The origin and evolution of mutations in acute myeloid leukemia.

Most mutations in cancer genomes are thought to be acquired after the initiating event, which may cause genomic instability and drive clonal evolution. However, for acute myeloid leukemia (AML), normal karyotypes are common, and genomic instability is unusual. To better understand clonal evolution in AML, we sequenced the genomes of M3-AML samples with a known initiating event (PML-RARA) versus the genomes of normal karyotype M1-AML samples and the exomes of hematopoietic stem/progenitor cells (HSPCs) from healthy people. Collectively, the data suggest that most of the mutations found in AML genomes are actually random events that occurred in HSPCs before they acquired the initiating mutation; the mutational history of that cell is "captured" as the clone expands. In many cases, only one or two additional, cooperating mutations are needed to generate the malignant founding clone. Cells from the founding clone can acquire additional cooperating mutations, yielding subclones that can contribute to disease progression and/or relapse.

A Randomized Trial of a Multifactorial Strategy to Prevent Serious Fall Injuries.

BACKGROUND: Injuries from falls are major contributors to complications and death in older adults. Despite evidence from efficacy trials that many falls can be prevented, rates of falls resulting in injury have not declined. METHODS: We conducted a pragmatic, cluster-randomized trial to evaluate the effectiveness of a multifactorial intervention that included risk assessment and individualized plans, administered by specially trained nurses, to prevent fall injuries. A total of 86 primary care practices across 10 health care systems were randomly assigned to the intervention or to enhanced usual care (the control) (43 practices each). The participants were community-dwelling adults, 70 years of age or older, who were at increased risk for fall injuries. The primary outcome, assessed in a time-to-event analysis, was the first serious fall injury, adjudicated with the use of participant report, electronic health records, and claims data. We hypothesized that the event rate would be lower by 20% in the intervention group than in the control group. RESULTS: The demographic and baseline characteristics of the participants were similar in the intervention group (2802 participants) and the control group (2649 participants); the mean age was 80 years, and 62.0% of the participants were women. The rate of a first adjudicated serious fall injury did not differ significantly between the groups, as assessed in a time-to-first-event analysis (events per 100 person-years of follow-up, 4.9 in the intervention group and 5.3 in the control group; hazard ratio, 0.92; 95% confidence interval [CI], 0.80 to 1.06; P = 0.25). The rate of a first participant-reported fall injury was 25.6 events per 100 person-years of follow-up in the intervention group and 28.6 events per 100 person-years of follow-up in the control group (hazard ratio, 0.90; 95% CI, 0.83 to 0.99; P = 0.004). The rates of hospitalization or death were similar in the two groups. CONCLUSIONS: A multifactorial intervention, administered by nurses, did not result in a significantly lower rate of a first adjudicated serious fall injury than enhanced usual care. (Funded by the Patient-Centered Outcomes Research Institute and others; STRIDE ClinicalTrials.gov number, NCT02475850.).

Collaborative Privacy-preserving Approaches for Distributed Deep Learning Using Multi-Institutional Data.

Deep learning (DL) algorithms have shown remarkable potential in automating various tasks in medical imaging and radiologic reporting. However, models trained on low quantities of data or only using data from a single institution often are not generalizable to other institutions, which may have different patient demographics or data acquisition characteristics. Therefore, training DL algorithms using data from multiple institutions is crucial to improving the robustness and generalizability of clinically useful DL models. In the context of medical data, simply pooling data from each institution to a central location to train a model poses several issues such as increased risk to patient privacy, increased costs for data storage and transfer, and regulatory challenges. These challenges of centrally hosting data have motivated the development of distributed machine learning techniques and frameworks for collaborative learning that facilitate the training of DL models without the need to explicitly share private medical data. The authors describe several popular methods for collaborative training and review the main considerations for deploying these models. They also highlight publicly available software frameworks for federated learning and showcase several real-world examples of collaborative learning. The authors conclude by discussing some key challenges and future research directions for distributed DL. They aim to introduce clinicians to the benefits, limitations, and risks of using distributed DL for the development of medical artificial intelligence algorithms. ©RSNA, 2023 Quiz questions for this article are available in the supplemental material.

Pretreatment optimization of tissue metabolomics in colorectal cancer.

BACKGROUND AND OBJECTIVES: To optimize the pretreatment method of colorectal cancer tissue samples for metabolomics research based on solid-phase nuclear magnetic resonance (NMR). METHODS AND STUDY DESIGN: The mucosal tissues of colorectal cancer were classified into five groups with a volume of 0.2 cm*0.2 cm*0.2 cm. The pretreatment methods for each group were as follows: I. Preservation with liquid nitrogen alone. Samples were also treated with liquid nitrogen for 10 (II), 20 (III), and 30 min (IV), respectively, immediately after isolation and then transferred to a -80℃ refrigerator; V. Only -80℃ refrigerator storage. No more than 30 minutes should pass between isolation and pretreatment of tumor samples. The tissue sample testing process was carried out on Bruker AVII-600 NMR Spectrometer. NMR signals were collected and analysed using partial least-squares discrimination analysis (PLS-DA) to explore the effects of different pretreatment methods on the metabolic changes of samples. RESULTS: The levels of pelargonic acid, stearic acid, D-Ribose, heptadecanoic acid, pyruvic acid, succinate, sarcosine, glycine, creatine, and L-lactate in the group I (only liquid nitrogen) were significantly lower than the other groups (p<0.05); the content of glycerophosphocholine in the group I (only liquid nitrogen) was lower than that in the other groups (p=0.055). These indicated that the glucose and choline phospholipid metabolism levels of the liquid nitrogen group were significantly lower than those of the other four groups. CONCLUSIONS: Liquid nitrogen storage can stop the metabolic process of glucose and choline phospholipid in colorectal cancer tissue samples in vitro, thus maintaining the metabolic state of tissue samples in vivo as much as possible.

Determinants of patient-reported xerostomia among long-term oropharyngeal cancer survivors.

BACKGROUND: This study was conducted to identify clinicodemographic risk factors for xerostomia among long-term oropharyngeal cancer (OPC) survivors. METHODS: This cross-sectional study included 906 disease-free, adult OPC survivors with a median survival duration at the time of survey of 6 years (range, 1-16 years); self-reported xerostomia scores were available for 877 participants. Study participants had completed curative treatment between January 2000 and December 2013 and responded to a survey administered from September 2015 to July 2016. The primary outcome variable was cancer patient-reported xerostomia measured with the MD Anderson Symptom Inventory Head and Neck Cancer Module. Clinicodemographic risk factors for moderate to severe xerostomia were identified via multivariable logistic regression. RESULTS: Moderate to severe xerostomia was reported by 343 of the respondents (39.1%). Female sex (odds ratio [OR], 1.82; 95% CI, 1.22-2.71; P = .003; Bayesian false-discovery probability [BFDP] = 0.568), high school or lower education (OR, 1.73; 95% CI, 1.19-2.52; P = .004; BFDP = 0.636), and current cigarette smoking at the time of survey (OR, 2.56; 95% CI, 1.19-5.47; P = .016; BFDP = 0.800) were risk factors for moderate to severe xerostomia, and bilateral intensity-modulated radiotherapy (IMRT) combined with proton therapy and ipsilateral IMRT were protective. CONCLUSIONS: In this large xerostomia study, modern radiotherapy was a protective factor, and continued cigarette smoking at the time of survey, female sex, and high school or lower education were identified as other contributing risk factors associated with moderate to severe xerostomia. Importantly, these findings need to be confirmed in prospective studies. These results can inform future research and targeted patient-centered interventions to monitor and manage radiation therapy-associated xerostomia and preserve quality of life among patients with OPC.

C11orf95-RELA fusions drive oncogenic NF-κB signalling in ependymoma.

Members of the nuclear factor-κB (NF-κB) family of transcriptional regulators are central mediators of the cellular inflammatory response. Although constitutive NF-κB signalling is present in most human tumours, mutations in pathway members are rare, complicating efforts to understand and block aberrant NF-κB activity in cancer. Here we show that more than two-thirds of supratentorial ependymomas contain oncogenic fusions between RELA, the principal effector of canonical NF-κB signalling, and an uncharacterized gene, C11orf95. In each case, C11orf95-RELA fusions resulted from chromothripsis involving chromosome 11q13.1. C11orf95-RELA fusion proteins translocated spontaneously to the nucleus to activate NF-κB target genes, and rapidly transformed neural stem cells--the cell of origin of ependymoma--to form these tumours in mice. Our data identify a highly recurrent genetic alteration of RELA in human cancer, and the C11orf95-RELA fusion protein as a potential therapeutic target in supratentorial ependymoma.

Minocycline for symptom reduction during radiation therapy for head and neck cancer: a randomized clinical trial.

PURPOSE: Local/systemic symptoms during cancer therapy may be exacerbated by dysregulated inflammation and its downstream toxic effects. Minocycline can suppress proinflammatory cytokine release; therefore, we investigated its potential to reduce patient-reported symptom severity during radiotherapy (RT) for head and neck cancer (HNC). METHODS: Eligible patients for this blinded, placebo-controlled trial were adults with T0-3, N-any, and M0 HNC receiving single-modality RT. Participants were randomized 1:1 to either minocycline (200 mg/day) or placebo during RT. The primary endpoint was the area under the curve (AUC) of 5 prespecified symptoms (pain, fatigue, disturbed sleep, poor appetite, difficulty swallowing/chewing) during RT, assessed with the MD Anderson Symptom Inventory for HNC (MDASI-HN). RESULTS: We analyzed data from 20 evaluable patients per arm. Overall, 75% had oropharyngeal cancer and 78% were male. No grade 3+ adverse events potentially related to study medication were observed. Two minocycline patients required a feeding tube during RT vs 5 placebo patients (P = 0.21). The average daily AUC during RT for the 5 MDASI-HN symptoms was 3.1 (SD = 1.0) for minocycline and 3.7 (SD = 1.7) for placebo (P = 0.16); the 0.37 effect size was less than our 0.70 target. AUC comparisons for several individual symptoms and symptom interference favored minocycline but were not statistically significant. The greatest numerical differences occurred for systemic symptoms, larger toward treatment end, and in early post-RT recovery. CONCLUSIONS: Minocycline was feasible, well tolerated, and achieved a positive signal toward reducing patient-reported symptom severity during RT for HNC, particularly for systemic symptoms. This justifies additional study and informs future trial design.

Development of drug-inducible CRISPR-Cas9 systems for large-scale functional screening.

BACKGROUND: Large-scale genetic screening using CRISPR-Cas9 technology has emerged as a powerful approach to uncover and validate gene functions. The ability to control the timing of genetic perturbation during CRISPR screens will facilitate precise dissection of dynamic and complex biological processes. Here, we report the optimization of a drug-inducible CRISPR-Cas9 system that allows high-throughput gene interrogation with a temporal control. RESULTS: We designed multiple drug-inducible sgRNA expression vectors and measured their activities using an EGFP gene disruption assay in 11 human and mouse cell lines. The optimal design allows for a tight and inducible control of gene knockout in vitro, and in vivo during a seven-week-long experiment following hematopoietic reconstitution in mice. We next performed parallel genome-wide loss-of-function screens using the inducible and constitutive CRISPR-Cas9 systems. In proliferation-based dropout screens, these two approaches have similar performance in discriminating essential and nonessential genes. In a more challenging phenotypic assay that requires cytokine stimulation and cell staining, we observed similar sensitivity of the constitutive and drug-induced screening approaches in detecting known hits. Importantly, we demonstrate minimal leakiness of our inducible CRISPR screening platforms in the absence of chemical inducers in large-scale settings. CONCLUSIONS: In this study, we have developed a drug-inducible CRISPR-Cas9 system that shows high cleavage efficiency upon induction but low background activity. Using this system, we have achieved inducible gene disruption in a wide range of cell types both in vitro and in vivo. For the first time, we present a systematic side-by-side comparison of constitutive and drug-inducible CRISPR-Cas9 platforms in large-scale functional screens. We demonstrate the tightness and efficiency of our drug-inducible CRISPR-Cas9 system in genome-wide pooled screening. Our design increases the versatility of CRISPR-based genetic screening and represents a significant upgrade on existing functional genomics toolbox.

Mutational landscape and significance across 12 major cancer types.

The Cancer Genome Atlas (TCGA) has used the latest sequencing and analysis methods to identify somatic variants across thousands of tumours. Here we present data and analytical results for point mutations and small insertions/deletions from 3,281 tumours across 12 tumour types as part of the TCGA Pan-Cancer effort. We illustrate the distributions of mutation frequencies, types and contexts across tumour types, and establish their links to tissues of origin, environmental/carcinogen influences, and DNA repair defects. Using the integrated data sets, we identified 127 significantly mutated genes from well-known (for example, mitogen-activated protein kinase, phosphatidylinositol-3-OH kinase, Wnt/ß-catenin and receptor tyrosine kinase signalling pathways, and cell cycle control) and emerging (for example, histone, histone modification, splicing, metabolism and proteolysis) cellular processes in cancer. The average number of mutations in these significantly mutated genes varies across tumour types; most tumours have two to six, indicating that the number of driver mutations required during oncogenesis is relatively small. Mutations in transcriptional factors/regulators show tissue specificity, whereas histone modifiers are often mutated across several cancer types. Clinical association analysis identifies genes having a significant effect on survival, and investigations of mutations with respect to clonal/subclonal architecture delineate their temporal orders during tumorigenesis. Taken together, these results lay the groundwork for developing new diagnostics and individualizing cancer treatment.

Metabolic dynamics in critically injured patients: a prospective cohort study integrated with 1H NMR metabolomics.

BACKGROUND AND OBJECTIVES: By combining the techniques of metabolomics and computational biology, this research aims to explore the mechanism of metabolic dynamics in critically injured patients and develop a new early warning method for mortality. METHODS AND STUDY DESIGN: A prospective cohort study was conducted, group plasma samples of critically injured patients were collected for 1H-NMR metabolomics analysis. The data was processed with partial least squares regression, to explore the role of enzyme-gene network regulatory mechanism in critically injured metabolic network regulation and to build a quantitative prediction model for early warning of fast death. RESULTS: In total, 60 patients were enrolled. There were significant differences in plasma metabolome between the surviving patients and the deceased ones. Compared to the surviving patients, 112 enzymes and genes regulating the 6 key metabolic marker disturbances of neopterin, corticosterone, 3-methylhistidine, homocysteine, Serine, tyrosine, prostaglandin E2, tryptophan, testosterone and estriol, were observed in the plasmas of deceased ones. Among patients of different injury stages, there were significant differences in plasma metabolome. Progressing from T0 to T50 stages of injury, increased levels of neopterin, corticosterone, prostaglandin E2, tryptophan and testosterone, together with decreased levels of homocysteine, and estriol, were observed. Eventually, the quantitative prediction model of death warning was established. Cross-validation results showed that the predictive effect was good (RMSE=0.18408, R2=0.87 p=0.036). CONCLUSIONS: Metabolomics approaches can be used to quantify the metabolic dynamics of patients with critically injuries and to predict death of critically injured patients by plasma 1H-NMR metabolomics.

A novel retinoblastoma therapy from genomic and epigenetic analyses.

Retinoblastoma is an aggressive childhood cancer of the developing retina that is initiated by the biallelic loss of RB1. Tumours progress very quickly following RB1 inactivation but the underlying mechanism is not known. Here we show that the retinoblastoma genome is stable, but that multiple cancer pathways can be epigenetically deregulated. To identify the mutations that cooperate with RB1 loss, we performed whole-genome sequencing of retinoblastomas. The overall mutational rate was very low; RB1 was the only known cancer gene mutated. We then evaluated the role of RB1 in genome stability and considered non-genetic mechanisms of cancer pathway deregulation. For example, the proto-oncogene SYK is upregulated in retinoblastoma and is required for tumour cell survival. Targeting SYK with a small-molecule inhibitor induced retinoblastoma tumour cell death in vitro and in vivo. Thus, retinoblastomas may develop quickly as a result of the epigenetic deregulation of key cancer pathways as a direct or indirect result of RB1 loss.

Clonal evolution in relapsed acute myeloid leukaemia revealed by whole-genome sequencing.

Most patients with acute myeloid leukaemia (AML) die from progressive disease after relapse, which is associated with clonal evolution at the cytogenetic level. To determine the mutational spectrum associated with relapse, we sequenced the primary tumour and relapse genomes from eight AML patients, and validated hundreds of somatic mutations using deep sequencing; this allowed us to define clonality and clonal evolution patterns precisely at relapse. In addition to discovering novel, recurrently mutated genes (for example, WAC, SMC3, DIS3, DDX41 and DAXX) in AML, we also found two major clonal evolution patterns during AML relapse: (1) the founding clone in the primary tumour gained mutations and evolved into the relapse clone, or (2) a subclone of the founding clone survived initial therapy, gained additional mutations and expanded at relapse. In all cases, chemotherapy failed to eradicate the founding clone. The comparison of relapse-specific versus primary tumour mutations in all eight cases revealed an increase in transversions, probably due to DNA damage caused by cytotoxic chemotherapy. These data demonstrate that AML relapse is associated with the addition of new mutations and clonal evolution, which is shaped, in part, by the chemotherapy that the patients receive to establish and maintain remissions.

Novel mutations target distinct subgroups of medulloblastoma.

Medulloblastoma is a malignant childhood brain tumour comprising four discrete subgroups. Here, to identify mutations that drive medulloblastoma, we sequenced the entire genomes of 37 tumours and matched normal blood. One-hundred and thirty-six genes harbouring somatic mutations in this discovery set were sequenced in an additional 56 medulloblastomas. Recurrent mutations were detected in 41 genes not yet implicated in medulloblastoma; several target distinct components of the epigenetic machinery in different disease subgroups, such as regulators of H3K27 and H3K4 trimethylation in subgroups 3 and 4 (for example, KDM6A and ZMYM3), and CTNNB1-associated chromatin re-modellers in WNT-subgroup tumours (for example, SMARCA4 and CREBBP). Modelling of mutations in mouse lower rhombic lip progenitors that generate WNT-subgroup tumours identified genes that maintain this cell lineage (DDX3X), as well as mutated genes that initiate (CDH1) or cooperate (PIK3CA) in tumorigenesis. These data provide important new insights into the pathogenesis of medulloblastoma subgroups and highlight targets for therapeutic development.

The genetic basis of early T-cell precursor acute lymphoblastic leukaemia.

Early T-cell precursor acute lymphoblastic leukaemia (ETP ALL) is an aggressive malignancy of unknown genetic basis. We performed whole-genome sequencing of 12 ETP ALL cases and assessed the frequency of the identified somatic mutations in 94 T-cell acute lymphoblastic leukaemia cases. ETP ALL was characterized by activating mutations in genes regulating cytokine receptor and RAS signalling (67% of cases; NRAS, KRAS, FLT3, IL7R, JAK3, JAK1, SH2B3 and BRAF), inactivating lesions disrupting haematopoietic development (58%; GATA3, ETV6, RUNX1, IKZF1 and EP300) and histone-modifying genes (48%; EZH2, EED, SUZ12, SETD2 and EP300). We also identified new targets of recurrent mutation including DNM2, ECT2L and RELN. The mutational spectrum is similar to myeloid tumours, and moreover, the global transcriptional profile of ETP ALL was similar to that of normal and myeloid leukaemia haematopoietic stem cells. These findings suggest that addition of myeloid-directed therapies might improve the poor outcome of ETP ALL.

Whole-genome analysis informs breast cancer response to aromatase inhibition.

To correlate the variable clinical features of oestrogen-receptor-positive breast cancer with somatic alterations, we studied pretreatment tumour biopsies accrued from patients in two studies of neoadjuvant aromatase inhibitor therapy by massively parallel sequencing and analysis. Eighteen significantly mutated genes were identified, including five genes (RUNX1, CBFB, MYH9, MLL3 and SF3B1) previously linked to haematopoietic disorders. Mutant MAP3K1 was associated with luminal A status, low-grade histology and low proliferation rates, whereas mutant TP53 was associated with the opposite pattern. Moreover, mutant GATA3 correlated with suppression of proliferation upon aromatase inhibitor treatment. Pathway analysis demonstrated that mutations in MAP2K4, a MAP3K1 substrate, produced similar perturbations as MAP3K1 loss. Distinct phenotypes in oestrogen-receptor-positive breast cancer are associated with specific patterns of somatic mutations that map into cellular pathways linked to tumour biology, but most recurrent mutations are relatively infrequent. Prospective clinical trials based on these findings will require comprehensive genome sequencing.

Radiation therapy dose is associated with improved survival for unresected anaplastic thyroid carcinoma: Outcomes from the National Cancer Data Base.

BACKGROUND: The outcomes of patients with unresected anaplastic thyroid carcinoma (ATC) from the National Cancer Data Base (NCDB) were assessed, and potential correlations were explored between radiation therapy (RT) dose and overall survival (OS). METHODS: The study cohort was comprised of patients who underwent either no surgery or grossly incomplete resection. Correlates of OS were explored using univariate analysis and multivariable analysis (MVA). RESULTS: In total, 1288 patients were analyzed. The mean patient age was 70.2 years, 59.7% of patients were women, and 47.6% received neck RT. The median OS was 2.27 months, and 11% of patients remained alive at 1 year. A positive RT dose-survival correlation was observed for the entire study cohort, for those who received systemic therapy, and for those with stage IVA/IVB and IVC disease. On MVA, older age (hazard ratio [HR], 1.317; 95% confidence interval [CI], 1.137-1.526), ≥ 1 comorbidity (HR, 1.587; 95% CI, 1.379-1.827), distant metastasis (HR, 1.385; 95% CI, 1.216-1.578), receipt of systemic therapy (HR, 0.637; 95% CI, 0.547-0.742), and receipt of RT compared with no RT (<45 grays [Gy]:HR, 0.843; 95% CI, 0.718-0.988; 45-59.9 Gy: HR, 0.596; 95% CI, 0.479-0.743; 60-75 Gy: HR, 0.419; 95% CI, 0.339-0.517) correlated with OS. The RT dose-survival correlation for patients who received higher (60-75 Gy) versus lower (45-59.9 Gy) therapeutic doses was confirmed by propensity-score matching. CONCLUSIONS: Survival was poor in this cohort of patients with unresected ATC, and more effective therapies are needed. However, the association of RT dose with OS highlights the importance of identifying patients with unresected ATC who may still yet benefit from multimodal locoregional treatment that incorporates higher dose RT. Cancer 2017;123:1653-1661. © 2017 American Cancer Society.

Targetable kinase-activating lesions in Ph-like acute lymphoblastic leukemia.

BACKGROUND: Philadelphia chromosome-like acute lymphoblastic leukemia (Ph-like ALL) is characterized by a gene-expression profile similar to that of BCR-ABL1-positive ALL, alterations of lymphoid transcription factor genes, and a poor outcome. The frequency and spectrum of genetic alterations in Ph-like ALL and its responsiveness to tyrosine kinase inhibition are undefined, especially in adolescents and adults. METHODS: We performed genomic profiling of 1725 patients with precursor B-cell ALL and detailed genomic analysis of 154 patients with Ph-like ALL. We examined the functional effects of fusion proteins and the efficacy of tyrosine kinase inhibitors in mouse pre-B cells and xenografts of human Ph-like ALL. RESULTS: Ph-like ALL increased in frequency from 10% among children with standard-risk ALL to 27% among young adults with ALL and was associated with a poor outcome. Kinase-activating alterations were identified in 91% of patients with Ph-like ALL; rearrangements involving ABL1, ABL2, CRLF2, CSF1R, EPOR, JAK2, NTRK3, PDGFRB, PTK2B, TSLP, or TYK2 and sequence mutations involving FLT3, IL7R, or SH2B3 were most common. Expression of ABL1, ABL2, CSF1R, JAK2, and PDGFRB fusions resulted in cytokine-independent proliferation and activation of phosphorylated STAT5. Cell lines and human leukemic cells expressing ABL1, ABL2, CSF1R, and PDGFRB fusions were sensitive in vitro to dasatinib, EPOR and JAK2 rearrangements were sensitive to ruxolitinib, and the ETV6-NTRK3 fusion was sensitive to crizotinib. CONCLUSIONS: Ph-like ALL was found to be characterized by a range of genomic alterations that activate a limited number of signaling pathways, all of which may be amenable to inhibition with approved tyrosine kinase inhibitors. Trials identifying Ph-like ALL are needed to assess whether adding tyrosine kinase inhibitors to current therapy will improve the survival of patients with this type of leukemia. (Funded by the American Lebanese Syrian Associated Charities and others.).

Genomic and epigenomic landscapes of adult de novo acute myeloid leukemia.

BACKGROUND: Many mutations that contribute to the pathogenesis of acute myeloid leukemia (AML) are undefined. The relationships between patterns of mutations and epigenetic phenotypes are not yet clear. METHODS: We analyzed the genomes of 200 clinically annotated adult cases of de novo AML, using either whole-genome sequencing (50 cases) or whole-exome sequencing (150 cases), along with RNA and microRNA sequencing and DNA-methylation analysis. RESULTS: AML genomes have fewer mutations than most other adult cancers, with an average of only 13 mutations found in genes. Of these, an average of 5 are in genes that are recurrently mutated in AML. A total of 23 genes were significantly mutated, and another 237 were mutated in two or more samples. Nearly all samples had at least 1 nonsynonymous mutation in one of nine categories of genes that are almost certainly relevant for pathogenesis, including transcription-factor fusions (18% of cases), the gene encoding nucleophosmin (NPM1) (27%), tumor-suppressor genes (16%), DNA-methylation-related genes (44%), signaling genes (59%), chromatin-modifying genes (30%), myeloid transcription-factor genes (22%), cohesin-complex genes (13%), and spliceosome-complex genes (14%). Patterns of cooperation and mutual exclusivity suggested strong biologic relationships among several of the genes and categories. CONCLUSIONS: We identified at least one potential driver mutation in nearly all AML samples and found that a complex interplay of genetic events contributes to AML pathogenesis in individual patients. The databases from this study are widely available to serve as a foundation for further investigations of AML pathogenesis, classification, and risk stratification. (Funded by the National Institutes of Health.).

Genome Modeling System: A Knowledge Management Platform for Genomics.

In this work, we present the Genome Modeling System (GMS), an analysis information management system capable of executing automated genome analysis pipelines at a massive scale. The GMS framework provides detailed tracking of samples and data coupled with reliable and repeatable analysis pipelines. The GMS also serves as a platform for bioinformatics development, allowing a large team to collaborate on data analysis, or an individual researcher to leverage the work of others effectively within its data management system. Rather than separating ad-hoc analysis from rigorous, reproducible pipelines, the GMS promotes systematic integration between the two. As a demonstration of the GMS, we performed an integrated analysis of whole genome, exome and transcriptome sequencing data from a breast cancer cell line (HCC1395) and matched lymphoblastoid line (HCC1395BL). These data are available for users to test the software, complete tutorials and develop novel GMS pipeline configurations. The GMS is available at https://github.com/genome/gms.