A Novel MKL Method for GBM Prognosis Prediction by Integrating Histopathological Image and Multi-Omics Data
TL;DR: The research shows that HI-MKL is an accurate, robust, and generalized MKL method, which performs well in a GBM prognosis task, and is built a system that could predict the G BM prognosis with high accuracy.
Abstract: Glioblastoma multiforme (GBM) is one of the most malignant brain tumors with very short prognosis expectation. To improve patients’ clinical treatment and their life quality after surgery, researches have developed tremendous in silico models and tools for predicting GBM prognosis based on molecular datasets and have earned great success. However, pathology still plays the most critical role in cancer diagnosis and prognosis in the clinic at present. Recent advancement of storing and processing histopathological images has drawn attention of researchers. Models based on histopathological images are developed, which show great potential for computer-aided pathological diagnoses. But models based on both molecular and histopathological images that could predict GBM prognosis with high accuracy are not present yet. In our previous research, we used the simple MKL method to integrate multi-omics data to improve GBM prognosis prediction successfully. In this paper, we have developed a novel multiple kernel learning (MKL) method, named histopathological integrating multiple kernel learning (HI-MKL), that could integrate both histopathological images and multi-omics data efficiently. By using datasets from The Cancer Genome Atlas project, we have built a system that could predict the GBM prognosis with high accuracy. Our research shows that HI-MKL is an accurate, robust, and generalized MKL method, which performs well in a GBM prognosis task.
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01 Jan 2013
TL;DR: In this article, the landscape of somatic genomic alterations based on multidimensional and comprehensive characterization of more than 500 glioblastoma tumors (GBMs) was described, including several novel mutated genes as well as complex rearrangements of signature receptors, including EGFR and PDGFRA.
Abstract: We describe the landscape of somatic genomic alterations based on multidimensional and comprehensive characterization of more than 500 glioblastoma tumors (GBMs). We identify several novel mutated genes as well as complex rearrangements of signature receptors, including EGFR and PDGFRA. TERT promoter mutations are shown to correlate with elevated mRNA expression, supporting a role in telomerase reactivation. Correlative analyses confirm that the survival advantage of the proneural subtype is conferred by the G-CIMP phenotype, and MGMT DNA methylation may be a predictive biomarker for treatment response only in classical subtype GBM. Integrative analysis of genomic and proteomic profiles challenges the notion of therapeutic inhibition of a pathway as an alternative to inhibition of the target itself. These data will facilitate the discovery of therapeutic and diagnostic target candidates, the validation of research and clinical observations and the generation of unanticipated hypotheses that can advance our molecular understanding of this lethal cancer.
2,616 citations
TL;DR: The random-forest-based model presented in this study may be helpful as part of a follow-up intervention decision support system and may lead to early detection of recurrence, early treatment, and more favorable outcomes.
67 citations
TL;DR: This research proposes gated attentive deep learning models stacked with random forest classifiers, which use multi-modal data and produce informative features to enhance the breast cancer prognosis prediction.
Abstract: Breast cancer is the most frequently occurring cancer and has compelling contributions to increasing mortality rates among women. The manual prognosis and diagnosis of this disease take long hours, even for a medical professional. A model with better predictive power can benefit cancer patients from going through the toxic side effects and extra medical expenses related to unnecessary treatment. Medical professionals can be benefited from early-stage detection and selection of the appropriate cancer treatment plan. The availability of multi-modal cancer data, i.e., genomic details, histopathology images, and clinical details, supports the researchers in proceeding with the development of multi-modal based advanced deep-learning models. This research proposes gated attentive deep learning models stacked with random forest classifiers, which use multi-modal data and produce informative features to enhance the breast cancer prognosis prediction. It is designed as a bi-phase model; phase one uses a sigmoid gated attention convolutional neural network to generate the stacked features, while phase two passes the stacked features to the random forest classifier. The comparative study of the proposed and other existing methods over METABRIC (1980 patients) and TCGA-BRCA (1080 patients) datasets illustrate significant enhancements, 5.1% in sensitivity values, in the survival estimation of breast cancer patients.
45 citations
TL;DR: A deep learning-based predictive model is developed using Deep Denoising Auto-encoder and Multi-layer Perceptron that can quantitatively capture how genetic and epigenetic alterations correlate with directionality of gene expression for liver hepatocellular carcinoma (LIHC).
32 citations
TL;DR: This paper presents the critical review of state-of-the-art techniques for omics, multi-omics, radiomics data analysis themed at disease prediction, disease recurrence, survival analysis, and biomarker discovery.
Abstract: The heterogeneous and high-dimensional nature of omics data presents various challenges in gaining insights while analysis. In the era of big data, omics data is available as genome, proteome, transcriptome, and metabolome. Apart from the single omics data type, integrative omics known as multi-omics, and omics imaging data known as radiomics approaching to big data are being used for predictive analysis. The various computational approaches such as data mining, machine learning, deep learning, statistical methods, metaheuristic techniques have gained attention to process, normalize, integrate, analyse omics data. This paper presents the critical review of state-of-the-art techniques for omics, multi-omics, radiomics data analysis themed at disease prediction, disease recurrence, survival analysis, and biomarker discovery. The paper investigates, compares and categorizes various existing tools and technologies based on common characteristics for integration and analysis of omics data. In addition, the significant research challenges and directions are discussed for futuristic omics research. This survey would guide researchers to understand the use of computationally intelligent approaches for efficient omics data analysis.
29 citations
References
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TL;DR: The addition of temozolomide to radiotherapy for newly diagnosed glioblastoma resulted in a clinically meaningful and statistically significant survival benefit with minimal additional toxicity.
Abstract: methods Patients with newly diagnosed, histologically confirmed glioblastoma were randomly assigned to receive radiotherapy alone (fractionated focal irradiation in daily fractions of 2 Gy given 5 days per week for 6 weeks, for a total of 60 Gy) or radiotherapy plus continuous daily temozolomide (75 mg per square meter of body-surface area per day, 7 days per week from the first to the last day of radiotherapy), followed by six cycles of adjuvant temozolomide (150 to 200 mg per square meter for 5 days during each 28-day cycle). The primary end point was overall survival. results A total of 573 patients from 85 centers underwent randomization. The median age was 56 years, and 84 percent of patients had undergone debulking surgery. At a median follow-up of 28 months, the median survival was 14.6 months with radiotherapy plus temozolomide and 12.1 months with radiotherapy alone. The unadjusted hazard ratio for death in the radiotherapy-plus-temozolomide group was 0.63 (95 percent confidence interval, 0.52 to 0.75; P<0.001 by the log-rank test). The two-year survival rate was 26.5 percent with radiotherapy plus temozolomide and 10.4 percent with radiotherapy alone. Concomitant treatment with radiotherapy plus temozolomide resulted in grade 3 or 4 hematologic toxic effects in 7 percent of patients.
16,653 citations
TL;DR: The fourth edition of the World Health Organization (WHO) classification of tumours of the central nervous system, published in 2007, lists several new entities, including angiocentric glioma, papillary glioneuronal tumour, rosette-forming glioneurs tumour of the fourth ventricle, Papillary tumourof the pineal region, pituicytoma and spindle cell oncocytoma of the adenohypophysis.
Abstract: The fourth edition of the World Health Organization (WHO) classification of tumours of the central nervous system, published in 2007, lists several new entities, including angiocentric glioma, papillary glioneuronal tumour, rosette-forming glioneuronal tumour of the fourth ventricle, papillary tumour of the pineal region, pituicytoma and spindle cell oncocytoma of the adenohypophysis. Histological variants were added if there was evidence of a different age distribution, location, genetic profile or clinical behaviour; these included pilomyxoid astrocytoma, anaplastic medulloblastoma and medulloblastoma with extensive nodularity. The WHO grading scheme and the sections on genetic profiles were updated and the rhabdoid tumour predisposition syndrome was added to the list of familial tumour syndromes typically involving the nervous system. As in the previous, 2000 edition of the WHO ‘Blue Book’, the classification is accompanied by a concise commentary on clinico-pathological characteristics of each tumour type. The 2007 WHO classification is based on the consensus of an international Working Group of 25 pathologists and geneticists, as well as contributions from more than 70 international experts overall, and is presented as the standard for the definition of brain tumours to the clinical oncology and cancer research communities world-wide.
13,134 citations
"A Novel MKL Method for GBM Prognosi..." refers background in this paper
...The severity of gliomas can be further divided according to cellular atypia, cell proliferation, angiogenesis, and necrosis to 4 grades (I-IV) [6]....
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TL;DR: The cBio Cancer Genomics Portal significantly lowers the barriers between complex genomic data and cancer researchers who want rapid, intuitive, and high-quality access to molecular profiles and clinical attributes from large-scale cancer genomics projects and empowers researchers to translate these rich data sets into biologic insights and clinical applications.
Abstract: The cBio Cancer Genomics Portal (http://cbioportal.org) is an open-access resource for interactive exploration of multidimensional cancer genomics data sets, currently providing access to data from more than 5,000 tumor samples from 20 cancer studies. The cBio Cancer Genomics Portal significantly lowers the barriers between complex genomic data and cancer researchers who want rapid, intuitive, and high-quality access to molecular profiles and clinical attributes from large-scale cancer genomics projects and empowers researchers to translate these rich data sets into biologic insights and clinical applications.
11,912 citations
"A Novel MKL Method for GBM Prognosi..." refers methods in this paper
...CBioportal [20], [21] is a data portal collects and processes data from various sources and articles, including TCGAGBM dataset....
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...Pre-processed multi-omics molecular datasets are downloaded from cBioportal [20], [21] TCGAGBM dataset....
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TL;DR: Overall cancer death rates have declined 20% from their peak in 1991 to 2009 and can be accelerated by applying existing cancer control knowledge across all segments of the population, with an emphasis on those groups in the lowest socioeconomic bracket and other underserved populations.
Abstract: Each year, the American Cancer Society estimates the numbers of new cancer cases and deaths expected in the United States in the current year and compiles the most recent data on cancer incidence, mortality, and survival based on incidence data from the National Cancer Institute, the Centers for Disease Control and Prevention, and the North American Association of Central Cancer Registries and mortality data from the National Center for Health Statistics. A total of 1,660,290 new cancer cases and 580,350 cancer deaths are projected to occur in the United States in 2013. During the most recent 5 years for which there are data (2005-2009), delay-adjusted cancer incidence rates declined slightly in men (by 0.6% per year) and were stable in women, while cancer death rates decreased by 1.8% per year in men and by 1.5% per year in women. Overall, cancer death rates have declined 20% from their peak in 1991 (215.1 per 100,000 population) to 2009 (173.1 per 100,000 population). Death rates continue to decline for all 4 major cancer sites (lung, colorectum, breast, and prostate). Over the past 10 years of data (2000-2009), the largest annual declines in death rates were for chronic myeloid leukemia (8.4%), cancers of the stomach (3.1%) and colorectum (3.0%), and non-Hodgkin lymphoma (3.0%). The reduction in overall cancer death rates since 1990 in men and 1991 in women translates to the avoidance of approximately 1.18 million deaths from cancer, with 152,900 of these deaths averted in 2009 alone. Further progress can be accelerated by applying existing cancer control knowledge across all segments of the population, with an emphasis on those groups in the lowest socioeconomic bracket and other underserved populations.
11,556 citations
"A Novel MKL Method for GBM Prognosi..." refers background in this paper
...Glioblastoma Multiforme (GBM) is the most malignant brain tumor which has only 12–16 months median survival in spite of multi-modal treatment approaches [1], [2]....
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Harvard University1, University of California, San Francisco2, University of Düsseldorf3, Heidelberg University4, Aix-Marseille University5, Ludwig Institute for Cancer Research6, International Agency for Research on Cancer7, German Cancer Research Center8, University of Zurich9, St. Jude Children's Research Hospital10
TL;DR: The 2016 World Health Organization Classification of Tumors of the Central Nervous System is both a conceptual and practical advance over its 2007 predecessor and is hoped that it will facilitate clinical, experimental and epidemiological studies that will lead to improvements in the lives of patients with brain tumors.
Abstract: The 2016 World Health Organization Classification of Tumors of the Central Nervous System is both a conceptual and practical advance over its 2007 predecessor. For the first time, the WHO classification of CNS tumors uses molecular parameters in addition to histology to define many tumor entities, thus formulating a concept for how CNS tumor diagnoses should be structured in the molecular era. As such, the 2016 CNS WHO presents major restructuring of the diffuse gliomas, medulloblastomas and other embryonal tumors, and incorporates new entities that are defined by both histology and molecular features, including glioblastoma, IDH-wildtype and glioblastoma, IDH-mutant; diffuse midline glioma, H3 K27M-mutant; RELA fusion-positive ependymoma; medulloblastoma, WNT-activated and medulloblastoma, SHH-activated; and embryonal tumour with multilayered rosettes, C19MC-altered. The 2016 edition has added newly recognized neoplasms, and has deleted some entities, variants and patterns that no longer have diagnostic and/or biological relevance. Other notable changes include the addition of brain invasion as a criterion for atypical meningioma and the introduction of a soft tissue-type grading system for the now combined entity of solitary fibrous tumor / hemangiopericytoma-a departure from the manner by which other CNS tumors are graded. Overall, it is hoped that the 2016 CNS WHO will facilitate clinical, experimental and epidemiological studies that will lead to improvements in the lives of patients with brain tumors.
11,197 citations