Throughput Analysis Research Articles

Proteome-scale tissue mapping using mass spectrometry based on label-free and multiplexed workflows.

Multiplexed bimolecular profiling of tissue microenvironment, or spatial omics, can provide deep insight into cellular compositions and interactions in healthy and diseased tissues. Proteome-scale tissue mapping, which aims to unbiasedly visualize all the proteins in a whole tissue section or region of interest, has attracted significant interest because it holds great potential to directly reveal diagnostic biomarkers and therapeutic targets. While many approaches are available, however, proteome mapping still exhibits significant technical challenges in both protein coverage and analytical throughput. Since many of these existing challenges are associated with mass spectrometry-based protein identification and quantification, we performed a detailed benchmarking study of three protein quantification methods for spatial proteome mapping, including label-free, TMT-MS2, and TMT-MS3. Our study indicates label-free method provided the deepest coverages of ~3500 proteins at a spatial resolution of 50 µm and the highest quantification dynamic range, while TMT-MS2 method holds great benefit in mapping throughput at >125 pixels per day. The evaluation also indicates both label-free and TMT-MS2 provide robust protein quantifications in identifying differentially abundant proteins and spatially co-variable clusters. In the study of pancreatic islet microenvironment, we demonstrated deep proteome mapping not only enables the identification of protein markers specific to different cell types, but more importantly, it also reveals unknown or hidden protein patterns by spatial co-expression analysis.

Weak Value Amplification Based Optical Sensor for High Throughput Real-Time Immunoassay of SARS-CoV-2 Spike Protein.

The demand for accurate and efficient immunoassays calls for the development of precise, high-throughput analysis methods. This paper introduces a novel approach utilizing a weak measurement interface sensor for immunoassays, offering a solution for high throughput analysis. Weak measurement is a precise quantum measurement method that amplifies the weak value of a system in the weak interaction through appropriate pre- and post-selection states. To facilitate the simultaneous analysis of multiple samples, we have developed a chip with six flow channels capable of conducting six immunoassays concurrently. We can perform real-time immunoassay to determine the binding characteristics of spike protein and antibody through real-time analysis of the flow channel images and calculating the relative intensity. The proposed method boasts a simple structure, eliminating the need for intricate nano processes. The spike protein concentration and relative intensity curve were fitted using the Log-Log fitting regression equation, and R2 was 0.91. Utilizing a pre-transformation approach to account for slight variations in detection sensitivity across different flow channels, the present method achieves an impressive limit of detection(LOD) of 0.85 ng/mL for the SARS-CoV-2 the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein, with a system standard deviation of 5.61. Furthermore, this method has been successfully verified for monitoring molecular-specific binding processes and differentiating binding capacities.

Hybrid Tasmanian Devil and Elephant Herding Model-Based Optimal Cluster Head Selection and Routing in MANET

MANETs (Mobile AdHoc Networks) have major problems due to the resource-constrained nature of mobile nodes. MANET topology is highly uncertain due to the mobility of MANET nodes that affect the stability of interconnected links. This scenario results in an increased traffic overhead which affects the routing protocol performance and results in increased energy consumption. A routing scheme designed for MANET should be able to handle energy depletion issues and nodes’ mobility. This paper designs two novel schemes to enhance energy efficiency and network lifetime by performing clustering and routing in MANET. The Hybrid Tasmanian devil and Elephant herding optimization (HTDEHO) is used for clustering the MANET nodes to minimize the overhead, enhance the stability of the network topology, and reduce collision. The HTDEHO selects the optimal cluster head (CH) from a set of nodes based on their fitness value calculated by various parameters such as mobility-based node ranking, residual energy, distance, node degree, and optimal next hop CH selection. Dwarf mongoose optimization algorithm with Fuzzy variable (FDMO) algorithm selects optimal routing cost from CH to the base station based on various parameters such as throughput, delay, and distance. The efficiency of the proposed model is evaluated with different performance metrics such as energy consumption, throughput, end-to-end delay, and packet-dropping analysis. The performance of the proposed HTDEHO-FDMO classifier is compared with different techniques such as RDOAICRP, BFOA, MARP-HO, and FEKHO-QBA. Even after 170 iterations, the HTDEHO model retains a total of 10 alive nodes.

TitrationAnalysis: a tool for high throughput binding kinetics data analysis for multiple label-free platforms

Label-free techniques including Surface Plasmon Resonance (SPR) and Biolayer Interferometry (BLI) are biophysical tools widely used to collect binding kinetics data of bimolecular interactions. To efficiently analyze SPR and BLI binding kinetics data, we have built a new high throughput analysis tool named the TitrationAnalysis. It can be used as a package in the Mathematica scripting environment and ultilize the non-linear curve-fitting module of Mathematica for its core function. This tool can fit the binding time course data and estimate association and dissociation rate constants (ka and kd respectively) for determining apparent dissociation constant (KD ) values. The high throughput fitting process is automatic, requires minimal knowledge on Mathematica scripting and can be applied to data from multiple label-free platforms. We demonstrate that the TitrationAnalysis is optimal to analyze antibody-antigen binding data acquired on Biacore T200 (SPR), Carterra LSA (SPR imaging) and ForteBio Octet Red384 (BLI) platforms. The ka , kd and KD values derived using TitrationAnalysis very closely matched the results from the commercial analysis software provided specifically for these instruments. Additionally, the TitrationAnalysis tool generates user-directed customizable results output that can be readily used in downstream Data Quality Control associated with Good Clinical Laboratory Practice operations. With the versatility in source of data input source and options of analysis result output, the TitrationAnalysis high throughput analysis tool offers investigators a powerful alternative in biomolecular interaction characterization.

Reanalysis of DIA Data Demonstrates the Capabilities of MS/MS-Free Proteomics to Reveal New Biological Insights in Disease-Related Samples.

Data-independent acquisition (DIA) at the shortened data acquisition time is becoming a method of choice for quantitative proteomic applications requiring high throughput analysis of large cohorts of samples. With the advent of the combination of high resolution mass spectrometry with an asymmetric track lossless analyzer, these DIA capabilities were further extended with the recent demonstration of quantitative analyses at the speed of up to hundreds of samples per day. In particular, the proteomic data for the brain samples related to multiple system atrophy disease were acquired using 7 and 28 min chromatography gradients (Guzman et al., Nat. Biotech. 2024). In this work, we applied the recently introduced DirectMS1 method to reanalysis of these data using only MS1 spectra. Both DirectMS1 and DIA results were matched against long gradient DDA analysis from the earlier study of the same sample cohort. While the quantitation efficiency of DirectMS1 was comparable with DIA on the same data sets, we found an additional five proteins of biological significance relevant to the analyzed tissue samples. Among the findings, DirectMS1 was able to detect decreased caspase activity for Vimentin protein in the multiple system atrophy samples missed by the MS/MS-based quantitation methods. Our study suggests that DirectMS1 can be an efficient MS1-only addition to the analysis of DIA data in high-throughput quantitative proteomic studies.

Analysis of diluted honey samples from the province of Lower Silesia (Poland) by inductively coupled plasma mass spectrometry

The direct determination of 18 elements (B, Ba, Ca, Cd, Cs, Cu, Fe, K, Li, Mg, Mn, Na, Ni, Pb, Rb, Sr, Tl, and Zn) in 1% aqueous honey solutions (dilution factor = 100) by ICP MS was presented. The presence of sugar in sample solutions caused a decrease in analytical signals, the appearance of carbon-containing polyatomic ions, and the deposition of carbonaceous products on a sampler cone. On the flip side, these effects did not destabilize the ICP, while the internal standard calibration (9Be, 89Y, and 153Eu as standards) made it possible to correct matrix effects. Additionally, in the case of 11B, 23Na, 43Ca, and 57Fe isotopes, using a collision cell (He, 2 mL min−1) was required. Due to lower sample blanks, the analysis of water-diluted honey samples offered on average 3 times lower detection limits of elements than achieved for microwave-digested samples. The proposed method, characterized by high analytical throughput, was used for the analysis of 120 samples of honey from the apiaries in the Lower Silesia province. The average content of the major elements in the analyzed honey samples was: B 10.4 mg kg−1, Ca 61.0 mg kg−1, Fe 1.0 mg kg−1, K 1049 mg kg−1, Mg 27.8 mg kg−1, Mn 3.4 mg kg−1, Na 9.1 mg kg−1, Rb 1.4 mg kg−1, Zn 2.0 mg kg−1, and the average content of the trace elements: Ba 130 μg kg−1, Cd 3.2 μg kg−1, Cs 4.8 μg kg−1, Cu 315 μg kg−1, Li 3.8 μg kg−1, Ni 90 μg kg−1, Pb 8.0 μg kg−1, Sr 119 μg kg−1, Tl 1.2 μg kg−1. It was found that (1) the concentrations of all tested elements (except B and Tl) were higher in dark honey, (2) the content of Cs and Rb in honey from the mountain areas of the voivodeship was much higher than this in honey from the lowland areas, and (3) the best discriminators in terms of the color and the botanical origin of honey were the concentrations of Cu and Mn, and to a lesser extent also B, Ca, K, Mg, and Na; e.g., honeydew and buckwheat honey contained much higher amounts of Cu and Mn, sunflower honey was characterized by a significantly higher content of Ca, and rape honey showed an increased B content. Given that the proposed sample preparation procedure allows the simultaneous, reliable, and fast determination of major, trace, and ultra-trace elements, it seems to be a convenient method for the profiling of many honey samples in a very short time.

Evaluation of lignin and dip coating for elemental analysis by thin film microextraction followed by laser-induced breakdown spectroscopy

Separation and preconcentration procedures are often necessary to eliminate interferents, enhance sensitivity, and improve detection limits. Conventional solid-phase extraction (batch) finds extensive applications in the determination of various analytes, particularly in low concentrations and complex matrices such as environmental, food, and biological samples. However, drawbacks include high reagent consumption, time-consuming sample processing and low analytical throughput. To address these issues and promote Green Analytical Chemistry (GAC), novel methods have emerged, with solid-phase and liquid-phase microextraction being notable examples. Thin Film Microextraction (TFME) represents an innovative approach involving a solid support coated with a thin layer of adsorbent material, attached to a rod and immersed in the sample solution. Extracted analytes can be quantified either after desorption (elution) or directly on the thin film. This article aims to explore TFME's potential when combined with Laser-Induced Breakdown Spectroscopy (LIBS) for Cd, Cr and Ni determination using lignin as adsorbent material deposited on a solid substrate by dip coating. Under optimized conditions, limits of detection obtained were 1.76 μg kg−1 (Cd), 5.72 μg kg−1 (Cr), and 3.27 μg kg−1 (Ni). The accuracy of the proposed method was evaluated through the analysis of a certified reference material (ERM® CA713), drinking and tap water.

Analytical Modeling and Throughput Computation of Blockchain Sharding

Analytical Modeling and Throughput Computation of Blockchain Sharding

3D imaging of SARS-CoV-2 infected hamster lungs by X-ray phase contrast tomography enables drug testing

X-ray Phase Contrast Tomography (XPCT) based on wavefield propagation has been established as a high resolution three-dimensional (3D) imaging modality, suitable to reconstruct the intricate structure of soft tissues, and the corresponding pathological alterations. However, for biomedical research, more is needed than 3D visualisation and rendering of the cytoarchitecture in a few selected cases. First, the throughput needs to be increased to cover a statistically relevant number of samples. Second, the cytoarchitecture has to be quantified in terms of morphometric parameters, independent of visual impression. Third, dimensionality reduction and classification are required for identification of effects and interpretation of results. To address these challenges, we here design and implement a novel integrated and high throughput XPCT imaging and analysis workflow for 3D histology, pathohistology and drug testing. Our approach uses semi-automated data acquisition, reconstruction and statistical quantification. We demonstrate its capability for the example of lung pathohistology in Covid-19. Using a small animal model, different Covid-19 drug candidates are administered after infection and tested in view of restoration of the physiological cytoarchitecture, specifically the alveolar morphology. To this end, we then use morphometric parameter determination followed by a dimensionality reduction and classification based on optimal transport. This approach allows efficient discrimination between physiological and pathological lung structure, thereby providing quantitative insights into the pathological progression and partial recovery due to drug treatment. Finally, we stress that the XPCT image chain implemented here only used synchrotron radiation for validation, while the data used for analysis was recorded with laboratory μ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mu$$\\end{document}CT radiation, more easily accessible for pre-clinical research.

Amanitin determination in bile samples by UHPLC-MS: LR-MS and HR-MS analytical performance

Consumption of misidentified foraged mushrooms containing bicyclic amanitin octapeptides is a worldwide public health and veterinary problem, being considered one of the deadliest accidental human and canine food ingestion due to acute liver failure (ALF). Reversal of advanced ALF and complete clinical recovery can be achieved following definitive removal of accumulated amatoxin laden bile from the gallbladder. An accurate means of quantifying amanitin content in aspirated bile is, therefore, urgently needed. Building on our prior work validating a method to detect and quantify amanitin in hepatic autopsy tissue, the development of an accurate method of measuring α- and β-amanitin in aspirated gallbladder bile was performed to evaluate the efficiency of this emergency procedure applied as a clinical treatment for intoxicated patients. A solid-phase extraction (SPE) procedure was optimized followed by detection based on ultra-high performance liquid chromatography coupled with mass spectrometry (UHPLC-MS). Low resolution mass spectrometry (LRMS) was compared with high resolution (HRMS) by the validation of UHPLC-MS/MS (triple quadrupole MS) and UHPLC-ToF-MS (time-of-flight MS). Both methods were able to detect amatoxins in bile with limits of detection and quantification ranging from 2.71 to 3.46 µg.kg-1, and 8.36–9.03 µg.kg-1 for α-amanitin and, 0.32–1.69 µg.kg-1 and 0.55–5.62 µg.kg-1 for β-amanitin, respectively. Validation was completed with the evaluation of linearity, specificity, robustness, recovery, and precision following the ICH guidelines and CIR 808/2021. The validated methods were finally applied to bile samples obtained 48–96 hours + post-ingestion from 4 amatoxin poisoning patients who underwent gallbladder drainage procedures in Vietnam, Canada, and California. Gallbladder bile from patients with amatoxin mushroom poisoning contained significant amanitin content, even when aspirated several days post-ingestion, thus confirming the important role of enterohepatic circulation in amatoxin hepatotoxicity. This work represents a high and unique analytical throughput in amanitin poisoning allowing to efficiently respond to this fatal health problem.

Algorithms and throughput analysis of cognitive radio‐based MIMO‐NOMA for the next generation of wireless communications

AbstractIn this paper, multi‐users (MU) communications are considered based on the cognitive ratio (CR) technique. Multiple‐input multiple‐output (MIMO) and non‐orthogonal multiple access (NOMA) are proposed to be merged with CR for the sake of improving the achievable throughput and satisfying user fairness. Moreover, utilizing MIMO‐NOMA enables the CR users to be served in the coverage network without the need for spectral sensing. Three scenarios are assumed in this paper to investigate the performance of each CR secondary user (SU) in the presence of single or multiple primary users (PUs). Pairing algorithms are proposed and applied to obtain optimum throughputs and outage probabilities, in which the algorithms are employed to select a PU with an SU to be coupled in a pair, depending on allocating the required power for each user, taking into account that any PU has the priority to reach its required throughput. Moreover, mathematical expressions have been derived for each case to evaluate the required power allocation factor and the achievable throughput for each user. Furthermore, the proposed algorithms show successful and satisfactory achievable rates and outage probabilities.

Ion Mobility Mass Spectrometry-Based Disaccharide Analysis of Glycosaminoglycans.

Glycosaminoglycans (GAGs) are linear and acidic polysaccharides. They are ubiquitous molecules, which are involved in a wide range of biological processes. Despite being structurally simple at first glance, with a repeating backbone of alternating hexuronic acid and hexosamine dimers, GAGs display a highly complex structure, which predominantly results from their heterogeneous sulfation patterns. The commonly applied method for compositional analysis of all GAGs is "disaccharide analysis." In this process, GAGs are enzymatically depolymerized into disaccharides, derivatized with a fluorescent label, and then analysed through liquid chromatography. The limiting factor in the high throughput analysis of GAG disaccharides is the time-consuming liquid chromatography. To address this limitation, we here utilized trapped ion mobility-mass spectrometry (TIM-MS) for the separation of isomeric GAG disaccharides, which reduces the measurement time from hours to a few minutes. A full set of disaccharides comprises twelve structures, with eight possessing isomers. Most disaccharides cannot be differentiated by TIM-MS in underivatized form. Therefore, we developed chemical modifications to reduce sample complexity and enhance differentiability. Quantification is performed using stable isotope labelled standards, which are easily available due to the nature of the performed modifications.

Spatial Multi-Omics in Alzheimer’s Disease: A Multi-Dimensional Approach to Understanding Pathology and Progression

Alzheimer’s Disease (AD) presents a complex neuropathological landscape characterized by hallmark amyloid plaques and neurofibrillary tangles, leading to progressive cognitive decline. Despite extensive research, the molecular intricacies contributing to AD pathogenesis are inadequately understood. While single-cell omics technology holds great promise for application in AD, particularly in deciphering the understanding of different cell types and analyzing rare cell types and transcriptomic expression changes, it is unable to provide spatial distribution information, which is crucial for understanding the pathological processes of AD. In contrast, spatial multi-omics research emerges as a promising and comprehensive approach to analyzing tissue cells, potentially better suited for addressing these issues in AD. This article focuses on the latest advancements in spatial multi-omics technology and compares various techniques. Additionally, we provide an overview of current spatial omics-based research results in AD. These technologies play a crucial role in facilitating new discoveries and advancing translational AD research in the future. Despite challenges such as balancing resolution, increasing throughput, and data analysis, the application of spatial multi-omics holds immense potential in revolutionizing our understanding of human disease processes and identifying new biomarkers and therapeutic targets, thereby potentially contributing to the advancement of AD research.

High-Throughput Compound Quality Assessment with High-Mass-Resolution Acoustic Ejection Mass Spectrometry: An Automatic Data Processing Toolkit.

Pharmacological screening heavily relies on the reliability of compound libraries. To ensure the accuracy of screening results, fast and reliable quality control (QC) of these libraries is essential. While liquid chromatography (LC) with ultraviolet (UV) or mass spectrometry (MS) detection has been employed for molecule QC on small sample sets, the analytical throughput becomes a bottleneck when dealing with large libraries. Acoustic ejection mass spectrometry (AEMS) is a high-throughput analytical platform that covers a broad range of chemical structural space. In this study, we present the utilization of an AEMS system equipped with a high-resolution MS analyzer for high-throughput compound QC. To facilitate efficient data processing, which is a key challenge for such a high-throughput application, we introduce an automatic data processing toolkit that allows for the high-throughput assessment of the sample standards' quantitative and qualitative characteristics, including purity calculation with the background processing option. Moreover, the toolkit includes a module for quantitatively comparing spectral similarity with the reference library. Integrating the described high-resolution AEMS system with the data processing toolkit effectively eliminates the analytical bottleneck, enabling a rapid and reliable compound quality assessment of large-scale compound libraries.

A Comprehensive Review on Advantages of Ultra Performance Liquid Chromatography (UPLC) for Analytical Method Development, Validation, and Stability Studies

Ultra Performance Liquid Chromatography (UPLC) represents a paradigm shift in analytical separation science, offering enhanced speed, sensitivity, and resolution while retaining the foundational principles of traditional liquid chromatography. This comprehensive review explores the principles, instrumentation, advantages, and applications of UPLC in analytical method development, validation, and stability studies, with a focus on pharmaceutical analysis. UPLC leverages finer particles and intensified flow rates to achieve unprecedented performance gains, facilitating rapid and precise separations without compromising analytical rigor. The discussion delves into the intricate instrumentation involved, including sample injection, column selection, and detector operation, highlighting the critical role of each component in ensuring optimal analytical performance. Advantages of UPLC include drastically reduced run times, streamlined method optimization, and increased sample throughput, while limitations such as higher back pressures and limited column regenerability are also discussed. Furthermore, the review outlines the diverse applications of UPLC across pharmaceutical, environmental, food and beverage, clinical, forensic, bioanalytical, and polymer analysis domains, underscoring its versatility and significance in advancing research, development, and quality control in various industries. In summary, UPLC represents a transformative tool in analytical chemistry, offering unparalleled capabilities for expedited and accurate analysis of complex sample matrices, ultimately driving innovation and scientific progress. Key Words: Ultra Performance Liquid Chromatography (UPLC), analytical method development, validation, stability studies, pharmaceutical analysis, instrumentation, advantages, applications, sample injection, column chemistry, detector technology, back pressures, method optimization, sample throughput, environmental analysis, food and beverage analysis, clinical diagnostics, forensic analysis, bioanalytical research, polymer analysis, analytical chemistry.

Nile red staining for rapid screening of plastic-suspect particles in edible seafood tissues

Concerns regarding microplastic (MP) contamination in aquatic ecosystems and its impact on seafood require a better understanding of human dietary MP exposure including extensive monitoring. While conventional techniques for MP analysis like infrared or Raman microspectroscopy provide detailed particle information, they are limited by low sample throughput, particularly when dealing with high particle numbers in seafood due to matrix-related residues. Consequently, more rapid techniques need to be developed to meet the requirements of large-scale monitoring. This study focused on semi-automated fluorescence imaging analysis after Nile red staining for rapid MP screening in seafood. By implementing RGB-based fluorescence threshold values, the need for high operator expertise to prevent misclassification was addressed. Food-relevant MP was identified with over 95% probability and differentiated from natural polymers with a 1% error rate. Comparison with laser direct infrared imaging (LDIR), a state-of-the-art method for rapid MP analysis, showed similar particle counts, indicating plausible results. However, highly variable recovery rates attributed to inhomogeneous particle spiking experiments highlight the need for future development of certified reference material including sample preparation. The proposed method demonstrated suitability of high throughput analysis for seafood samples, requiring 0.02–0.06 h/cm2 filter surface compared to 4.5–14.7 h/cm with LDIR analysis. Overall, the method holds promise as a screening tool for more accurate yet resource-intensive MP analysis methods such as spectroscopic or thermoanalytical techniques.Graphical

Forecast combination using grey relational analysis and Choquet fuzzy integral for container throughput forecasting

Utilizing the accurate and reliable demand forecasting model for the port logistics industry decision makers, can prevent misguided decisions ensuing from inaccurate forecasting. Current forecasting models of container throughput forecasting focus on the development of single or related hybrid models. However, these studies focus on improving the forecast accuracy, while neglecting the risk of failure. Combination forecasting can improve the forecast accuracy and reduce the risks from model selection failure. The previous research on port container throughput (PCT) combination forecasting focused on using a small number of linear models to generate the combined values, and neglected the nonlinear combined models reflecting the interaction between variables. Given the significance of combination forecasting for the PCT prediction, this study establishes a nonlinear combined model using the grey relational analysis (GRA) and Choquet fuzzy integral. The empirical results indicate that the proposed combined model outperforms both the other considered linear and nonlinear models. The findings assist the port decision-makers in both the public and private sectors for understanding the future PCT, and devising timely response plans aligned with actual circumstances and trends, besides formulating the appropriate development plans and decisions to modify the current situation or sustain competitive advantages.

CAPITAL BUDGETING

The project talks about a capital budgeting and its methods including discounted cash flow, payback, and throughput analyses. The process involves analyzing a project’s cash inflows and outflows to determine whether the expected return meets a set benchmark, we are going to talk about how capital budgeting is also important for small and big companies to evaluate major projects and investments, such as new plants or equipment. In the present scenario the efficient allocation of capital resources is a most important function of project management. This function involves firm’s decision to invest its funds in long-term assets like plant, machinery land, building, equipment etc. These assets are extremely important to the firm because the organizational profits are derived from the use of its capital investment in assets which represent a long-term commitment of funds. The future development of an enterprise depends on the capital investment projects. These projects may be the replacement of existing capital assets which turns out to be less attractive to the firm or expansion of business for implementing new ideas and planning. Thus, long term investment decisions of an enterprise fall within the definition of project budgeting or capital expenditure decisions. These decisions are concerned with the acquisition of assets in which funds will be invested by an enterprise. The assets of business include long term assets and short-term assets. Long term assets will yield a return over a period of time whereas short term assets are those assets which are easily convertible into cash within one accounting period, normally a year. The long-term investment decision is known as project budgeting/capital budgeting and the short-term investment decision are identified as working capital management.

Abstract PO4-01-02: High throughput analysis in HER2 positive locally advanced breast cancer: pCR and mutational status

Abstract Background Pathologic complete response (pCR) after neoadjuvant treatment (NAD) is a strong prognostic biomarker associated with improved survival in breast cancer patients, especially with HER2-positive and Triple negative subtypes. PIK3CA is mutated in up to 20% of HER2 positive breast cancers, contributes to anti-HER-2 resistance and may be predictive of the lack of response to anti HER2 NAD. PIK3CA mutations in breast cancer occur primarily at hotspots E545K at exon 9 and H1047R at exon 20. Next generation sequencing has improved our knowledge regarding the biology of the mechanisms behind treatment resistance. The aim of this study is to evaluate the genomic landscape of tissue samples obtained from locally advanced breast cancer patients treated in an neoadjuvant setting, in order to correlate the genomic analysis to response and clinical outcome. Methods DNA extracted from the archival 84 formalin-fixed paraffin-embedded (FFPE) samples from core needle biopsies was subjected to deep sequencing using the TruSight Oncology (TSO) 500 panel (Illumina, San Diego, USA; 523 genes, size: 1.94 Mb), following the manufacturer’s protocol, which assesses microsatellite instability (MSI) status, tumor mutation burden (TMB), recurrent somatic copy number variations (sCNV), somatic single nucleotide variants (SNVs). Libraries were sequenced on a NovaSeq 6000 instrument (Illumina) to reach a minimum of 500 × read depth. All samples were evaluated for RNA single cell transcriptional profile. 73 of 84 patients were treated with traditional neoadjuvant chemotherapy plus trastuzumab meanwhile 13 of 84 received neoadjuvant chemotherapy plus trastuzumab and pertuzumab. Statistical analysis was performed to detect the genes whose mutation involved in the progression and/or non-response of the disease using the Statistical Package for Social Science (SPSS), release 23.0. Continuous variables were expressed as mean ± SD or median [range], categorical variables were displayed as frequencies and the appropriate parametric (Student‘s t-test) non-parametric test (X2-test or Mann-Whitney test) was used to assess significance of the differences between subgroups (patients with or without pCR). Results In total we analyzed samples of 84 patients. The median age was 48 years. 64 of 84 (76.2%) were triple positive, 66 (78.6%) with G3 and 29 (34.5%) with stage III. The most common frequently mutated genes were PIK3CA (16/84, 19%) and BRCA1 (7/84, 8.3%). The pathogenic variant of BRCA1 (sBRCA1) had a significant association with resistance to treatment: we found a pCR in only 14.3% of sBRCA1 versus 62.3% in sBRCA1 wild type (wt) patients (p=0.014). We also identified 11 somatic mutations in hereditary genes (ATM, BRCA1, BRCA2, PALB2, CHEK2, RAD51C, RAD51D). From a germline standpoint we detected 7 pathogenic variants (two affecting BRCA1, two affecting CHEK2, one affecting PALB2, 7/84, 8.3%), which will be clinically confirmed. Among patients with a mutated PI3KCA, 43.8% obtained a pCR versus 61.8% of patients with PI3KCA wt (p=0.19). This data probably presents an imbalance due to the sample size A PTEN mutation was identified in 2% of pts (2/84) which obtained no benefit in terms of pCR after NAD versus 59.8% in wt patients (p=0.09). Conclusions In this preliminary report, genetic drivers such as BRCA1 status may have a clinical implication for prognosis and treatment response after NAD in locally advanced HER2 positive breast cancer. More samples are needed to evaluate the significance of PI3K and PTEN mutations in this patient setting. We are waiting for the results of the RNA single cell transcriptional profile of these samples. Event free survival data is not yet mature at this moment. We would like to acknowledge the contribution of Multi-specialistic Biobank Research Core Facility G-STeP, Fondazione Policlinico Universitario “A. Gemelli” IRCCS (Biobank-FPG) who provided the bio-resources. Citation Format: Ida Paris, Angelo Minucci, Daniele Generali, simona Nanni, Tatiana D'angelo, Francesco Pavese, Alessandra Fabi, Maria De Bonis, Katia Cannita, Alessia Piermattei, Giordana Tiberi, Paola Fuso, Luisa Carbognin, Silvia Rotondaro, Camilla Nero, Flavia Giacomini, Tina Pasciuto, Maria Marrazzo, Luciano Giaco', Antonino Mulè, Riccardo Masetti, Gianluca Franceschini, Giorgia Garganese, Diana Giannarelli, Giovanni Scambia. High throughput analysis in HER2 positive locally advanced breast cancer: pCR and mutational status [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 PO4-01-02.

Physio-omics? Robotic development, software engineering, and machine learning for high throughput, whole animal physiology assays as a path to disease mechanism and drug discovery

Measurement and analysis of cardio-respiratory function in animal models is essential for our understanding of functional and disease mechanisms that pave the way for human therapeutics and treatments. However, physiological phenotyping in conscious animals is often an onerous process requiring significant observer time, attention, and manual effort while being susceptible to bias and variability that ultimately limits the kinds of questions that can be asked and answered. Once data is collected, comprehensive and accurate analysis is challenging as it often requires either the use of expensive commercial software(s) with limited flexibility to meet the investigator’s needs or lab developed scripts that are often arduous and require specialized skills to utilize, resulting in incremental insights. Here we present an overview of our high throughput physiology assessment and analysis pipeline for interrogating disease mechanisms and enabling drug discovery in congenital cardio-respiratory disorders such as the lethal sudden infant death syndrome (SIDS). We first developed a closed loop robotic platform for automated neonate cardio-respiratory experiments (Looper) that enables automated interventional studies (i.e. hypoxic induction or drug application) based on real-time physiological feedback during the experiment. We show that deployment of multiple Looper platforms in parallel enables an order of magnitude increase in data collection for a single observer over a single workday and is highly scalable to achieve yet greater outputs. When combined with well designed genetically engineered or other disease mouse models, large scale genetic, molecular, and cellular mechanistic insights and drug discovery become feasible through physiological screening paradigms. Next, we developed an open-sourced cardio-respiratory assessment software tool (Breathe Easy) that enables facile deep interrogation of terabytes of data in a high throughput fashion while offering extensive customization capabilities to allow for unique case by case analysis by more skilled users. Breathe Easy output offers near publication ready graphical outputs with significance markings and statistical tables for rapid presentation of results through manuscripts and web-publishing. Lastly, we will present our early work leveraging our robotic and software pipeline to build an analytical machine learning framework that can predict cardio-respiratory failure (death) with high temporal specificity (more than 10 min out) in our SIDS like assay and our efforts to utilize the trained algorithms for 1) real-time prediction, feedback, and intervention application in our SIDS like assays on the Looper platform to screen small molecules for novel therapeutics; 2) elucidating unique physiological mechanisms through comparative training in distinct SIDS models; and 3) determining the potential for these machine learning approaches to be used in human polysomnographic recordings to identify at risk infants. R01HL161142, R01HL130249. 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.