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Mass spectrometry-based targeted proteomics employs heavy isotope-labeled proteins or peptides as standards to improve accuracy and precision. The input sample amount is often determined by the total quantity of endogenous proteins or peptides, as defined by spectrophotometric assays, before the heavy-isotope standards are spiked into the samples. Errors in spectrophotometric measurements, which may be due to low sensitivity or chemical or biological interference, have a direct impact on the quantitative mass spectrometry results. Currently used targeted proteomics workflows cannot identify or correct deviations that arise from differences in the input sample amount. We have developed a workflow, global extraction from parallel reaction monitoring (PRM), to identify and quantify thousands of background peptides that are inherently acquired by PRM experiments. These background peptides were used to identify differences in the input sample amount and to reduce this variance by intensity-based, post-acquisition normalization. This approach was then applied to a xenograft study to improve the quantification of human proteins in the presence of mouse tissue contamination. In addition, these background peptides also provided a direct source of quality control metrics related to sample handling and preparation.

Mass spectrometry-based targeted proteomics allows objective protein quantitation of clinical biomarkers from a single section of formalin-fixed, paraffin-embedded (FFPE) tumor tissue biopsies. We combined high-field asymmetric waveform ion mobility spectrometry (FAIMS) and parallel reaction monitoring (PRM) to increase assay sensitivity. The modular nature of the FAIMS source allowed direct comparison of the performance of FAIMS-PRM to PRM. Limits of quantitation were determined by spiking synthetic peptides into a human spleen matrix. In addition, 20 clinical samples were analyzed using FAIMS-PRM and the quantitation of HER2 was compared with that obtained with the Ventana immunohistochemistry assay. FAIMS-PRM improved the overall signal-to-noise ratio over that from PRM and increased assay sensitivity in FFPE tissue analysis for four (HER2, EGFR, cMET, and KRAS) of five proteins of clinical interest. FAIMS-PRM enabled sensitive quantitation of basal HER2 expression in breast cancer samples classified as HER2 negative by immunohistochemistry. Furthermore, we determined the degree of FAIMS-dependent background reduction and showed that this correlated with an improved lower limit of quantitation with FAIMS. FAIMS-PRM is anticipated to benefit clinical trials in which multiple biomarker questions must be addressed and the availability of tumor biopsy samples is limited.

ObjectiveLupus nephritis (LN) is diagnosed by biopsy, but longitudinal monitoring assessment methods are needed. Here, in this preliminary and hypothesis-generating study, we evaluate the potential for using urine proteomics as a non-invasive method to monitor disease activity and damage. Urinary biomarkers were identified and used to develop two novel algorithms that were used to predict LN activity and chronicity.MethodsBaseline urine samples were collected for four cohorts (healthy donors (HDs, n=18), LN (n=42), SLE (n=17) or non-LN kidney disease biopsy control (n=9)), and over 1 year for patients with LN (n=42). Baseline kidney biopsies were available for the LN (n=46) and biopsy control groups (n=9). High-throughput proteomics platforms were used to identify urinary analytes ≥1.5 SD from HD means, which were subjected to stepwise, univariate and multivariate logistic regression modelling to develop predictive algorithms for National Institutes of Health Activity Index (NIH-AI)/National Institutes of Health Chronicity Index (NIH-CI) scores. Kidney biopsies were analysed for macrophage and neutrophil markers using immunohistochemistry (IHC).ResultsIn total, 112 urine analytes were identified from LN, SLE and biopsy control patients as both quantifiable and overexpressed compared with HDs. Regression analysis identified proteins associated with the NIH-AI (n=30) and NIH-CI (n=26), with four analytes common to both groups, demonstrating a difference in the mechanisms associated with NIH-AI and NIH-CI. Pathway analysis of the NIH-AI and NIH-CI analytes identified granulocyte-associated and macrophage-associated pathways, and the presence of these cells was confirmed by IHC in kidney biopsies. Four markers each for the NIH-AI and NIH-CI were identified and used in the predictive algorithms. The NIH-AI algorithm sensitivity and specificity were both 93% with a false-positive rate (FPR) of 7%. The NIH-CI algorithm sensitivity was 88%, specificity 96% and FPR 4%. The accuracy for both models was 93%.ConclusionsLongitudinal predictions suggested that patients with baseline NIH-AI scores of ≥8 were most sensitive to improvement over 6–12 months. Viable approaches such as this may enable the use of urine samples to monitor LN over time.

TOPIC: Chest Infections TYPE: Original Investigations PURPOSE: Severe COVID-19 infections have been associated with alterations in blood eosinophil counts and elevated blood interleukin (IL)-5 levels [1,2]. The aim of this study was to explore whether eosinophils and IL-5 receptors are observed in post-mortem lung tissue from patients with COVID-19 pneumonia. METHODS: Hematoxylin & eosin (HE584(7821):463–9. 2. Rodriguez L, Pekkarinen PT, Lakshmikanth T, et al. Systems-level immunomonitoring from acute to recovery phase of severe COVID-19. Cell Rep Med. 2020;1(5):100078. DISCLOSURES: Employee relationship with AstraZeneca Please note: 7.5 years Added 04/30/2021 by Jennifer Cann, source=Web Response, value=stock grants No relevant relationships by David Chain, source=Web Response Employee relationship with AstraZeneca Please note: 15/06/2020 Added 04/30/2021 by Ron Chen, source=Web Response, value=Salary No relevant relationships by Karma Dacosta, source=Web Response No relevant relationships by Adrian Freeman, source=Web Response Employee relationship with AstraZeneca Please note: 1996 to date Added 04/29/2021 by Andrew Jones, source=Web Response, value=Salary Stock Owner relationship with AstraZeneca Please note: 1996 to date Added 04/29/2021 by Andrew Jones, source=Web Response, value=Stock Owner Employee relationship with astrazeneca Please note: current Added 04/30/2021 by Rohit Katial, source=Web Response, value=Salary Employee relationship with AstraZeneca Please note: July 2019-present Added 04/29/2021 by Allen McAlexander, source=Web Response, value=Salary Employee relationship with AstraZeneca Please note: Jul 1999 to present Added 04/29/2021 by Christopher McCrae, source=Web Response, value=Salary Employee relationship with AstraZeneca Please note: Jul 1999 to present Added 04/29/2021 by Christopher McCrae, source=Web Response, value=Ownership interest No relevant relationships by Hitesh Sanganee, source=Web Response No relevant relationships by WEIGUANG ZHAO, source=Web Response

Antibody-Drug-Conjugates (ADCs) are biopharmaceutical drugs designed for targeted tumor therapy, meant to improve therapeutic index by restricting drug delivery to tumor cells that express the target antigen. ADCs bind to the target molecule on the cell membrane, which triggers internalization, linker cleavage, and ultimately drug release inside the target cell. Prospective patient selection can be done by quantifying the level of target expression in the tumor using immuno-histochemistry (IHC). However, this process typically involves pathologists and is time consuming, expensive, and prone to human bias. We have developed a supervised deep learning algorithm that segments IHC images of invasive tumor epithelium into individual epithelial cells and their membrane, cytoplasm and nucleus with high accuracy. On unseen test data its performance for epithelial cell detection and segmentation is comparable to the inter-pathologist consensus. With our algorithm, we can describe the target molecule distribution of individual cells in a fully quantitative fashion after using standard IHC methods: we call our approach Quantitative Continuous Score (QCS). We applied QCS to interrogate the mechanism of action of AZD8205, a B7-H4 directed ADC incorporating a novel topoisomerase I linker-warhead. Pharmacodynamic effects were evaluated in vivo, using a human tumor xenograft mouse model and the cell line HT29-huB7-H4 Clone 26, engineered to express human B7-H4. After tumors grew in volume to approximately 250 to 300 mm3, animals were randomized and each mouse received an IV injection of either AZD8205 (1.25, 3.5, or 7 mg/kg) or control articles. Tumors were collected at designated timepoints, fixed in 10% neutral buffered formalin and subsequently embedded into paraffin blocks. IHC and QCS were then used to examine human IgG, γH2AX foci, cleaved caspase-3, and epithelial cell density in tumor samples over time. Using our novel approach we could quantitatively measure the level of AZD8205 bound to tumor cells, with the highest level of ADC on the cell membrane detected at 24-48 hrs. Increased dose levels accelerated the binding kinetics of the drug and led to to a 4- and 3-fold excess of γH2AX and CC-3 respectively, as well as more cells being killed, with up to 2/3 of all epithelial cells dead at the highest dose studied. In summary, we here set the basis for future mechanistic investigation of model systems using computational pathology to improve our understanding of ADC effects. Computational pathology has the potential to determine molecule abundance quantitatively, increase throughput and avoid human bias. Our data implies QCS has the potential to identify patients who may respond to AZD8205, which we will interrogate further and integrate into future clinical studies. Citation Format: Philipp Wortmann, Tze Heng Tan, Susanne Haneder, Andrea Ennio Storti, Ansh Kapil, Jon Chesebrough, Daniel Sutton, Michal Sulikowski, Arthur Lewis, Sofia Koch, Steve Sweet, Zifeng Song, David Chain, Yeoun Jin Kim, Nadia Luheshi, Krista Kinneer, Zachary A. Cooper, Marlon Rebelatto, Günter Schmidt, Hadassah Sade, J. Carl Barrett. Development and implementation of image analysis-based Quantitative Continuous Score (QCS) for B7-H4 IHC to understand AZD8205 pharmacodynamics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 452.