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Supplementary Information from Human Bone Marrow Organoids for Disease Modeling, Discovery, and Validation of Therapeutic Targets in Hematologic Malignancies
<p>Supplementary Materials and Methods, Supplementary Figures S1-S12</p>
Supp Table 7 from Human Bone Marrow Organoids for Disease Modeling, Discovery, and Validation of Therapeutic Targets in Hematologic Malignancies
<p>Supplementary Table 7 NGS Panel</p>
Data from Human Bone Marrow Organoids for Disease Modeling, Discovery, and Validation of Therapeutic Targets in Hematologic Malignancies
<div>Abstract<p>A lack of models that recapitulate the complexity of human bone marrow has hampered mechanistic studies of normal and malignant hematopoiesis and the validation of novel therapies. Here, we describe a step-wise, directed-differentiation protocol in which organoids are generated from induced pluripotent stem cells committed to mesenchymal, endothelial, and hematopoietic lineages. These 3D structures capture key features of human bone marrow—stroma, lumen-forming sinusoids, and myeloid cells including proplatelet-forming megakaryocytes. The organoids supported the engraftment and survival of cells from patients with blood malignancies, including cancer types notoriously difficult to maintain <i>ex vivo</i>. Fibrosis of the organoid occurred following TGFβ stimulation and engraftment with myelofibrosis but not healthy donor–derived cells, validating this platform as a powerful tool for studies of malignant cells and their interactions within a human bone marrow–like milieu. This enabling technology is likely to accelerate the discovery and prioritization of novel targets for bone marrow disorders and blood cancers.</p>Significance:<p>We present a human bone marrow organoid that supports the growth of primary cells from patients with myeloid and lymphoid blood cancers. This model allows for mechanistic studies of blood cancers in the context of their microenvironment and provides a much-needed <i>ex vivo</i> tool for the prioritization of new therapeutics.</p><p><i><a href="https://aacrjournals.org/cancerdiscovery/article/doi/10.1158/2159-8290.CD-22-1303" target="_blank">See related commentary by Derecka and Crispino, p. 263</a>.</i></p><p><i><a href="https://aacrjournals.org/cancerdiscovery/article/doi/10.1158/2159-8290.CD-13-2-ITI" target="_blank">This article is highlighted in the In This Issue feature, p. 247</a></i></p></div>
Multi-organ single-cell RNA sequencing in mice reveals early hyperglycemia responses that converge on fibroblast dysregulation.
Diabetes causes a range of complications that can affect multiple organs. Hyperglycemia is an important driver of diabetes-associated complications, mediated by biological processes such as dysfunction of endothelial cells, fibrosis, and alterations in leukocyte number and function. Here, we dissected the transcriptional response of key cell types to hyperglycemia across multiple tissues using single-cell RNA sequencing (scRNA-seq) and identified conserved, as well as organ-specific, changes associated with diabetes complications. By studying an early time point of diabetes, we focus on biological processes involved in the initiation of the disease, before the later organ-specific manifestations had supervened. We used a mouse model of type 1 diabetes and performed scRNA-seq on cells isolated from the heart, kidney, liver, and spleen of streptozotocin-treated and control male mice after 8 weeks and assessed differences in cell abundance, gene expression, pathway activation, and cell signaling across organs and within organs. In response to hyperglycemia, endothelial cells, macrophages, and monocytes displayed organ-specific transcriptional responses, whereas fibroblasts showed similar responses across organs, exhibiting altered metabolic gene expression and increased myeloid-like fibroblasts. Furthermore, we found evidence of endothelial dysfunction in the kidney, and of endothelial-to-mesenchymal transition in streptozotocin-treated mouse organs. In summary, our study represents the first single-cell and multi-organ analysis of early dysfunction in type 1 diabetes-associated hyperglycemia, and our large-scale dataset (comprising 67 611 cells) will serve as a starting point, reference atlas, and resource for further investigating the events leading to early diabetic disease.
Adipokines and stroke: A systematic review and meta-analysis of disease risk and patient outcomes.
Obesity is reported to increase stroke risk, with adipocyte-derived cytokines or adipokines implicated as mediators. However, the relationship between adipokines and stroke is not well clarified. Thus, we aimed to evaluate the association of adipokines with stroke using fully adjusted risk estimates that incorporated body mass index in a meta-analysis. Data from 52 studies (62,428 patients) were pooled in a random-effects meta-analysis. Adiponectin was independently associated with a lower risk of pre-existing stroke (adjusted odds ratio: 0.64 [95% confidence interval: 0.46-0.88], p
Dihydropyrimidine dehydrogenase gene variants for predicting grade 4-5 fluoropyrimidine-induced toxicity: FUSAFE individual patient data meta-analysis.
BACKGROUND: Dihydropyrimidine dehydrogenase (DPD) deficiency is the main known cause of life-threatening fluoropyrimidine (FP)-induced toxicities. We conducted a meta-analysis on individual patient data to assess the contribution of deleterious DPYD variants *2A/D949V/*13/HapB3 (recommended by EMA) and clinical factors, for predicting G4-5 toxicity. METHODS: Study eligibility criteria included recruitment of Caucasian patients without DPD-based FP-dose adjustment. Main endpoint was 12-week haematological or digestive G4-5 toxicity. The value of DPYD variants *2A/p.D949V/*13 merged, HapB3, and MIR27A rs895819 was evaluated using multivariable logistic models (AUC). RESULTS: Among 25 eligible studies, complete clinical variables and primary endpoint were available in 15 studies (8733 patients). Twelve-week G4-5 toxicity prevalence was 7.3% (641 events). The clinical model included age, sex, body mass index, schedule of FP-administration, concomitant anticancer drugs. Adding *2A/p.D949V/*13 variants (at least one allele, prevalence 2.2%, OR 9.5 [95%CI 6.7-13.5]) significantly improved the model (p
Automated Echocardiographic Detection of Heart Failure With Preserved Ejection Fraction Using Artificial Intelligence
Background: Detection of heart failure with preserved ejection fraction (HFpEF) involves integration of multiple imaging and clinical features which are often discordant or indeterminate. Objectives: The authors applied artificial intelligence (AI) to analyze a single apical 4-chamber transthoracic echocardiogram video clip to detect HFpEF. Methods: A 3-dimensional convolutional neural network was developed and trained on apical 4-chamber video clips to classify patients with HFpEF (diagnosis of heart failure, ejection fraction ≥50%, and echocardiographic evidence of increased filling pressure; cases) vs without HFpEF (ejection fraction ≥50%, no diagnosis of heart failure, normal filling pressure; controls). Model outputs were classified as HFpEF, no HFpEF, or nondiagnostic (high uncertainty). Performance was assessed in an independent multisite data set and compared to previously validated clinical scores. Results: Training and validation included 2,971 cases and 3,785 controls (validation holdout, 16.8% patients), and demonstrated excellent discrimination (area under receiver-operating characteristic curve: 0.97 [95% CI: 0.96-0.97] and 0.95 [95% CI: 0.93-0.96] in training and validation, respectively). In independent testing (646 cases, 638 controls), 94 (7.3%) were nondiagnostic; sensitivity (87.8%; 95% CI: 84.5%-90.9%) and specificity (81.9%; 95% CI: 78.2%-85.6%) were maintained in clinically relevant subgroups, with high repeatability and reproducibility. Of 701 and 776 indeterminate outputs from the Heart Failure Association-Pretest Assessment, Echocardiographic and Natriuretic Peptide Score, Functional Testing (HFA-PEFF), and Final Etiology and Heavy, Hypertensive, Atrial Fibrillation, Pulmonary Hypertension, Elder, and Filling Pressure (H2FPEF) scores, the AI HFpEF model correctly reclassified 73.5% and 73.6%, respectively. During follow-up (median: 2.3 [IQR: 0.5-5.6] years), 444 (34.6%) patients died; mortality was higher in patients classified as HFpEF by AI (HR: 1.9 [95% CI: 1.5-2.4]). Conclusions: An AI HFpEF model based on a single, routinely acquired echocardiographic video demonstrated excellent discrimination of patients with vs without HFpEF, more often than clinical scores, and identified patients with higher mortality.
Classification, risk stratification and response assessment in myelodysplastic syndromes/neoplasms (MDS): A state-of-the-art report on behalf of the International Consortium for MDS (icMDS).
The guidelines for classification, prognostication, and response assessment of myelodysplastic syndromes/neoplasms (MDS) have all recently been updated. In this report on behalf of the International Consortium for MDS (icMDS) we summarize these developments. We first critically examine the updated World Health Organization (WHO) classification and the International Consensus Classification (ICC) of MDS. We then compare traditional and molecularly based risk MDS risk assessment tools. Lastly, we discuss limitations of criteria in measuring therapeutic benefit and highlight how the International Working Group (IWG) 2018 and 2023 response criteria addressed these deficiencies and are endorsed by the icMDS. We also address the importance of patient centered care by discussing the value of quality-of-life assessment. We hope that the reader of this review will have a better understanding of how to classify MDS, predict clinical outcomes and evaluate therapeutic outcomes.
Pan-cancer analysis of whole genomes identifies driver rearrangements promoted by LINE-1 retrotransposition.
About half of all cancers have somatic integrations of retrotransposons. Here, to characterize their role in oncogenesis, we analyzed the patterns and mechanisms of somatic retrotransposition in 2,954 cancer genomes from 38 histological cancer subtypes within the framework of the Pan-Cancer Analysis of Whole Genomes (PCAWG) project. We identified 19,166 somatically acquired retrotransposition events, which affected 35% of samples and spanned a range of event types. Long interspersed nuclear element (LINE-1; L1 hereafter) insertions emerged as the first most frequent type of somatic structural variation in esophageal adenocarcinoma, and the second most frequent in head-and-neck and colorectal cancers. Aberrant L1 integrations can delete megabase-scale regions of a chromosome, which sometimes leads to the removal of tumor-suppressor genes, and can induce complex translocations and large-scale duplications. Somatic retrotranspositions can also initiate breakage-fusion-bridge cycles, leading to high-level amplification of oncogenes. These observations illuminate a relevant role of L1 retrotransposition in remodeling the cancer genome, with potential implications for the development of human tumors.
Disruption of chromatin folding domains by somatic genomic rearrangements in human cancer.
Chromatin is folded into successive layers to organize linear DNA. Genes within the same topologically associating domains (TADs) demonstrate similar expression and histone-modification profiles, and boundaries separating different domains have important roles in reinforcing the stability of these features. Indeed, domain disruptions in human cancers can lead to misregulation of gene expression. However, the frequency of domain disruptions in human cancers remains unclear. Here, as part of the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA), which aggregated whole-genome sequencing data from 2,658 cancers across 38 tumor types, we analyzed 288,457 somatic structural variations (SVs) to understand the distributions and effects of SVs across TADs. Notably, SVs can lead to the fusion of discrete TADs, and complex rearrangements markedly change chromatin folding maps in the cancer genomes. Notably, only 14% of the boundary deletions resulted in a change in expression in nearby genes of more than twofold.
Comprehensive molecular characterization of mitochondrial genomes in human cancers.
Mitochondria are essential cellular organelles that play critical roles in cancer. Here, as part of the International Cancer Genome Consortium/The Cancer Genome Atlas Pan-Cancer Analysis of Whole Genomes Consortium, which aggregated whole-genome sequencing data from 2,658 cancers across 38 tumor types, we performed a multidimensional, integrated characterization of mitochondrial genomes and related RNA sequencing data. Our analysis presents the most definitive mutational landscape of mitochondrial genomes and identifies several hypermutated cases. Truncating mutations are markedly enriched in kidney, colorectal and thyroid cancers, suggesting oncogenic effects with the activation of signaling pathways. We find frequent somatic nuclear transfers of mitochondrial DNA, some of which disrupt therapeutic target genes. Mitochondrial copy number varies greatly within and across cancers and correlates with clinical variables. Co-expression analysis highlights the function of mitochondrial genes in oxidative phosphorylation, DNA repair and the cell cycle, and shows their connections with clinically actionable genes. Our study lays a foundation for translating mitochondrial biology into clinical applications.
Comprehensive analysis of chromothripsis in 2,658 human cancers using whole-genome sequencing.
Chromothripsis is a mutational phenomenon characterized by massive, clustered genomic rearrangements that occurs in cancer and other diseases. Recent studies in selected cancer types have suggested that chromothripsis may be more common than initially inferred from low-resolution copy-number data. Here, as part of the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA), we analyze patterns of chromothripsis across 2,658 tumors from 38 cancer types using whole-genome sequencing data. We find that chromothripsis events are pervasive across cancers, with a frequency of more than 50% in several cancer types. Whereas canonical chromothripsis profiles display oscillations between two copy-number states, a considerable fraction of events involve multiple chromosomes and additional structural alterations. In addition to non-homologous end joining, we detect signatures of replication-associated processes and templated insertions. Chromothripsis contributes to oncogene amplification and to inactivation of genes such as mismatch-repair-related genes. These findings show that chromothripsis is a major process that drives genome evolution in human cancer.
Deciphering signaling pathways in hematopoietic stem cells: the molecular complexity of Myelodysplastic Syndromes (MDS) and leukemic progression.
Myelodysplastic Syndromes, a heterogeneous group of hematological disorders, are characterized by abnormalities in phosphoinositide-dependent signaling, epigenetic regulators, apoptosis, and cytokine interactions within the bone marrow microenvironment, contributing to disease pathogenesis and neoplastic growth. Comprehensive knowledge of these pathways is crucial for the development of innovative therapies that aim to restore normal apoptosis and improve patient outcomes.
Pathway and network analysis of more than 2500 whole cancer genomes.
The catalog of cancer driver mutations in protein-coding genes has greatly expanded in the past decade. However, non-coding cancer driver mutations are less well-characterized and only a handful of recurrent non-coding mutations, most notably TERT promoter mutations, have been reported. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2658 cancer across 38 tumor types, we perform multi-faceted pathway and network analyses of non-coding mutations across 2583 whole cancer genomes from 27 tumor types compiled by the ICGC/TCGA PCAWG project that was motivated by the success of pathway and network analyses in prioritizing rare mutations in protein-coding genes. While few non-coding genomic elements are recurrently mutated in this cohort, we identify 93 genes harboring non-coding mutations that cluster into several modules of interacting proteins. Among these are promoter mutations associated with reduced mRNA expression in TP53, TLE4, and TCF4. We find that biological processes had variable proportions of coding and non-coding mutations, with chromatin remodeling and proliferation pathways altered primarily by coding mutations, while developmental pathways, including Wnt and Notch, altered by both coding and non-coding mutations. RNA splicing is primarily altered by non-coding mutations in this cohort, and samples containing non-coding mutations in well-known RNA splicing factors exhibit similar gene expression signatures as samples with coding mutations in these genes. These analyses contribute a new repertoire of possible cancer genes and mechanisms that are altered by non-coding mutations and offer insights into additional cancer vulnerabilities that can be investigated for potential therapeutic treatments.
Reconstructing evolutionary trajectories of mutation signature activities in cancer using TrackSig.
The type and genomic context of cancer mutations depend on their causes. These causes have been characterized using signatures that represent mutation types that co-occur in the same tumours. However, it remains unclear how mutation processes change during cancer evolution due to the lack of reliable methods to reconstruct evolutionary trajectories of mutational signature activity. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole-genome sequencing data from 2658 cancers across 38 tumour types, we present TrackSig, a new method that reconstructs these trajectories using optimal, joint segmentation and deconvolution of mutation type and allele frequencies from a single tumour sample. In simulations, we find TrackSig has a 3-5% activity reconstruction error, and 12% false detection rate. It outperforms an aggressive baseline in situations with branching evolution, CNA gain, and neutral mutations. Applied to data from 2658 tumours and 38 cancer types, TrackSig permits pan-cancer insight into evolutionary changes in mutational processes.