Single-cell developmental classification of B cell precursor acute lymphoblastic leukemia at diagnosis reveals predictors of relapse

Concepts in B cell acute lymphoblastic leukemia pathogenesis

B cell acute lymphoblastic leukemia (B-ALL) arises from genetic alterations impacting B cell progenitors, ultimately leading to clinically overt disease. Extensive collaborative efforts in basic and clinical research have significantly improved patient prognoses. Nevertheless, a subset of patients demonstrate resistance to conventional chemotherapeutic approaches and emerging immunotherapeutic interventions. This review highlights the mechanistic underpinnings governing B-ALL transformation. Beginning with exploring normative B cell lymphopoiesis, we delineate the influence of recurrent germline and somatic genetic aberrations on the perturbation of B cell progenitor differentiation and protumorigenic signaling, thereby facilitating the neoplastic transformation underlying B-ALL progression. Additionally, we highlight recent advances in the multifaceted landscape of B-ALL, encompassing metabolic reprogramming, microbiome influences, inflammation, and the discernible impact of socioeconomic and racial disparities on B-ALL transformation and patient survival.

Monitoring measurable residual disease in paediatric acute lymphoblastic leukaemia using immunoglobulin gene clonality based on next-generation sequencing

BACKGROUND

Assessment of measurable residual disease (MRD) is an essential prognostic tool for B-lymphoblastic leukaemia (B-ALL). In this study, we evaluated the utility of next-generation sequencing (NGS)-based MRD assessment in real-world clinical practice.

METHOD

The study included 93 paediatric patients with B-ALL treated at our institution between January 2017 and June 2022. Clonality for IGH or IGK rearrangements was identified in most bone marrow samples (91/93, 97.8%) obtained at diagnosis.

RESULTS

In 421 monitoring samples, concordance was 74.8% between NGS and multiparameter flow cytometry and 70.7% between NGS and reverse transcription-PCR. Elevated quantities of clones of IGH alone (P < 0.001; hazard ratio [HR], 22.2; 95% confidence interval [CI], 7.1-69.1), IGK alone (P = 0.011; HR, 5.8; 95% CI, 1.5-22.5), and IGH or IGK (P < 0.001; HR, 7.2; 95% CI, 2.6-20.0) were associated with an increased risk of relapse. Detection of new clone(s) in NGS was also associated with inferior relapse-free survival (P < 0.001; HR, 18.1; 95% CI, 3.0-108.6). Multivariable analysis confirmed age at diagnosis, BCR::ABL1-like mutation, TCF3::PBX1 mutation, and increased quantity of IGH or IGK clones during monitoring as unfavourable factors.

CONCLUSION

In conclusion, this study highlights the usefulness of NGS-based MRD as a routine assessment tool for prognostication of paediatric patients with B-ALL.

Mutation and cell state compatibility is required and targetable in Ph+ acute lymphoblastic leukemia minimal residual disease

Efforts to cure BCR::ABL1 B cell acute lymphoblastic leukemia (Ph+ ALL) solely through inhibition of ABL1 kinase activity have thus far been insufficient despite the availability of tyrosine kinase inhibitors (TKIs) with broad activity against resistance mutants. The mechanisms that drive persistence within minimal residual disease (MRD) remain poorly understood and therefore untargeted. Utilizing 13 patient-derived xenograft (PDX) models and clinical trial specimens of Ph+ ALL, we examined how genetic and transcriptional features co-evolve to drive progression during prolonged TKI response. Our work reveals a landscape of cooperative mutational and transcriptional escape mechanisms that differ from those causing resistance to first generation TKIs. By analyzing MRD during remission, we show that the same resistance mutation can either increase or decrease cellular fitness depending on transcriptional state. We further demonstrate that directly targeting transcriptional state-associated vulnerabilities at MRD can overcome BCR::ABL1 independence, suggesting a new paradigm for rationally eradicating MRD prior to relapse. Finally, we illustrate how cell mass measurements of leukemia cells can be used to rapidly monitor dominant transcriptional features of Ph+ ALL to help rationally guide therapeutic selection from low-input samples.

SCIPAC: quantitative estimation of cell-phenotype associations

Numerous algorithms have been proposed to identify cell types in single-cell RNA sequencing data, yet a fundamental problem remains: determining associations between cells and phenotypes such as cancer. We develop SCIPAC, the first algorithm that quantitatively estimates the association between each cell in single-cell data and a phenotype. SCIPAC also provides a p-value for each association and applies to data with virtually any type of phenotype. We demonstrate SCIPAC's accuracy in simulated data. On four real cancerous or noncancerous datasets, insights from SCIPAC help interpret the data and generate new hypotheses. SCIPAC requires minimum tuning and is computationally very fast.

The Promises and Possibilities of Artificial Intelligence in the Delivery of Behavior Analytic Services

Artificial intelligence (AI) has begun to affect nearly every aspect of our daily lives and nearly every industry and profession. Many readers of this journal likely work in one or more areas of behavioral health. For readers who work in behavioral health and who are interested in AI, the purpose of this article is to highlight the pervasiveness of AI research being conducted around many facets of behavioral health service delivery. To do this, we first provide a brief overview of some of the areas within AI and the types of problems each area of AI attempts to solve. We then outline the prototypical client journey in behavioral healthcare beginning with diagnosis/assessment and ending with intervention withdrawal or ongoing monitoring. Next, for each stage in the client journey, we highlight several areas that parallel existing behavior analytic practice where researchers have begun to use AI, often to improve the efficiency of service delivery or to learn new things that improve the effectiveness of behavioral health services. Finally, for those whose appetite has been whet for getting involved with AI, we close by describing three roles they might consider trying out and that parallel the three main domains of behavior analysis. These three roles are an AI tool designer (akin to EAB), AI tool implementer (akin to ABA), or AI tool supporter (akin to practice).

Immune determinants of CAR-T cell expansion in solid tumor patients receiving GD2 CAR-T cell therapy

Chimeric antigen receptor T cells (CAR-Ts) have remarkable efficacy in liquid tumors, but limited responses in solid tumors. We conducted a Phase I trial (NCT02107963) of GD2 CAR-Ts (GD2-CAR.OX40.28.z.iC9), demonstrating feasibility and safety of administration in children and young adults with osteosarcoma and neuroblastoma. Since CAR-T efficacy requires adequate CAR-T expansion, patients were grouped into good or poor expanders across dose levels. Patient samples were evaluated by multi-dimensional proteomic, transcriptomic, and epigenetic analyses. T cell assessments identified naive T cells in pre-treatment apheresis associated with good expansion, and exhausted T cells in CAR-T products with poor expansion. Myeloid cell assessment identified CXCR3+ monocytes in pre-treatment apheresis associated with good expansion. Longitudinal analysis of post-treatment samples identified increased CXCR3- classical monocytes in all groups as CAR-T numbers waned. Together, our data uncover mediators of CAR-T biology and correlates of expansion that could be utilized to advance immunotherapies for solid tumor patients.

Genome editing-induced t(4;11) chromosomal translocations model B cell precursor acute lymphoblastic leukemias with KMT2A-AFF1 fusion

A t(4;11) leukemia model established from CRISPR-engineered chromosomal translocations between the KMT2A and AFF1 genes recapitulate proteomic, epigenomic, and transcriptomic features of primary patient leukemias.

Stem cell-like reprogramming is required for leukemia-initiating activity in B-ALL

B cell acute lymphoblastic leukemia (B-ALL) is a multistep disease characterized by the hierarchical acquisition of genetic alterations. However, the question of how a primary oncogene reprograms stem cell-like properties in committed B cells and leads to a preneoplastic population remains unclear. Here, we used the PAX5::ELN oncogenic model to demonstrate a causal link between the differentiation blockade, the self-renewal, and the emergence of preleukemic stem cells (pre-LSCs). We show that PAX5::ELN disrupts the differentiation of preleukemic cells by enforcing the IL7r/JAK-STAT pathway. This disruption is associated with the induction of rare and quiescent pre-LSCs that sustain the leukemia-initiating activity, as assessed using the H2B-GFP model. Integration of transcriptomic and chromatin accessibility data reveals that those quiescent pre-LSCs lose B cell identity and reactivate an immature molecular program, reminiscent of human B-ALL chemo-resistant cells. Finally, our transcriptional regulatory network reveals the transcription factor EGR1 as a strong candidate to control quiescence/resistance of PAX5::ELN pre-LSCs as well as of blasts from human B-ALL.

Single cell proteomics characterization of bone marrow hematopoiesis with distinct Ras pathway lesions

Normal hematopoiesis requires constant prolific production of different blood cell lineages by multipotent hematopoietic stem cells (HSC). Stem- and progenitor- cells need to balance dormancy with proliferation. How genetic alterations impact frequency, lineage potential, and metabolism of HSC is largely unknown. Here, we compared induced expression of KRAS G12D or RasGRP1 to normal hematopoiesis. At low-resolution, both Ras pathway lesions result in skewing towards myeloid lineages. Single-cell resolution CyTOF proteomics unmasked an expansion of HSC- and progenitor- compartments for RasGRP1, contrasted by a depletion for KRAS G12D . SCENITH™ quantitates protein synthesis with single-cell precision and corroborated that immature cells display low metabolic SCENITH™ rates. Both RasGRP1 and KRAS G12D elevated mean SCENITH™ signals in immature cells. However, RasGRP1-overexpressing stem cells retain a metabolically quiescent cell-fraction, whereas this fraction diminishes for KRAS G12D . Our temporal single cell proteomics and metabolomics datasets provide a resource of mechanistic insights into altered hematopoiesis at single cell resolution.

Chimeric Antigen Receptor T Cells in Acute Myeloid Leukemia

Up to 30% of patients with acute myeloid leukemia (AML) who undergo chimeric antigen receptor (CAR) T-cell therapy have evidence of response, although trials are highly heterogeneous. These responses are rarely deep or durable. CD123, CD33, and CLL-1 have emerged as the most common targets for CAR T cells in AML. CAR T cells against myeloid antigens cause myeloablation as well as cytokine release syndrome, although neurotoxicity is rarely seen. Future efforts should focus on AML-specific antigen discovery or engineering, and on further enhancing the activity of CAR T cells.

Detection of continuous hierarchical heterogeneity by single-cell surface antigen analysis in the prognosis evaluation of acute myeloid leukaemia

BACKGROUND

Acute myeloid leukaemia (AML) is characterised by the malignant accumulation of myeloid progenitors with a high recurrence rate after chemotherapy. Blasts (leukaemia cells) exhibit a complete myeloid differentiation hierarchy hiding a wide range of temporal information from initial to mature clones, including genesis, phenotypic transformation, and cell fate decisions, which might contribute to relapse in AML patients.

METHODS

Based on the landscape of AML surface antigens generated by mass cytometry (CyTOF), we combined manifold analysis and principal curve-based trajectory inference algorithm to align myelocytes on a single-linear evolution axis by considering their phenotype continuum that correlated with differentiation order. Backtracking the trajectory from mature clusters located automatically at the terminal, we recurred the molecular dynamics during AML progression and confirmed the evolution stage of single cells. We also designed a 'dispersive antigens in neighbouring clusters exhibition (DANCE)' feature selection method to simplify and unify trajectories, which enabled the exploration and comparison of relapse-related traits among 43 paediatric AML bone marrow specimens.

RESULTS

The feasibility of the proposed trajectory analysis method was verified with public datasets. After aligning single cells on the pseudotime axis, primitive clones were recognized precisely from AML blasts, and the expression of the inner molecules before and after drug stimulation was accurately plotted on the trajectory. Applying DANCE to 43 clinical samples with different responses for chemotherapy, we selected 12 antigens as a general panel for myeloblast differentiation performance, and obtain trajectories to those patients. For the trajectories with unified molecular dynamics, CD11c overexpression in the primitive stage indicated a good chemotherapy outcome. Moreover, a later initial peak of stemness heterogeneity tended to be associated with a higher risk of relapse compared with complete remission.

CONCLUSIONS

In this study, pseudotime was generated as a new single-cell feature. Minute differences in temporal traits among samples could be exhibited on a trajectory, thus providing a new strategy for predicting AML relapse and monitoring drug responses over time scale.

Advances in Clinical Mass Cytometry

The advent of high-dimensional single-cell technologies has enabled detection of cellular heterogeneity and functional diversity of immune cells during health and disease conditions. Because of its multiplexing capabilities and limited compensation requirements, mass cytometry or cytometry by time of flight (CyTOF) has played a superior role in immune monitoring compared with flow cytometry. Further, it has higher throughput and lower cost compared with other single-cell techniques. Several published articles have utilized CyTOF to identify cellular phenotypes and features associated with disease outcomes. This article introduces CyTOF-based assays to profile immune cell-types, cell-states, and their applications in clinical research.

The shape of cancer relapse: Topological data analysis predicts recurrence in paediatric acute lymphoblastic leukaemia

Although children and adolescents with acute lymphoblastic leukaemia (ALL) have high survival rates, approximately 15-20% of patients relapse. Risk of relapse is routinely estimated at diagnosis by biological factors, including flow cytometry data. This high-dimensional data is typically manually assessed by projecting it onto a subset of biomarkers. Cell density and "empty spaces" in 2D projections of the data, i.e. regions devoid of cells, are then used for qualitative assessment. Here, we use topological data analysis (TDA), which quantifies shapes, including empty spaces, in data, to analyse pre-treatment ALL datasets with known patient outcomes. We combine these fully unsupervised analyses with Machine Learning (ML) to identify significant shape characteristics and demonstrate that they accurately predict risk of relapse, particularly for patients previously classified as 'low risk'. We independently confirm the predictive power of CD10, CD20, CD38, and CD45 as biomarkers for ALL diagnosis. Based on our analyses, we propose three increasingly detailed prognostic pipelines for analysing flow cytometry data from ALL patients depending on technical and technological availability: 1. Visual inspection of specific biological features in biparametric projections of the data; 2. Computation of quantitative topological descriptors of such projections; 3. A combined analysis, using TDA and ML, in the four-parameter space defined by CD10, CD20, CD38 and CD45. Our analyses readily extend to other haematological malignancies.

tidytof: a user-friendly framework for scalable and reproducible high-dimensional cytometry data analysis

Summary

While many algorithms for analyzing high-dimensional cytometry data have now been developed, the software implementations of these algorithms remain highly customized-this means that exploring a dataset requires users to learn unique, often poorly interoperable package syntaxes for each step of data processing. To solve this problem, we developed {tidytof}, an open-source R package for analyzing high-dimensional cytometry data using the increasingly popular 'tidy data' interface.

Availability and implementation

{tidytof} is available at https://github.com/keyes-timothy/tidytof and is released under the MIT license. It is supported on Linux, MS Windows and MacOS. Additional documentation is available at the package website (https://keyes-timothy.github.io/tidytof/).

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

Systems biology approaches to unravel lymphocyte subsets and function

Single-cell technologies have revealed the extensive heterogeneity and complexity of the immune system. Systems biology approaches in immunology have taken advantage of the high-parameter, high-throughput data and analyzed immune cell types in a 'bottom-up' data-driven method. This approach has discovered previously unrecognized cell types and functions. Especially for human immunology, in which experimental manipulations are challenging, systems approach has become a successful means to investigate physiologically relevant contexts. This review focuses on the recent findings in lymphocyte biology, from their development, differentiation into subsets, and heterogeneity in their functions, enabled by these systems approaches. Furthermore, we review examples of the application of findings from systems approach studies and discuss how now to leave the rich dataset in the curse of high dimensionality.

Dasatinib overcomes glucocorticoid resistance in B-cell acute lymphoblastic leukemia

Resistance to glucocorticoids (GC) is associated with an increased risk of relapse in B-cell progenitor acute lymphoblastic leukemia (BCP-ALL). Performing transcriptomic and single-cell proteomic studies in healthy B-cell progenitors, we herein identify coordination between the glucocorticoid receptor pathway with B-cell developmental pathways. Healthy pro-B cells most highly express the glucocorticoid receptor, and this developmental expression is conserved in primary BCP-ALL cells from patients at diagnosis and relapse. In-vitro and in vivo glucocorticoid treatment of primary BCP-ALL cells demonstrate that the interplay between B-cell development and the glucocorticoid pathways is crucial for GC resistance in leukemic cells. Gene set enrichment analysis in BCP-ALL cell lines surviving GC treatment show enrichment of B cell receptor signaling pathways. In addition, primary BCP-ALL cells surviving GC treatment in vitro and in vivo demonstrate a late pre-B cell phenotype with activation of PI3K/mTOR and CREB signaling. Dasatinib, a multi-kinase inhibitor, most effectively targets this active signaling in GC-resistant cells, and when combined with glucocorticoids, results in increased cell death in vitro and decreased leukemic burden and prolonged survival in an in vivo xenograft model. Targeting the active signaling through the addition of dasatinib may represent a therapeutic approach to overcome GC resistance in BCP-ALL.

Mapping the Evidence for Opioid-Mediated Changes in Malignancy and Chemotherapeutic Efficacy: Protocol for a Scoping Review

BACKGROUND

Numerous reports contend opioids can augment or inhibit malignancy. At present, there is no consensus on the risk or benefit posed by opioids on malignancy or chemotherapeutic activity. Distinguishing the consequences of opioid use from pain and its management is challenging. Additionally, opioid concentration data is often lacking in clinical studies. A scoping review approach inclusive of preclinical and clinical data will improve our understanding of the risk-benefit relationship concerning commonly prescribed opioids and cancer and cancer treatment.

OBJECTIVE

The aim of the study is to map diverse studies spanning from preclinical to clinical regarding opioids with malignancy and its treatment.

METHODS

This scoping review will use the Arksey six stages framework to (1) identify the research question; (2) identify relevant studies; (3) select studies meeting criteria; (4) extract and chart data; (5) collate, summarize, and report results; and (6) conduct expert consultation. An initial pilot study was undertaken to (1) parameterize the extent and scale of existing data for an evidence review, (2) identify key factors to be extracted in systematic charting efforts, and (3) assess opioid concentration as a variable for its relevance to the central hypothesis. Six databases will be searched with no filters: MEDLINE, Embase, CINAHL Complete, Cochrane Library, Biological Sciences Collection, and International Pharmaceutical Abstracts. Trial registries will include ClinicalTrials.gov, Cochrane CENTRAL, International Standard Randomised Controlled Trial Number Registry, European Union Clinical Trials Register, and World Health Organization International Clinical Trials Registry. Eligibility criteria will include preclinical and clinical study data on opioids effects on tumor growth or survival, or alteration on the antineoplastic activity of chemotherapeutics. We will chart data on (1) opioid concentration from human subjects with cancer, yielding a "physiologic range" to better interpret available preclinical data; (2) patterns of opioid exposure with disease and treatment-related patient outcomes; and (3) the influence of opioids on cancer cell survival, as well as opioid-related changes to cancer cell susceptibility for chemotherapeutics.

RESULTS

This scoping review will present results in narrative forms as well as with the use of tables and diagrams. Initiated in February 2021 at the University of Utah, this protocol is anticipated to generate a scoping review by August 2023. The results of the scoping review will be disseminated through scientific conference proceedings and presentations, stakeholder meetings, and by publication in a peer-reviewed journal.

CONCLUSIONS

The findings of this scoping review will provide a comprehensive description of the consequences of prescription opioids on malignancy and its treatment. By incorporating preclinical and clinical data, this scoping review will invite novel comparisons across study types that could inform new basic, translational, and clinical studies regarding risks and benefits of opioid use among patients with cancer.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

PRR1-10.2196/38167.

Single-cell analysis highlights a population of Th17-polarized CD4+ naïve T cells showing IL6/JAK3/STAT3 activation in pediatric severe aplastic anemia

Acquired aplastic anemia (AA) is recognized as an immune-mediated disorder resulting from active destruction of hematopoietic cells in bone marrow (BM) by effector T lymphocytes. Bulk genomic landscape analysis and transcriptomic profiling have contributed to a better understanding of the recurrent cytogenetic abnormalities and immunologic cues associated with the onset of hematopoietic destruction. However, the functional mechanistic determinants underlying the complexity of heterogeneous T lymphocyte populations as well as their correlation with clinical outcomes remain to be elucidated. To uncover dysfunctional mechanisms acting within the heterogeneous marrow-infiltrating immune environment and examine their pathogenic interplay with the hematopoietic stem/progenitor pool, we exploited single-cell mass cytometry for BM mononuclear cells of severe AA (SAA) patients pre- and post-immunosuppressive therapy, in contrast to those of healthy donors. Alignment of BM cellular composition with hematopoietic developmental trajectories revealed potential functional roles for non-canonically activated CD4⁺ naïve T cells in newly-diagnosed pediatric cases of SAA. Furthermore, single-cell transcriptomic profiling highlighted a population of Th17-polarized CD4⁺CAMK4⁺ naïve T cells showing activation of the IL-6/JAK3/STAT3 pathway, while gene signature dissection indicated a predisposition to proinflammatory pathogenesis. Retrospective validation from our SAA cohort of 231 patients revealed high plasma levels of IL-6 as an independent risk factor of delayed hematopoietic response to antithymocyte globulin-based immunosuppressive therapy. Thus, IL-6 warrants further investigation as a putative therapeutic target in SAA.

Single-cell analysis of acute lymphoblastic and lineage-ambiguous leukemia: approaches and molecular insights

Despite recent progress in identifying the genetic drivers of acute lymphoblastic leukemia (ALL), prognosis remains poor for those individuals who experience disease recurrence. Moreover, acute leukemias of ambiguous lineage lack a biologically informed framework to guide classification and therapy. These needs have driven the adoption of multiple complementary single-cell sequencing approaches to explore key issues in the biology of these leukemias, including cell of origin, developmental hierarchy and ontogeny, and the molecular heterogeneity driving pathogenesis, progression, and therapeutic responsiveness. There are multiple single-cell techniques for profiling a specific modality, including RNA, DNA, chromatin accessibility and methylation; and an expanding range of approaches for simultaneous analysis of multiple modalities. Single-cell sequencing approaches have also enabled characterization of cell-intrinsic and -extrinsic features of ALL biology. In this review we describe these approaches and highlight the extensive heterogeneity that underpins ALL gene expression, cellular differentiation, and clonal architecture throughout disease pathogenesis and treatment resistance. In addition, we discuss the importance of the dynamic interactions that occur between leukemia cells and the nonleukemia microenvironment. We discuss potential opportunities and limitations of single-cell sequencing for the study of ALL biology and treatment responsiveness.

Single-cell technologies uncover intra-tumor heterogeneity in childhood cancers

Childhood cancer is the second leading cause of death in children aged 1 to 14. Although survival rates have vastly improved over the past 40 years, cancer resistance and relapse remain a significant challenge. Advances in single-cell technologies enable dissection of tumors to unprecedented resolution. This facilitates unraveling the heterogeneity of childhood cancers to identify cell subtypes that are prone to treatment resistance. The rapid accumulation of single-cell data from different modalities necessitates the development of novel computational approaches for processing, visualizing, and analyzing single-cell data. Here, we review single-cell approaches utilized or under development in the context of childhood cancers. We review computational methods for analyzing single-cell data and discuss best practices for their application. Finally, we review the impact of several studies of childhood tumors analyzed with these approaches and future directions to implement single-cell studies into translational cancer research in pediatric oncology.

A Hashing-Based Framework for Enhancing Cluster Delineation of High-Dimensional Single-Cell Profiles

Although many methods have been developed to explore the function of cells by clustering high-dimensional (HD) single-cell omics data, the inconspicuously differential expressions of biomarkers of proteins or genes across all cells disturb the cell cluster delineation and downstream analysis. Here, we introduce a hashing-based framework to improve the delineation of cell clusters, which is based on the hypothesis that one variable with no significant differences can be decomposed into more diversely latent variables to distinguish cells. By projecting the original data into a sparse HD space, fly and densefly hashing preprocessing retain the local structure of data, and improve the cluster delineation of existing clustering methods, such as PhenoGraph. Moreover, the analyses on mass cytometry dataset show that our hashing-based framework manages to unveil new hidden heterogeneities in cell clusters. The proposed framework promotes the utilization of cell biomarkers and enriches the biological findings by introducing more latent variables.

Supplementary Information

The online version contains supplementary material available at 10.1007/s43657-022-00056-z.

Integrated plasma proteomic and single-cell immune signaling network signatures demarcate mild, moderate, and severe COVID-19

The biological determinants underlying the range of coronavirus 2019 (COVID-19) clinical manifestations are not fully understood. Here, over 1,400 plasma proteins and 2,600 single-cell immune features comprising cell phenotype, endogenous signaling activity, and signaling responses to inflammatory ligands are cross-sectionally assessed in peripheral blood from 97 patients with mild, moderate, and severe COVID-19 and 40 uninfected patients. Using an integrated computational approach to analyze the combined plasma and single-cell proteomic data, we identify and independently validate a multi-variate model classifying COVID-19 severity (multi-class area under the curve [AUC]training = 0.799, p = 4.2e-6; multi-class AUCvalidation = 0.773, p = 7.7e-6). Examination of informative model features reveals biological signatures of COVID-19 severity, including the dysregulation of JAK/STAT, MAPK/mTOR, and nuclear factor κB (NF-κB) immune signaling networks in addition to recapitulating known hallmarks of COVID-19. These results provide a set of early determinants of COVID-19 severity that may point to therapeutic targets for prevention and/or treatment of COVID-19 progression.

New insights into Human Hematopoietic Stem and Progenitor Cells via Single-Cell Omics

Residing at the apex of the hematopoietic hierarchy, hematopoietic stem and progenitor cells (HSPCs) give rise to all mature blood cells. In the last decade, significant progress has been made in single-cell RNA sequencing as well as multi-omics technologies that have facilitated elucidation of the heterogeneity of previously defined human HSPCs. From the embryonic stage through the adult stage to aging, single-cell studies have enabled us to trace the origins of hematopoietic stem cells (HSCs), demonstrating different hematopoietic differentiation during development, as well as identifying novel cell populations. In both hematological benign diseases and malignancies, single-cell omics technologies have begun to reveal tissue heterogeneity and have permitted mapping of microenvironmental ecosystems and tracking of cell subclones, thereby greatly broadening our understanding of disease development. Furthermore, advances have also been made in elucidating the molecular mechanisms for relapse and identifying therapeutic targets of hematological disorders and other non-hematological diseases. Extensive exploration of hematopoiesis at the single-cell level may thus have great potential for broad clinical applications of HSPCs, as well as disease prognosis.

Rise of the SARS-CoV-2 Variants: can proteomics be the silver bullet?

INTRODUCTION

The challenges posed by emergent strains of SARS-CoV-2 need to be tackled by contemporary scientific approaches, with proteomics playing a significant role.

AREAS COVERED

In this review, we provide a brief synthesis of the impact of proteomics technologies in elucidating disease pathogenesis and classifiers for the prognosis of COVID-19 and propose proteomics methodologies that could play a crucial role in understanding emerging variants and their altered disease pathology. From aiding the design of novel drug candidates to facilitating the identification of T cell vaccine targets, we have discussed the impact of proteomics methods in COVID-19 research. Techniques varied as mass spectrometry, single-cell proteomics, multiplexed ELISA arrays, high-density proteome arrays, surface plasmon resonance, immunopeptidomics, and in silico docking studies that have helped augment the fight against existing diseases were useful in preparing us to tackle SARS-CoV-2 variants. We also propose an action plan for a pipeline to combat emerging pandemics using proteomics technology by adopting uniform standard operating procedures and unified data analysis paradigms.

EXPERT OPINION

The knowledge about the use of diverse proteomics approaches for COVID-19 investigation will provide a framework for future basic research, better infectious disease prevention strategies, improved diagnostics, and targeted therapeutics.

CytofIn enables integrated analysis of public mass cytometry datasets using generalized anchors

The increasing use of mass cytometry for analyzing clinical samples offers the possibility to perform comparative analyses across public datasets. However, challenges in batch normalization and data integration limit the comparison of datasets not intended to be analyzed together. Here, we present a data integration strategy, CytofIn, using generalized anchors to integrate mass cytometry datasets from the public domain. We show that low-variance controls, such as healthy samples and stable channels, are inherently homogeneous, robust against stimulation, and can serve as generalized anchors for batch correction. Single-cell quantification comparing mass cytometry data from 989 leukemia files pre- and post normalization with CytofIn demonstrates effective batch correction while recapitulating the gold-standard bead normalization. CytofIn integration of public cancer datasets enabled the comparison of immune features across histologies and treatments. We demonstrate the ability to integrate public datasets without necessitating identical control samples or bead standards for fast and robust analysis using CytofIn.

Challenges in batch normalization and data integration limit the comparison of existing mass cytometry datasets. Here, the authors report CytofIn that can integrate mass cytometry datasets from the public domain and reveal cellular features associated with immune oncology by analyzing five public cancer datasets.

Revealing new biology from multiplexed, metal-isotope-tagged, single-cell readouts

Mass cytometry (MC) is a recent technology that pairs plasma-based ionization of cells in suspension with time-of-flight (TOF) mass spectrometry to sensitively quantify the single-cell abundance of metal-isotope-tagged affinity reagents to key proteins, RNA, and peptides. Given the ability to multiplex readouts (~50 per cell) and capture millions of cells per experiment, MC offers a robust way to assay rare, transitional cell states that are pertinent to human development and disease. Here, we review MC approaches that let us probe the dynamics of cellular regulation across multiple conditions and sample types in a single experiment. Additionally, we discuss current limitations and future extensions of MC as well as computational tools commonly used to extract biological insight from single-cell proteomic datasets.

Cell surface expression of GRP78 and CXCR4 is associated with childhood high-risk acute lymphoblastic leukemia at diagnostics

Acute lymphocytic leukemia is the most common type of cancer in pediatric individuals. Glucose regulated protein (GRP78) is an endoplasmic reticulum chaperone that facilitates the folding and assembly of proteins and regulates the unfolded protein response pathway. GRP78 has a role in survival of cancer and metastasis and cell-surface associated GRP78 (sGRP78) is expressed on cancer cells but not in normal cells. Here, we explored the presence of sGRP78 in pediatric B-ALL at diagnosis and investigated the correlation with bona fide markers of leukemia. By using a combination of flow cytometry and high multidimensional analysis, we found a distinctive cluster containing high levels of sGRP78, CD10, CD19, and CXCR4 in bone marrow samples obtained from High-risk leukemia patients, which was absent in the compartment of Standard-risk leukemia. We confirmed that sGRP78+CXCR4+ blood-derived cells were more frequent in High-risk leukemia patients. Finally, we analyzed the dissemination capacity of sGRP78 leukemia cells in a model of xenotransplantation. sGRP78+ cells emigrated to the bone marrow and lymph nodes, maintaining the expression of CXCR4. Testing the presence of sGRP78 and CXCR4 together with conventional markers may help to achieve a better categorization of High and Standard-risk pediatric leukemia at diagnosis.

An instructive role for Interleukin-7 receptor α in the development of human B-cell precursor leukemia

Kinase signaling fuels growth of B-cell precursor acute lymphoblastic leukemia (BCP-ALL). Yet its role in leukemia initiation is unclear and has not been shown in primary human hematopoietic cells. We previously described activating mutations in interleukin-7 receptor alpha (IL7RA) in poor-prognosis "ph-like" BCP-ALL. Here we show that expression of activated mutant IL7RA in human CD34+ hematopoietic stem and progenitor cells induces a preleukemic state in transplanted immunodeficient NOD/LtSz-scid IL2Rγnull mice, characterized by persistence of self-renewing Pro-B cells with non-productive V(D)J gene rearrangements. Preleukemic CD34+CD10highCD19+ cells evolve into BCP-ALL with spontaneously acquired Cyclin Dependent Kinase Inhibitor 2 A (CDKN2A) deletions, as commonly observed in primary human BCP-ALL. CRISPR mediated gene silencing of CDKN2A in primary human CD34+ cells transduced with activated IL7RA results in robust development of BCP-ALLs in-vivo. Thus, we demonstrate that constitutive activation of IL7RA can initiate preleukemia in primary human hematopoietic progenitors and cooperates with CDKN2A silencing in progression into BCP-ALL.

Alignment of single-cell trajectories by tuMap enables high-resolution quantitative comparison of cancer samples

Single-cell technologies allow characterization of cancer samples as continuous developmental trajectories. Yet, the obtained temporal resolution cannot be leveraged for a comparative analysis due to the large phenotypic heterogeneity existing between patients. Here, we present the tuMap algorithm that exploits high-dimensional single-cell data of cancer samples exhibiting an underlying developmental structure to align them with the healthy development, yielding the tuMap pseudotime axis that allows their systematic, meaningful comparison. We applied tuMap on single-cell mass cytometry data of acute lymphoblastic and myeloid leukemia to reveal associations between the tuMap pseudotime axis and clinics that outperform cellular assignment into developmental populations. Application of the tuMap algorithm on single-cell RNA sequencing data further identified gene signatures of stem cells residing at the very-early parts of the cancer trajectories. The quantitative framework provided by tuMap allows generation of metrics for cancer patients evaluation.

Interleukin-7 receptor α mutational activation can initiate precursor B-cell acute lymphoblastic leukemia

Interleukin-7 receptor α (encoded by IL7R) is essential for lymphoid development. Whether acute lymphoblastic leukemia (ALL)-related IL7R gain-of-function mutations can trigger leukemogenesis remains unclear. Here, we demonstrate that lymphoid-restricted mutant IL7R, expressed at physiological levels in conditional knock-in mice, establishes a pre-leukemic stage in which B-cell precursors display self-renewal ability, initiating leukemia resembling PAX5 P80R or Ph-like human B-ALL. Full transformation associates with transcriptional upregulation of oncogenes such as Myc or Bcl2, downregulation of tumor suppressors such as Ikzf1 or Arid2, and major IL-7R signaling upregulation (involving JAK/STAT5 and PI3K/mTOR), required for leukemia cell viability. Accordingly, maximal signaling drives full penetrance and early leukemia onset in homozygous IL7R mutant animals. Notably, we identify 2 transcriptional subgroups in mouse and human Ph-like ALL, and show that dactolisib and sphingosine-kinase inhibitors are potential treatment avenues for IL-7R-related cases. Our model, a resource to explore the pathophysiology and therapeutic vulnerabilities of B-ALL, demonstrates that IL7R can initiate this malignancy.

New interpretable machine-learning method for single-cell data reveals correlates of clinical response to cancer immunotherapy

We introduce a new method for single-cell cytometry studies, FAUST, which performs unbiased cell population discovery and annotation. FAUST processes experimental data on a per-sample basis and returns biologically interpretable cell phenotypes, making it well suited for the analysis of complex datasets. We provide simulation studies that compare FAUST with existing methodology, exemplifying its strength. We apply FAUST to data from a Merkel cell carcinoma anti-PD-1 trial and discover pre-treatment effector memory T cell correlates of outcome co-expressing PD-1, HLA-DR, and CD28. Using FAUST, we then validate these correlates in cryopreserved peripheral blood mononuclear cell samples from the same study, as well as an independent CyTOF dataset from a published metastatic melanoma trial. Finally, we show how FAUST's phenotypes can be used to perform cross-study data integration in the presence of diverse staining panels. Together, these results establish FAUST as a powerful new approach for unbiased discovery in single-cell cytometry.

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An interpretable machine-learning method for cytometry data analysis is developed

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Using this, candidate biomarkers of response to therapy are identified and visualized

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The method is used to validate our findings on two additional cytometry datasets

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It is shown how to integrate findings across datasets with heterogeneous marker panels

Our article introduces a new method, FAUST, which combines novel algorithms for clustering, cluster matching, variable selection, and feature selection. While these algorithms were developed for application to high-dimensional single-cell data—and our article validates this application area with multiple case studies—they are general purpose and can be applied to any collection of related real-valued matrices one wishes to partition.

Some useful features of these algorithms to the broader data science community include the following: they estimate the number of clusters across a dataset, they can be applied independently to each matrix in the set of matrices one wishes to cluster, they match clusters across matrices on the basis of data-driven annotations, and the annotations are interpretable in relation to the initial measurement variables. We provide an open-source implementation of our method, https://github.com/RGLab/FAUST, targeting data structures optimized for use in cytometry 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.

MECHANISTIC AND DATA-DRIVEN MODELS OF CELL SIGNALING: TOOLS FOR FUNDAMENTAL DISCOVERY AND RATIONAL DESIGN OF THERAPY

A full understanding of cell signaling processes requires knowledge of protein structure/function relationships, protein-protein interactions, and the abilities of pathways to control phenotypes. Computational models offer a valuable framework for integrating that knowledge to predict the effects of system perturbations and interventions in health and disease. Whereas mechanistic models are well suited for understanding the biophysical basis for signal transduction and principles of therapeutic design, data-driven models are particularly suited to distill complex signaling relationships among samples and between multivariate signaling changes and phenotypes. Both approaches have limitations and provide incomplete representations of signaling biology, but their careful implementation and integration can provide new understanding for how manipulating system variables impacts cellular decisions.

Multiplex Single-Cell Analysis of Cancer Cells Enables Unbiased Uncovering Subsets Associated with Cancer Relapse: Heterogeneity of Multidrug Resistance in Precursor B-ALL

Earlier detection of biomarkers responsible for cancer relapse facilitates more rational cancer treatment regimens to be designed. Herein, we develop a mass cytometry-based strategy for unbiased mining of cell subsets that potentially contribute to cancer recurrence through panoramic examination of the immunophenotypic features and multidrug resistance characteristics. The incorporation of metal tags enables multiplexed information of single cells to be interrogated based on metal fingerprint. Using acute lymphoblastic leukemia (B-ALL) as a showcase, we show overexpressed multidrug resistance biomarkers, i. e., BCRP, Bcl-2, MRP1, and P-gp in B-ALL cells compared with healthy control, and a positive correlation among different multidrug resistance biomarkers. Different cell subsets with multidrug resistance are well-defined, featured with CD34⁺ CD38⁺ CD10^- and CD34⁺ CD38^+/int CD10⁺ . Importantly, we uncovered that CD34 expression level is positively correlated to multidrug resistance, indicative of a higher potential of immature cells to induce B-ALL relapse. In addition, the cell subsets positively expressing CD73 and CD304 (CD34⁺ CD10⁺ CD304⁺ ; CD34⁺ CD38^+/int CD10⁺ CD73⁺ ) also overexpress multidrug resistance biomarkers, suggesting that they may serve as additional new biomarkers for B-ALL stratification and prognosis. Our data provide the first evidence that highly expressed multidrug resistance biomarkers in certain cell subpopulations with specific immunophenotypes may potentially induce B-ALL recurrence. The incorporation of multidrug resistance features with cell phenotypes using mass cytometry proposed in this study provides a general strategy for risk assessment and the prediction of recurrence of different types of cancers.

Oncogene-Induced Reprogramming in Acute Lymphoblastic Leukemia: Towards Targeted Therapy of Leukemia-Initiating Cells

Simple Summary

Acute lymphoblastic leukemia is a heterogeneous disease characterized by a diversity of genetic alterations, following a sophisticated and controversial organization. In this review, we present and discuss the concepts exploring the cellular, molecular and functional heterogeneity of leukemic cells. We also review the emerging evidence indicating that cell plasticity and oncogene-induced reprogramming should be considered at the biological and clinical levels as critical mechanisms for identifying and targeting leukemia-initiating cells.

Abstract

Our understanding of the hierarchical structure of acute leukemia has yet to be fully translated into therapeutic approaches. Indeed, chemotherapy still has to take into account the possibility that leukemia-initiating cells may have a distinct chemosensitivity profile compared to the bulk of the tumor, and therefore are spared by the current treatment, causing the relapse of the disease. Therefore, the identification of the cell-of-origin of leukemia remains a longstanding question and an exciting challenge in cancer research of the last few decades. With a particular focus on acute lymphoblastic leukemia, we present in this review the previous and current concepts exploring the phenotypic, genetic and functional heterogeneity in patients. We also discuss the benefits of using engineered mouse models to explore the early steps of leukemia development and to identify the biological mechanisms driving the emergence of leukemia-initiating cells. Finally, we describe the major prospects for the discovery of new therapeutic strategies that specifically target their aberrant stem cell-like functions.

Raman and quantitative phase imaging allow morpho-molecular recognition of malignancy and stages of B-cell acute lymphoblastic leukemia

Acute lymphoblastic leukemia (ALL) is one of the most common malignancies that account for nearly one-third of all pediatric cancers. The current diagnostic assays are time-consuming, labor-intensive, and require expensive reagents. Here, we report a label-free approach featuring diffraction phase imaging and Raman microscopy that can retrieve both morphological and molecular attributes for label-free optical phenotyping of individual B cells. By investigating leukemia cell lines of early and late stages along with the healthy B cells, we show that phase images can capture subtle morphological differences among the healthy, early, and late stages of leukemic cells. By exploiting its biomolecular specificity, we demonstrate that Raman microscopy is capable of accurately identifying not only different stages of leukemia cells but also individual cell lines at each stage. Overall, our study provides a rationale for employing this hybrid modality to screen leukemia cells using the widefield QPI and using Raman microscopy for accurate differentiation of early and late-stage phenotypes. This contrast-free and rapid diagnostic tool exhibits great promise for clinical diagnosis and staging of leukemia in the near future.

FlowCT for the analysis of large immunophenotypic datasets and biomarker discovery in cancer immunology

Large-scale immune monitoring is becoming routinely used in clinical trials to identify determinants of treatment responsiveness, particularly to immunotherapies. Flow cytometry remains one of the most versatile and high throughput approaches for single-cell analysis; however, manual interpretation of multidimensional data poses a challenge to capture full cellular diversity and provide reproducible results. We present FlowCT, a semi-automated workspace empowered to analyze large datasets that includes pre-processing, normalization, multiple dimensionality reduction techniques, automated clustering and predictive modeling tools. As a proof of concept, we used FlowCT to compare the T cell compartment in bone marrow (BM) vs peripheral blood (PB) of patients with smoldering multiple myeloma (MM); identify minimally-invasive immune biomarkers of progression from smoldering to active MM; define prognostic T cell subsets in the BM of patients with active MM after treatment intensification; and assess the longitudinal effect of maintenance therapy in BM T cells. A total of 354 samples were analyzed and immune signatures predictive of malignant transformation in 150 smoldering MM patients (hazard ratio [HR]: 1.7; P <.001), and of progression-free (HR: 4.09; P <.0001) and overall survival (HR: 3.12; P =.047) in 100 active MM patients, were identified. New data also emerged about stem cell memory T cells, the concordance between immune profiles in BM vs PB and the immunomodulatory effect of maintenance therapy. FlowCT is a new open-source computational approach that can be readily implemented by research laboratories to perform quality-control, analyze high-dimensional data, unveil cellular diversity and objectively identify biomarkers in large immune monitoring studies.

Chemotherapy induces canalization of cell state in childhood B-cell precursor acute lymphoblastic leukemia

Comparison of intratumor genetic heterogeneity in cancer at diagnosis and relapse suggests that chemotherapy induces bottleneck selection of subclonal genotypes. However, evolutionary events subsequent to chemotherapy could also explain changes in clonal dominance seen at relapse. We, therefore, investigated the mechanisms of selection in childhood B-cell precursor acute lymphoblastic leukemia (BCP-ALL) during induction chemotherapy where maximal cytoreduction occurs. To distinguish stochastic versus deterministic events, individual leukemias were transplanted into multiple xenografts and chemotherapy administered. Analyses of the immediate post-treatment leukemic residuum at single-cell resolution revealed that chemotherapy has little impact on genetic heterogeneity. Rather, it acts on extensive, previously unappreciated, transcriptional and epigenetic heterogeneity in BCP-ALL, dramatically reducing the spectrum of cell states represented, leaving a genetically polyclonal but phenotypically uniform population with hallmark signatures relating to developmental stage, cell cycle and metabolism. Hence, canalization of cell state accounts for a significant component of bottleneck selection during induction chemotherapy.

Identification and characterization of relapse-initiating cells in MLL-rearranged infant ALL by single-cell transcriptomics

Infants with MLL-rearranged infant acute lymphoblastic leukemia (MLL-r iALL) undergo intense therapy to counter a highly aggressive malignancy with survival rates of only 30–40%. The majority of patients initially show therapy response, but in two-thirds of cases the leukemia returns, typically during treatment. The glucocorticoid drug prednisone is established as a major player in the treatment of leukemia and the in vivo response to prednisone monotreatment is currently the best indicator of risk for MLL-r iALL. We used two different single-cell RNA sequencing technologies to analyze the expression of a prednisone-dependent signature, derived from an independent study, in diagnostic bone marrow and peripheral blood biopsies. This allowed us to classify individual leukemic cells as either resistant or sensitive to treatment and show that quantification of these two groups can be used to better predict the occurrence of future relapse in individual patients. This work also sheds light on the nature of the therapy-resistant subpopulation of relapse-initiating cells. Leukemic cells associated with high relapse risk are characterized by basal activation of glucocorticoid response, smaller size, and a quiescent gene expression program with cell stemness properties. These results improve current risk stratification and elucidate leukemic therapy-resistant subpopulations at diagnosis.

Training Novices in Generation and Analysis of High-Dimensional Human Cell Phospho-Flow Cytometry Data

This article presents a single experiment designed to introduce a trainee to multiple advanced bench and analysis techniques, including high-dimensional cytometry, profiling cell signaling networks, functional assays with primary human tissue, and single-cell analysis with machine learning tools. The trainee is expected to have only minimal laboratory experience and is not required to have any prior training in flow cytometry, immunology, or data science. This article aims to introduce the advanced research areas with a design that is robust enough that novice trainees will succeed, flexible enough to allow some project customization, and fundamental enough that the skills and knowledge gained will provide a template for future experiments. For advanced users, the updated phospho-flow protocol and the established controls, best practices, and expected outcomes presented here also provide a framework for adapting these tools in new areas with unexplored biology. © 2020 by John Wiley & Sons, Inc. Basic Protocol: Phospho-protein stimulation and mass cytometry data collection Support Protocol: Analysis of signaling mass cytometry data.

Discriminating Leukemia Cellular Heterogeneity and Screening Metabolite Biomarker Candidates using Label-Free Mass Cytometry

Discriminating various leukocyte subsets with specific functions is critical due to their important roles in the development of many diseases. Here, we proposed a general strategy to unravel leukocytes heterogeneity and screen differentiated metabolites as biomarker candidates for leukocyte subtypes using the label-free mass cytometry (CyESI-MS) combined with a homemade data processing workflow. Taking leukemia cells as an example, metabolic fingerprints of single leukemia cells were obtained from 472 HL-60, 416 THP-1, 313 U937, 356 Jurkat, and 366 Ramos cells, with throughput up to 40 cells/min. Five leukemia subtypes were clearly distinguished by unsupervised learning t-SNE analysis of the single-cell metabolic fingerprints. Cell discrimination in the mixed leukemia cell samples was also realized by supervised learning of the single-cell metabolic fingerprints with high recovery and good repetition (98.31 ± 0.24%, -102.35 ± 4.82%). Statistical analysis and metabolite assignment were carried out to screen characteristic metabolites for discrimination and 36 metabolites with significant differences were annotated. Then, differentiated metabolites for pairwise discrimination of five leukemia subtypes were further selected as biomarker candidates. Furthermore, discriminating cultured leukemia cells from human normal leukocytes, separated from fresh human peripheral blood, was performed based on single-cell metabolic fingerprints as well as the proposed biomarker candidates, unveiling the potential of this strategy in clinical research. This work makes efforts to realize high-throughput single-leukocyte metabolic analysis and metabolite-based discrimination of leukocytes. It is expected to be a powerful means for the clinical molecular diagnosis of hematological diseases.

Applications of Single-Cell Omics in Tumor Immunology

The tumor microenvironment (TME) is an ecosystem that contains various cell types, including cancer cells, immune cells, stromal cells, and many others. In the TME, cancer cells aggressively proliferate, evolve, transmigrate to the circulation system and other organs, and frequently communicate with adjacent immune cells to suppress local tumor immunity. It is essential to delineate this ecosystem's complex cellular compositions and their dynamic intercellular interactions to understand cancer biology and tumor immunology and to benefit tumor immunotherapy. But technically, this is extremely challenging due to the high complexities of the TME. The rapid developments of single-cell techniques provide us powerful means to systemically profile the multiple omics status of the TME at a single-cell resolution, shedding light on the pathogenic mechanisms of cancers and dysfunctions of tumor immunity in an unprecedently resolution. Furthermore, more advanced techniques have been developed to simultaneously characterize multi-omics and even spatial information at the single-cell level, helping us reveal the phenotypes and functionalities of disease-specific cell populations more comprehensively. Meanwhile, the connections between single-cell data and clinical characteristics are also intensively interrogated to achieve better clinical diagnosis and prognosis. In this review, we summarize recent progress in single-cell techniques, discuss their technical advantages, limitations, and applications, particularly in tumor biology and immunology, aiming to promote the research of cancer pathogenesis, clinically relevant cancer diagnosis, prognosis, and immunotherapy design with the help of single-cell techniques.

Single-cell technologies and analyses in hematopoiesis and hematological malignancies

In recent years, single-cell technologies have emerged as breakthrough techniques that enable the characterization of hematopoietic cell populations of normal and malignant tissue samples and will be combined in the near future with bulk technologies, currently used in clinical practice, to improve diagnosis, prognosis, and the search for novel molecular targets. These single-cell methods have the advantage of not masking cell-to-cell variation features and involve the study of genetic, epigenetic, transcriptional, and proteomic landscapes from a single-cell perspective. Latest advances in this field have enabled the development of novel strategies that significantly increase both sensitivity and high throughput. In this review, we emphasize emerging techniques aimed at assessing individual or multiomic parameters at single-cell resolution and analyze how these technologies have helped us understand hematopoietic variability and identify unknown and/or rare subpopulations. We also summarize the impact of these single-cell profiling strategies on the characterization of cell diversity within the tumor and the clonal evolution of multiple hematological malignancies in samples from untreated and treated patients, which provide valuable information for diagnosis, prognosis, and future treatments and explain why current therapies may fail. However, despite these improvements, new challenges lie ahead.

Biological stratification of clinical disease courses in childhood immune thrombocytopenia

BACKGROUND

In childhood immune thrombocytopenia (ITP), an autoimmune bleeding disorder, there is a need for better prediction of individual disease courses and treatment outcomes.

OBJECTIVE

To predict the response to intravenous immunoglobulins (IVIg) and ITP disease course using genetic and immune markers.

METHODS

Children aged younger than 7 years with newly diagnosed ITP (N = 147) from the Treatment With or Without IVIG for Kids with ITP study were included, which randomized children to an IVIg or observation group. A total of 46 variables were available: clinical characteristics, targeted genotyping, lymphocyte immune phenotyping, and platelet autoantibodies.

RESULTS

In the treatment arm, 48/80 children (60%) showed a complete response (platelets ≥100 × 109 /L) that lasted for at least 1 month (complete sustained response [CSR]) and 32 exhibited no or a temporary response (absence of a sustained response [ASR]). For a biological risk score, five variables were selected by regularized logistic regression that predicted ASR vs CSR: (1) hemoglobin; (2) platelet count; (3) genetic polymorphisms of Fc-receptor (FcγR) IIc; (4) the presence of immunoglobulin G (IgG) anti-platelet antibodies; and (5) preceding vaccination. The ASR sensitivity was 0.91 (95% confidence interval, 0.80-1.00) and specificity was 0.67 (95% confidence interval, 0.53-0.80). In the 67 patients of the observation arm, this biological score was also associated with recovery during 1 year of follow-up. The addition of the biological score to a predefined clinical score further improved the discrimination of favorable ITP disease courses.

CONCLUSIONS

The prediction of disease courses and IVIg treatment responses in ITP is improved by using both clinical and biological stratification.

Childhood Acute Leukemias in Developing Nations: Successes and Challenges

PURPOSE OF REVIEW

Acute leukemias represent a tremendous threat to public health around the globe and the main cause of death due to disease in scholar age children from developing nations. Here, we review their current status in Mexico, as a paradigm of study, and the major challenges to control systemic diseases like childhood cancer.

RECENT FINDINGS

A unique molecular epidemiology, late/low precision diagnosis, limited access to treatment, toxicity associated with therapy, continuous exposure to environmental risk factors, and the high frequency of early relapses are some of the factors cooperating to low rates of survival in low-to-medium-income countries. Deliberative dialogues and exhaustive programs have emerged as promising means of advancing evidence-informed policy, by providing a structured forum for key stakeholders to integrate scientific and pragmatic knowledge about complex health concerns. A system-wide strategy based on the comprehensive leukemia identity is essential for a meaningful decline in early childhood mortality.

Integrated plasma proteomic and single-cell immune signaling network signatures demarcate mild, moderate, and severe COVID-19

The biological determinants of the wide spectrum of COVID-19 clinical manifestations are not fully understood. Here, over 1400 plasma proteins and 2600 single-cell immune features comprising cell phenotype, basal signaling activity, and signaling responses to inflammatory ligands were assessed in peripheral blood from patients with mild, moderate, and severe COVID-19, at the time of diagnosis. Using an integrated computational approach to analyze the combined plasma and single-cell proteomic data, we identified and independently validated a multivariate model classifying COVID-19 severity (multi-class AUCtraining = 0.799, p-value = 4.2e-6; multi-class AUCvalidation = 0.773, p-value = 7.7e-6). Features of this high-dimensional model recapitulated recent COVID-19 related observations of immune perturbations, and revealed novel biological signatures of severity, including the mobilization of elements of the renin-angiotensin system and primary hemostasis, as well as dysregulation of JAK/STAT, MAPK/mTOR, and NF-κB immune signaling networks. These results provide a set of early determinants of COVID-19 severity that may point to therapeutic targets for the prevention of COVID-19 progression.

Nanoscopic subcellular imaging enabled by ion beam tomography

Multiplexed ion beam imaging (MIBI) has been previously used to profile multiple parameters in two dimensions in single cells within tissue slices. Here, a mathematical and technical framework for three-dimensional (3D) subcellular MIBI is presented. Ion-beam tomography (IBT) compiles ion beam images that are acquired iteratively across successive, multiple scans, and later assembled into a 3D format without loss of depth resolution. Algorithmic deconvolution, tailored for ion beams, is then applied to the transformed ion image series, yielding 4-fold enhanced ion beam data cubes. To further generate 3D sub-ion-beam-width precision visuals, isolated ion molecules are localized in the raw ion beam images, creating an approach coined as SILM, secondary ion beam localization microscopy, providing sub-25 nm accuracy in original ion images. Using deep learning, a parameter-free reconstruction method for ion beam tomograms with high accuracy is developed for low-density targets. In cultured cancer cells and tissues, IBT enables accessible visualization of 3D volumetric distributions of genomic regions, RNA transcripts, and protein factors with 5 nm axial resolution using isotope-enrichments and label-free elemental analyses. Multiparameter imaging of subcellular features at near macromolecular resolution is implemented by the IBT tools as a general biocomputation pipeline for imaging mass spectrometry.

Role of HMOX1 Promoter Genetic Variants in Chemoresistance and Chemotherapy Induced Neutropenia in Children with Acute Lymphoblastic Leukemia

Whilst the survival rates of childhood acute lymphoblastic leukemia (ALL) have increased remarkably over the last decades, the therapy resistance and toxicity are still the major causes of treatment failure. It was shown that overexpression of heme oxygenase-1 (HO-1) promotes proliferation and chemoresistance of cancer cells. In humans, the HO-1 gene (HMOX1) expression is modulated by two polymorphisms in the promoter region: (GT)n-length polymorphism and single-nucleotide polymorphism (SNP) A(−413)T, with short GT repeat sequences and 413-A variants linked to an increased HO-1 inducibility. We found that the short alleles are significantly more frequent in ALL patients in comparison to the control group, and that their presence may be associated with a higher risk of treatment failure, reflecting the role of HO-1 in chemoresistance. We also observed that the presence of short alleles may predispose to develop chemotherapy-induced neutropenia. In case of SNP, the 413-T variant co-segregated with short or long alleles, while 413-A almost selectively co-segregated with long alleles, hence it is not possible to determine if SNPs are actually of phenotypic significance. Our results suggest that HO-1 can be a potential target to overcome the treatment failure in ALL patients.

A mathematical model of tumor regression and recurrence after therapeutic oncogene inactivation

The targeted inactivation of individual oncogenes can elicit regression of cancers through a phenomenon called oncogene addiction. Oncogene addiction is mediated by cell-autonomous and immune-dependent mechanisms. Therapeutic resistance to oncogene inactivation leads to recurrence but can be counteracted by immune surveillance. Predicting the timing of resistance will provide valuable insights in developing effective cancer treatments. To provide a quantitative understanding of cancer response to oncogene inactivation, we developed a new 3-compartment mathematical model of oncogene-driven tumor growth, regression and recurrence, and validated the model using a MYC-driven transgenic mouse model of T-cell acute lymphoblastic leukemia. Our mathematical model uses imaging-based measurements of tumor burden to predict the relative number of drug-sensitive and drug-resistant cancer cells in MYC-dependent states. We show natural killer (NK) cell adoptive therapy can delay cancer recurrence by reducing the net-growth rate of drug-resistant cells. Our studies provide a novel way to evaluate combination therapy for personalized cancer treatment.

Emerging molecular subtypes and therapeutic targets in B-cell precursor acute lymphoblastic leukemia

B-cell precursor acute lymphoblastic leukemia (BCP-ALL) is characterized by genetic alterations with high heterogeneity. Precise subtypes with distinct genomic and/or gene expression patterns have been recently revealed using high-throughput sequencing technology. Most of these profiles are associated with recurrent non-overlapping rearrangements or hotspot point mutations that are analogous to the established subtypes, such as DUX4 rearrangements, MEF2D rearrangements, ZNF384/ZNF362 rearrangements, NUTM1 rearrangements, BCL2/MYC and/or BCL6 rearrangements, ETV6-RUNX1-like gene expression, PAX5alt (diverse PAX5 alterations, including rearrangements, intragenic amplifications, or mutations), and hotspot mutations PAX5 (p.Pro80Arg) with biallelic PAX5 alterations, IKZF1 (p.Asn159Tyr), and ZEB2 (p.His1038Arg). These molecular subtypes could be classified by gene expression patterns with RNA-seq technology. Refined molecular classification greatly improved the treatment strategy. Multiagent therapy regimens, including target inhibitors (e.g., imatinib), immunomodulators, monoclonal antibodies, and chimeric antigen receptor T-cell (CAR-T) therapy, are transforming the clinical practice from chemotherapy drugs to personalized medicine in the field of risk-directed disease management. We provide an update on our knowledge of emerging molecular subtypes and therapeutic targets in BCP-ALL.