Publications by authors named "Aedin C Culhane"

57 Publications

Characteristics and Outcomes of Over 300,000 Patients with COVID-19 and History of Cancer in the United States and Spain.

Cancer Epidemiol Biomarkers Prev 2021 10 16;30(10):1884-1894. Epub 2021 Jul 16.

Tennessee Valley Healthcare System, Veterans Affairs Medical Center, Nashville, Tennessee.

Background: We described the demographics, cancer subtypes, comorbidities, and outcomes of patients with a history of cancer and coronavirus disease 2019 (COVID-19). Second, we compared patients hospitalized with COVID-19 to patients diagnosed with COVID-19 and patients hospitalized with influenza.

Methods: We conducted a cohort study using eight routinely collected health care databases from Spain and the United States, standardized to the Observational Medical Outcome Partnership common data model. Three cohorts of patients with a history of cancer were included: (i) diagnosed with COVID-19, (ii) hospitalized with COVID-19, and (iii) hospitalized with influenza in 2017 to 2018. Patients were followed from index date to 30 days or death. We reported demographics, cancer subtypes, comorbidities, and 30-day outcomes.

Results: We included 366,050 and 119,597 patients diagnosed and hospitalized with COVID-19, respectively. Prostate and breast cancers were the most frequent cancers (range: 5%-18% and 1%-14% in the diagnosed cohort, respectively). Hematologic malignancies were also frequent, with non-Hodgkin's lymphoma being among the five most common cancer subtypes in the diagnosed cohort. Overall, patients were aged above 65 years and had multiple comorbidities. Occurrence of death ranged from 2% to 14% and from 6% to 26% in the diagnosed and hospitalized COVID-19 cohorts, respectively. Patients hospitalized with influenza ( = 67,743) had a similar distribution of cancer subtypes, sex, age, and comorbidities but lower occurrence of adverse events.

Conclusions: Patients with a history of cancer and COVID-19 had multiple comorbidities and a high occurrence of COVID-19-related events. Hematologic malignancies were frequent.

Impact: This study provides epidemiologic characteristics that can inform clinical care and etiologic studies.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1158/1055-9965.EPI-21-0266DOI Listing
October 2021

Genome-scale screens identify factors regulating tumor cell responses to natural killer cells.

Nat Genet 2021 08 12;53(8):1196-1206. Epub 2021 Jul 12.

Department of Transplantation Immunology, Maastricht University Medical Center+, Maastricht, the Netherlands.

To systematically define molecular features in human tumor cells that determine their degree of sensitivity to human allogeneic natural killer (NK) cells, we quantified the NK cell responsiveness of hundreds of molecularly annotated 'DNA-barcoded' solid tumor cell lines in multiplexed format and applied genome-scale CRISPR-based gene-editing screens in several solid tumor cell lines, to functionally interrogate which genes in tumor cells regulate the response to NK cells. In these orthogonal studies, NK cell-sensitive tumor cells tend to exhibit 'mesenchymal-like' transcriptional programs; high transcriptional signature for chromatin remodeling complexes; high levels of B7-H6 (NCR3LG1); and low levels of HLA-E/antigen presentation genes. Importantly, transcriptional signatures of NK cell-sensitive tumor cells correlate with immune checkpoint inhibitor (ICI) resistance in clinical samples. This study provides a comprehensive map of mechanisms regulating tumor cell responses to NK cells, with implications for future biomarker-driven applications of NK cell immunotherapies.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s41588-021-00889-wDOI Listing
August 2021

An Embryonic Diapause-like Adaptation with Suppressed Myc Activity Enables Tumor Treatment Persistence.

Cancer Cell 2021 02 7;39(2):240-256.e11. Epub 2021 Jan 7.

Department of Data Sciences, Dana-Farber Cancer Institute & Harvard T.H. Chan School of Public Health, Boston, MA, USA.

Treatment-persistent residual tumors impede curative cancer therapy. To understand this cancer cell state we generated models of treatment persistence that simulate the residual tumors. We observe that treatment-persistent tumor cells in organoids, xenografts, and cancer patients adopt a distinct and reversible transcriptional program resembling that of embryonic diapause, a dormant stage of suspended development triggered by stress and associated with suppressed Myc activity and overall biosynthesis. In cancer cells, depleting Myc or inhibiting Brd4, a Myc transcriptional co-activator, attenuates drug cytotoxicity through a dormant diapause-like adaptation with reduced apoptotic priming. Conversely, inducible Myc upregulation enhances acute chemotherapeutic activity. Maintaining residual cells in dormancy after chemotherapy by inhibiting Myc activity or interfering with the diapause-like adaptation by inhibiting cyclin-dependent kinase 9 represent potential therapeutic strategies against chemotherapy-persistent tumor cells. Our study demonstrates that cancer co-opts a mechanism similar to diapause with adaptive inactivation of Myc to persist during treatment.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.ccell.2020.12.002DOI Listing
February 2021

Functional Genomics Identify Distinct and Overlapping Genes Mediating Resistance to Different Classes of Heterobifunctional Degraders of Oncoproteins.

Cell Rep 2021 01;34(1):108532

Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA; Harvard Medical School, Boston, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA.

Heterobifunctional proteolysis-targeting chimeric compounds leverage the activity of E3 ligases to induce degradation of target oncoproteins and exhibit potent preclinical antitumor activity. To dissect the mechanisms regulating tumor cell sensitivity to different classes of pharmacological "degraders" of oncoproteins, we performed genome-scale CRISPR-Cas9-based gene editing studies. We observed that myeloma cell resistance to degraders of different targets (BET bromodomain proteins, CDK9) and operating through CRBN (degronimids) or VHL is primarily mediated by prevention of, rather than adaptation to, breakdown of the target oncoprotein; and this involves loss of function of the cognate E3 ligase or interactors/regulators of the respective cullin-RING ligase (CRL) complex. The substantial gene-level differences for resistance mechanisms to CRBN- versus VHL-based degraders explains mechanistically the lack of cross-resistance with sequential administration of these two degrader classes. Development of degraders leveraging more diverse E3 ligases/CRLs may facilitate sequential/alternating versus combined uses of these agents toward potentially delaying or preventing resistance.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.celrep.2020.108532DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8485877PMC
January 2021

Deep phenotyping of 34,128 adult patients hospitalised with COVID-19 in an international network study.

Nat Commun 2020 10 6;11(1):5009. Epub 2020 Oct 6.

Clinical Pharmacology Unit, Zealand University Hospital, Køge, Denmark.

Comorbid conditions appear to be common among individuals hospitalised with coronavirus disease 2019 (COVID-19) but estimates of prevalence vary and little is known about the prior medication use of patients. Here, we describe the characteristics of adults hospitalised with COVID-19 and compare them with influenza patients. We include 34,128 (US: 8362, South Korea: 7341, Spain: 18,425) COVID-19 patients, summarising between 4811 and 11,643 unique aggregate characteristics. COVID-19 patients have been majority male in the US and Spain, but predominantly female in South Korea. Age profiles vary across data sources. Compared to 84,585 individuals hospitalised with influenza in 2014-19, COVID-19 patients have more typically been male, younger, and with fewer comorbidities and lower medication use. While protecting groups vulnerable to influenza is likely a useful starting point in the response to COVID-19, strategies will likely need to be broadened to reflect the particular characteristics of individuals being hospitalised with COVID-19.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s41467-020-18849-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7538555PMC
October 2020

Risk of hydroxychloroquine alone and in combination with azithromycin in the treatment of rheumatoid arthritis: a multinational, retrospective study.

Lancet Rheumatol 2020 Nov 21;2(11):e698-e711. Epub 2020 Aug 21.

Janssen Research and Development, Titusville, NJ, USA.

Background: Hydroxychloroquine, a drug commonly used in the treatment of rheumatoid arthritis, has received much negative publicity for adverse events associated with its authorisation for emergency use to treat patients with COVID-19 pneumonia. We studied the safety of hydroxychloroquine, alone and in combination with azithromycin, to determine the risk associated with its use in routine care in patients with rheumatoid arthritis.

Methods: In this multinational, retrospective study, new user cohort studies in patients with rheumatoid arthritis aged 18 years or older and initiating hydroxychloroquine were compared with those initiating sulfasalazine and followed up over 30 days, with 16 severe adverse events studied. Self-controlled case series were done to further establish safety in wider populations, and included all users of hydroxychloroquine regardless of rheumatoid arthritis status or indication. Separately, severe adverse events associated with hydroxychloroquine plus azithromycin (compared with hydroxychloroquine plus amoxicillin) were studied. Data comprised 14 sources of claims data or electronic medical records from Germany, Japan, the Netherlands, Spain, the UK, and the USA. Propensity score stratification and calibration using negative control outcomes were used to address confounding. Cox models were fitted to estimate calibrated hazard ratios (HRs) according to drug use. Estimates were pooled where the value was less than 0·4.

Findings: The study included 956 374 users of hydroxychloroquine, 310 350 users of sulfasalazine, 323 122 users of hydroxychloroquine plus azithromycin, and 351 956 users of hydroxychloroquine plus amoxicillin. No excess risk of severe adverse events was identified when 30-day hydroxychloroquine and sulfasalazine use were compared. Self-controlled case series confirmed these findings. However, long-term use of hydroxychloroquine appeared to be associated with increased cardiovascular mortality (calibrated HR 1·65 [95% CI 1·12-2·44]). Addition of azithromycin appeared to be associated with an increased risk of 30-day cardiovascular mortality (calibrated HR 2·19 [95% CI 1·22-3·95]), chest pain or angina (1·15 [1·05-1·26]), and heart failure (1·22 [1·02-1·45]).

Interpretation: Hydroxychloroquine treatment appears to have no increased risk in the short term among patients with rheumatoid arthritis, but in the long term it appears to be associated with excess cardiovascular mortality. The addition of azithromycin increases the risk of heart failure and cardiovascular mortality even in the short term. We call for careful consideration of the benefit-risk trade-off when counselling those on hydroxychloroquine treatment.

Funding: National Institute for Health Research (NIHR) Oxford Biomedical Research Centre, NIHR Senior Research Fellowship programme, US National Institutes of Health, US Department of Veterans Affairs, Janssen Research and Development, IQVIA, Korea Health Industry Development Institute through the Ministry of Health and Welfare Republic of Korea, Versus Arthritis, UK Medical Research Council Doctoral Training Partnership, Foundation Alfonso Martin Escudero, Innovation Fund Denmark, Novo Nordisk Foundation, Singapore Ministry of Health's National Medical Research Council Open Fund Large Collaborative Grant, VINCI, Innovative Medicines Initiative 2 Joint Undertaking, EU's Horizon 2020 research and innovation programme, and European Federation of Pharmaceutical Industries and Associations.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/S2665-9913(20)30276-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7442425PMC
November 2020

Pleiotropic Mechanisms Drive Endocrine Resistance in the Three-Dimensional Bone Microenvironment.

Cancer Res 2021 01 28;81(2):371-383. Epub 2020 Aug 28.

Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts.

Although hormonal therapy (HT) inhibits the growth of hormone receptor-positive (HR) breast and prostate cancers, HT resistance frequently develops within the complex metastatic microenvironment of the host organ (often the bone), a setting poorly recapitulated in 2D culture systems. To address this limitation, we cultured HR breast cancer and prostate cancer spheroids and patient-derived organoids in 3D extracellular matrices (ECM) alone or together with bone marrow stromal cells (BMSC). In 3D monocultures, antiestrogens and antiandrogens induced anoikis by abrogating anchorage-independent growth of HR cancer cells but exhibited only modest effects against tumor cells residing in the ECM niche. In contrast, BMSC induced hormone-independent growth of breast cancer and prostate cancer spheroids and restored lumen filling in the presence of HR-targeting agents. Molecular and functional characterization of BMSC-induced hormone independence and HT resistance in anchorage-independent cells revealed distinct context-dependent mechanisms. Cocultures of ZR75-1 and LNCaP with BMSCs exhibited paracrine IL6-induced HT resistance via attenuation of HR protein expression, which was reversed by inhibition of IL6 or JAK signaling. Paracrine IL6/JAK/STAT3-mediated HT resistance was confirmed in patient-derived organoids cocultured with BMSCs. Distinctly, MCF7 and T47D spheroids retained ER protein expression in cocultures but acquired redundant compensatory signals enabling anchorage independence via ERK and PI3K bypass cascades activated in a non-IL6-dependent manner. Collectively, these data characterize the pleiotropic hormone-independent mechanisms underlying acquisition and restoration of anchorage-independent growth in HR tumors. Combined analysis of tumor and microenvironmental biomarkers in metastatic biopsies of HT-resistant patients can help refine treatment approaches. SIGNIFICANCE: This study uncovers a previously underappreciated dependency of tumor cells on HR signaling for anchorage-independent growth and highlights how the metastatic microenvironment restores this malignant property of cancer cells during hormone therapy.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1158/0008-5472.CAN-20-0571DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8445072PMC
January 2021

Impact of Data Preprocessing on Integrative Matrix Factorization of Single Cell Data.

Front Oncol 2020 23;10:973. Epub 2020 Jun 23.

Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, United States.

Integrative, single-cell analyses may provide unprecedented insights into cellular and spatial diversity of the tumor microenvironment. The sparsity, noise, and high dimensionality of these data present unique challenges. Whilst approaches for integrating single-cell data are emerging and are far from being standardized, most data integration, cell clustering, cell trajectory, and analysis pipelines employ a dimension reduction step, frequently principal component analysis (PCA), a matrix factorization method that is relatively fast, and can easily scale to large datasets when used with sparse-matrix representations. In this review, we provide a guide to PCA and related methods. We describe the relationship between PCA and singular value decomposition, the difference between PCA of a correlation and covariance matrix, the impact of scaling, log-transforming, and standardization, and how to recognize a horseshoe or arch effect in a PCA. We describe canonical correlation analysis (CCA), a popular matrix factorization approach for the integration of single-cell data from different platforms or studies. We discuss alternatives to CCA and why additional preprocessing or weighting datasets within the joint decomposition should be considered.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.3389/fonc.2020.00973DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7324639PMC
June 2020

Deep phenotyping of 34,128 patients hospitalised with COVID-19 and a comparison with 81,596 influenza patients in America, Europe and Asia: an international network study.

medRxiv 2020 Jun 28. Epub 2020 Jun 28.

Background In this study we phenotyped individuals hospitalised with coronavirus disease 2019 (COVID-19) in depth, summarising entire medical histories, including medications, as captured in routinely collected data drawn from databases across three continents. We then compared individuals hospitalised with COVID-19 to those previously hospitalised with influenza. Methods We report demographics, previously recorded conditions and medication use of patients hospitalised with COVID-19 in the US (Columbia University Irving Medical Center [CUIMC], Premier Healthcare Database [PHD], UCHealth System Health Data Compass Database [UC HDC], and the Department of Veterans Affairs [VA OMOP]), in South Korea (Health Insurance Review & Assessment [HIRA]), and Spain (The Information System for Research in Primary Care [SIDIAP] and HM Hospitales [HM]). These patients were then compared with patients hospitalised with influenza in 2014-19. Results 34,128 (US: 8,362, South Korea: 7,341, Spain: 18,425) individuals hospitalised with COVID-19 were included. Between 4,811 (HM) and 11,643 (CUIMC) unique aggregate characteristics were extracted per patient, with all summarised in an accompanying interactive website (http://evidence.ohdsi.org/Covid19CharacterizationHospitalization/). Patients were majority male in the US (CUIMC: 52%, PHD: 52%, UC HDC: 54%, VA OMOP: 94%,) and Spain (SIDIAP: 54%, HM: 60%), but were predominantly female in South Korea (HIRA: 60%). Age profiles varied across data sources. Prevalence of asthma ranged from 4% to 15%, diabetes from 13% to 43%, and hypertensive disorder from 24% to 70% across data sources. Between 14% and 33% were taking drugs acting on the renin-angiotensin system in the 30 days prior to hospitalisation. Compared to 81,596 individuals hospitalised with influenza in 2014-19, patients admitted with COVID-19 were more typically male, younger, and healthier, with fewer comorbidities and lower medication use. Conclusions We provide a detailed characterisation of patients hospitalised with COVID-19. Protecting groups known to be vulnerable to influenza is a useful starting point to minimize the number of hospital admissions needed for COVID-19. However, such strategies will also likely need to be broadened so as to reflect the particular characteristics of individuals hospitalised with COVID-19.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1101/2020.04.22.20074336DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7239064PMC
June 2020

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

Cancer Epidemiol Biomarkers Prev 2020 02 23;29(2):509-519. Epub 2019 Dec 23.

Department of Data Sciences, Dana-Farber Cancer Institute, Boston, Massachusetts.

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.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1158/1055-9965.EPI-18-1359DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7448721PMC
February 2020

MOGSA: Integrative Single Sample Gene-set Analysis of Multiple Omics Data.

Mol Cell Proteomics 2019 08 26;18(8 suppl 1):S153-S168. Epub 2019 Jun 26.

Department of Data Science, Division of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, Massachusetts 02215; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts 02215. Electronic address:

Gene-set analysis (GSA) summarizes individual molecular measurements to more interpretable pathways or gene-sets and has become an indispensable step in the interpretation of large-scale omics data. However, GSA methods are limited to the analysis of single omics data. Here, we introduce a new computation method termed multi-omics gene-set analysis (MOGSA), a multivariate single sample gene-set analysis method that integrates multiple experimental and molecular data types measured over the same set of samples. The method learns a low dimensional representation of most variant correlated features (genes, proteins, etc.) across multiple omics data sets, transforms the features onto the same scale and calculates an integrated gene-set score from the most informative features in each data type. MOGSA does not require filtering data to the intersection of features (gene IDs), therefore, all molecular features, including those that lack annotation may be included in the analysis. Using simulated data, we demonstrate that integrating multiple diverse sources of molecular data increases the power to discover subtle changes in gene-sets and may reduce the impact of unreliable information in any single data type. Using real experimental data, we demonstrate three use-cases of MOGSA. First, we show how to remove a source of noise (technical or biological) in integrative MOGSA of NCI60 transcriptome and proteome data. Second, we apply MOGSA to discover similarities and differences in mRNA, protein and phosphorylation profiles of a small study of stem cell lines and assess the influence of each data type or feature on the total gene-set score. Finally, we apply MOGSA to cluster analysis and show that three molecular subtypes are robustly discovered when copy number variation and mRNA data of 308 bladder cancers from The Cancer Genome Atlas are integrated using MOGSA. MOGSA is available in the Bioconductor R package "mogsa."
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1074/mcp.TIR118.001251DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6692785PMC
August 2019

BRCA1-IRIS promotes human tumor progression through PTEN blockade and HIF-1α activation.

Proc Natl Acad Sci U S A 2018 10 25;115(41):E9600-E9609. Epub 2018 Sep 25.

Department of Genetics, Harvard Medical School, Boston, MA 02115;

is an established breast and ovarian tumor suppressor gene that encodes multiple protein products whose individual contributions to human cancer suppression are poorly understood. BRCA1-IRIS (also known as "IRIS"), an alternatively spliced product and a chromatin-bound replication and transcription regulator, is overexpressed in various primary human cancers, including breast cancer, lung cancer, acute myeloid leukemia, and certain other carcinomas. Its naturally occurring overexpression can promote the metastasis of patient-derived xenograft (PDX) cells and other human cancer cells in mouse models. The IRIS-driven metastatic mechanism results from IRIS-dependent suppression of phosphatase and tensin homolog () transcription, which in turn perturbs the PI3K/AKT/GSK-3β pathway leading to prolyl hydroxylase-independent HIF-1α stabilization and activation in a normoxic environment. Thus, despite the tumor-suppressing genetic origin of IRIS, its properties more closely resemble those of an oncoprotein that, when spontaneously overexpressed, can, paradoxically, drive human tumor progression.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1073/pnas.1807112115DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6187201PMC
October 2018

Enter the Matrix: Factorization Uncovers Knowledge from Omics.

Trends Genet 2018 10 22;34(10):790-805. Epub 2018 Aug 22.

Department of Oncology, Division of Biostatistics and Bioinformatics, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins School of Medicine, Baltimore, MD, USA. Electronic address:

Omics data contain signals from the molecular, physical, and kinetic inter- and intracellular interactions that control biological systems. Matrix factorization (MF) techniques can reveal low-dimensional structure from high-dimensional data that reflect these interactions. These techniques can uncover new biological knowledge from diverse high-throughput omics data in applications ranging from pathway discovery to timecourse analysis. We review exemplary applications of MF for systems-level analyses. We discuss appropriate applications of these methods, their limitations, and focus on the analysis of results to facilitate optimal biological interpretation. The inference of biologically relevant features with MF enables discovery from high-throughput data beyond the limits of current biological knowledge - answering questions from high-dimensional data that we have not yet thought to ask.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.tig.2018.07.003DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6309559PMC
October 2018

The Immune Landscape of Cancer.

Immunity 2018 04 5;48(4):812-830.e14. Epub 2018 Apr 5.

Department of Systems Biology and Department of Electrical Engineering, Columbia University, New York, NY 10027, USA.

We performed an extensive immunogenomic analysis of more than 10,000 tumors comprising 33 diverse cancer types by utilizing data compiled by TCGA. Across cancer types, we identified six immune subtypes-wound healing, IFN-γ dominant, inflammatory, lymphocyte depleted, immunologically quiet, and TGF-β dominant-characterized by differences in macrophage or lymphocyte signatures, Th1:Th2 cell ratio, extent of intratumoral heterogeneity, aneuploidy, extent of neoantigen load, overall cell proliferation, expression of immunomodulatory genes, and prognosis. Specific driver mutations correlated with lower (CTNNB1, NRAS, or IDH1) or higher (BRAF, TP53, or CASP8) leukocyte levels across all cancers. Multiple control modalities of the intracellular and extracellular networks (transcription, microRNAs, copy number, and epigenetic processes) were involved in tumor-immune cell interactions, both across and within immune subtypes. Our immunogenomics pipeline to characterize these heterogeneous tumors and the resulting data are intended to serve as a resource for future targeted studies to further advance the field.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.immuni.2018.03.023DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5982584PMC
April 2018

Software for the Integration of Multiomics Experiments in Bioconductor.

Cancer Res 2017 11;77(21):e39-e42

Graduate School of Public Health & Health Policy, City University of New York, New York, New York.

Multiomics experiments are increasingly commonplace in biomedical research and add layers of complexity to experimental design, data integration, and analysis. R and Bioconductor provide a generic framework for statistical analysis and visualization, as well as specialized data classes for a variety of high-throughput data types, but methods are lacking for integrative analysis of multiomics experiments. The MultiAssayExperiment software package, implemented in R and leveraging Bioconductor software and design principles, provides for the coordinated representation of, storage of, and operation on multiple diverse genomics data. We provide the unrestricted multiple 'omics data for each cancer tissue in The Cancer Genome Atlas as ready-to-analyze MultiAssayExperiment objects and demonstrate in these and other datasets how the software simplifies data representation, statistical analysis, and visualization. The MultiAssayExperiment Bioconductor package reduces major obstacles to efficient, scalable, and reproducible statistical analysis of multiomics data and enhances data science applications of multiple omics datasets. .
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1158/0008-5472.CAN-17-0344DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5679241PMC
November 2017

In Reply.

Oncologist 2017 12 11;22(12):1561. Epub 2017 Sep 11.

Departments of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA.

View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1634/theoncologist.2017-0283DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5728026PMC
December 2017

Molecular Subtypes Improve Prognostic Value of International Metastatic Renal Cell Carcinoma Database Consortium Prognostic Model.

Oncologist 2017 03 20;22(3):286-292. Epub 2017 Feb 20.

Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA

Introduction: Gene-expression signatures for prognosis have been reported in localized renal cell carcinoma (RCC). The aim of this study was to test the predictive power of two different signatures, ClearCode34, a 34-gene signature model [Eur Urol 2014;66:77-84], and an 8-gene signature model [Eur Urol 2015;67:17-20], in the setting of systemic therapy for metastatic disease.

Materials And Methods: Metastatic RCC (mRCC) patients from five institutions who were part of TCGA were identified and clinical data were retrieved. We trained and implemented each gene model as described by the original study. The latter was demonstrated by faithful regeneration of a figure and results from the original study. mRCC patients were dichotomized to good or poor prognostic risk groups using each gene model. Cox proportional hazard regression and concordance index (C-Index) analysis were used to investigate an association between each prognostic risk model and overall survival (OS) from first-line therapy.

Results: Overall, 54 patients were included in the final analysis. The primary endpoint was OS. Applying the ClearCode34 model, median survival for the low-risk-ccA ( = 17)-and the high-risk-ccB ( = 37)-subtypes were 27.6 and 22.3 months (hazard ratio (HR): 2.33;  = .039), respectively. ClearCode34 ccA/ccB and International Metastatic Renal Cell Carcinoma Database Consortium (IMDC) classifications appear to represent distinct risk criteria in mRCC, and we observed no significant overlap in classification ( > .05, chi-square test). On multivariable analyses and adjusting for IMDC groups, ccB remained independently associated with a worse OS ( = .044); the joint model of ccA/ccB and IMDC was significantly more accurate in predicting OS than a model with IMDC alone ( = .045, F-test). This was also observed in C-Index analysis; a model with both ccA and ccB subtypes had higher accuracy (C-Index 0.63, 95% confidence interval [CI] = 0.51-0.75) and 95% CIs of the C-Index that did not include the null value of 0.5 in contrast to a model with IMDC alone (0.60, CI = 0.47-0.72). The 8-gene signature molecular subtype model was a weak but insignificant predictor of survival in this cohort ( = .13). A model that included both the 8-gene signature and IMDC (C-Index 0.62, CI = 0.49-0.76) was more prognostic than IMDC alone but did not reach significance, as the 95% CI included the null value of 0.5. These two genomic signatures share no genes in common and are enriched in different biological pathways. The ClearCode34 included genes and (also known as HIF2a), which are involved in regulation of gene expression by hypoxia-inducible factor.

Conclusion: The ClearCode34 but not the 8-gene molecular model improved the prognostic predictive power of the IMDC model in this cohort of 54 patients with metastatic clear cell RCC. 2017;22:286-292 IMPLICATIONS FOR PRACTICE: The clinical and laboratory factors included in the International Metastatic Renal Cell Carcinoma Database Consortium model provide prognostic information in metastatic renal cell carcinoma (mRCC). The present study shows that genomic signatures, originally validated in localized RCC, may add further complementary prognostic information in the metastatic setting. This study may provide new insights into the molecular basis of certain mRCC subgroups. The integration of clinical and molecular data has the potential to redefine mRCC classification, enhance the understanding of mRCC biology, and potentially predict response to treatment in the future.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1634/theoncologist.2016-0078DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5344647PMC
March 2017

Dimension reduction techniques for the integrative analysis of multi-omics data.

Brief Bioinform 2016 07 11;17(4):628-41. Epub 2016 Mar 11.

State-of-the-art next-generation sequencing, transcriptomics, proteomics and other high-throughput 'omics' technologies enable the efficient generation of large experimental data sets. These data may yield unprecedented knowledge about molecular pathways in cells and their role in disease. Dimension reduction approaches have been widely used in exploratory analysis of single omics data sets. This review will focus on dimension reduction approaches for simultaneous exploratory analyses of multiple data sets. These methods extract the linear relationships that best explain the correlated structure across data sets, the variability both within and between variables (or observations) and may highlight data issues such as batch effects or outliers. We explore dimension reduction techniques as one of the emerging approaches for data integration, and how these can be applied to increase our understanding of biological systems in normal physiological function and disease.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1093/bib/bbv108DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4945831PMC
July 2016

MECP2 Is a Frequently Amplified Oncogene with a Novel Epigenetic Mechanism That Mimics the Role of Activated RAS in Malignancy.

Cancer Discov 2016 Jan 6;6(1):45-58. Epub 2015 Nov 6.

Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, Massachusetts. Department of Medicine, Harvard Medical School, Boston, Massachusetts. Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts. Center for Cancer Systems Biology, Dana-Farber Cancer Institute, Boston, Massachusetts.

Unlabelled: An unbiased genome-scale screen for unmutated genes that drive cancer growth when overexpressed identified methyl cytosine-guanine dinucleotide (CpG) binding protein 2 (MECP2) as a novel oncogene. MECP2 resides in a region of the X-chromosome that is significantly amplified across 18% of cancers, and many cancer cell lines have amplified, overexpressed MECP2 and are dependent on MECP2 expression for growth. MECP2 copy-number gain and RAS family member alterations are mutually exclusive in several cancer types. The MECP2 splicing isoforms activate the major growth factor pathways targeted by activated RAS, the MAPK and PI3K pathways. MECP2 rescued the growth of a KRAS(G12C)-addicted cell line after KRAS downregulation, and activated KRAS rescues the growth of an MECP2-addicted cell line after MECP2 downregulation. MECP2 binding to the epigenetic modification 5-hydroxymethylcytosine is required for efficient transformation. These observations suggest that MECP2 is a commonly amplified oncogene with an unusual epigenetic mode of action.

Significance: MECP2 is a commonly amplified oncogene in human malignancies with a unique epigenetic mechanism of action. Cancer Discov; 6(1); 45-58. ©2015 AACR.This article is highlighted in the In This Issue feature, p. 1.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1158/2159-8290.CD-15-0341DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4775099PMC
January 2016

Public data and open source tools for multi-assay genomic investigation of disease.

Brief Bioinform 2016 07 12;17(4):603-15. Epub 2015 Oct 12.

Molecular interrogation of a biological sample through DNA sequencing, RNA and microRNA profiling, proteomics and other assays, has the potential to provide a systems level approach to predicting treatment response and disease progression, and to developing precision therapies. Large publicly funded projects have generated extensive and freely available multi-assay data resources; however, bioinformatic and statistical methods for the analysis of such experiments are still nascent. We review multi-assay genomic data resources in the areas of clinical oncology, pharmacogenomics and other perturbation experiments, population genomics and regulatory genomics and other areas, and tools for data acquisition. Finally, we review bioinformatic tools that are explicitly geared toward integrative genomic data visualization and analysis. This review provides starting points for accessing publicly available data and tools to support development of needed integrative methods.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1093/bib/bbv080DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4945830PMC
July 2016

RelA-Induced Interferon Response Negatively Regulates Proliferation.

PLoS One 2015 13;10(10):e0140243. Epub 2015 Oct 13.

Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, Massachusetts, United States of America; Department of Surgery, Brigham and Women's Hospital, Boston, Massachusetts, United States of America.

Both oncogenic and tumor-suppressor activities are attributed to the Nuclear Factor kappa B (NF-kB) pathway. Moreover, NF-kB may positively or negatively regulate proliferation. The molecular determinants of these opposing roles of NF-kB are unclear. Using primary human mammary epithelial cells (HMEC) as a model, we show that increased RelA levels and consequent increase in basal transcriptional activity of RelA induces IRF1, a target gene. Induced IRF1 upregulates STAT1 and IRF7, and in consort, these factors induce the expression of interferon response genes. Activation of the interferon pathway down-regulates CDK4 and up-regulates p27 resulting in Rb hypo-phosphorylation and cell cycle arrest. Stimulation of HMEC with IFN-γ elicits similar phenotypic and molecular changes suggesting that basal activity of RelA and IFN-γ converge on IRF1 to regulate proliferation. The anti-proliferative RelA-IRF1-CDK4 signaling axis is retained in ER+/HER2- breast tumors analyzed by The Cancer Genome Atlas (TCGA). Using immuno-histochemical analysis of breast tumors, we confirm the negative correlation between RelA levels and proliferation rate in ER+/HER2- breast tumors. These findings attribute an anti-proliferative tumor-suppressor role to basal RelA activity. Inactivation of Rb, down-regulation of RelA or IRF1, or upregulation of CDK4 or IRF2 rescues the RelA-IRF1-CDK4 induced proliferation arrest in HMEC and are points of disruption in aggressive tumors. Activity of the RelA-IRF1-CDK4 axis may explain favorable response to CDK4/6 inhibition observed in patients with ER+ Rb competent tumors.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0140243PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4604146PMC
June 2016

A multivariate approach to the integration of multi-omics datasets.

BMC Bioinformatics 2014 May 29;15:162. Epub 2014 May 29.

Chair of Proteomics and Bioanalytics, Technische Universität München, Freising, Germany.

Background: To leverage the potential of multi-omics studies, exploratory data analysis methods that provide systematic integration and comparison of multiple layers of omics information are required. We describe multiple co-inertia analysis (MCIA), an exploratory data analysis method that identifies co-relationships between multiple high dimensional datasets. Based on a covariance optimization criterion, MCIA simultaneously projects several datasets into the same dimensional space, transforming diverse sets of features onto the same scale, to extract the most variant from each dataset and facilitate biological interpretation and pathway analysis.

Results: We demonstrate integration of multiple layers of information using MCIA, applied to two typical "omics" research scenarios. The integration of transcriptome and proteome profiles of cells in the NCI-60 cancer cell line panel revealed distinct, complementary features, which together increased the coverage and power of pathway analysis. Our analysis highlighted the importance of the leukemia extravasation signaling pathway in leukemia that was not highly ranked in the analysis of any individual dataset. Secondly, we compared transcriptome profiles of high grade serous ovarian tumors that were obtained, on two different microarray platforms and next generation RNA-sequencing, to identify the most informative platform and extract robust biomarkers of molecular subtypes. We discovered that the variance of RNA-sequencing data processed using RPKM had greater variance than that with MapSplice and RSEM. We provided novel markers highly associated to tumor molecular subtype combined from four data platforms. MCIA is implemented and available in the R/Bioconductor "omicade4" package.

Conclusion: We believe MCIA is an attractive method for data integration and visualization of several datasets of multi-omics features observed on the same set of individuals. The method is not dependent on feature annotation, and thus it can extract important features even when there are not present across all datasets. MCIA provides simple graphical representations for the identification of relationships between large datasets.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1186/1471-2105-15-162DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4053266PMC
May 2014

Risk prediction for late-stage ovarian cancer by meta-analysis of 1525 patient samples.

J Natl Cancer Inst 2014 Apr 3;106(5). Epub 2014 Apr 3.

Affiliations of authors: Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA (MR, ACC, LT, FM, CH, GP); Department of Biostatistics, Harvard School of Public Health, Boston, MA (MR, ACC, LT, FM, CH, GP); Center for Cancer Research (WW, S-hK, MB) and Department of Pathology (EO), Massachusetts General Hospital, Boston, MA; City University of New York School of Public Health, Hunter College, New York, NY (LW); Sung-hoon Kim, Yonsei University College of Medicine, Seoul, Korea (S-HK).

Background: Ovarian cancer causes more than 15000 deaths per year in the United States. The survival of patients is quite heterogeneous, and accurate prognostic tools would help with the clinical management of these patients.

Methods: We developed and validated two gene expression signatures, the first for predicting survival in advanced-stage, serous ovarian cancer and the second for predicting debulking status. We integrated 13 publicly available datasets totaling 1525 subjects. We trained prediction models using a meta-analysis variation on the compound covariable method, tested models by a "leave-one-dataset-out" procedure, and validated models in additional independent datasets. Selected genes from the debulking signature were validated by immunohistochemistry and quantitative reverse-transcription polymerase chain reaction (qRT-PCR) in two further independent cohorts of 179 and 78 patients, respectively. All statistical tests were two-sided.

Results: The survival signature stratified patients into high- and low-risk groups (hazard ratio = 2.19; 95% confidence interval [CI] = 1.84 to 2.61) statistically significantly better than the TCGA signature (P = .04). POSTN, CXCL14, FAP, NUAK1, PTCH1, and TGFBR2 were validated by qRT-PCR (P < .05) and POSTN, CXCL14, and phosphorylated Smad2/3 were validated by immunohistochemistry (P < .001) as independent predictors of debulking status. The sum of immunohistochemistry intensities for these three proteins provided a tool that classified 92.8% of samples correctly in high- and low-risk groups for suboptimal debulking (area under the curve = 0.89; 95% CI = 0.84 to 0.93).

Conclusions: Our survival signature provides the most accurate and validated prognostic model for early- and advanced-stage high-grade, serous ovarian cancer. The debulking signature accurately predicts the outcome of cytoreductive surgery, potentially allowing for stratification of patients for primary vs secondary cytoreduction.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1093/jnci/dju048DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4580556PMC
April 2014

Comparative meta-analysis of prognostic gene signatures for late-stage ovarian cancer.

J Natl Cancer Inst 2014 Apr 3;106(5). Epub 2014 Apr 3.

Affiliations of authors: City University of New York School of Public Health, Hunter College, New York, NY (LW); Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA (LW, AC, MR, JD, XVW, ST, TR, BG, GP); Department of Biostatistics, Harvard School of Public Health, Boston, MA (LW, AC, CH, GP); Center for Cancer Research, Massachusetts General Hospital, Boston, MA (MB); Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada (BH); Medical Biophysics Department, University of Toronto, Toronto, Ontario, Canada (BH);Institute for Medical Information Sciences, Biometry, and Epidemiology, LMU Munich, Munich, Germany (CB).

Background: Ovarian cancer is the fifth most common cause of cancer deaths in women in the United States. Numerous gene signatures of patient prognosis have been proposed, but diverse data and methods make these difficult to compare or use in a clinically meaningful way. We sought to identify successful published prognostic gene signatures through systematic validation using public data.

Methods: A systematic review identified 14 prognostic models for late-stage ovarian cancer. For each, we evaluated its 1) reimplementation as described by the original study, 2) performance for prognosis of overall survival in independent data, and 3) performance compared with random gene signatures. We compared and ranked models by validation in 10 published datasets comprising 1251 primarily high-grade, late-stage serous ovarian cancer patients. All tests of statistical significance were two-sided.

Results: Twelve published models had 95% confidence intervals of the C-index that did not include the null value of 0.5; eight outperformed 97.5% of signatures including the same number of randomly selected genes and trained on the same data. The four top-ranked models achieved overall validation C-indices of 0.56 to 0.60 and shared anticorrelation with expression of immune response pathways. Most models demonstrated lower accuracy in new datasets than in validation sets presented in their publication.

Conclusions: This analysis provides definitive support for a handful of prognostic models but also confirms that these require improvement to be of clinical value. This work addresses outstanding controversies in the ovarian cancer literature and provides a reproducible framework for meta-analytic evaluation of gene signatures.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1093/jnci/dju049DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4580554PMC
April 2014

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

J Clin Invest 2014 Feb 27;124(2):859-70. Epub 2014 Jan 27.

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.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1172/JCI70941DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3904619PMC
February 2014

Palb2 synergizes with Trp53 to suppress mammary tumor formation in a model of inherited breast cancer.

Proc Natl Acad Sci U S A 2013 May 8;110(21):8632-7. Epub 2013 May 8.

Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA.

Germ-line mutations in PALB2 lead to a familial predisposition to breast and pancreatic cancer or to Fanconi Anemia subtype N. PALB2 performs its tumor suppressor role, at least in part, by supporting homologous recombination-type double strand break repair (HR-DSBR) through physical interactions with BRCA1, BRCA2, and RAD51. To further understand the mechanisms underlying PALB2-mediated DNA repair and tumor suppression functions, we targeted Palb2 in the mouse. Palb2-deficient murine ES cells recapitulated DNA damage defects caused by PALB2 depletion in human cells, and germ-line deletion of Palb2 led to early embryonic lethality. Somatic deletion of Palb2 driven by K14-Cre led to mammary tumor formation with long latency. Codeletion of both Palb2 and Tumor protein 53 (Trp53) accelerated mammary tumor formation. Like BRCA1 and BRCA2 mutant breast cancers, these tumors were defective in RAD51 focus formation, reflecting a defect in Palb2 HR-DSBR function, a strongly suspected contributor to Brca1, Brca2, and Palb2 mammary tumor development. However, unlike the case of Brca1-mutant cells, Trp53bp1 deletion failed to rescue the genomic instability of Palb2- or Brca2-mutant primary lymphocytes. Therefore, Palb2-driven DNA damage control is, in part, distinct from that executed by Brca1 and more similar to that of Brca2. The mechanisms underlying Palb2 mammary tumor suppression functions can now be explored genetically in vivo.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1073/pnas.1305362110DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3666744PMC
May 2013

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

PLoS One 2013 11;8(3):e57799. Epub 2013 Mar 11.

Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, Massachusetts, United States of America.

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.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0057799PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3594231PMC
December 2013
-->