Deep profiling of apoptotic pathways with mass cytometry identifies a synergistic drug combination for killing myeloma cells

Immunometabolic Adaptation of CD19-Targeted CAR T Cells in the Central Nervous System Microenvironment of Patients Promotes Memory Development

Metabolic reprogramming is a hallmark of T-cell activation, and metabolic fitness is fundamental for T-cell-mediated antitumor immunity. Insights into the metabolic plasticity of chimeric antigen receptor (CAR) T cells in patients could help identify approaches to improve their efficacy in treating cancer. Here, we investigated the spatiotemporal immunometabolic adaptation of CD19-targeted CAR T cells using clinical samples from CAR T-cell-treated patients. Context-dependent immunometabolic adaptation of CAR T cells demonstrated the link between their metabolism, activation, differentiation, function, and local microenvironment. Specifically, compared with the peripheral blood, low lipid availability, high IL15, and low TGFβ in the central nervous system microenvironment promoted immunometabolic adaptation of CAR T cells, including upregulation of a lipolytic signature and memory properties. Pharmacologic inhibition of lipolysis in cerebrospinal fluid led to decreased CAR T-cell survival. Furthermore, manufacturing CAR T cells in cerebrospinal fluid enhanced their metabolic fitness and antileukemic activity. Overall, this study elucidates spatiotemporal immunometabolic rewiring of CAR T cells in patients and demonstrates that these adaptations can be exploited to maximize the therapeutic efficacy of CAR T cells.

SIGNIFICANCE

The spatiotemporal immunometabolic landscape of CD19-targeted CAR T cells from patients reveals metabolic adaptations in specific microenvironments that can be exploited to maximize the therapeutic efficacy of CAR T cells.

Identification of aberrant luminal progenitors and mTORC1 as a potential breast cancer prevention target in BRCA2 mutation carriers

Inheritance of a BRCA2 pathogenic variant conveys a substantial life-time risk of breast cancer. Identification of the cell(s)-of-origin of BRCA2-mutant breast cancer and targetable perturbations that contribute to transformation remains an unmet need for these individuals who frequently undergo prophylactic mastectomy. Using preneoplastic specimens from age-matched, premenopausal females, here we show broad dysregulation across the luminal compartment in BRCA2mut/+ tissue, including expansion of aberrant ERBB3lo luminal progenitor and mature cells, and the presence of atypical oestrogen receptor (ER)-positive lesions. Transcriptional profiling and functional assays revealed perturbed proteostasis and translation in ERBB3lo progenitors in BRCA2mut/+ breast tissue, independent of ageing. Similar molecular perturbations marked tumours bearing BRCA2-truncating mutations. ERBB3lo progenitors could generate both ER+ and ER- cells, potentially serving as cells-of-origin for ER-positive or triple-negative cancers. Short-term treatment with an mTORC1 inhibitor substantially curtailed tumorigenesis in a preclinical model of BRCA2-deficient breast cancer, thus uncovering a potential prevention strategy for BRCA2 mutation carriers.

Single-cell profiling in multiple myeloma: insights, problems, and promises

In a short time, single-cell platforms have become the norm in many fields of research, including multiple myeloma (MM). In fact, the large amount of cellular heterogeneity in MM makes single-cell platforms particularly attractive because bulk assessments can miss valuable information about cellular subpopulations and cell-to-cell interactions. The decreasing cost and increasing accessibility of single-cell platform, combined with breakthroughs in obtaining multiomics data for the same cell and innovative computational programs for analyzing data, have allowed single-cell studies to make important insights into MM pathogenesis; yet, there is still much to be done. In this review, we will first focus on the types of single-cell profiling and the considerations for designing a single-cell profiling experiment. Then, we will discuss what have learned from single-cell profiling about myeloma clonal evolution, transcriptional reprogramming, and drug resistance, and about the MM microenvironment during precursor and advanced disease.

To metabolomics and beyond: a technological portfolio to investigate cancer metabolism

Tumour cells have exquisite flexibility in reprogramming their metabolism in order to support tumour initiation, progression, metastasis and resistance to therapies. These reprogrammed activities include a complete rewiring of the bioenergetic, biosynthetic and redox status to sustain the increased energetic demand of the cells. Over the last decades, the cancer metabolism field has seen an explosion of new biochemical technologies giving more tools than ever before to navigate this complexity. Within a cell or a tissue, the metabolites constitute the direct signature of the molecular phenotype and thus their profiling has concrete clinical applications in oncology. Metabolomics and fluxomics, are key technological approaches that mainly revolutionized the field enabling researchers to have both a qualitative and mechanistic model of the biochemical activities in cancer. Furthermore, the upgrade from bulk to single-cell analysis technologies provided unprecedented opportunity to investigate cancer biology at cellular resolution allowing an in depth quantitative analysis of complex and heterogenous diseases. More recently, the advent of functional genomic screening allowed the identification of molecular pathways, cellular processes, biomarkers and novel therapeutic targets that in concert with other technologies allow patient stratification and identification of new treatment regimens. This review is intended to be a guide for researchers to cancer metabolism, highlighting current and emerging technologies, emphasizing advantages, disadvantages and applications with the potential of leading the development of innovative anti-cancer therapies.

Advances in Mass Spectrometry-Based Single Cell Analysis

Technological developments and improvements in single-cell isolation and analytical platforms allow for advanced molecular profiling at the single-cell level, which reveals cell-to-cell variation within the admixture cells in complex biological or clinical systems. This helps to understand the cellular heterogeneity of normal or diseased tissues and organs. However, most studies focused on the analysis of nucleic acids (e.g., DNA and RNA) and mass spectrometry (MS)-based analysis for proteins and metabolites of a single cell lagged until recently. Undoubtedly, MS-based single-cell analysis will provide a deeper insight into cellular mechanisms related to health and disease. This review summarizes recent advances in MS-based single-cell analysis methods and their applications in biology and medicine.

Heterogeneity of B cell lymphopoiesis in patients with premalignant and active myeloma

To better characterize the heterogeneity of multiple myeloma (MM), we profiled plasma cells (PCs) and their B cell lymphopoiesis in the BM samples from patients with monoclonal gammopathy of undetermined significance, smoldering MM, and active MM by mass cytometry (CyTOF) analysis. Characterization of intra- and interneoplastic heterogeneity of malignant plasmablasts and PCs revealed overexpression of the MM SET domain (MMSET), Notch-1, and CD47. Variations in upregulation of B cell signaling regulators (IFN regulatory factor 4 [IRF-4], CXCR4, B cell lymphoma 6 [Bcl-6], c-Myc, myeloid differentiation primary response protein 88 [MYD88], and spliced X box-binding protein 1 [sXBP-1]) and aberrant markers (CD319, CD269, CD200, CD117, CD56, and CD28) were associated with different clinical outcomes in clonal PC subsets. In addition, prognosis was related to heterogeneity in subclonal expression of stemness markers, including neuroepithelial stem cell protein (Nestin), SRY-box transcription factor 2 (Sox2), Krüppel-like factor 4 (KLF-4), and Nanog. Furthermore, we have defined significantly elevated levels of MMSET, MYD88, c-Myc, CD243, Notch-1, and CD47 from hematopoietic stem cells to PCs in myeloma B cell lymphopoiesis, noted even in premalignant conditions, with variably modulated expression of B cell development regulators, including IRF-4, Bcl-2, Bcl-6, and sXBP-1; aberrant PC markers (such as CD52, CD44, CD200, CD81, CD269, CD117, and CXCR4); and stemness-controlling regulators, including Nanog, KLF-4, octamer-binding transcription factor 3/4 (Oct3/4), Sox2, and retinoic acid receptor α2 (RARα2). This study provides the rationale for precise molecular profiling of patients with MM by CyTOF technology to define disease heterogeneity and prognosis.

Exploiting endogenous and therapy-induced apoptotic vulnerabilities in immunoglobulin light chain amyloidosis with BH3 mimetics

Immunoglobulin light chain (AL) amyloidosis is an incurable hematologic disorder typically characterized by the production of amyloidogenic light chains by clonal plasma cells. These light chains misfold and aggregate in healthy tissues as amyloid fibrils, leading to life-threatening multi-organ dysfunction. Here we show that the clonal plasma cells in AL amyloidosis are highly primed to undergo apoptosis and dependent on pro-survival proteins MCL-1 and BCL-2. Notably, this MCL-1 dependency is indirectly targeted by the proteasome inhibitor bortezomib, currently the standard of care for this disease and the related plasma cell disorder multiple myeloma, due to upregulation of pro-apoptotic Noxa and its inhibitory binding to MCL-1. BCL-2 inhibitors sensitize clonal plasma cells to multiple front-line therapies including bortezomib, dexamethasone and lenalidomide. Strikingly, in mice bearing AL amyloidosis cell line xenografts, single agent treatment with the BCL-2 inhibitor ABT-199 (venetoclax) produces deeper remissions than bortezomib and triples median survival. Mass spectrometry-based proteomic analysis reveals rewiring of signaling pathways regulating apoptosis, proliferation and mitochondrial metabolism between isogenic AL amyloidosis and multiple myeloma cells that divergently alter their sensitivity to therapies. These findings provide a roadmap for the use of BH3 mimetics to exploit endogenous and induced apoptotic vulnerabilities in AL amyloidosis.

Hybrid Fluorescent Mass-Tag Nanotrackers as Universal Reagents for Long-Term Live-Cell Barcoding

Barcoding and pooling cells for processing as a composite sample are critical to minimize technical variability in multiplex technologies. Fluorescent cell barcoding has been established as a standard method for multiplexing in flow cytometry analysis. In parallel, mass-tag barcoding is routinely used to label cells for mass cytometry. Barcode reagents currently used label intracellular proteins in fixed and permeabilized cells and, therefore, are not suitable for studies with live cells in long-term culture prior to analysis. In this study, we report the development of fluorescent palladium-based hybrid-tag nanotrackers to barcode live cells for flow and mass cytometry dual-modal readout. We describe the preparation, physicochemical characterization, efficiency of cell internalization, and durability of these nanotrackers in live cells cultured over time. In addition, we demonstrate their compatibility with standardized cytometry reagents and protocols. Finally, we validated these nanotrackers for drug response assays during a long-term coculture experiment with two barcoded cell lines. This method represents a new and widely applicable advance for fluorescent and mass-tag barcoding that is independent of protein expression levels and can be used to label cells before long-term drug studies.

Routine blood biomarkers for the detection of multiple myeloma using machine learning

INTRODUCTION

Primary laboratory tests performed in the diagnosis of multiple myeloma (MM) include bone marrow examination and free light chain assay; however, these may only be ordered after clinical suspicion of disease. In contrast, routine blood test results are readily available.

METHODS

Machine learning algorithms (ML) combined with routine blood tests were used to detect MM. Feature selection was performed to achieve improved classification performance. The robustness of the classification models was assessed in an internal and external validation data set. To minimize the divergence, the training and validation data sets were combined and used to assess the performance of the ML algorithms.

RESULTS

The AdaBoost-DecisionTable produced the best performance (accuracy =94.75%, sensitivity =87.70%, positive predictive value (PPV) =92.50%, F-measure =90.00%, and areas under the receiver operating characteristic curves (AUC) =97.50%) in the training data set using a 10-fold cross-validation. Performance in the validation data sets was affected by the divergence of the data sets, with accuracy greater than 85% and AUC greater than 90% in the validation data sets. The ML algorithm achieved a high accuracy of 92.61%, high AUC (96.80%), a sensitivity value of 85.20%, a PPV value of 88.50%, and an F-measure of 86.80% in a test set that was randomly selected from the combined data set.

CONCLUSIONS

Combining ML and routine serum biomarkers hold a potential benefit in MM diagnosis.

Machine Learning and Deep Learning Applications in Multiple Myeloma Diagnosis, Prognosis, and Treatment Selection

Simple Summary

Multiple myeloma is a malignant neoplasm of plasma cells with complex pathogenesis. With major progresses in multiple myeloma research, it is essential that we reconsider our methods for diagnosing and monitoring multiple myeloma disease. This fact needs the integration of serology, histology, radiology, and genetic data; therefore, multiple myeloma study has generated massive quantities of granular high-dimensional data exceeding human understanding. With improved computational techniques, artificial intelligence tools for data processing and analysis are becoming more and more relevant. Artificial intelligence represents a wide set of algorithms for which machine learning and deep learning are presently among the most impactful. This review focuses on artificial intelligence applications in multiple myeloma research, first illustrating machine learning and deep learning procedures and workflow, followed by how these algorithms are used for multiple myeloma diagnosis, prognosis, bone lesions identification, and evaluation of response to the treatment.

Abstract

Artificial intelligence has recently modified the panorama of oncology investigation thanks to the use of machine learning algorithms and deep learning strategies. Machine learning is a branch of artificial intelligence that involves algorithms that analyse information, learn from that information, and then employ their discoveries to make abreast choice, while deep learning is a field of machine learning basically represented by algorithms inspired by the organization and function of the brain, named artificial neural networks. In this review, we examine the possibility of the artificial intelligence applications in multiple myeloma evaluation, and we report the most significant experimentations with respect to the machine and deep learning procedures in the relevant field. Multiple myeloma is one of the most common haematological malignancies in the world, and among them, it is one of the most difficult ones to cure due to the high occurrence of relapse and chemoresistance. Machine learning- and deep learning-based studies are expected to be among the future strategies to challenge this negative-prognosis tumour via the detection of new markers for their prompt discovery and therapy selection and by a better evaluation of its relapse and survival.

Application of Machine Learning for Cytometry Data

Modern cytometry technologies present opportunities to profile the immune system at a single-cell resolution with more than 50 protein markers, and have been widely used in both research and clinical settings. The number of publicly available cytometry datasets is growing. However, the analysis of cytometry data remains a bottleneck due to its high dimensionality, large cell numbers, and heterogeneity between datasets. Machine learning techniques are well suited to analyze complex cytometry data and have been used in multiple facets of cytometry data analysis, including dimensionality reduction, cell population identification, and sample classification. Here, we review the existing machine learning applications for analyzing cytometry data and highlight the importance of publicly available cytometry data that enable researchers to develop and validate machine learning methods.

Single-Cell Profiling of the Intrinsic Apoptotic Pathway by Mass Cytometry (CyTOF)

Mass cytometry time-of-flight (CyTOF) is a technology for the study of complex biological processes at the single-cell level. The technology enables measurement of >50 protein moieties on the surface and inside the cell. The power of CyTOF lies in the application of purpose-built panels of antibody probes that resolve features of key biological processes in a cell. Here, we describe this technology's use to profile changes in the intrinsic apoptotic (cell death) protein machinery at a single-cell level. We provide a comprehensive overview of a tailor-made set of cell survival/death antibodies, ideal staining conditions, and high-dimensional data analysis.

Mass Cytometry in Hematologic Malignancies: Research Highlights and Potential Clinical Applications

Recent advances in global gene sequencing technologies and the effect they have had on disease diagnosis, therapy, and research have fueled interest in technologies capable of more broadly profiling not only genes but proteins, metabolites, cells, and almost any other component of biological systems. Mass cytometry is one such technology, which enables simultaneous characterization of over 40 parameters per cell, significantly more than can be achieved by even the most state-of-the-art flow cytometers. This mini-review will focus on how mass cytometry has been utilized to help advance the field of neoplastic hematology. Common themes among published studies include better defining lineage sub-populations, improved characterization of tumor microenvironments, and profiling intracellular signaling across multiple pathways simultaneously in various cell types. Reviewed studies highlight potential applications for disease diagnosis, prognostication, response to therapy, measurable residual disease analysis, and identifying new therapies.

treekoR: identifying cellular-to-phenotype associations by elucidating hierarchical relationships in high-dimensional cytometry data

High-throughput single-cell technologies hold the promise of discovering novel cellular relationships with disease. However, analytical workflows constructed for these technologies to associate cell proportions with disease often employ unsupervised clustering techniques that overlook the valuable hierarchical structures that have been used to define cell types. We present treekoR, a framework that empirically recapitulates these structures, facilitating multiple quantifications and comparisons of cell type proportions. Our results from twelve case studies reinforce the importance of quantifying proportions relative to parent populations in the analyses of cytometry data — as failing to do so can lead to missing important biological insights.

Serine and one-carbon metabolisms bring new therapeutic venues in prostate cancer

Serine and one-carbon unit metabolisms are essential biochemical pathways implicated in fundamental cellular functions such as proliferation, biosynthesis of important anabolic precursors and in general for the availability of methyl groups. These two distinct but interacting pathways are now becoming crucial in cancer, the de novo cytosolic serine pathway and the mitochondrial one-carbon metabolism. Apart from their role in physiological conditions, such as epithelial proliferation, the serine metabolism alterations are associated to several highly neoplastic proliferative pathologies. Accordingly, prostate cancer shows a deep rearrangement of its metabolism, driven by the dependency from the androgenic stimulus. Several new experimental evidence describes the role of a few of the enzymes involved in the serine metabolism in prostate cancer pathogenesis. The aim of this study is to analyze gene and protein expression data publicly available from large cancer specimens dataset, in order to further dissect the potential role of the abovementioned metabolism in the complex reshaping of the anabolic environment in this kind of neoplasm. The data suggest a potential role as biomarkers as well as in cancer therapy for the genes (and enzymes) belonging to the one-carbon metabolism in the context of prostatic cancer.

Machine Learning in Oncology: What Should Clinicians Know?

The volume and complexity of scientific and clinical data in oncology have grown markedly over recent years, including but not limited to the realms of electronic health data, radiographic and histologic data, and genomics. This growth holds promise for a deeper understanding of malignancy and, accordingly, more personalized and effective oncologic care. Such goals require, however, the development of new methods to fully make use of the wealth of available data. Improvements in computer processing power and algorithm development have positioned machine learning, a branch of artificial intelligence, to play a prominent role in oncology research and practice. This review provides an overview of the basics of machine learning and highlights current progress and challenges in applying this technology to cancer diagnosis, prognosis, and treatment recommendations, including a discussion of current takeaways for clinicians.

Making the most of high-dimensional cytometry data

High-dimensional cytometry represents an exciting new era of immunology research, enabling the discovery of new cells and prediction of patient responses to therapy. A plethora of analysis and visualization tools and programs are now available for both new and experienced users; however, the transition from low- to high-dimensional cytometry requires a change in the way users think about experimental design and data analysis. Data from high-dimensional cytometry experiments are often underutilized, because of both the size of the data and the number of possible combinations of markers, as well as to a lack of understanding of the processes required to generate meaningful data. In this article, we explain the concepts behind designing high-dimensional cytometry experiments and provide considerations for new and experienced users to design and carry out high-dimensional experiments to maximize quality data collection.

Direct P70S6K1 inhibition to replace dexamethasone in synergistic combination with MCL-1 inhibition in multiple myeloma

Novel combination therapies have markedly improved the lifespan of patients with multiple myeloma (MM), but drug resistance and disease relapse remain major clinical problems. Dexamethasone and other glucocorticoids are a cornerstone of conventional and new combination therapies for MM, although their use is accompanied by serious side effects. We aimed to uncover drug combinations that act in synergy and, as such, allow reduced dosing while remaining effective. Dexamethasone and the myeloid cell leukemia 1 (MCL-1) inhibitor S63845 (MCL-1i) proved the most potent combination in our lethality screen and induced apoptosis of human myeloma cell lines (HMCLs) that was 50% higher compared with an additive drug effect. Kinome analysis of dexamethasone-treated HMCLs revealed a reduction in serine/threonine peptide phosphorylation, which was predicted to result from reduced Akt activity. Biochemical techniques showed no dexamethasone-induced effects on FOXO protein or GSK3 but did show a 50% reduction in P70S6K phosphorylation, downstream of the Akt-mTORC1 axis. Replacing dexamethasone by the P70S6K1 isoform-specific inhibitor PF-4708671 (S6K1i) revealed similar and statistically significant synergistic apoptosis of HMCLs in combination with MCL-1i. Interestingly, apoptosis induced by the P70S6K1i and MCL-1i combination was more-than-additive in all 9 primary MM samples tested; this effect was observed for 6 of 9 samples with the dexamethasone and MCL-1i combination. Toxicity on stem and progenitor cell subsets remained minimal. Combined, our results show a strong rationale for combination treatments using the P70S6K inhibitor in MM. Direct and specific inhibition of P70S6K may also provide a solution for patients ineligible or insensitive to dexamethasone or other glucocorticoids.

Extended live-cell barcoding approach for multiplexed mass cytometry

Sample barcoding is essential in mass cytometry analysis, since it can eliminate potential procedural variations, enhance throughput, and allow simultaneous sample processing and acquisition. Sample pooling after prior surface staining termed live-cell barcoding is more desirable than intracellular barcoding, where samples are pooled after fixation and permeabilization, since it does not depend on fixation-sensitive antigenic epitopes. In live-cell barcoding, the general approach uses two tags per sample out of a pool of antibodies paired with five palladium (Pd) isotopes in order to preserve appreciable signal-to-noise ratios and achieve higher yields after sample deconvolution. The number of samples that can be pooled in an experiment using live-cell barcoding is limited, due to weak signal intensities associated with Pd isotopes and the relatively low number of available tags. Here, we describe a novel barcoding technique utilizing 10 different tags, seven cadmium (Cd) tags and three Pd tags, with superior signal intensities that do not impinge on lanthanide detection, which enables enhanced pooling of samples with multiple experimental conditions and markedly enhances sample throughput.

Targeted Therapies for Multiple Myeloma

Multiple myeloma continues to be a challenging disorder to treat despite improved therapies and the widespread use of proteasome inhibitors and immunomodulatory drugs. Although patient outcomes have improved, the disease continues to invariably relapse, and in the majority of cases, a cure remains elusive. In the last decade, there has been an explosion of novel drugs targeting cellular proteins essential for malignant plasma cell proliferation and survival. In this review, we focus on novel druggable targets leading to the development of monoclonal antibodies and cellular therapies against surface antigens (CD38, CD47, CD138, BCMA, SLAMF7, GPRC5D, FcRH5), inhibitors of epigenetic regulators such as histone deacetylase (HDAC), and agents targeting anti-apoptotic (BCL-2), ribosomal (eEF1A2) and nuclear export (XPO1) proteins.

Characterizing Highly Cited Papers in Mass Cytometry through H-Classics

Mass cytometry (CyTOF) is a relatively novel technique for the multiparametric analysis of single-cell features with an increasing central role in cell biology, immunology, pharmacology, and biomedicine. This technique mixes the fundamentals of flow cytometry with mass spectrometry and is mainly used for in-depth studies of the immune system and diseases with a significant immune load, such as cancer, autoimmune diseases, and viral diseases like HIV or the recently emerged COVID-19, produced by the SARS-CoV-2 coronavirus. The objective of this study was to provide a useful insight into the evolution of the mass cytometry research field, revealing the knowledge structure (conceptual and social) and authors, countries, sources, documents, and organizations that have made the most significant contribution to its development. We retrieved 937 articles from the Web of Science (2010-2019), analysed 71 Highly Cited Papers (HCP) through the H-Classics methodology and computed the data by using Bibliometrix R package. HCP sources corresponded to high-impact journals, such as Nature Biotechnology and Cell, and its production was concentrated in the US, and specifically Stanford University, affiliation of the most relevant authors in the field. HCPs analysis confirmed great interest in the study of the immune system and complex data processing in the mass cytometry research field.

Ex Vivo Analysis of Primary Tumor Specimens for Evaluation of Cancer Therapeutics

The use of ex vivo drug sensitivity testing to predict drug activity in individual patients has been actively explored for almost 50 years without delivering a generally useful predictive capability. However, extended failure should not be an indicator of futility. This is especially true in cancer research where ultimate success is often preceded by less successful attempts. For example, both immune- and genetic-based targeted therapies for cancer underwent numerous failed attempts before biological understanding, improved targets, and optimized drug development matured to facilitate an arsenal of transformational drugs. Similarly, the concept of directly assessing drug sensitivity of primary tumor biopsies-and the use of this information to help direct therapeutic approaches-has a long history with a definitive learning curve. In this review, we will survey the history of ex vivo testing as well as the current state of the art for this field. We will present an update on methodologies and approaches, describe the use of these technologies to test cutting-edge drug classes, and describe an increasingly nuanced understanding of tumor types and models for which this strategy is most likely to succeed. We will consider the relative strengths and weaknesses of predicting drug activity across the broad biological context of cancer patients and tumor types. This will include an analysis of the potential for ex vivo drug sensitivity testing to accurately predict drug activity within each of the biological hallmarks of cancer pathogenesis.

Removing unwanted variation with CytofRUV to integrate multiple CyTOF datasets

Mass cytometry (CyTOF) is a technology that has revolutionised single-cell biology. By detecting over 40 proteins on millions of single cells, CyTOF allows the characterisation of cell subpopulations in unprecedented detail. However, most CyTOF studies require the integration of data from multiple CyTOF batches usually acquired on different days and possibly at different sites. To date, the integration of CyTOF datasets remains a challenge due to technical differences arising in multiple batches. To overcome this limitation, we developed an approach called CytofRUV for analysing multiple CyTOF batches, which includes an R-Shiny application with diagnostic plots. CytofRUV can correct for batch effects and integrate data from large numbers of patients and conditions across batches, to confidently compare cellular changes and correlate these with clinically relevant outcomes.