Discovery Phase Research Articles

Heart proteomic profiling discovers MYH6 and COX5B as biomarkers for sudden unexplained death

Sudden unexplained death (SUD) is not uncommon in forensic pathology. Yet, diagnosis of SUD remains challenging due to lack of specific biomarkers. This study aimed to screen differentially expressed proteins (DEPs) and validate their usefulness as diagnostic biomarkers for SUD cases. We designed a three-phase investigation, where in the discovery phase, formalin-fixed paraffin-embedded (FFPE) heart specimens were screened through label-free proteomic analysis of cases dying from SUD, mechanical injury and carbon monoxide (CO) intoxication. A total of 26 proteins were identified to be DEPs for the SUD cases after rigorous criterion. Bioinformatics and Adaboost-recursive feature elimination (RFE) analysis further revealed that three of the 26 proteins (MYH6, COX5B and TNNT2) were potential discriminative biomarkers. In the training phase, MYH6 and COX5B were verified to be true DEPs in cardiac tissues from 29 independent SUD cases as compared with a serial of control cases (n = 42). Receiver operating characteristic (ROC) analysis illustrated that combination of MYH6 and COX5B achieved optimal diagnostic sensitivity (89.7 %) and specificity (84.4 %), with area under the curve (AUC) being 0.91. A diagnostic software based on the logistic regression formula derived from the training phase was then constructed. In the validation phase, the diagnostic software was applied to eight authentic SUD cases, seven (87.5 %) of which were accurately recognized. Our study provides a valid strategy towards practical diagnosis of SUD by integrating cardiac MYH6 and COX5B as dual diagnostic biomarkers.

From stigma to solutions: harnessing local wisdom to tackle harms associated with menstrual seclusion (chhaupadi) in Nepal

In Nepal, menstrual practices, and particularly chhaupadi, impose restrictive norms affecting women’s daily lives. Chhaupadi is a tradition that involves isolating women and girls during menstruation and after childbirth, along with following other restrictions, which have physical and mental health implications. To date, interventions have yet to fully and sustainably address harms associated with chhaupadi across the country. This two-phase study conducted in Dailekh, Nepal facilitated the development of community-created solutions to mitigate chhaupadi’s adverse impacts on women’s health. Using Human Centred Design and a community-engaged approach, the discovery phase identified key stakeholders and contextualised chhaupadi, while the subsequent design phase facilitated the development of five community-created interventions. These included leveraging female community health volunteers (FCHVs) for counselling and awareness, targeting mothers to drive behavioural change, engaging the wider community in behaviour change efforts, empowering fathers to catalyse change at home, and training youth for advocacy. The FCHV intervention concept was selected as the most promising intervention by the women co-design team, warranting broader exploration and testing. Additionally, while it is imperative for interventions to prioritise tackling deleterious aspects of chhaupadi, interventions must also acknowledge its deep-rooted cultural significance and history and recognise the positive aspects that some women may wish to preserve.

Utility of methylated DNA markers for the diagnosis of malignant biliary strictures.

Early identification of malignant biliary strictures (MBS) is challenging, with up to 20% classified as indeterminants after preliminary testing and tissue sampling with endoscopic retrograde cholangiopancreatography (ERCP). We aimed to evaluate the use of methylated DNA markers (MDM) from biliary brushings to enhance MBS detection in a prospective cohort. Candidate MDMs were evaluated for their utility in MBS diagnosis through a series of discovery and validation phases. DNA was extracted from biliary brushing samples, quantified, bisulfite-converted, and then subjected to methylation-specific Droplet Digital Polymerase Chain Reaction (ddPCR). Patients were considered to have no malignancy if the sampling was negative and there was no evidence of malignancy after 1 year or definitive negative surgical histopathology. Fourteen candidate MDMs were evaluated in the discovery phase, with top-performing and new markers evaluated in the technical validation phase. The top four MDMs were TWIST1, HOXA1, VSTM2B, and CLEC11A, which individually achieved AUC values of 0.82, 0.81, 0.83, and 0.78, respectively, with sensitivities of 59.4%, 53.1%, 62.5%, and 50.0%, respectively, at high specificities for malignancy of 95.2-95.3% for the final biologic validation phase. When combined as a panel, the AUC was 0.86, achieving 73.4% sensitivity and 92.9% specificity, which outperformed cytology and fluorescent in situ hybridization (FISH). The selected methylated DNA markers demonstrated improved performance characteristics for the detection of MBS compared to cytology and FISH. ​ Therefore, MDMs should be considered viable candidates for inclusion in diagnostic testing algorithms.​.

Shotgun Proteomics Links Proteoglycan-4+ Extracellular Vesicles to Cognitive Protection in Amyotrophic Lateral Sclerosis.

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disorder lacking reliable biomarkers for early diagnosis and disease progression monitoring. This study aimed to identify the novel biomarkers in plasmatic extracellular vesicles (EVs) isolated from ALS patients and healthy controls (HCs). A total of 61 ALS patients and 30 age-matched HCs were enrolled in the study and the protein content of circulating EVs was analyzed by shotgun proteomics. The study was divided into a discovery phase (involving 12 ALS and 12 HC patients) and a validation one (involving 49 ALS and 20 HC patients). In the discovery phase, more than 300 proteins were identified, with 32 proteins showing differential regulation in ALS patients compared to HCs. In the validation phase, over 400 proteins were identified, with 20 demonstrating differential regulation in ALS patients compared to HCs. Notably, seven proteins were found to be common to both phases, all of which were significantly upregulated in EVs from ALS patients. Most of them have previously been linked to ALS since they have been detected in the serum or cerebrospinal fluid of ALS patients. Among them, proteoglycan (PRG)-4, also known as lubricin, was of particular interest since it was significantly increased in ALS patients with normal cognitive and motor functions. This study highlights the significance of EVs as a promising avenue for biomarker discovery in ALS. Moreover, it sheds light on the unexpected role of PRG-4 in relation to cognitive status in ALS patients.

Task-Similarity is a Crucial Factor for Few-Shot Meta-Learning of Structure-Activity Relationships.

Machine learning models support computer-aided molecular design and compound optimization. However, the initial phases of drug discovery often face a scarcity of training data for these models. Meta-learning has emerged as a potentially promising strategy, harnessing the wealth of structure-activity data available for known targets to facilitate efficient few-shot model training for the specific target of interest. In this study, we assessed the effectiveness of two different meta-learning methods, namely model-agnostic meta-learning (MAML) and adaptive deep kernel fitting (ADKF), specifically in the regression setting. We investigated how factors such as dataset size and the similarity of training tasks impact predictability. The results indicate that ADKF significantly outperformed both MAML and a single-task baseline model on the inhibition data. However, the performance of ADKF varied across different test tasks. Our findings suggest that considerable enhancements in performance can be anticipated primarily when the task of interest is similar to the tasks incorporated in the meta-learning process.

Implementation of a LC-MS based multi-attribute method (MAM) and intact multi-attribute method (iMAM) workflow for the characterisation of a GLP-Fc fusion protein

Over the past few years, the implementation of mass spectrometry (MS) in QC laboratories has become a more common occurrence. The multi-attribute method (MAM), and emerging intact multi-attribute method (iMAM), are powerful analytical tools utilising liquid chromatography−mass spectrometry (LC-MS) methods that enable the monitoring of critical quality attributes (CQAs) in biotherapeutic proteins in compliant settings. Both MAM and iMAM are intended to replace or supplement several conventional assays with a single LC-MS method utilising MS data in combination with robust, semi-automated data processing workflows. MAM and iMAM workflows can also be implemented into current Good Manufacturing Practices environments due to the availability of CFR 11 compliant chromatography data system software. In this study, MAM and iMAM are employed for the analysis of 4 batches of a glucagon-like peptide-Fc fusion protein. MAM approach involved a first the discovery phase for the identification of CQAs and second, the target monitoring phase of the selected CQAs in other samples. New peak detection was performed on the data set to determine the appearance, absence or change of any peak. For native iMAM workflow both size exclusion and strong cation exchange chromatography were optimized for the identification and monitoring of CQAs at the intact level.

Development of a Novel Non-invasive Metabolomics Assay to Predict Implantation Potential of Human Embryos.

Can a set of metabolites present in embryo culture media correlate with embryo implantation? Case-control study in two phases: discovery phase (101 samples) and validation phase (169 samples), collected between 2018 and 2022, with a total of 218 participants. Culture media samples with known implantation outcomes were collected after blastocyst embryo transfer (including both PGT and non-PGT cycles) and were analyzed using chromatography followed by mass spectrometry. The spectra were processed and analyzed using statistical and machine learning techniques to identify biomarkers associated with embryo implantation, and to develop a predictive model. In the discovery phase, 148 embryo implantation biomarkers were identified using high resolution equipment, and 47 of them were characterized. Our results indicate a significant enrichment of tryptophan metabolism, arginine and proline metabolism, and lysine degradation biochemical pathways. After transferring the method to a lower resolution equipment, a model able to assign a Metabolite Pregnancy Index (MPI) to each embryo culture media was developed, taking the concentration of 36 biomarkers as input. Applying this model to 20% of the validation samples (N=34) used as the test set, an accuracy of 85.29% was achieved, with a PPV (Positive Predictive Value) of 88% and a NPV (Negative Predictive Value) of 77.78%. Additionally, informative results were obtained for all the analyzed samples. Metabolite concentration in the media after in vitro culture shows correlation with embryo implantation potential. Furthermore, the mathematical combination of biomarker concentrations using Artificial Intelligence techniques can be used to predict embryo implantation outcome with an accuracy of around 85%.

Scoping review of the use of mesenchymal stem and stromal cell products in cats, Part 1: current logistics and safety.

Despite a pressing need for new therapies to address unmet veterinary medical need, no approved stem cell products are available for use in cats in the US. To evaluate the current state of mesenchymal stem or stromal cell (MSC) research in cats, a scoping review of published literature was performed, which identified 108 publications related to feline MSCs. Twenty-six of the articles described administration of MSC products to a total of 215 cats. Twelve of the studies included a control group. These experimental and clinical trials used 7 cell sources, 9 administration routes, 12 delivery vehicles, and a 300-fold range in dosages for initial studies in healthy cats and cats with 12 naturally occurring and induced diseases. The majority of studies administered 2 doses of allogeneic, adipose-derived MSC IV and monitored a median of 6.5 treated cats for a median of 90 days. The majority (150/215 [69.8%]) of cats had no reported adverse events associated with treatment. Although an increase in feline MSC publications in the past 10 years indicates progress, the wide variety and small number of studies using MSCs and MSC products in cats demonstrates that current evaluations are mostly still in the discovery phase, and several issues remain related to larger scale trials using MSC products in cats. The current available publications provide information to direct further clinical study development and informed owner consent for study enrollment.

A case study of a bispecific antibody manufacturability assessment and optimization during discovery stage and its implications.

The manufacturability assessment and optimization of bispecific antibodies (bsAbs) during the discovery stage are crucial for the success of the drug development process, impacting the speed and cost of advancing such therapeutics to the Investigational New Drug (IND) stage and ultimately to the market. The complexity of bsAbs creates challenges in employing effective evaluation methods to detect developability risks in early discovery stage, and poses difficulties in identifying the root causes and implementing subsequent engineering solutions. This study presents a case of engineering a bsAb that displayed a normal solution appearance during the discovery phase but underwent significant precipitation when subjected to agitation stress during 15L Chemistry, Manufacturing, and Control (CMC) production Leveraging analytical tools, structural analysis, in silico prediction, and wet-lab validations, the key molecular origins responsible for the observed precipitation were identified and addressed. Sequence engineering to reduce protein surface hydrophobicity and enhance conformational stability proved effective in resolving agitation-induced aggregation. The refined bsAb sequences enabled successful mass production in CMC department. The findings of this case study contribute to the understanding of the fundamental mechanism of agitation-induced aggregation and offer a potential protein engineering procedure for addressing similar issues in bsAb. Furthermore, this case study emphasizes the significance of a close partnership between Discovery and CMC teams. Integrating CMC's rigorous evaluation methods with Discovery's engineering capability can facilitate a streamlined development process for bsAb molecules.

Plasma proteome association with coronary heart disease and carotid intima media thickness: results from the KORA F4 study

Background and aimsAtherosclerosis is the main cause of stroke and coronary heart disease (CHD), both leading mortality causes worldwide. Proteomics, as a high-throughput method, could provide helpful insights into the pathological mechanisms underlying atherosclerosis. In this study, we characterized the associations of plasma protein levels with CHD and with carotid intima-media thickness (CIMT), as a surrogate measure of atherosclerosis.MethodsThe discovery phase included 1000 participants from the KORA F4 study, whose plasma protein levels were quantified using the aptamer-based SOMAscan proteomics platform. We evaluated the associations of plasma protein levels with CHD using logistic regression, and with CIMT using linear regression. For both outcomes we applied two models: an age-sex adjusted model, and a model additionally adjusted for body mass index, smoking status, physical activity, diabetes status, hypertension status, low density lipoprotein, high density lipoprotein, and triglyceride levels (fully-adjusted model). The replication phase included a matched case-control sample from the independent KORA F3 study, using ELISA-based measurements of galectin-4. Pathway analysis was performed with nominally associated proteins (p-value < 0.05) from the fully-adjusted model.ResultsIn the KORA F4 sample, after Bonferroni correction, we found CHD to be associated with five proteins using the age-sex adjusted model: galectin-4 (LGALS4), renin (REN), cathepsin H (CTSH), and coagulation factors X and Xa (F10). The fully-adjusted model yielded only the positive association of galectin-4 (OR = 1.58, 95% CI = 1.30–1.93), which was successfully replicated in the KORA F3 sample (OR = 1.40, 95% CI = 1.09–1.88). For CIMT, we found four proteins to be associated using the age-sex adjusted model namely: cytoplasmic protein NCK1 (NCK1), insulin-like growth factor-binding protein 2 (IGFBP2), growth hormone receptor (GHR), and GDNF family receptor alpha-1 (GFRA1). After assessing the fully-adjusted model, only NCK1 remained significant (β = 0.017, p-value = 1.39e-06). Upstream regulators of galectin-4 and NCK1 identified from pathway analysis were predicted to be involved in inflammation pathways.ConclusionsOur proteome-wide association study identified galectin-4 to be associated with CHD and NCK1 to be associated with CIMT. Inflammatory pathways underlying the identified associations highlight the importance of inflammation in the development and progression of CHD.

Integrating classic AI and agriculture: A novel model for predicting insecticide-likeness to enhance efficiency in insecticide development

The integration of artificial intelligence (AI) into smart agriculture boosts production and management efficiency, facilitating sustainable agricultural development. In intensive agricultural management, adopting eco-friendly and effective pesticides is crucial to promote green agricultural practices. However, exploring new insecticides species is a difficult and time-consuming task that involves significant risks. Enhancing compound druggability in the lead discovery phase could considerably shorten the discovery cycle, accelerating insecticides research and development. The Insecticide Activity Prediction (IAPred) model, a novel classic artificial intelligence-based method for evaluating the potential insecticidal activity of unknown functional compounds, is introduced in this study. The IAPred model utilized 27 insecticide-likeness features from PaDEL descriptors and employed an ensemble of Support Vector Machine (SVM) and Random Forest (RF) algorithms using the hard-vote mechanism, achieving an accuracy rate of 86 %. Notably, the IAPred model outperforms current models by accurately predicting the efficacy of novel insecticides such as nicofluprole, overcoming the limitations inherent in existing insecticide structures. Our research presents a practical approach for discovering and optimizing novel insecticide lead compounds quickly and efficiently.

Integrative analysis of genetics, epigenetics and RNA expression data reveal three susceptibility loci for smoking behavior in Chinese Han population.

Despite numerous studies demonstrate that genetics and epigenetics factors play important roles on smoking behavior, our understanding of their functional relevance and coordinated regulation remains largely unknown. Here we present a multiomics study on smoking behavior for Chinese smoker population with the goal of not only identifying smoking-associated functional variants but also deciphering the pathogenesis and mechanism underlying smoking behavior in this under-studied ethnic population. After whole-genome sequencing analysis of 1329 Chinese Han male samples in discovery phase and OpenArray analysis of 3744 samples in replication phase, we discovered that three novel variants located near FOXP1 (rs7635815), and between DGCR6 and PRODH (rs796774020), and in ARVCF (rs148582811) were significantly associated with smoking behavior. Subsequently cis-mQTL and cis-eQTL analysis indicated that these variants correlated significantly with the differential methylation regions (DMRs) or differential expressed genes (DEGs) located in the regions where these variants present. Finally, our in silico multiomics analysis revealed several hub genes, like DRD2, PTPRD, FOXP1, COMT, CTNNAP2, to be synergistic regulated each other in the etiology of smoking.

Clinical Biomarkers of Tumour Radiosensitivity and Predicting Benefit from Radiotherapy: A Systematic Review.

Modern advanced radiotherapy techniques have improved the precision and accuracy of radiotherapy delivery, with resulting plans being highly personalised based on individual anatomy. Adaptation for individual tumour biology remains elusive. There is an unmet need for biomarkers of intrinsic radiosensitivity that can predict tumour response to radiation to facilitate individualised decision-making, dosing and treatment planning. Over the last few decades, the use of high throughput molecular biology technologies has led to an explosion of newly discovered cancer biomarkers. Gene expression signatures are now used routinely in clinic to aid decision-making regarding adjuvant systemic therapy. They have great potential as radiotherapy biomarkers. A previous systematic review published in 2015 reported only five studies of signatures evaluated for their ability to predict radiotherapy benefits in clinical cohorts. This updated systematic review encompasses the expanded number of studies reported in the last decade. An additional 27 studies were identified. In total, 22 distinct signatures were recognised (5 pre-2015, 17 post-2015). Seventeen signatures were 'radiosensitivity' signatures and five were breast cancer prognostic signatures aiming to identify patients at an increased risk of local recurrence and therefore were more likely to benefit from adjuvant radiation. Most signatures (15/22) had not progressed beyond the discovery phase of development, with no suitable validated clinical-grade assay for application. Very few signatures (4/17 'radiosensitivity' signatures) had undergone any laboratory-based biological validation of their ability to predict tumour radiosensitivity. No signatures have been assessed prospectively in a phase III biomarker-led trial to date and none are recommended for routine use in clinical guidelines. A phase III prospective evaluation is ongoing for two breast cancer prognostic signatures. The most promising radiosensitivity signature remains the radiosensitivity index (RSI), which is used to calculate a genomic adjusted radiation dose (GARD). There is an ongoing phase II prospective biomarker-led study of RSI/GARD in triple negative breast cancer. The results of these trials are eagerly anticipated over the coming years. Future work in this area should focus on (1) robust biological validation; (2) building biobanks alongside large radiotherapy randomised controlled trials with dose variance (to demonstrate an interaction between radiosensitivity signature and dose); (3) a validation of clinical-grade cost-effective assays that are deliverable within current healthcare infrastructure; and (4) an integration with biomarkers of other determinants of radiation response.

Effects of gut microbiome on type 1 diabetes susceptibility and complications: A large-scale bidirectional Mendelian randomization and external validation study.

To assess and verify the effect of the gut microbiome on the susceptibility and complications of type 1 diabetes (T1D). To achieve this aim, a two-sample and reverse Mendelian randomization (MR) analysis was conducted. In addition, an external validation study was performed using individual microbiome data of patients with T1D from the gutMEGA datasets and the National Clinical Research Center for Metabolic Diseases. The circulating metabolites facilitated two-sample MR analysis, mediation and multivariable MR analysis to evaluate the direct relationship between the gut microbiome and T1D complications. The MR analysis results from the discovery and validation phases confirmed that Veillonellaceae can potentially reduce the susceptibility of T1D. In the gutMEGA dataset, the average relative abundance of Veillonellaceae in patients with T1D was 0.66%, compared with 1.09% in the controls. Furthermore, the external validation, which included 60 patients with T1D and 30 matched healthy controls, found that the median relative abundance of Veillonellaceae was also lower than controls at 1.10% (95% CI 0.50%-1.80%). Specifically, the Eubacterium coprostanoligenes group, known for its ability to regulate cholesterol, was significantly associated with a lower risk of developing renal, neurological and ophthalmic complications in T1D. Moreover, high cholesterol in small high-density lipoprotein and cholesteryl esters in high-density lipoprotein were associated with a reduced risk of T1D renal and ophthalmic complications. The mediation and multivariable MR analysis combining cholesterol indicated that the E. coprostanoligenes group is the most dominant factor influencing T1D complications. Our findings supported the potential causal effect of gut microbiota on the susceptibility and complications of T1D.

GWAS of Folate Metabolism With Gene-environment Interaction Analysis Revealed the Possible Role of Lifestyles in the Control of Blood Folate Metabolites in Japanese: The J-MICC Study.

The present genome-wide association study (GWAS) aimed to reveal the genetic loci associated with folate metabolites, as well as to detect related gene-environment interactions in Japanese. We conducted the GWAS of plasma homocysteine (Hcy), folic acid (FA), and vitamin B12 (VB12) levels in the Japan Multi-Institutional Collaborative Cohort (J-MICC) Study participants who joined from 2005 to 2012, and also estimated gene-environment interactions. In the replication phase, we used data from the Yakumo Study conducted in 2009. In the discovery phase, data of 2,263 participants from four independent study sites of the J-MICC Study were analyzed. In the replication phase, data of 573 participants from the Yakumo Study were analyzed. For Hcy, MTHFR locus on chr 1, NOX4 on chr 11, CHMP1A on chr 16, and DPEP1 on chr 16 reached genome-wide significance (P < 5 × 10-8). MTHFR also associated with FA, and FUT2 on chr 19 associated with VB12. We investigated gene-environment interactions in both studies and found significant interactions between MTHFR C677T and ever drinking, current drinking, and physical activity >33% on Hcy (β = 0.039, 0.038 and -0.054, P = 0.018, 0.021 and <0.001, respectively) and the interaction of MTHFR C677T with ever drinking on FA (β = 0.033, P = 0.048). The present GWAS revealed the folate metabolism-associated genetic loci and gene-environment interactions with drinking and physical activity in Japanese, suggesting the possibility of future personalized cardiovascular disease prevention.

Identification and validation of the surface proteins FIBG, PDGF-β, and TGF-β on serum extracellular vesicles for non-invasive detection of colorectal cancer: experimental study.

The absence of non-invasive biomarkers for the early diagnosis of colorectal cancer (CRC) has contributed to poor prognosis. Extracellular vesicles (EVs) have emerged as promising candidates for cancer monitoring using liquid biopsy. However, the complexity of EVs isolation procedures and the absence of clear targets for detecting serum-derived EVs have hindered the clinical application of EVs in early CRC diagnosis. In the discovery phase, we conducted a comprehensive 4D-DIA proteomic analysis of serum-derived EVs samples from 37 individuals, performing an initial screening of EVs surface proteins. In the technical validation phase, we developed an extraction-free CRC-EVArray microarray to assess the expression of these potential EVs surface proteins in a multi-centre study comprising 404 individuals. In the application phase, the authors evaluated the diagnostic efficacy of the CRC-EVArray model based on machine-learning algorithms. Through 4D-DIA proteomic analysis, the authors identified seven potential EVs surface proteins showing significantly differential expression in CRC patients compared to healthy controls. Utilizing our developed high-throughput CRC-EVArray microarray, we further confirmed the differential expression of three EVs surface proteins, FIBG, PDGF-β and TGF-β, in a large sample population. Moreover, we established an optimal CRC-EVArray model using the NNET algorithm, demonstrating superior diagnostic efficacy with an area under the curve (AUC) of 0.882 in the train set and 0.937 in the test set. Additionally, we predicted the functions and potential origins of these EVs-derived proteins through a series of multi-omics approaches. Our systematic exploration of surface protein expression profiles on serum-derived EVs has identified FIBG, PDGF-β, and TGF-β as novel diagnostic biomarkers for CRC. The development of CRC-EVArray diagnostic model based on these findings provided an effective tool for the large-scale CRC screening, thus facilitating its translation into clinical practice.

Abstract PO2-16-02: RBsig gene-expression signature in patients with endocrine resistant metastatic breast cancer treated with palbociclib and fulvestrant in the PYTHIA trial

Abstract Background: We have previously identified RBsig, a gene-expression signature of resistance to CDK4/6 inhibitors composed of E2F1/E2F2 dependent genes, which is associated with genetic loss of RB1. Although RBsig has been previously tested in vitro and in clinical trials with CDK4/6 inhibitors, further clinical validation is needed. Additionally, data on the correlation of RBsig with other prognostic/predictive biomarkers for CDK4/6 inhibitors such as serum Thymidine Kinase activity (sTKa), is lacking. Here, we aimed to investigate RBsig in patients (pts) treated with palbociclib (P) + fulvestrant (F) within the PYTHIA trial (IBCSG 53- 14/BIG 14-04; NCT02536742), a downstream trial of the AURORA molecular program (BIG 14-01; NCT02102165). Methods: PYTHIA is a biomarker discovery phase II trial which enrolled 122 pts with endocrine-resistant ER+ and HER2 - metastatic breast cancer (MBC) who received P+F at standard schedule and dose. We analyzed a subset of 38 pts enrolled in PYTHIA and successfully included in AURORA who had available RNAseq from tumor tissue samples (24 primary, 24 metastatic, 10 both). Ki67 by IHC was also available. The PYTHIA biomarker assessment included sTKa at baseline and during treatment (D15 and D28) measured using DiviTumÒ TKa (Biovica International). Evaluation of the prognostic role of RBsig was a pre-specified primary objective of PYTHIA. RBsig is composed of 87 genes and was calculated as previously described. Wilcoxon rank sum test was used to compare continuous RBsig values between groups defined by sTKa. Median progression free survival (mPFS) was estimated using Kaplan-Meier method for groups defined by dichotomizing RBsig at zero. Results: Median RBsig was higher in metastatic than in primary samples; in paired samples, RBsig increased in metastatic for 7/10 pts vs primary. When evaluated as continuous variables, RBsig correlated with tissue Ki67 both in primary (Spearman R 0.8) and in metastatic (R 0.68) samples. Additionally, RBsig was higher in Basal and Luminal B intrinsic subtypes in metastatic samples. RBsig was significantly higher in pts with high baseline sTKa (median cut-off) (Wilcoxon test, p=0.04 and p=0.03 in primary and metastatic samples respectively), with a similar direction, albeit not statistically significant, observed in the group of pts with no sTKa clearance at D15 (below limit of detection cut-off). When evaluated in the combined cohort, where RBSig values were taken from metastatic samples when available and from primary samples otherwise, higher RBsig values were associated with poorer PFS as compared to lower RBsig values [mPFS 5.5 months (95% CI 3.7, 14) vs 13 months (95% CI 8.6, 28); Log-rank p-value=0.004]. Conclusions: Higher RBsig was associated with higher tumor proliferation (measured by both tissue ki67 and sTKa), luminal B status, and shorter PFS on treatment with P+F. This analysis reinforces prior data on RBsig prognostic significance in pts treated with CDK4/6 inhibitors. Further investigation on the added value of RBsig as a novel biomarker for patients’ stratification, including in the early setting, is warranted. Citation Format: Matteo Benelli, Svitlana Tyekucheva, Thayane Crestani, Michail Ignatiadis, Patrick Neven, Marco Colleoni, Stéphanie Henry, Konstantinos Papadimitriou, Antonio Bernardo, Elena Seles, François Duhoux, Iain Macpherson, Alastair Thomson, David Mark Davies, Ilenia Migliaccio, Gabriele Zoppoli, Roswitha Kammler, Heidi De Swert, Barbara Ruepp, Philippe Aftimos, Jose Seoane, Dario Romagnoli, David Venet, Theodora Goulioti, David Cameron, Sherene Loi, Martine Piccart, Meredith Regan, Luca Malorni. RBsig gene-expression signature in patients with endocrine resistant metastatic breast cancer treated with palbociclib and fulvestrant in the PYTHIA trial [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-16-02.

Proteomic Signatures of Coronary Artery Disease and Hypercholesterolemia

Background: Coronary artery disease (CAD) is the most common type of cardiovascular disease (CVD), it causes narrowing of the blood vessel lumen due to atherosclerotic plaque formation. Atherosclerosis, is strongly influenced by hypercholesterolemia (HC). Proteomics offers advantages over traditional studies that focus on individual biomarkers. This method provides a holistic view by examining the entire proteomic spectrum in a biological sample, making it an indispensable asset for spotting potential novel protein biomarkers directly linked to atherosclerosis occurrence, progression, and complications such as plaque instability or rupture. Methods: Slow Off-rate Modified Aptamer (SOMAmer)-based protein array was used to quantify proteins. Our cross-sectional study involved healthy controls (n=45), and patients diagnosed with HC (n=51) or CAD (n=32). Proteome data was analyzed using multiple statistical methods. Results: The orthogonal partial least square discriminant analysis (OPLS-DA) revealed a clear separation of the 3 study groups based on two principal components, with R2X= 0.267, R2Y= 0.9, and Q2= 0.28, indicating distinct protein alterations between groups. A total of 1,305 proteins were analyzed and 156 of them in CAD vs control, 140 of them in HC vs control, and 326 of the proteins in CAD vs HC comparisons, were significantly differentially expressed with a p-value&lt;0.05. Among those, q-value&lt;0.05 were determined. As a result, in CAD patients, 2 proteins exhibited significantly differential expressions compared to healthy controls. In contrast, HC patients had 15 significantly altered proteins compared to controls. Furthermore, a comparison between CAD and HC patients revealed 87 significantly regulated proteins. The receiver operating characteristic (ROC) was performed on proteins with distinct expressions ( q&lt;0.05) in pairwise group comparisons. The AUC was calculated to identify potential biomarker candidates for the disease groups. In the CAD vs. control analysis, both significant proteins (PCSK9, SDF-1) exhibited an AUC of 0.75. In the comparison between HC and control groups, out of 15 significantly altered proteins, 7 had an AUC of 0.75 or higher (LRP1B, Apo E, QORL1, Apo E3, Apo B, Transferrin, MMP-3). Among the 87 proteins significantly differentially expressed in CAD versus HC, 59 had an AUC ≥ 0.75. Sixteen of these proteins had an AUC ≥ 0.80. Conclusion: Distinct protein patterns associated with each condition were identified. CAD patients showed a significant increase in the cholesterol-metabolizing protein PCSK9 and varying levels of the angiogenesis-related protein SDF-1. Several other proteins (LRP1B, VAV, CSRP3, HSP 60, and TAK1-TAB1) also exhibited unique expressions in disease groups, suggesting potential roles in atherosclerosis development. Moreover, a statistically significant correlation between SDF-1, ApoB levels, and LDL levels was found in CAD patients but not in healthy controls. This discovery phase cohort study unveils potential biomarkers for atherosclerosis within the HC context. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Global untargeted and individual targeted plasma metabolomics of breast cancer recurrence modified by hormone receptors.

Breast cancer is a heterogeneous and complex etiological disease. Understanding perturbations of circulating metabolites could improve prognosis. We recruited breast cancer patients from Kaohsiung Medical University (KMU) to perform untargeted (case-control design) and targeted (patient cohort) metabolomics analyses in the discovery and validation phases to evaluate interaction effects between clinical factors and plasma metabolites using multivariable Cox proportional hazards model. In the discovery phase, partial least squares-discriminant analysis (PLS-DA) showed that plasma metabolites were significantly different between recurrent and non-recurrent breast cancer patients. Metabolite set enrichment analysis (MSEA) and metabolomic pathway analysis (MetPA) showed that valine, leucine, and isoleucine degradation was the significant pathway, and volcano plot showed significant ten upregulated and two downregulated metabolites between recurrent and non-recurrent cases. Combined with receiver operating characteristic (ROC) curve and biological significance, creatine, valine, methionine, and mannose were selected for the validation phase. In this patient cohort with 41 new-recurrent vs. 248 non-recurrent breast cancer cases, followed for 720.49 person-years, compared with low level of valine, high valine level was significantly negatively associated with recurrent breast cancer (aHR: 0.36, 95% CI: 0.18-0.72, P = 0.004), especially in ER-negative and PR-negative status. There were interaction effects between valine and ER (Pinteraction = 0.006) as well as PR (Pinteraction = 0.002) on recurrent breast cancer. After Bonferroni correction, stratification effects between valine and hormone receptors were still significant. Our study revealed that plasma metabolites were significantly different between recurrent and non-recurrent patients, proposing therapeutic insights for breast cancer prognosis.

Exhaled Volatile Organic Compounds for Asthma Control Classification in Children with Moderate to Severe Asthma: Results from the SysPharmPediA Study.

Early identification of children with poorly controlled asthma is imperative for optimizing treatment strategies. The analysis of exhaled volatile organic compounds (VOCs) is an emerging approach to identify prognostic and diagnostic biomarkers in pediatric asthma. To assess the accuracy of gas chromatography-mass spectrometry based exhaled metabolite analysis to differentiate between controlled and uncontrolled pediatric asthma. This study encompassed a discovery (SysPharmPediA) and validation phase (U-BIOPRED, PANDA). Firstly, exhaled VOCs that discriminated asthma control levels were identified. Subsequently, outcomes were validated in two independent cohorts. Patients were classified as controlled or uncontrolled, based on asthma control test scores and number of severe attacks in the past year. Additionally, potential of VOCs in predicting two or more future severe asthma attacks in SysPharmPediA was evaluated. Complete data were available for 196 children (SysPharmPediA=100, U-BIOPRED=49, PANDA=47). In SysPharmPediA, after randomly splitting the population into training (n=51) and test sets (n=49), three compounds (acetophenone, ethylbenzene, and styrene) distinguished between uncontrolled and controlled asthmatics. The area under the receiver operating characteristic curve (AUROCC) for training and test sets were respectively: 0.83 (95% CI: 0.65-1.00) and 0.77 (95% CI: 0.58-0.96). Combinations of these VOCs resulted in AUROCCs of 0.74 ±0.06 (UBIOPRED) and 0.68 ±0.05 (PANDA). Attacks prediction tests, resulted in AUROCCs of 0.71 (95% CI 0.51-0.91) and 0.71 (95% CI 0.52-0.90) for training and test sets. Exhaled metabolites analysis might enable asthma control classification in children. This should stimulate further development of exhaled metabolites-based point-of-care tests in asthma.