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.

Site IQ in mitochondrial complex I generates S1QEL-sensitive superoxide/hydrogen peroxide in both the reverse and forward reactions.

Superoxide/hydrogen peroxide production by site IQ in complex I of the electron transport chain is conventionally assayed during reverse electron transport (RET) from ubiquinol to NAD. However, S1QELs (specific suppressors of superoxide/hydrogen peroxide production by site IQ) have potent effects in cells and in vivo during presumed forward electron transport (FET). Therefore, we tested whether site IQ generates S1QEL-sensitive superoxide/hydrogen peroxide during FET (site IQf), or alternatively, whether RET and associated S1QEL-sensitive superoxide/hydrogen peroxide production (site IQr) occurs in cells under normal conditions. We introduce an assay to determine if electron flow through complex I is thermodynamically forward or reverse: on blocking electron flow through complex I, the endogenous matrix NAD pool will become more reduced if flow before the challenge was forward, but more oxidised if flow was reverse. Using this assay we show in the model system of isolated rat skeletal muscle mitochondria that superoxide/hydrogen peroxide production by site IQ can be equally great whether RET or FET is running. We show that sites IQr and IQf are equally sensitive to S1QELs, and to rotenone and piericidin A, inhibitors that block the Q-site of complex I. We exclude the possibility that some sub-fraction of the mitochondrial population running site IQr during FET is responsible for S1QEL-sensitive superoxide/hydrogen peroxide production by site IQ. Finally, we show that superoxide/hydrogen peroxide production by site IQ in cells occurs during FET, and is S1QEL-sensitive.

Comment on the Definition and Labeling of pK50.

We propose a more rigorous definition for the recently introduced concept of pK50. The value of pK50 should be associated not with a "functional group", as originally postulated, but instead with an atom. The proposed clarification is meant to improve the interpretation and labeling of pK50.

A conserved role of the insulin-like signaling pathway in diet-dependent uric acid pathologies in Drosophila melanogaster.

Elevated uric acid (UA) is a key risk factor for many disorders, including metabolic syndrome, gout and kidney stones. Despite frequent occurrence of these disorders, the genetic pathways influencing UA metabolism and the association with disease remain poorly understood. In humans, elevated UA levels resulted from the loss of the of the urate oxidase (Uro) gene around 15 million years ago. Therefore, we established a Drosophila melanogaster model with reduced expression of the orthologous Uro gene to study the pathogenesis arising from elevated UA. Reduced Uro expression in Drosophila resulted in elevated UA levels, accumulation of concretions in the excretory system, and shortening of lifespan when reared on diets containing high levels of yeast extract. Furthermore, high levels of dietary purines, but not protein or sugar, were sufficient to produce the same effects of shortened lifespan and concretion formation in the Drosophila model. The insulin-like signaling (ILS) pathway has been shown to respond to changes in nutrient status in several species. We observed that genetic suppression of ILS genes reduced both UA levels and concretion load in flies fed high levels of yeast extract. Further support for the role of the ILS pathway in modulating UA metabolism stems from a human candidate gene study identifying SNPs in the ILS genes AKT2 and FOXO3 being associated with serum UA levels or gout. Additionally, inhibition of the NADPH oxidase (NOX) gene rescued the reduced lifespan and concretion phenotypes in Uro knockdown flies. Thus, components of the ILS pathway and the downstream protein NOX represent potential therapeutic targets for treating UA associated pathologies, including gout and kidney stones, as well as extending human healthspan.

Controlled power: how biology manages succinate-driven energy release.

Oxidation of succinate by mitochondria can generate a higher protonmotive force (pmf) than can oxidation of NADH-linked substrates. Fundamentally, this is because of differences in redox potentials and gearing. Biology adds kinetic constraints that tune the oxidation of NADH and succinate to ensure that the resulting mitochondrial pmf is suitable for meeting cellular needs without triggering pathology. Tuning within an optimal range is used, for example, to shift ATP consumption between different consumers. Conditions that overcome these constraints and allow succinate oxidation to drive pmf too high can cause pathological generation of reactive oxygen species. We discuss the thermodynamic properties that allow succinate oxidation to drive pmf higher than NADH oxidation, and discuss the evidence for kinetic tuning of ATP production and for pathologies resulting from substantial succinate oxidation in vivo.

Pattern-free generation and quantum mechanical scoring of ring-chain tautomers.

In contrast to the computational generation of conventional tautomers, the analogous operation that would produce ring-chain tautomers is rarely available in cheminformatics codes. This is partly due to the perceived unimportance of ring-chain tautomerism and partly because specialized algorithms are required to realize the non-local proton transfers that occur during ring-chain rearrangement. Nevertheless, for some types of organic compounds, including sugars, warfarin analogs, fluorescein dyes and some drug-like compounds, ring-chain tautomerism cannot be ignored. In this work, a novel ring-chain tautomer generation algorithm is presented. It differs from previously proposed solutions in that it does not rely on hard-coded patterns of proton migrations and bond rearrangements, and should therefore be more general and maintainable. We deploy this algorithm as part of a workflow which provides an automated solution for tautomer generation and scoring. The workflow identifies protonatable and deprotonatable sites in the molecule using a previously described approach based on rapid micro-pKa prediction. These data are used to distribute the active protons among the protonatable sites exhaustively, at which point alternate resonance structures are considered to obtain pairs of atoms with opposite formal charge. These pairs are connected with a single bond and a 3D undistorted geometry is generated. The scoring of the generated tautomers is performed with a subsequent density functional theory calculation employing an implicit solvent model. We demonstrate the performance of our workflow on several types of organic molecules known to exist in ring-chain tautomeric equilibria in solution. In particular, we show that some ring-chain tautomers not found using previously published algorithms are successfully located by ours.

Gene expression analysis to detect disseminated tumor cells in the bone marrow of triple-negative breast cancer patients predicts metastatic relapse.

PURPOSE: Disseminated tumor cells (DTCs) in the BM of breast cancer patients predict early disease relapse, but the molecular heterogeneity of these cells is less well characterized. Expression of a 46-gene panel was used to detect DTCs and classify patient BM samples to determine whether a composite set of biomarkers could better predict metastatic relapse. METHODS: Using a high-throughput qRT-PCR assay platform, BM specimens collected from 70 breast cancer patients prior to neoadjuvant therapy were analyzed for the expression of 46 gene transcripts. Gene expression was scored positive (detectable) relative to a reference pool of 16 healthy female control BM specimens. To validate findings from a subset of 28 triple-negative breast cancer (TNBC) patients in the initial 70 patient cohort, an independent set of pre-therapeutic BM specimens from 16 TNBC patients was analyzed. RESULTS: Expression of each of the 46 gene transcripts was highly variable between patients. Individual gene expression was detected in 0-84% of BM specimens analyzed and all but two patient BM specimens expressed at least one transcript. Among a subset of 28 patients with TNBC, positivity of one or more of eight transcripts correlated with time to distant relapse (p = 0.03). In an independent set of 16 triple-negative patient BM samples, detection of five of these same eight gene transcripts also correlated with time to distant relapse (p = 0.03) with a positive predictive value of 89%. CONCLUSIONS: We identified a set of gene transcripts whose detection in the BM of TNBC patients, prior to any treatment intervention, predicts time to first distant relapse, thus identifying a TNBC patient population which requires additional treatment intervention. Because these genes are presumably expressed in populations of DTCs and many encode proteins that are known therapeutic targets (e.g., ERBB2), these results also suggest a potential approach for targeted DTC therapy to mitigate distant metastases in TNBC.

Use of S1QELs and S3QELs to link mitochondrial sites of superoxide and hydrogen peroxide generation to physiological and pathological outcomes.

Changes in mitochondrial superoxide and hydrogen peroxide production may contribute to various pathologies, and even aging, given that over time and in certain conditions, they damage macromolecules and disrupt normal redox signalling. Mitochondria-targeted antioxidants such as mitoQ, mitoVitE, and mitoTEMPO have opened up the study of the importance of altered mitochondrial matrix superoxide/hydrogen peroxide in disease. However, the use of such tools has caveats and they are unable to distinguish precise sites of production within the reactions of substrate oxidation and the electron transport chain. S1QELs are specific small-molecule Suppressors of site IQElectron Leak and S3QELs are specific small-molecule Suppressors of site IIIQoElectron Leak; they prevent superoxide/hydrogen production at specific sites without affecting electron transport or oxidative phosphorylation. We discuss the benefits of using S1QELs and S3QELs as opposed to mitochondria-targeted antioxidants, mitochondrial poisons, and genetic manipulation. We summarise pathologies in which site IQ in mitochondrial complex I and site IIIQo in mitochondrial complex III have been implicated using S1QELs and S3QELs.

Generation of Tautomers Using Micro-p Ka's.

Solutions of organic molecules containing one or more heterocycles with conjugated bonds may exist as a mixture of tautomers, but typically only a few of them are significantly populated even though the potential number grows combinatorially with the number of protonation and deprotonation sites. Generating the most stable tautomers from a given input structure is an important and challenging task, and numerous algorithms to tackle it have been proposed in the literature. This work describes a novel approach for tautomer prediction that involves the combined use of molecular mechanics, semiempirical quantum chemistry, and density functional theory. The key idea in our method is to identify the protonation and deprotonation sites using estimated micro-p Ka's for every atom in the molecule as well as in its nearest protonated and deprotonated forms. To generate tautomers in a systematic way with minimal bias, we then consider the full set of tautomers that arise from the combinatorial distribution of all such mobile protons among all protonatable sites, with efficient postprocessing to screen away high-energy species. To estimate the micro-p Ka's, we present a new method designed for the current task, but we emphasize that any alternative method can be used in conjunction with our basic algorithm. Our approach is therefore grounded in the computational prediction of physical properties in aqueous solution, in contrast to other approaches that may rely on the use of hard-coded rules of proton distribution, previously observed tautomerization patterns from a known chemical space, or human input. We present examples of the application of our algorithm to organic and drug-like molecules, with a focus on novel structures where traditional methods are expected to perform worse.

Identifying biomarkers of breast cancer micrometastatic disease in bone marrow using a patient-derived xenograft mouse model.

BACKGROUND: Disseminated tumor cells (DTCs) found in the bone marrow (BM) of patients with breast cancer portend a poor prognosis and are thought to be intermediaries in the metastatic process. To assess the clinical relevance of a mouse model for identifying possible prognostic and predictive biomarkers of these cells, we have employed patient-derived xenografts (PDX) for propagating and molecularly profiling human DTCs. METHODS: Previously developed mouse xenografts from five breast cancer patients were further passaged by implantation into NOD/SCID mouse mammary fat pads. BM was collected from long bones at early, serial passages and analyzed for human-specific gene expression by qRT-PCR as a surrogate biomarker for the detection of DTCs. Microarray-based gene expression analyses were performed to compare expression profiles between primary xenografts, solid metastasis, and populations of BM DTCs. Differential patterns of gene expression were then compared to previously generated microarray data from primary human BM aspirates from patients with breast cancer and healthy volunteers. RESULTS: Human-specific gene expression of SNAI1, GSC, FOXC2, KRT19, and STAM2, presumably originating from DTCs, was detected in the BM of all xenograft mice that also developed metastatic tumors. Human-specific gene expression was undetectable in the BM of those xenograft lines with no evidence of distant metastases and in non-transplanted control mice. Comparative gene expression analysis of BM DTCs versus the primary tumor of one mouse line identified multiple gene transcripts associated with epithelial-mesenchymal transition, aggressive clinical phenotype, and metastatic disease development. Sixteen of the PDX BM associated genes also demonstrated a statistically significant difference in expression in the BM of healthy volunteers versus the BM of breast cancer patients with distant metastatic disease. CONCLUSION: Unique and reproducible patterns of differential gene expression can be identified that presumably originate from BM DTCs in mouse PDX lines. Several of these identified genes are also detected in the BM of patients with breast cancer who develop early metastases, which suggests that they may be clinically relevant biomarkers. The PDX model may also provide a clinically relevant system for analyzing and targeting these intermediaries of metastases.

Weighted Averaging Scheme and Local Atomic Descriptor for pKa Prediction Based on Density Functional Theory.

As a continuation of our work on developing a density functional theory-based pKa predictor, we present conceptual improvements to our previously published shell model, which is a hierarchical organization of pKa training sets and which, in principle, covers all chemical space. The improvements concern the way the studied chemical compound is associated with the data points from the training sets. By introducing a new descriptor of the local atomic environment which foregoes dependence on chemical bonding and connectivity, we are able to automatically locate molecules from the training set that are most relevant to the proton dissociation equilibrium under study. This new scheme leads to the prediction of a single pKa value weighted across multiple training sets and thus patches a defect disclosed in the formulation of our previous model. Using the new parametrization approach, the pKa prediction gets rid of outliers reported in previous applications of our approach, eliminates ambiguity in interpreting the results, and improves the overall accuracy. Our new treatment accounts for multiple conformations both on the level of energetics and parametrization. Illustrative results are shown for several types of chemical structures containing guanidine, amidine, amine, and phenol functional groups, and which are representative of practically important large and flexible drug-like molecules. Our method's performance is compared to the performance of other previously published pKa prediction methods. Further possible improvements to the organization of the training sets and the potential application of our new local atomic descriptor to other kinds of parametrizations are discussed.

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.

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.).

Paired-like Homeodomain Transcription factor 2 expression by breast cancer bone marrow disseminated tumor cells is associated with early recurrent disease development.

The presence of disseminated tumor cells (DTCs) in the bone marrow (BM) of breast cancer patients is prognostic for early relapse. In the present study, we analyzed the gene expression profiles from BM cells of breast cancer patients to identify molecular signatures associated with DTCs and their relevance to metastatic outcome. We analyzed BM from 30 patients with stage II/III breast cancer by gene expression profiling and correlated expression with metastatic disease development. A candidate gene, PITX2, was analyzed for expression and phenotype in breast cancer cell lines. PITX2 was knocked down in the MDAMB231 cell lines for gene expression analysis and cell invasiveness. Expression of various signaling pathway molecules was confirmed by RT-PCR. We found that the expression of Paired-like Homeobox Transcription factor-2 (PITX2) is absent in the BM of normal healthy volunteers and, when detected in the BM of breast cancer patients, is significantly correlated with early metastatic disease development (p = 0.0062). Suppression of PITX2 expression significantly reduced invasiveness in MDAMB231 cells. Three genes-NKD1, LEF1, and DKK4-were significantly downregulated in response to PITX2 suppression. Expression of PITX2 in BM of early-stage breast cancer patients is associated with risk for early disease recurrence. Furthermore, PITX2 likely plays a role in the metastatic process through its effect on the expression of genes associated with the Wnt/beta-Catenin signaling pathway.

caTissue Suite to OpenSpecimen: Developing an extensible, open source, web-based biobanking management system.

The National Cancer Institute (NCI) Cancer Biomedical Informatics Grid® (caBIG®) program established standards and best practices for biorepository data management by creating an infrastructure to propagate biospecimen resource sharing while maintaining data integrity and security. caTissue Suite, a biospecimen data management software tool, has evolved from this effort. More recently, the caTissue Suite continues to evolve as an open source initiative known as OpenSpecimen. The essential functionality of OpenSpecimen includes the capture and representation of highly granular, hierarchically-structured data for biospecimen processing, quality assurance, tracking, and annotation. Ideal for multi-user and multi-site biorepository environments, OpenSpecimen permits role-based access to specific sets of data operations through a user-interface designed to accommodate varying workflows and unique user needs. The software is interoperable, both syntactically and semantically, with an array of other bioinformatics tools given its integration of standard vocabularies thus enabling research involving biospecimens. End-users are encouraged to share their day-to-day experiences in working with the application, thus providing to the community board insight into the needs and limitations which need be addressed. Users are also requested to review and validate new features through group testing environments and mock screens. Through this user interaction, application flexibility and interoperability have been recognized as necessary developmental focuses essential for accommodating diverse adoption scenarios and biobanking workflows to catalyze advances in biomedical research and operations. Given the diversity of biobanking practices and workforce roles, efforts have been made consistently to maintain robust data granularity while aiding user accessibility, data discoverability, and security within and across applications by providing a lower learning curve in using OpenSpecimen. Iterative development and testing cycles provide continuous maintenance and up-to-date capabilities for this freely available, open-access, web-based software application that is globally-adopted at over 25 institutions.

Retraction: A genomic strategy to refine prognosis in early-stage non-small-cell lung cancer. N Engl J Med 2006;355:570-80.

To the Editor: We would like to retract our article, "A Genomic Strategy to Refine Prognosis in Early-Stage Non-Small-Cell Lung Cancer,"(1) which was published in the Journal on August 10, 2006. Using a sample set from a study by the American College of Surgeons Oncology Group (ACOSOG) and a collection of samples from a study by the Cancer and Leukemia Group B (CALGB), we have tried and failed to reproduce results supporting the validation of the lung metagene model described in the article. We deeply regret the effect of this action on the work of other investigators.

Endocrine-Sensitive Disease Rate in Postmenopausal Patients With Estrogen Receptor-Rich/ERBB2-Negative Breast Cancer Receiving Neoadjuvant Anastrozole, Fulvestrant, or Their Combination: A Phase 3 Randomized Clinical Trial.

Importance: Adding fulvestrant to anastrozole (A+F) improved survival in postmenopausal women with advanced estrogen receptor (ER)-positive/ERBB2 (formerly HER2)-negative breast cancer. However, the combination has not been tested in early-stage disease. Objective: To determine whether neoadjuvant fulvestrant or A+F increases the rate of pathologic complete response or ypT1-2N0/N1mic/Ki67 2.7% or less residual disease (referred to as endocrine-sensitive disease) over anastrozole alone. Design, Setting, and Participants: A phase 3 randomized clinical trial assessing differences in clinical and correlative outcomes between each of the fulvestrant-containing arms and the anastrozole arm. Postmenopausal women with clinical stage II to III, ER-rich (Allred score 6-8 or >66%)/ERBB2-negative breast cancer were included. All analyses were based on data frozen on March 2, 2023. Interventions: Patients received anastrozole, fulvestrant, or a combination for 6 months preoperatively. Tumor Ki67 was assessed at week 4 and optionally at week 12, and if greater than 10% at either time point, the patient switched to neoadjuvant chemotherapy or immediate surgery. Main Outcomes and Measures: The primary outcome was the endocrine-sensitive disease rate (ESDR). A secondary outcome was the percentage change in Ki67 after 4 weeks of neoadjuvant endocrine therapy (NET) (week 4 Ki67 suppression). Results: Between February 2014 and November 2018, 1362 female patients (mean [SD] age, 65.0 [8.2] years) were enrolled. Among the 1298 evaluable patients, ESDRs were 18.7% (95% CI, 15.1%-22.7%), 22.8% (95% CI, 18.9%-27.1%), and 20.5% (95% CI, 16.8%-24.6%) with anastrozole, fulvestrant, and A+F, respectively. Compared to anastrozole, neither fulvestrant-containing regimen significantly improved ESDR or week 4 Ki67 suppression. The rate of week 4 or week 12 Ki67 greater than 10% was 25.1%, 24.2%, and 15.7% with anastrozole, fulvestrant, and A+F, respectively. Pathologic complete response/residual cancer burden class I occurred in 8 of 167 patients and 17 of 167 patients, respectively (15.0%; 95% CI, 9.9%-21.3%), after switching to neoadjuvant chemotherapy due to week 4 or week 12 Ki67 greater than 10%. PAM50 subtyping derived from RNA sequencing of baseline biopsies available for 753 patients (58%) identified 394 luminal A, 304 luminal B, and 55 nonluminal tumors. A+F led to a greater week 4 Ki67 suppression than anastrozole alone in luminal B tumors (median [IQR], -90.4% [-95.2 to -81.9%] vs -76.7% [-89.0 to -55.6%]; P < .001), but not luminal A tumors. Thirty-six nonluminal tumors (65.5%) had a week 4 or week 12 Ki67 greater than 10%. Conclusions and Relevance: In this randomized clinical trial, neither fulvestrant nor A+F significantly improved the 6-month ESDR over anastrozole in ER-rich/ERBB2-negative breast cancer. Aromatase inhibition remains the standard-of-care NET. Differential NET response by PAM50 subtype in exploratory analyses warrants further investigation. Trial Registration: ClinicalTrials.gov Identifier: NCT01953588.

DNMT3A mutations in acute myeloid leukemia.

BACKGROUND: The genetic alterations responsible for an adverse outcome in most patients with acute myeloid leukemia (AML) are unknown. METHODS: Using massively parallel DNA sequencing, we identified a somatic mutation in DNMT3A, encoding a DNA methyltransferase, in the genome of cells from a patient with AML with a normal karyotype. We sequenced the exons of DNMT3A in 280 additional patients with de novo AML to define recurring mutations. RESULTS: A total of 62 of 281 patients (22.1%) had mutations in DNMT3A that were predicted to affect translation. We identified 18 different missense mutations, the most common of which was predicted to affect amino acid R882 (in 37 patients). We also identified six frameshift, six nonsense, and three splice-site mutations and a 1.5-Mbp deletion encompassing DNMT3A. These mutations were highly enriched in the group of patients with an intermediate-risk cytogenetic profile (56 of 166 patients, or 33.7%) but were absent in all 79 patients with a favorable-risk cytogenetic profile (P<0.001 for both comparisons). The median overall survival among patients with DNMT3A mutations was significantly shorter than that among patients without such mutations (12.3 months vs. 41.1 months, P<0.001). DNMT3A mutations were associated with adverse outcomes among patients with an intermediate-risk cytogenetic profile or FLT3 mutations, regardless of age, and were independently associated with a poor outcome in Cox proportional-hazards analysis. CONCLUSIONS: DNMT3A mutations are highly recurrent in patients with de novo AML with an intermediate-risk cytogenetic profile and are independently associated with a poor outcome. (Funded by the National Institutes of Health and others.).

Detection of disseminated tumor cells in the bone marrow of breast cancer patients using multiplex gene expression measurements identifies new therapeutic targets in patients at high risk for the development of metastatic disease.

Disseminated tumor cells (DTCs) detected in the bone marrow (BM) of breast cancer patients identify women at high risk of recurrence. DTCs are traditionally detected by immunocytochemical staining for cytokeratins or single gene expression measurements, which limit both specificity and sensitivity. We evaluated the Nanostring nCounter™ platform for multi-marker, gene expression-based detection and classification of DTCs in the BM of breast cancer patients. Candidate genes exhibiting tumor cell-specific expression were identified from microarray datasets and validated by qRT-PCR analysis in non-malignant human BM and identical samples spiked with predefined numbers of molecularly diverse breast tumor cell lines. Thirty-eight validated transcripts were designed for the nCounter™ platform and a subset of these transcripts was technically validated against qRT-PCR measurements using identical spiked BM controls. Bilateral iliac crest BM aspirates were collected and analyzed from twenty breast cancer patients, prior to neoadjuvant therapy, using the full 38-gene nCounter™ code set. Tumor cell-specific gene expression by nCounter™ was detected with a sensitivity of one cancer cell per 1 × 10(6) nucleated BM cells after optimization. Measurements were quantitative, log linear over a 20-fold range, and correlated with qRT-PCR measurements. Using the nCounter™ 38-gene panel, 6 of 8 patients (75 %) who developed metastatic disease had detectable expression of at least one transcript. Notably, three of these patients had detectable expression of ERBB2 in their BM, despite the fact that their corresponding primary tumors were HER2/ERBB2 negative and therefore did not receive trastuzumab therapy. Four of these patients also expressed the PTCH1 receptor, a newly recognized therapeutic target based on hedgehog signaling pathway inhibition. The presumptive detection and classification of DTCs in the BM of breast cancer patients, based on sensitive and quantitative multi-marker detection of gene expression using the nCounter™ platform, provide an opportunity to both predict early distant recurrence and, more importantly, identify opportunities for preventing the spread of disease based on the expression of unique, therapeutically actionable gene targets. This study demonstrates the application of a new technology for multiplexed gene expression-based detection of DTCs in the BM of breast cancer patients and identifies at least two therapeutically targetable genes that are frequently expressed in the BM of patients who develop metastatic disease.

Automatic detection of circulating tumor cells and cancer associated fibroblasts using deep learning.

Circulating tumor cells (CTCs) and cancer-associated fibroblasts (CAFs) from whole blood are emerging as important biomarkers that potentially aid in cancer diagnosis and prognosis. The microfilter technology provides an efficient capture platform for them but is confounded by two challenges. First, uneven microfilter surfaces makes it hard for commercial scanners to obtain images with all cells in-focus. Second, current analysis is labor-intensive with long turnaround time and user-to-user variability. Here we addressed the first challenge through developing a customized imaging system and data pre-processing algorithms. Utilizing cultured cancer and CAF cells captured by microfilters, we showed that images from our custom system are 99.3% in-focus compared to 89.9% from a top-of-the-line commercial scanner. Then we developed a deep-learning-based method to automatically identify tumor cells serving to mimic CTC (mCTC) and CAFs. Our deep learning method achieved precision and recall of 94% (± 0.2%) and 96% (± 0.2%) for mCTC detection, and 93% (± 1.7%) and 84% (± 3.1%) for CAF detection, significantly better than a conventional computer vision method, whose numbers are 92% (± 0.2%) and 78% (± 0.3%) for mCTC and 58% (± 3.9%) and 56% (± 3.5%) for CAF. Our custom imaging system combined with deep learning cell identification method represents an important advance on CTC and CAF analysis.