survival and ablation failure. STUDY DESIGN: Retrospective review of patients who underwent a surgical microwave ablation at a single center high-volume institution from October 2006 through September 2022 using a prospectively maintained database. Primary outcome was overall survival. RESULTS: A total of 394 operations were performed on 328 patients with 842 tumors undergoing MWA. Median tumor size was 1.5 cm (range 0.4-7.0 cm), with the median number of tumors ablated per operation being 1 (range 1-11). A laparoscopic approach was used 77.9% of the time. Concomitant procedures were performed 63% of the time, most commonly hepatectomy (22.3%), cholecystectomy (17.5%), and colectomy (6.6%). Clavien-Dindo Grade III or IV complications occurred in 12 patients (3.6%), and all of these patients had undergone concomitant procedures. Mortality within 30 days occurred in 4 patients (1.2%). The rate of incomplete ablation (IA) was 1.5% per tumor. Local recurrence (LR) occurred at a rate of 6.3% per tumor. African Americans were found to have a higher incidence of IA and LR. One year survival probability was 91% [95% CI: 87.9 -94.3], with a mean overall survival of 57.6 months [95% CI: 49.9-65.4 months]. CONCLUSION: Surgical MWA offers a low-morbidity approach to treatment of colorectal liver metastasis (CRLM), with low rates of failure. This large series reviews the outcomes of MWA as definitive treatment for CRLM.

Investigating mouse hepatic lipidome dysregulation following exposure to emerging per- and polyfluoroalkyl substances (PFAS).

Per- and polyfluoroalkyl substances (PFAS) are environmental pollutants that have been associated with adverse health effects including liver damage, decreased vaccine responses, cancer, developmental toxicity, thyroid dysfunction, and elevated cholesterol. The specific molecular mechanisms impacted by PFAS exposure to cause these health effects remain poorly understood, however there is some evidence of lipid dysregulation. Thus, lipidomic studies that go beyond clinical triglyceride and cholesterol tests are greatly needed to investigate these perturbations. Here, we have utilized a platform coupling liquid chromatography, ion mobility spectrometry, and mass spectrometry (LC-IMS-MS) separations to simultaneously evaluate PFAS bioaccumulation and lipid metabolism disruptions. For the study, liver samples collected from C57BL/6 mice exposed to either of the emerging PFAS hexafluoropropylene oxide dimer acid (HFPO-DA or "GenX") or Nafion byproduct 2 (NBP2) were assessed. Sex-specific differences in PFAS accumulation and liver size were observed for both PFAS, in addition to disturbed hepatic liver lipidomic profiles. Interestingly, GenX resulted in less hepatic bioaccumulation than NBP2 yet gave a higher number of significantly altered lipids when compared to the control group, implying that the accumulation of substances in the liver may not be a reliable measure of the substance's capacity to disrupt the liver's natural metabolic processes. Specifically, phosphatidylglycerols, phosphatidylinositols, and various specific fatty acyls were greatly impacted, indicating alteration of inflammation, oxidative stress, and cellular signaling processes due to emerging PFAS exposure. Overall, these results provide valuable insight into the liver bioaccumulation and molecular mechanisms of GenX- and NBP2-induced hepatotoxicity.

Altered neurological and neurobehavioral phenotypes in a mouse model of the recurrent KCNB1-p.R306C voltage-sensor variant.

Pathogenic variants in KCNB1 are associated with a neurodevelopmental disorder spectrum that includes global developmental delays, cognitive impairment, abnormal electroencephalogram (EEG) patterns, and epilepsy with variable age of onset and severity. Additionally, there are prominent behavioral disturbances, including hyperactivity, aggression, and features of autism spectrum disorder. The most frequently identified recurrent variant is KCNB1-p.R306C, a missense variant located within the S4 voltage-sensing transmembrane domain. Individuals with the R306C variant exhibit mild to severe developmental delays, behavioral disorders, and a diverse spectrum of seizures. Previous in vitro characterization of R306C described altered sensitivity and cooperativity of the voltage sensor and impaired capacity for repetitive firing of neurons. Existing Kcnb1 mouse models include dominant negative missense variants, as well as knockout and frameshifts alleles. While all models recapitulate key features of KCNB1 encephalopathy, mice with dominant negative alleles were more severely affected. In contrast to existing loss-of-function and dominant-negative variants, KCNB1-p.R306C does not affect channel expression, but rather affects voltage-sensing. Thus, modeling R306C in mice provides a novel opportunity to explore impacts of a voltage-sensing mutation in Kcnb1. Using CRISPR/Cas9 genome editing, we generated the Kcnb1R306C mouse model and characterized the molecular and phenotypic effects. Consistent with the in vitro studies, neurons from Kcnb1R306C mice showed altered excitability. Heterozygous and homozygous R306C mice exhibited hyperactivity, altered susceptibility to chemoconvulsant-induced seizures, and frequent, long runs of slow spike wave discharges on EEG, reminiscent of the slow spike and wave activity characteristic of Lennox Gastaut syndrome. This novel model of channel dysfunction in Kcnb1 provides an additional, valuable tool to study KCNB1 encephalopathies. Furthermore, this allelic series of Kcnb1 mouse models will provide a unique platform to evaluate targeted therapies.

Metagenomic, metabolomic, and lipidomic shifts associated with fecal microbiota transplantation for recurrent Clostridioides difficile infection.

Recurrent C. difficile infection (rCDI) is an urgent public health threat for which the last resort and lifesaving treatment is a fecal microbiota transplant (FMT). However, the exact mechanisms which mediate a successful FMT are not well understood. Here we use longitudinal stool samples collected from patients undergoing FMT to evaluate changes in the microbiome, metabolome, and lipidome after successful FMTs. We show changes in the abundance of many lipids, specifically acylcarnitines and bile acids, in response to FMT. These changes correlate with Enterobacteriaceae, which encode carnitine metabolism genes, and Lachnospiraceae, which encode bile salt hydrolases and baiA genes. LC-IMS-MS revealed a shift from microbial conjugation of primary bile acids pre-FMT to secondary bile acids post-FMT. Here we define the structural and functional changes in successful FMTs. This information will help guide targeted Live Biotherapeutic Product development for the treatment of rCDI and other intestinal diseases.

Deciphering ApoE Genotype-Driven Proteomic and Lipidomic Alterations in Alzheimer's Disease Across Distinct Brain Regions.

Alzheimer's disease (AD) is a neurodegenerative disease with a complex etiology influenced by confounding factors such as genetic polymorphisms, age, sex, and race. Traditionally, AD research has not prioritized these influences, resulting in dramatically skewed cohorts such as three times the number of Apolipoprotein E (APOE) ε4-allele carriers in AD relative to healthy cohorts. Thus, the resulting molecular changes in AD have previously been complicated by the influence of apolipoprotein E disparities. To explore how apolipoprotein E polymorphism influences AD progression, 62 post-mortem patients consisting of 33 AD and 29 controls (Ctrl) were studied to balance the number of ε4-allele carriers and facilitate a molecular comparison of the apolipoprotein E genotype. Lipid and protein perturbations were assessed across AD diagnosed brains compared to Ctrl brains, ε4 allele carriers (APOE4+ for those carrying 1 or 2 ε4s and APOE4- for non-ε4 carriers), and differences in ε3ε3 and ε3ε4 Ctrl brains across two brain regions (frontal cortex (FCX) and cerebellum (CBM)). The region-specific influences of apolipoprotein E on AD mechanisms showcased mitochondrial dysfunction and cell proteostasis at the core of AD pathophysiology in the post-mortem brains, indicating these two processes may be influenced by genotypic differences and brain morphology.

From big data to big insights: statistical and bioinformatic approaches for exploring the lipidome.

The goal of lipidomic studies is to provide a broad characterization of cellular lipids present and changing in a sample of interest. Recent lipidomic research has significantly contributed to revealing the multifaceted roles that lipids play in fundamental cellular processes, including signaling, energy storage, and structural support. Furthermore, these findings have shed light on how lipids dynamically respond to various perturbations. Continued advancement in analytical techniques has also led to improved abilities to detect and identify novel lipid species, resulting in increasingly large datasets. Statistical analysis of these datasets can be challenging not only because of their vast size, but also because of the highly correlated data structure that exists due to many lipids belonging to the same metabolic or regulatory pathways. Interpretation of these lipidomic datasets is also hindered by a lack of current biological knowledge for the individual lipids. These limitations can therefore make lipidomic data analysis a daunting task. To address these difficulties and shed light on opportunities and also weaknesses in current tools, we have assembled this review. Here, we illustrate common statistical approaches for finding patterns in lipidomic datasets, including univariate hypothesis testing, unsupervised clustering, supervised classification modeling, and deep learning approaches. We then describe various bioinformatic tools often used to biologically contextualize results of interest. Overall, this review provides a framework for guiding lipidomic data analysis to promote a greater assessment of lipidomic results, while understanding potential advantages and weaknesses along the way.

Reverse metabolomics for the discovery of chemical structures from humans.

Determining the structure and phenotypic context of molecules detected in untargeted metabolomics experiments remains challenging. Here we present reverse metabolomics as a discovery strategy, whereby tandem mass spectrometry spectra acquired from newly synthesized compounds are searched for in public metabolomics datasets to uncover phenotypic associations. To demonstrate the concept, we broadly synthesized and explored multiple classes of metabolites in humans, including N-acyl amides, fatty acid esters of hydroxy fatty acids, bile acid esters and conjugated bile acids. Using repository-scale analysis1,2, we discovered that some conjugated bile acids are associated with inflammatory bowel disease (IBD). Validation using four distinct human IBD cohorts showed that cholic acids conjugated to Glu, Ile/Leu, Phe, Thr, Trp or Tyr are increased in Crohn's disease. Several of these compounds and related structures affected pathways associated with IBD, such as interferon-γ production in CD4+ T cells3 and agonism of the pregnane X receptor4. Culture of bacteria belonging to the Bifidobacterium, Clostridium and Enterococcus genera produced these bile amidates. Because searching repositories with tandem mass spectrometry spectra has only recently become possible, this reverse metabolomics approach can now be used as a general strategy to discover other molecules from human and animal ecosystems.

Surgical microwave ablation for the treatment of hepatocellular carcinoma in 791 operations.

INTRODUCTION: Hepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality and often arises in the setting of cirrhosis. The present series reviews outcomes following 791 operations. METHODS: Retrospective review surgical MWA for HCC from March 2007 through December 2022 at a high-volume institution was performed using a prospective database. Primary outcome was overall survival. RESULTS: A total of 791 operations in 623 patients and 1156 HCC tumors were treated with surgical MWA. Median tumor size was 2 cm (range 0.25-10 cm) with an average of 1 tumor ablated per operation (range 1-7 tumors). Nearly 90 % of patients had cirrhosis with a median MELD score of 8 (IQR = 6-11). Mortality within 30 days occurred in 13 patients (1.6 %). Per tumor, the rate of incomplete ablation was 2.25 % and local recurrence was 2.95 %. Previous ablation and tumor size were risk factors for recurrence. One-year overall survival was 82.0 % with a median overall survival of 36.5 months (95 % CI 15.7-93.7) and median disease-free survival of 15.9 months (range 5.7-37.3 months). CONCLUSION: Surgical MWA offers a low-morbidity approach for treatment of HCC, affording low rates of incomplete ablation and local recurrence.

Characterizing Adeno-Associated Virus Capsids with Both Denaturing and Intact Analysis Methods.

Adeno-associated virus (AAV) capsids are among the leading gene delivery platforms used to treat a vast array of human diseases and conditions. AAVs exist in a variety of serotypes due to differences in viral protein (VP) sequences with distinct serotypes targeting specific cells and tissues. As the utility of AAVs in gene therapy increases, ensuring their specific composition is imperative for the correct targeting and gene delivery. From a quality control perspective, current analytical tools are limited in their selectivity for viral protein (VP) subunits due to their sequence similarities, instrumental difficulties in assessing the large molecular weights of intact capsids, and the uncertainty in distinguishing empty and filled capsids. To address these challenges, we combined two distinct analytical workflows that assess the intact capsids and VP subunits separately. First, a selective temporal overview of resonant ion (STORI)-based charge detection-mass spectrometry (CD-MS) was applied for characterization of the intact capsids. Liquid chromatography, ion mobility spectrometry, and mass spectrometry (LC-IMS-MS) separations were then used for the capsid denaturing measurements. This multimethod combination was applied to three AAV serotypes (AAV2, AAV6, and AAV8) to evaluate their intact empty and filled capsid ratios and then examine the distinct VP sequences and modifications present.

Assessing How Chemical Exposures Affect Human Health.

Measuring chemical exposure is extremely challenging due to the range and number of anthropogenic molecules encountered in our daily lives, as well as their complex transformations throughout the body. To broadly characterize how chemical exposures influence human health, a combination of genomic, transcriptomic, proteomic, endogenous metabolomic, and xenobiotic measurements must be performed. However, while genomic, transcriptomic, and proteomic analyses have rapidly progressed over the last two decades, advancements in instrumentation and computations for nontargeted xenobiotic and endogenous small molecule measurements are still greatly needed.

Uncovering per- and polyfluoroalkyl substances (PFAS) with nontargeted ion mobility spectrometry-mass spectrometry analyses.

Because of environmental and health concerns, legacy per- and polyfluoroalkyl substances (PFAS) have been voluntarily phased out, and thousands of emerging PFAS introduced as replacements. Traditional analytical methods target a limited number of mainly legacy PFAS; therefore, many species are not routinely assessed in the environment. Nontargeted approaches using high-resolution mass spectrometry methods have therefore been used to detect and characterize unknown PFAS. However, their ability to elucidate chemical structures relies on generation of informative fragments, and many low concentration species are not fragmented in typical data-dependent acquisition approaches. Here, a data-independent method leveraging ion mobility spectrometry (IMS) and size-dependent fragmentation was developed and applied to characterize aquatic passive samplers deployed near a North Carolina fluorochemical manufacturer. From the study, 11 PFAS structures for various per- and polyfluorinated ether sulfonic acids and multiheaded perfluorinated ether acids were elucidated in addition to 36 known PFAS. Eight of these species were previously unreported in environmental media, and three suspected species were validated.

Using Multidimensional Separations to Distinguish Isomeric Amino Acid-Bile Acid Conjugates and Assess Their Presence and Perturbations in Model Systems.

Bile acids play key roles in nutrient uptake, inflammation, signaling, and microbiome composition. While previous bile acid analyses have primarily focused on profiling 5 canonical primary and secondary bile acids and their glycine and taurine amino acid-bile acid (AA-BA) conjugates, recent studies suggest that many other microbial conjugated bile acids (or MCBAs) exist. MCBAs are produced by the gut microbiota and serve as biomarkers, providing information about early disease onset and gut health. Here we analyzed 8 core bile acids synthetically conjugated with 22 proteinogenic and nonproteogenic amino acids totaling 176 MCBAs. Since many of the conjugates were isomeric and only 42 different m/z values resulted from the 176 MCBAs, a platform coupling liquid chromatography, ion mobility spectrometry, and mass spectrometry (LC-IMS-MS) was used for their separation. Their molecular characteristics were then used to create an in-house extended bile acid library for a combined total of 182 unique compounds. Additionally, ∼250 rare bile acid extracts were also assessed to provide additional resources for bile acid profiling and identification. This library was then applied to healthy mice dosed with antibiotics and humans having fecal microbiota transplantation (FMT) to assess the MCBA presence and changes in the gut before and after each perturbation.

Assessing Per- and Polyfluoroalkyl Substances (PFAS) in Fish Fillet Using Non-Targeted Analyses.

Per- and polyfluoroalkyl substances (PFAS) are a class of thousands of man-made chemicals that are persistent and highly stable in the environment. The diverse structures of PFAS give them different chemical properties that influence their solubility in different environmental matrices and biological tissues. PFAS in drinking water have been extensively studied, but information on their presence in fish and other exposure routes is limited. To address this, a non-targeted analysis using liquid chromatography, ion mobility spectrometry, and mass spectrometry (LC-IMS-MS) was performed to evaluate PFAS in fish fillets from in central North Carolina and compare with PFAS data from previously published water. A total of 22 different PFAS were detected in the fillets, including only 4 of the PFAS reported in water. Both more PFAS types and higher concentrations were observed in fish caught near a known PFAS point-source compared to those from a reservoir used for drinking water and recreation. Median fillet PFOS levels were 54 ppb in fish closest to the point source and 14-20 ppb in fish from the reservoir. Thus, future PFAS monitoring should include both targeted and non-targeted analyses of both water and fish to increase understanding of human exposure risks and ecosystem impacts. SYNOPSIS: Fish fillet samples were collected from five sites in North Carolina. PFAS were detected in all samples and differences in analytes and abundances were observed at the different sites. GRAPHICAL ABSTRACT: For use in table of contents only.

Combining native mass spectrometry and lipidomics to uncover specific membrane protein-lipid interactions from natural lipid sources.

While it is known that lipids play an essential role in regulating membrane protein structure and function, it remains challenging to identify specific protein-lipid interactions. Here, we present an innovative approach that combines native mass spectrometry (MS) and lipidomics to identify lipids retained by membrane proteins from natural lipid extracts. Our results reveal that the bacterial ammonia channel (AmtB) enriches specific cardiolipin (CDL) and phosphatidylethanolamine (PE) from natural headgroup extracts. When the two extracts are mixed, AmtB retains more species, wherein selectivity is tuned to bias headgroup selection. Using a series of natural headgroup extracts, we show TRAAK, a two-pore domain K+ channel (K2P), retains specific acyl chains that is independent of the headgroup. A brain polar lipid extract was then combined with the K2Ps, TRAAK and TREK2, to understand lipid specificity. More than a hundred lipids demonstrated affinity for each protein, and both channels were found to retain specific fatty acids and lysophospholipids known to stimulate channel activity, even after several column washes. Natural lipid extracts provide the unique opportunity to not only present natural lipid diversity to purified membrane proteins but also identify lipids that may be important for membrane protein structure and function.

Aggregated Molecular Phenotype Scores: Enhancing Assessment and Visualization of Mass Spectrometry Imaging Data for Tissue-Based Diagnostics.

Mass spectrometry imaging (MSI) has gained increasing popularity for tissue-based diagnostics due to its ability to identify and visualize molecular characteristics unique to different phenotypes within heterogeneous samples. Data from MSI experiments are often assessed and visualized using various supervised and unsupervised statistical approaches. However, these approaches tend to fall short in identifying and concisely visualizing subtle, phenotype-relevant molecular changes. To address these shortcomings, we developed aggregated molecular phenotype (AMP) scores. AMP scores are generated using an ensemble machine learning approach to first select features differentiating phenotypes, weight the features using logistic regression, and combine the weights and feature abundances. AMP scores are then scaled between 0 and 1, with lower values generally corresponding to class 1 phenotypes (typically control) and higher scores relating to class 2 phenotypes. AMP scores, therefore, allow the evaluation of multiple features simultaneously and showcase the degree to which these features correlate with various phenotypes. Due to the ensembled approach, AMP scores are able to overcome limitations associated with individual models, leading to high diagnostic accuracy and interpretability. Here, AMP score performance was evaluated using metabolomic data collected from desorption electrospray ionization MSI. Initial comparisons of cancerous human tissues to their normal or benign counterparts illustrated that AMP scores distinguished phenotypes with high accuracy, sensitivity, and specificity. Furthermore, when combined with spatial coordinates, AMP scores allow visualization of tissue sections in one map with distinguished phenotypic borders, highlighting their diagnostic utility.

Multidimensional profiles of Head Start children's social behaviors predict their interpretations of physical aggression.

Preschool children's reasoning regarding moral events differs according to adversity and relates to aggression. Understanding morality in young children is paramount for understanding their aggressive behaviors. The study aims to identify patterns of aggression and prosocial behavior using Latent Class Analysis (LCA) and investigate how patterns of aggression and prosocial behavior relate to reasoning about prototypic moral events. One hundred six children (Mage = 4.40 years old, SD = 0.55 years old, Range: 3.08-5.33 years old, 51% boys) enrolled in Head Start programs and their caregivers participated. In the fall caregivers completed surveys on forms (i.e., the manifestation of behavior) and functions of aggression (i.e., motivation of behavior), and prosocial behavior. The following spring children completed two moral reasoning tasks that measured children's judgment and reasoning of harm, and their attributions of transgressors' reasoning. The LCA revealed a 3-class solution: (1) high levels of relational aggression and moderate levels of prosocial behavior (bistrategic controllers), (2) low levels of both aggression and average prosocial behavior (uninvolved), (3) high levels of all types of aggression and low levels of prosocial behavior (high aggression). Subsequent analyses suggest that uninvolved children prioritize adhering to authority over other concerns, and bistrategic controllers focused on goal-oriented reasoning. Overall, our findings support that recognizing patterns of behavior may be useful in understanding children's moral reasoning.