Feature Selection Enhances Peptide Binding Predictions for TCR-Specific Interactions.

T-cell receptors (TCRs) play a critical role in the immune response by recognizing specific ligand peptides presented by major histocompatibility complex (MHC) molecules. Accurate prediction of peptide binding to TCRs is essential for advancing immunotherapy, vaccine design, and understanding mechanisms of autoimmune disorders. This study presents a novel theoretical method that explores the impact of feature selection techniques on enhancing the predictive accuracy of peptide binding models tailored for specific TCRs. To evaluate the universality of our approach across different TCR systems, we utilized a dataset that includes peptide libraries tested against three distinct murine TCRs. A broad range of physicochemical properties, including amino acid composition, dipeptide composition, and tripeptide features, were integrated into the machine learning-based feature selection framework to identify key features contributing to binding affinity. Our analysis reveals that leveraging optimized feature subsets not only simplifies the model complexity but also enhances predictive performance, enabling more precise identification of TCR-peptide interactions. The results of our feature selection method are consistent with findings from hybrid approaches that utilize both sequence and structural data as input as well as experimental data. Our theoretical approach highlights the role of feature selection in peptide-TCR interactions, providing a powerful tool for uncovering the molecular mechanisms of the T-cell response and assisting in the design of more advanced targeted therapeutics.

Cell-state transitions and density-dependent interactions together explain the dynamics of spontaneous epithelial-mesenchymal heterogeneity.

Cancer cell populations comprise phenotypes distributed among the epithelial-mesenchymal (E-M) spectrum. However, it remains unclear which population-level processes give rise to the observed experimental distribution and dynamical changes in E-M heterogeneity, including (1) differential growth, (2) cell-state switching, and (3) population density-dependent growth or state-transition rates. Here, we analyze the necessity of these three processes in explaining the dynamics of E-M population distributions as observed in PMC42-LA and HCC38 breast cancer cells. We find that, while cell-state transition is necessary to reproduce experimental observations of dynamical changes in E-M fractions, including density-dependent growth interactions (cooperation or suppression) better explains the data. Further, our models predict that treatment of HCC38 cells with transforming growth factor ß (TGF-ß) signaling and Janus kinase 2/signal transducer and activator of transcription 3 (JAK2/3) inhibitors enhances the rate of mesenchymal-epithelial transition (MET) instead of lowering that of E-M transition (EMT). Overall, our study identifies the population-level processes shaping the dynamics of spontaneous E-M heterogeneity in breast cancer cells.

Real-World Experience With Avacopan in Antineutrophil Cytoplasmic Autoantibody-Associated Vasculitis.

Introduction: Postmarketing data on outcomes of avacopan use in antineutrophil cytoplasmic autoantibody (ANCA)-associated vasculitis (AAV) are lacking. Methods: We performed a multicenter retrospective analysis of 92 patients with newly diagnosed or relapsing AAV who received therapy with avacopan. The coprimary outcome measures were clinical remission at 26 and 52 weeks. We use descriptive statistics and univariate logistic regression to assess outcomes and predictors of remission, respectively. Results: Of the 92 patients, 23% (n = 21) had a baseline estimated glomerular filtration rate (eGFR) < 15 ml/min per 1.73 m2 and 10% on kidney replacement therapy at baseline. Among those with kidney involvement, mean (SD) enrollment eGFR was 33 (27) ml/min per 1.73 m2 with a mean (SD) change of +12 (25) and +20 (23) ml/min per 1.73 m2 at weeks 26 and 52, respectively. In addition to avacopan, 47% of patients received combination therapy of rituximab and low-dose cyclophosphamide, and 14% of patients received plasma exchange (PLEX). After induction, the median (interquartile range [IQR]) time to start avacopan was 3.6 (2.1-7.7) weeks, and the median time to discontinue prednisone after starting avacopan was 5.6 (3.3-9.5) weeks. Clinical remission was achieved in 90% of patients at week 26 and 84% of patients at week 52. Of the patients, 20% stopped avacopan due to adverse events, with the most common being elevated serum aminotransferases (4.3%). Conclusion: A high rate of remission and an acceptable safety profile were observed with the use of avacopan in the treatment of AAV in this postmarketing analysis, including the populations excluded from the ADVOCATE trial.

RACER-m leverages structural features for sparse T cell specificity prediction.

Reliable prediction of T cell specificity against antigenic signatures is a formidable task, complicated by the immense diversity of T cell receptor and antigen sequence space and the resulting limited availability of training sets for inferential models. Recent modeling efforts have demonstrated the advantage of incorporating structural information to overcome the need for extensive training sequence data, yet disentangling the heterogeneous TCR-antigen interface to accurately predict MHC-allele-restricted TCR-peptide interactions has remained challenging. Here, we present RACER-m, a coarse-grained structural model leveraging key biophysical information from the diversity of publicly available TCR-antigen crystal structures. Explicit inclusion of structural content substantially reduces the required number of training examples and maintains reliable predictions of TCR-recognition specificity and sensitivity across diverse biological contexts. Our model capably identifies biophysically meaningful point-mutant peptides that affect binding affinity, distinguishing its ability in predicting TCR specificity of point-mutants from alternative sequence-based methods. Its application is broadly applicable to studies involving both closely related and structurally diverse TCR-peptide pairs.

Protocol for inferring epithelial-to-mesenchymal transition trajectories from single-cell RNA sequencing data using R.

The epithelial-to-mesenchymal transition (EMT) provides crucial insights into the metastatic process and possesses prognostic value within the cancer context. Here, we present COMET, an R package for inferring EMT trajectories and inter-state transition rates from single-cell RNA sequencing data. We describe steps for finding the optimal number of EMT genes for a specific context, estimating EMT-related trajectories, optimal fitting of continuous-time Markov chain to inferred trajectories, and estimating inter-state transition rates.

Optimal phenotypic adaptation in fluctuating environments.

Phenotypic adaptation is a universal feature of biological systems navigating highly variable environments. Recent empirical data support the role of memory-driven decision making in cellular systems navigating uncertain future nutrient landscapes, wherein a distinct growth phenotype emerges in fluctuating conditions. We develop a simple stochastic mathematical model to describe memory-driven cellular adaptation required for systems to optimally navigate such uncertainty. In this framework, adaptive populations traverse dynamic environments by inferring future variation from a memory of prior states, and memory capacity imposes a fundamental trade-off between the speed and accuracy of adaptation to new fluctuating environments. Our results suggest that the observed growth reductions that occur in fluctuating environments are a direct consequence of optimal decision making and result from bet hedging and occasional phenotypic-environmental mismatch. We anticipate that this modeling framework will be useful for studying the role of memory in phenotypic adaptation, including in the design of temporally varying therapies against adaptive systems.

Quantitative approaches for decoding the specificity of the human T cell repertoire.

T cell receptor (TCR)-peptide-major histocompatibility complex (pMHC) interactions play a vital role in initiating immune responses against pathogens, and the specificity of TCRpMHC interactions is crucial for developing optimized therapeutic strategies. The advent of high-throughput immunological and structural evaluation of TCR and pMHC has provided an abundance of data for computational approaches that aim to predict favorable TCR-pMHC interactions. Current models are constructed using information on protein sequence, structures, or a combination of both, and utilize a variety of statistical learning-based approaches for identifying the rules governing specificity. This review examines the current theoretical, computational, and deep learning approaches for identifying TCR-pMHC recognition pairs, placing emphasis on each method's mathematical approach, predictive performance, and limitations.

Systematic review of preoperative n-3 fatty acids in major gastrointestinal surgery.

Objectives: Perioperative nutrition aims to replenish nutritional stores before surgery and reduce postoperative complications. 'Immunonutrition' (including omega-3 fatty acids) may modulate the immune system and attenuate the postoperative inflammatory response. Hitherto, immunonutrition has overwhelmingly been administered in the postoperative period-however, this may be too late to provide benefit. Design: A systematic literature search using MEDLINE and EMBASE for randomized controlled trials (RCTs). Setting: Perioperative major gastrointestinal surgery. Participants: Patients undergoing major gastrointestinal surgery. Interventions: Omega-3 fatty acid supplementation commenced in the preoperative period, with or without continuation into postoperative period. Main outcome measures: The effect of preoperative omega-3 fatty acids on inflammatory response and clinical outcomes. Results: 833 studies were identified. After applying inclusion and exclusion criteria, 12 RCTs, involving 1456 randomized patients, were included. Ten articles exclusively enrolled patients with cancer. Seven studies used a combination of EPA (eicosapentaenoic acid) and DHA (docosahexaenoic acid) as the intervention and five studies used EPA alone. Eight out of 12 studies continued preoperative nutritional support into the postoperative period.Of the nine studies reporting mortality, no difference was seen. Duration of hospitalisation ranged from 4.5 to 18 days with intervention and 3.5 to 23.5 days with control. Omega-3 fatty acids had no effect on postoperative C-reactive protein and the effect on cytokines (including tumor necrosis factor-α, interleukin (IL)-6 and IL-10) was inconsistent. Ten of the 12 studies had low risk of bias, with one study having moderate bias from allocation and blinding. Conclusions: There is insufficient evidence to support routine preoperative omega-3 fatty acid supplementation for major gastrointestinal surgery, even when this is continued after surgery. PROSPERO registration number: CRD42018108333.

Population dynamics of EMT elucidates the timing and distribution of phenotypic intra-tumoral heterogeneity.

The Epithelial-to-Mesenchymal Transition (EMT) is a hallmark of cancer metastasis and morbidity. EMT is a non-binary process, and cells can be stably arrested en route to EMT in an intermediate hybrid state associated with enhanced tumor aggressiveness and worse patient outcomes. Understanding EMT progression in detail will provide fundamental insights into the mechanisms underlying metastasis. Despite increasingly available single-cell RNA sequencing (scRNA-seq) data that enable in-depth analyses of EMT at the single-cell resolution, current inferential approaches are limited to bulk microarray data. There is thus a great need for computational frameworks to systematically infer and predict the timing and distribution of EMT-related states at single-cell resolution. Here, we develop a computational framework for reliable inference and prediction of EMT-related trajectories from scRNA-seq data. Our model can be utilized across a variety of applications to predict the timing and distribution of EMT from single-cell sequencing data.

The New Zealand drug harms ranking study: A multi-criteria decision analysis.

AIMS: The harms arising from psychoactive drug use are complex, and harm reduction strategies should be informed by a detailed understanding of the extent and nature of that harm. Drug harm is also context specific, and so any comprehensive assessment of drug harm should be relevant to the characteristics of the population in question. This study aimed to evaluate and rank drug harms within Aotearoa New Zealand using a multi-criteria decision analysis (MCDA) framework, and to separately consider harm within the total population, and among youth. METHODS: Two facilitated workshops involved the separate ranking of harm for the total population, and then for youth aged 12-17, by two expert panels. In the total population workshop, 23 drugs were scored against 17 harm criteria, and those criteria were then evaluated using a swing weighting process. Scoring and weighting were subsequently updated during the youth-specific workshop. All results were recorded and analysed using specialised MCDA software. RESULTS: When considering overall harm, the MCDA modelling results indicated that alcohol, methamphetamine and synthetic cannabinoids were the most harmful to both the overall population and the youth, followed by tobacco in the total population. Alcohol remained the most harmful drug for the total population when separately considering harm to those who use it, and harm to others. CONCLUSIONS: The results provide detailed and context-specific insight into the harm associated with psychoactive drugs use within Aotearoa New Zealand. The findings also demonstrate the value of separately considering harm for different countries, and for different population subgroups.

Non-Traditional Physical Education Classes Improve High School Students' Movement Competency and Fitness: A Mixed-Methods Program Evaluation Study.

Non-traditional physical education (PE) programs may facilitate functional movement patterns and develop fitness and work capacity to facilitate long-term physical activity. This program evaluation study compared changes in body composition, movement competency, work capacity, and fitness for high school students in CrossFit or weight training PE; both classes were hypothesized to improve each area, with greater improvements in the CrossFit class. Students participated in 57 min classes 4 days per week for 9 months. Measures including body composition, movement competencies (squat, lunge, push-up, pull-up, hinge, and brace), work capacity (two CrossFit workouts), and fitness (air squats, push-ups, inverted row, plank hold, horizontal and vertical jumps, 5 rep max back squat and press, 500 m bike, and 12 min run) were taken at baseline, midpoint, and post-test. Focus groups to assess students' experiences and outcomes were conducted at post-test. Students significantly improved in movement competencies (ps = 0.034 to <0.001), work capacity (ps < 0.001), and all fitness tests (ps = 0.036 to <0.001). The CrossFit class was only superior on the 500 m bike. Four themes were identified from the focus groups: (1) increased self-confidence, (2) health improvements, (3) newfound community, and (4) translational sports improvements. Future research should examine changes using an experimental design.

Optimal cancer evasion in a dynamic immune microenvironment generates diverse post-escape tumor antigenicity profiles.

The failure of cancer treatments, including immunotherapy, continues to be a major obstacle in preventing durable remission. This failure often results from tumor evolution, both genotypic and phenotypic, away from sensitive cell states. Here, we propose a mathematical framework for studying the dynamics of adaptive immune evasion that tracks the number of tumor-associated antigens available for immune targeting. We solve for the unique optimal cancer evasion strategy using stochastic dynamic programming and demonstrate that this policy results in increased cancer evasion rates compared to a passive, fixed strategy. Our foundational model relates the likelihood and temporal dynamics of cancer evasion to features of the immune microenvironment, where tumor immunogenicity reflects a balance between cancer adaptation and host recognition. In contrast with a passive strategy, optimally adaptive evaders navigating varying selective environments result in substantially heterogeneous post-escape tumor antigenicity, giving rise to immunogenically hot and cold tumors.

Epigenetic memory acquired during long-term EMT induction governs the recovery to the epithelial state.

Epithelial-mesenchymal transition (EMT) and its reverse mesenchymal-epithelial transition (MET) are critical during embryonic development, wound healing and cancer metastasis. While phenotypic changes during short-term EMT induction are reversible, long-term EMT induction has been often associated with irreversibility. Here, we show that phenotypic changes seen in MCF10A cells upon long-term EMT induction by TGFß need not be irreversible, but have relatively longer time scales of reversibility than those seen in short-term induction. Next, using a phenomenological mathematical model to account for the chromatin-mediated epigenetic silencing of the miR-200 family by ZEB family, we highlight how the epigenetic memory gained during long-term EMT induction can slow the recovery to the epithelial state post-TGFß withdrawal. Our results suggest that epigenetic modifiers can govern the extent and time scale of EMT reversibility and advise caution against labelling phenotypic changes seen in long-term EMT induction as 'irreversible'.

Contact map dependence of a T-cell receptor binding repertoire.

The T-cell arm of the adaptive immune system provides the host protection against unknown pathogens by discriminating between host and foreign material. This discriminatory capability is achieved by the creation of a repertoire of cells each carrying a T-cell receptor (TCR) specific to non-self-antigens displayed as peptides bound to the major histocompatibility complex (pMHC). The understanding of the dynamics of the adaptive immune system at a repertoire level is complex, due to both the nuanced interaction of a TCR-pMHC pair and to the number of different possible TCR-pMHC pairings, making computationally exact solutions currently unfeasible. To gain some insight into this problem, we study an affinity-based model for TCR-pMHC binding in which a crystal structure is used to generate a distance-based contact map that weights the pairwise amino acid interactions. We find that the TCR-pMHC binding energy distribution strongly depends both on the number of contacts and the repeat structure allowed by the topology of the contact map of choice; this in turn influences T-cell recognition probability during negative selection, with higher variances leading to higher survival probabilities. In addition, we quantify the degree to which neoantigens with mutations in sites with higher contacts are recognized at a higher rate.

Effect of annealing on properties and performance of HfO2/SiO2 optical coatings for UV-applications.

The field of ultraviolet (UV)-laser applications is currently experiencing rapid growth in the semiconductor processing, laser micromachining and biomedical markets. Key enablers for these technologies are optical coatings used to manipulate and guide laser beams in a targeted manner. As laser power, laser fluence and pulse frequencies increase, the demands on the physical properties of the coating materials become more stringent. Ion beam sputtering is a technique that allows producing optical coatings with the low losses required in these applications. In this study, we investigate the influence of ion beam sputtering (IBS) parameters on the optical properties of HfO2 and SiO2 single layers as well as the impact of annealing duration at 475 °C for anti-reflective (AR) and highly reflective (HR) optical coatings at 355 nm. For HfO2 sputtered from a metal target the O2 flow during the coating process is a key parameter to reduce absorption. SiO2 single layers exhibit improved transmission in the UV-range as the ion beam energy for the sputtering process is reduced. Furthermore, a complex behavior for film stress, absorption, surface roughness and coating structure was unraveled as a function of annealing duration for AR- and HR-coatings at 355 nm. The reflectance of the HR-mirror after optimized annealing exceeded 99.94% at 355 nm and a high laser induced damage threshold (LIDT) of 6.9 J/cm2 was measured after 2 hours of annealing. For the AR-coating a LIDT-value of 15.7 J/cm2 was observed after 12 hours of annealing.

Changes in Triple-Negative Breast Cancer Molecular Subtypes in Patients Without Pathologic Complete Response After Neoadjuvant Systemic Chemotherapy.

PURPOSE: Lehmann et al have identified four molecular subtypes of triple-negative breast cancer (TNBC)-basal-like (BL) 1, BL2, mesenchymal (M), and luminal androgen receptor-and an immunomodulatory (IM) gene expression signature modifier. Our group previously showed that the response of TNBC to neoadjuvant systemic chemotherapy (NST) differs by molecular subtype, but whether NST affects the subtype was unknown. Here, we tested the hypothesis that in patients without pathologic complete response, TNBC subtypes can change after NST. Moreover, in cases with the changed subtype, we determined whether epithelial-to-mesenchymal transition (EMT) had occurred. MATERIALS AND METHODS: From the Pan-Pacific TNBC Consortium data set containing TNBC patient samples from four countries, we examined 64 formalin-fixed, paraffin-embedded pairs of matched pre- and post-NST tumor samples. The TNBC subtype was determined using the TNBCtype-IM assay. We analyzed a partial EMT gene expression scoring metric using mRNA data. RESULTS: Of the 64 matched pairs, 36 (56%) showed a change in the TNBC subtype after NST. The most frequent change was from BL1 to M subtypes (38%). No tumors changed from M to BL1. The IM signature was positive in 14 (22%) patients before NST and eight (12.5%) patients after NST. The EMT score increased after NST in 28 (78%) of the 36 patients with the changed subtype (v 39% of the 28 patients without change; P = .002254). CONCLUSION: We report, to our knowledge, for the first time that the TNBC molecular subtype and IM signature frequently change after NST. Our results also suggest that EMT is promoted by NST. Our findings may lead to innovative adjuvant therapy strategies in TNBC cases with residual tumor after NST.

Obstructive jaundice, a rare presentation of lung cancer: A case report.

Lung cancer metastases to the pancreas are rare but potentially life-threatening. Oftentimes, the presence of symptoms is indicative of extensive disease burden. This report describes a case of primary lung adenocarcinoma metastasizing to the head of the pancreas presenting as obstructive jaundice. The patient was a 61-year-old female veteran who presented with a chronic dyspnea, weight loss, and 3-weeks of nausea and vomiting found to have jaundice, elevated alkaline phosphatase levels, hyperbilirubinemia, and transaminitis. Imaging of her chest revealed large pulmonary parenchymal nodules throughout both lungs with a large left lower lobe mass and consolidation. Abdominal imaging showed a large heterogeneous mass in the pancreatic head, a grossly dilated common bile duct, and enlarged retroperitoneal lymph nodes contiguous with the mass. Pancreatic head biopsies revealed metastatic cancer cells from her lung adenocarcinoma which was confirmed via cytology and the presence of thyroid transcription factor - 1 and cytokeritin-7 expression and the absence of tumor protein 63 staining. Lung adenocarcinomas commonly metastasize to the bones, liver, and central nervous system but very rarely to the pancreas. There have been only a few reported cases of pancreatic tumors that manifested clinically as a result of primary lung cancer metastases however, even though uncommon, hematogenous spread of cancerous tissue should be considered on the differential as a cause for obstructive jaundice in the setting of lung adenocarcinoma.

Pulmonary Large Cell Neuroendocrine Carcinoma: A Rare Type of Non-Small Cell Lung Cancer.

Pulmonary large cell neuroendocrine carcinoma (LCNEC) is an uncommon type of non-small cell lung cancer (NSCLC) with an incidence of approximately 3% of all lung cancer diagnoses. The patient was a 60-year-old male with a 90-pack year smoking history who presented with dyspnea on exertion and productive cough for five weeks. Decreased breath sounds without respiratory distress and generalized cachexia were noted on the initial physical exam. Laboratory results were unremarkable except for chronic microcytic anemia. Computed tomography revealed extensive lymphadenopathy of the paratracheal, paraaortic, hilar, and nodes surrounding the left pulmonary arteries. Additionally, there were areas of necrosis in the left upper lobe, lingula, and left lower lobe with extensive pleural thickening extending to the abdomen and subcutaneous tissue of the anterior chest wall. Biopsy and staining showed disorganized tight cell clusters with irregular and prominent nuclei and numerous lymphocytes consistent with LCNEC. Immunohistochemistry was positive for neural cell adhesion molecule CD56 and synaptophysin, which was indicative of neuroendocrine origin. It was also positive for pan-cytokeratin antibody AE1 and AE3 and cytokeratin (CAM) 5.2, which arise from epithelial origin consistent with NSCLCs. Lastly, the patient's tissue was positive for thyroid transcription factor-1, which confirmed the tumor's primary lung origin. This combination of neuroendocrine and primary lung tumor markers, in conjunction with the histology, confirmed the patient's diagnosis of LCNEC.

Gene expression profiles of inflammatory breast cancer reveal high heterogeneity across the epithelial-hybrid-mesenchymal spectrum.

Inflammatory breast cancer (IBC) is a highly aggressive breast cancer that metastasizes largely via tumor emboli, and has a 5-year survival rate of less than 30%. No unique genomic signature has yet been identified for IBC nor has any specific molecular therapeutic been developed to manage the disease. Thus, identifying gene expression signatures specific to IBC remains crucial. Here, we compare various gene lists that have been proposed as molecular footprints of IBC using different clinical samples as training and validation sets and using independent training algorithms, and determine their accuracy in identifying IBC samples in three independent datasets. We show that these gene lists have little to no mutual overlap, and have limited predictive accuracy in identifying IBC samples. Despite this inconsistency, single-sample gene set enrichment analysis (ssGSEA) of IBC samples correlate with their position on the epithelial-hybrid-mesenchymal spectrum. This positioning, together with ssGSEA scores, improves the accuracy of IBC identification across the three independent datasets. Finally, we observed that IBC samples robustly displayed a higher coefficient of variation in terms of EMT scores, as compared to non-IBC samples. Pending verification that this patient-to-patient variability extends to intratumor heterogeneity within a single patient, these results suggest that higher heterogeneity along the epithelial-hybrid-mesenchymal spectrum can be regarded to be a hallmark of IBC and a possibly useful biomarker.