Mutation order in acute myeloid leukemia identifies uncommon patterns of evolution and illuminates phenotypic heterogeneity.

Acute myeloid leukemia (AML) has a poor prognosis and a heterogeneous mutation landscape. Although common mutations are well-studied, little research has characterized how the sequence of mutations relates to clinical features. Using published, single-cell DNA sequencing data from three institutions, we compared clonal evolution patterns in AML to patient characteristics, disease phenotype, and outcomes. Mutation trees, which represent the order of select mutations, were created for 207 patients from targeted panel sequencing data using 1 639 162 cells, 823 mutations, and 275 samples. In 224 distinct orderings of mutated genes, mutations related to DNA methylation typically preceded those related to cell signaling, but signaling-first cases did occur, and had higher peripheral cell counts, increased signaling mutation homozygosity, and younger patient age. Serial sample analysis suggested that NPM1 and DNA methylation mutations provide an advantage to signaling mutations in AML. Interestingly, WT1 mutation evolution shared features with signaling mutations, such as WT1-early being proliferative and occurring in younger individuals, trends that remained in multivariable regression. Some mutation orderings had a worse prognosis, but this was mediated by unfavorable mutations, not mutation order. These findings add a dimension to the mutation landscape of AML, identifying uncommon patterns of leukemogenesis and shedding light on heterogeneous phenotypes.

Web-Based Scaffolds: The Feasibility of a Constructivist Approach to Oncology Fellow Learning.

In this 2-institution feasibility pilot, oncology fellows used and updated freely available web-based learning tools (scaffolds) in a constructivist fashion.

Mutation order in acute myeloid leukemia identifies uncommon patterns of evolution and illuminates phenotypic heterogeneity.

Acute myeloid leukemia (AML) has a poor prognosis and a heterogeneous mutation landscape. Although common mutations are well-studied, little research has characterized how the sequence of mutations relates to clinical features. Using published, single-cell DNA sequencing data from three institutions, we compared clonal evolution patterns in AML to patient characteristics, disease phenotype, and outcomes. Mutation trees, which represent the order of select mutations, were created for 207 patients from targeted panel sequencing data using 1 639 162 cells, 823 mutations, and 275 samples. In 224 distinct orderings of mutated genes, mutations related to DNA methylation typically preceded those related to cell signaling, but signaling-first cases did occur, and had higher peripheral cell counts, increased signaling mutation homozygosity, and younger patient age. Serial sample analysis suggested that NPM1 and DNA methylation mutations provide an advantage to signaling mutations in AML. Interestingly, WT1 mutation evolution shared features with signaling mutations, such as WT1-early being proliferative and occurring in younger individuals, trends that remained in multivariable regression. Some mutation orderings had a worse prognosis, but this was mediated by unfavorable mutations, not mutation order. These findings add a dimension to the mutation landscape of AML, identifying uncommon patterns of leukemogenesis and shedding light on heterogenous phenotypes.

Graph-based clinical recommender: Predicting specialists procedure orders using graph representation learning.

OBJECTIVE: To determine whether graph neural network based models of electronic health records can predict specialty consultation care needs for endocrinology and hematology more accurately than the standard of care checklists and other conventional medical recommendation algorithms in the literature. METHODS: Demand for medical expertise far outstrips supply, with tens of millions in the US alone with deficient access to specialty care. Rather than potentially months long delays to initiate diagnostic workup and medical treatment with a specialist, referring primary care supported by an automated recommender algorithm could anticipate and directly initiate patient evaluation that would otherwise be needed at subsequent a specialist appointment. We propose a novel graph representation learning approach with a heterogeneous graph neural network to model structured electronic health records and formulate recommendation/prediction of subsequent specialist orders as a link prediction problem. RESULTS: Models are trained and assessed in two specialty care sites: endocrinology and hematology. Our experimental results show that our model achieves an 8% improvement in ROC-AUC for endocrinology (ROC-AUC = 0.88) and 5% improvement for hematology (ROC-AUC = 0.84) personalized procedure recommendations over prior medical recommender systems. These recommender algorithm approaches provide medical procedure recommendations for endocrinology referrals more effectively than manual clinical checklists (recommender: precision = 0.60, recall = 0.27, F1-score = 0.37) vs. (checklist: precision = 0.16, recall = 0.28, F1-score = 0.20), and similarly for hematology referrals (recommender: precision = 0.44, recall = 0.38, F1-score = 0.41) vs. (checklist: precision = 0.27, recall = 0.71, F1-score = 0.39). CONCLUSION: Embedding graph neural network models into clinical care can improve digital specialty consultation systems and expand the access to medical experience of prior similar cases.

Diagnosis and management of neutrophilic myeloid neoplasms.

Chronic neutrophilia is commonly seen with persistent infections, inflammatory disorders, smoking, solid tumors, and specific medications. However, after reactive causes have been excluded, a workup for primary (clonal) neutrophilic disorders, such as myeloproliferative neoplasms (MPNs) and myelodysplastic/myeloproliferative overlap syndromes, should be pursued. Except for chronic myeloid leukemia, which is defined by the presence of the Philadelphia (Ph) chromosome, and the classic Ph chromosome-negative MPNs (polycythemia vera, essential thrombocythemia, and primary myelofibrosis), clonal neutrophilic neoplasms historically have been challenging to diagnose and classify. The 2016 revised World Health Organization classification of these disorders has been based mainly on clinicopathologic features. However, recent discoveries of the molecular alterations underlying these disorders have served to supplement our knowledge of their morphologic and clinical features, opening new therapeutic avenues. In this review, we discuss the diagnostic approach, prognostic features, and treatments of neutrophilic myeloid neoplasms, with a focus on chronic neutrophilic leukemia, atypical chronic myeloid leukemia, and chronic myelomonocytic leukemia.

Proinflammatory cytokines and ARDS pulmonary edema fluid induce CD40 on human mesenchymal stromal cells-A potential mechanism for immune modulation.

Human mesenchymal stem/stromal cells (hMSCs) are a promising therapy for acute respiratory distress syndrome (ARDS) and other inflammatory conditions. While considerable research has focused on paracrine effects and mitochondrial transfer that improve lung fluid balance, hMSCs are well known to have immunomodulatory properties as well. Some of these immunomodulatory properties have been related to previously reported paracrine effectors such as indoleamine-2,3-dioxygenase (IDO), but these effects cannot fully account for cell-contact dependent immunomodulation. Here, we report that CD40 is upregulated on hMSCs under the same conditions previously reported to induce IDO. Further, CD40 transcription is also upregulated on hMSCs by ARDS pulmonary edema fluid but not by hydrostatic pulmonary edema fluid. Transcription of CD40, as well as paracrine effectors TSG6 and PTGS2 remained significantly upregulated for at least 12 hours after withdrawal of cytokine stimulation. Finally, induction of this immune phenotype altered the transdifferentiation of hMSCs, one of their hallmark properties. CD40 may play an important role in the immunomodulatory effects of hMSCs in ARDS and inflammation.

Clinician Recognition of the Acute Respiratory Distress Syndrome: Risk Factors for Under-Recognition and Trends Over Time.

OBJECTIVES: The acute respiratory distress syndrome is common in critically ill patients. Recognition is crucial because acute respiratory distress syndrome is associated with a high mortality rate, and low tidal volume ventilation improves mortality. However, acute respiratory distress syndrome often goes unrecognized. Risk factors for under-recognition and trends over time have not been fully described. DESIGN: Retrospective chart review of patients with acute respiratory distress syndrome from a prospective cohort study of critically ill patients. For each patient's ICU stay, we searched the chart for terms that indicated that acute respiratory distress syndrome was diagnosed, in the differential diagnosis, or treated with low tidal volume ventilation. SETTING: ICUs at a tertiary hospital at the University of California, San Francisco between 2008 and 2016. PATIENTS: Critically ill patients with acute respiratory distress syndrome. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Acute respiratory distress syndrome was recognized in 70% of patients, and recognition increased from 60% in 2008-2009 to 92% in 2016 (p = 0.004). Use of tidal volumes less than 6.5 mL/kg also increased (p < 0.001) from 20% to 92%. Increased acute respiratory distress syndrome severity (p = 0.01) and vasopressor use (p = 0.04) were associated with greater recognition. Clinician diagnosis of acute respiratory distress syndrome and inclusion of acute respiratory distress syndrome in the differential diagnosis were associated with tidal volumes less than 6.5 mL/kg (51% use of tidal volume ≤ 6.5 mL/kg if acute respiratory distress syndrome recognized vs 15% if not recognized; p = 0.002). Diagnosing acute respiratory distress syndrome was associated with lower tidal volume in multivariate analysis. CONCLUSIONS: Although acute respiratory distress syndrome recognition and low tidal volume ventilation use have increased over time, they remain less than universal. Clinician recognition of acute respiratory distress syndrome is associated with both systemic and respiratory severity of illness and is also associated with use of low tidal volume ventilation.

The Impact of Stroma Admixture on Molecular Subtypes and Prognostic Gene Signatures in Serous Ovarian Cancer.

BACKGROUND: Recent efforts to improve outcomes for high-grade serous ovarian cancer, a leading cause of cancer death in women, have focused on identifying molecular subtypes and prognostic gene signatures, but existing subtypes have poor cross-study robustness. We tested the contribution of cell admixture in published ovarian cancer molecular subtypes and prognostic gene signatures. METHODS: Gene signatures of tumor and stroma were developed using paired microdissected tissue from two independent studies. Stromal genes were investigated in two molecular subtype classifications and 61 published gene signatures. Prognostic performance of gene signatures of stromal admixture was evaluated in 2,527 ovarian tumors (16 studies). Computational simulations of increasing stromal cell proportion were performed by mixing gene-expression profiles of paired microdissected ovarian tumor and stroma. RESULTS: Recently described ovarian cancer molecular subtypes are strongly associated with the cell admixture. Tumors were classified as different molecular subtypes in simulations where the percentage of stromal cells increased. Stromal gene expression in bulk tumors was associated with overall survival (hazard ratio, 1.17; 95% confidence interval, 1.11-1.23), and in one data set, increased stroma was associated with anatomic sampling location. Five published prognostic gene signatures were no longer prognostic in a multivariate model that adjusted for stromal content. CONCLUSIONS: Cell admixture affects the interpretation and reproduction of ovarian cancer molecular subtypes and gene signatures derived from bulk tissue. Elucidating the role of stroma in the tumor microenvironment and in prognosis is important. IMPACT: Single-cell analyses may be required to refine the molecular subtypes of high-grade serous ovarian cancer.

Effects of bone marrow-derived mesenchymal stromal cells on gene expression in human alveolar type II cells exposed to TNF-α, IL-1ß, and IFN-γ.

The acute respiratory distress syndrome (ARDS) is common in critically ill patients and has a high mortality rate. Mesenchymal stromal cells (MSCs) have demonstrated therapeutic potential in animal models of ARDS, and their benefits occur in part through interactions with alveolar type II (ATII) cells. However, the effects that MSCs have on human ATII cells have not been well studied. Using previously published microarray data, we performed genome-wide differential gene expression analyses of human ATII cells that were (1) unstimulated, (2) exposed to proinflammatory cytokines (CytoMix), or (3) exposed to proinflammatory cytokines plus MSCs. Findings were validated by qPCR. Alveolar type II cells differentially expressed hundreds of genes when exposed either to proinflammatory cytokines or to proinflammatory cytokines plus MSCs. Stimulation with proinflammatory cytokines increased expression of inflammatory genes and downregulated genes related to surfactant function and alveolar fluid clearance. Some of these changes, including expression of some cytokines and genes related to surfactant, were reversed by exposure to MSCs. In addition, MSCs induced upregulation of other potentially beneficial genes, such as those related to extracellular matrix remodeling. We confirmed several of these gene expression changes by qPCR. Thus, ATII cells downregulate genes associated with surfactant and alveolar fluid clearance when exposed to inflammatory cytokines, and mesenchymal stromal cells partially reverse many of these gene expression changes.

Strong correlation of downregulated genes related to synaptic transmission and mitochondria in post-mortem autism cerebral cortex.

BACKGROUND: Genetic studies in autism have pinpointed a heterogeneous group of loci and genes. Further, environment may be an additional factor conferring susceptibility to autism. Transcriptome studies investigate quantitative differences in gene expression between patient-derived tissues and control. These studies may pinpoint genes relevant to pathophysiology yet circumvent the need to understand genetic architecture or gene-by-environment interactions leading to disease. METHODS: We conducted alternate gene set enrichment analyses using differentially expressed genes from a previously published RNA-seq study of post-mortem autism cerebral cortex. We used three previously published microarray datasets for validation and one of the microarray datasets for additional differential expression analysis. The RNA-seq study used 26 autism and 33 control brains in differential gene expression analysis, and the largest microarray dataset contained 15 autism and 16 control post-mortem brains. RESULTS: While performing a gene set enrichment analysis of genes differentially expressed in the RNA-seq study, we discovered that genes associated with mitochondrial function were downregulated in autism cerebral cortex, as compared to control. These genes were correlated with genes related to synaptic function. We validated these findings across the multiple microarray datasets. We also did separate differential expression and gene set enrichment analyses to confirm the importance of the mitochondrial pathway among downregulated genes in post-mortem autism cerebral cortex. CONCLUSIONS: We found that genes related to mitochondrial function were differentially expressed in autism cerebral cortex and correlated with genes related to synaptic transmission. Our principal findings replicate across all datasets investigated. Further, these findings may potentially replicate in other diseases, such as in schizophrenia.

Mutations in mitochondrial enzyme GPT2 cause metabolic dysfunction and neurological disease with developmental and progressive features.

Mutations that cause neurological phenotypes are highly informative with regard to mechanisms governing human brain function and disease. We report autosomal recessive mutations in the enzyme glutamate pyruvate transaminase 2 (GPT2) in large kindreds initially ascertained for intellectual and developmental disability (IDD). GPT2 [also known as alanine transaminase 2 (ALT2)] is one of two related transaminases that catalyze the reversible addition of an amino group from glutamate to pyruvate, yielding alanine and α-ketoglutarate. In addition to IDD, all affected individuals show postnatal microcephaly and ∼80% of those followed over time show progressive motor symptoms, a spastic paraplegia. Homozygous nonsense p.Arg404* and missense p.Pro272Leu mutations are shown biochemically to be loss of function. The GPT2 gene demonstrates increasing expression in brain in the early postnatal period, and GPT2 protein localizes to mitochondria. Akin to the human phenotype, Gpt2-null mice exhibit reduced brain growth. Through metabolomics and direct isotope tracing experiments, we find a number of metabolic abnormalities associated with loss of Gpt2. These include defects in amino acid metabolism such as low alanine levels and elevated essential amino acids. Also, we find defects in anaplerosis, the metabolic process involved in replenishing TCA cycle intermediates. Finally, mutant brains demonstrate misregulated metabolites in pathways implicated in neuroprotective mechanisms previously associated with neurodegenerative disorders. Overall, our data reveal an important role for the GPT2 enzyme in mitochondrial metabolism with relevance to developmental as well as potentially to neurodegenerative mechanisms.

Taxonomy of breast cancer based on normal cell phenotype predicts outcome.

Accurate classification is essential for understanding the pathophysiology of a disease and can inform therapeutic choices. For hematopoietic malignancies, a classification scheme based on the phenotypic similarity between tumor cells and normal cells has been successfully used to define tumor subtypes; however, use of normal cell types as a reference by which to classify solid tumors has not been widely emulated, in part due to more limited understanding of epithelial cell differentiation compared with hematopoiesis. To provide a better definition of the subtypes of epithelial cells comprising the breast epithelium, we performed a systematic analysis of a large set of breast epithelial markers in more than 15,000 normal breast cells, which identified 11 differentiation states for normal luminal cells. We then applied information from this analysis to classify human breast tumors based on normal cell types into 4 major subtypes, HR0-HR3, which were differentiated by vitamin D, androgen, and estrogen hormone receptor (HR) expression. Examination of 3,157 human breast tumors revealed that these HR subtypes were distinct from the current classification scheme, which is based on estrogen receptor, progesterone receptor, and human epidermal growth factor receptor 2. Patient outcomes were best when tumors expressed all 3 hormone receptors (subtype HR3) and worst when they expressed none of the receptors (subtype HR0). Together, these data provide an ontological classification scheme associated with patient survival differences and provides actionable insights for treating breast tumors.

Gene expression signature of normal cell-of-origin predicts ovarian tumor outcomes.

The potential role of the cell-of-origin in determining the tumor phenotype has been raised, but not adequately examined. We hypothesized that distinct cells-of-origin may play a role in determining ovarian tumor phenotype and outcome. Here we describe a new cell culture medium for in vitro culture of paired normal human ovarian (OV) and fallopian tube (FT) epithelial cells from donors without cancer. While these cells have been cultured individually for short periods of time, to our knowledge this is the first long-term culture of both cell types from the same donors. Through analysis of the gene expression profiles of the cultured OV/FT cells we identified a normal cell-of-origin gene signature that classified primary ovarian cancers into OV-like and FT-like subgroups; this classification correlated with significant differences in clinical outcomes. The identification of a prognostically significant gene expression signature derived solely from normal untransformed cells is consistent with the hypothesis that the normal cell-of-origin may be a source of ovarian tumor heterogeneity and the associated differences in tumor outcome.

Stem cell-like gene expression in ovarian cancer predicts type II subtype and prognosis.

Although ovarian cancer is often initially chemotherapy-sensitive, the vast majority of tumors eventually relapse and patients die of increasingly aggressive disease. Cancer stem cells are believed to have properties that allow them to survive therapy and may drive recurrent tumor growth. Cancer stem cells or cancer-initiating cells are a rare cell population and difficult to isolate experimentally. Genes that are expressed by stem cells may characterize a subset of less differentiated tumors and aid in prognostic classification of ovarian cancer. The purpose of this study was the genomic identification and characterization of a subtype of ovarian cancer that has stem cell-like gene expression. Using human and mouse gene signatures of embryonic, adult, or cancer stem cells, we performed an unsupervised bipartition class discovery on expression profiles from 145 serous ovarian tumors to identify a stem-like and more differentiated subgroup. Subtypes were reproducible and were further characterized in four independent, heterogeneous ovarian cancer datasets. We identified a stem-like subtype characterized by a 51-gene signature, which is significantly enriched in tumors with properties of Type II ovarian cancer; high grade, serous tumors, and poor survival. Conversely, the differentiated tumors share properties with Type I, including lower grade and mixed histological subtypes. The stem cell-like signature was prognostic within high-stage serous ovarian cancer, classifying a small subset of high-stage tumors with better prognosis, in the differentiated subtype. In multivariate models that adjusted for common clinical factors (including grade, stage, age), the subtype classification was still a significant predictor of relapse. The prognostic stem-like gene signature yields new insights into prognostic differences in ovarian cancer, provides a genomic context for defining Type I/II subtypes, and potential gene targets which following further validation may be valuable in the clinical management or treatment of ovarian cancer.

An unbalanced translocation involving loss of 10q26.2 and gain of 11q25 in a pedigree with autism spectrum disorder and cerebellar juvenile pilocytic astrocytoma.

We report on a pedigree with a pair of brothers each with minor anomalies, developmental delay, and autistic-symptoms who share an unbalanced translocation (not detectable by karyotype). The unbalanced translocation involves a 7.1 Mb loss of the terminal portion of 10q, and a 4.2 Mb gain of 11q. One of the brothers also developed a cerebellar juvenile pilocytic astrocytoma. The father was found to be a balanced carrier and the couple had a previous miscarriage. We demonstrate that the breakpoint for the triplicated region from chromosome 11 is adjacent to two IgLON genes, namely Neurotrimin (NTM) and Opioid Binding Protein/Cell Adhesion Molecule-like (OPCML). These genes are highly similar neural cell adhesion molecules that have been implicated in synaptogenesis and oncogenesis, respectively. The children also have a 10q deletion and are compared to other children with the 10q deletion syndrome which generally does not involve autism spectrum disorders (ASDs) or cancer. Together these data support a role for NTM and OPCML in developmental delay and potentially in cancer susceptibility.

Profiles of genomic instability in high-grade serous ovarian cancer predict treatment outcome.

PURPOSE: High-grade serous cancer (HGSC) is the most common cancer of the ovary and is characterized by chromosomal instability. Defects in homologous recombination repair (HRR) are associated with genomic instability in HGSC, and are exploited by therapy targeting DNA repair. Defective HRR causes uniparental deletions and loss of heterozygosity (LOH). Our purpose is to profile LOH in HGSC and correlate our findings to clinical outcome, and compare HGSC and high-grade breast cancers. EXPERIMENTAL DESIGN: We examined LOH and copy number changes using single nucleotide polymorphism array data from three HGSC cohorts and compared results to a cohort of high-grade breast cancers. The LOH profiles in HGSC were matched to chemotherapy resistance and progression-free survival (PFS). RESULTS: LOH-based clustering divided HGSC into two clusters. The major group displayed extensive LOH and was further divided into two subgroups. The second group contained remarkably less LOH. BRCA1 promoter methylation was associated with the major group. LOH clusters were reproducible when validated in two independent HGSC datasets. LOH burden in the major cluster of HGSC was similar to triple-negative, and distinct from other high-grade breast cancers. Our analysis revealed an LOH cluster with lower treatment resistance and a significant correlation between LOH burden and PFS. CONCLUSIONS: Separating HGSC by LOH-based clustering produces remarkably stable subgroups in three different cohorts. Patients in the various LOH clusters differed with respect to chemotherapy resistance, and the extent of LOH correlated with PFS. LOH burden may indicate vulnerability to treatment targeting DNA repair, such as PARP1 inhibitors.