scholarly journals Multi-omic tumor data reveal diversity of molecular mechanisms that correlate with survival

2018 ◽  
Author(s):  
Daniele Ramazzotti ◽  
Avantika Lal ◽  
Bo Wang ◽  
Serafim Batzoglou ◽  
Arend Sidow
Keyword(s):  
Molecular Mechanisms ◽  
Molecular Subtypes ◽  
Point Mutations ◽  
Integrated Analysis ◽  
Tumor Type ◽  
Cancer Subtypes ◽  
Cancer Types ◽  
Copy Number Changes ◽  
Omic Data

Outcomes for cancer patients vary greatly even within the same tumor type, and characterization of molecular subtypes of cancer holds important promise for improving prognosis and personalized treatment. This promise has motivated recent efforts to produce large amounts of multidimensional genomic (‘multi-omic’) data, but current algorithms still face challenges in the integrated analysis of such data. Here we present Cancer Integration via Multikernel Learning (CIMLR), a new cancer subtyping method that integrates multi-omic data to reveal molecular subtypes of cancer. We apply CIMLR to multi-omic data from 36 cancer types and show significant improvements in both computational efficiency and ability to extract biologically meaningful cancer subtypes. The discovered subtypes exhibit significant differences in patient survival for 27 of 36 cancer types. Our analysis reveals integrated patterns of gene expression, methylation, point mutations and copy number changes in multiple cancers and highlights patterns specifically associated with poor patient outcomes.

scholarly journals Tumor angiogenesis: causes, consequences, challenges and opportunities

2019 ◽  
Vol 77 (9) ◽  
pp. 1745-1770 ◽  
Author(s):  
Roberta Lugano ◽  
Mohanraj Ramachandran ◽  
Anna Dimberg
Keyword(s):  
Tumor Angiogenesis ◽  
Molecular Mechanisms ◽  
Kinase Inhibitors ◽  
Cancer Tissue ◽  
Tumor Type ◽  
Biological Processes ◽  
Cancer Subtypes ◽  
Secreted Factors ◽  
Challenges And Opportunities ◽  
Single Tumor

Abstract Tumor vascularization occurs through several distinct biological processes, which not only vary between tumor type and anatomic location, but also occur simultaneously within the same cancer tissue. These processes are orchestrated by a range of secreted factors and signaling pathways and can involve participation of non-endothelial cells, such as progenitors or cancer stem cells. Anti-angiogenic therapies using either antibodies or tyrosine kinase inhibitors have been approved to treat several types of cancer. However, the benefit of treatment has so far been modest, some patients not responding at all and others acquiring resistance. It is becoming increasingly clear that blocking tumors from accessing the circulation is not an easy task to accomplish. Tumor vessel functionality and gene expression often differ vastly when comparing different cancer subtypes, and vessel phenotype can be markedly heterogeneous within a single tumor. Here, we summarize the current understanding of cellular and molecular mechanisms involved in tumor angiogenesis and discuss challenges and opportunities associated with vascular targeting.

scholarly journals miRNome Profiling Reveals Shared Features in Breast Cancer Subtypes and Highlights miRNAs That Potentially Regulate MYB and EZH2 Expression

2021 ◽  
Vol 11 ◽  
Author(s):  
Stephany Corrêa ◽  
Francisco P. Lopes ◽  
Carolina Panis ◽  
Thais Basili ◽  
Renata Binato ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cell Lines ◽  
Molecular Mechanisms ◽  
Enrichment Analysis ◽  
Normal Breast ◽  
Cancer Subtypes ◽  
Interaction Prediction ◽  
Ezh2 Expression ◽  
Cancer Types ◽  
Disease Subtypes

Breast cancer (BC) has been extensively studied, as it is one of the more commonly diagnosed cancer types worldwide. The study of miRNAs has increased what is known about the complexity of pathways and signaling and has identified potential biomarkers and therapeutic targets. Thus, miRNome profiling could provide important information regarding the molecular mechanisms involved in BC. On average, more than 430 miRNAs were identified as differentially expressed between BC cell lines and normal breast HMEC cells. From these, 110 miRNAs were common to BC subtypes. The miRNome enrichment analysis and interaction maps highlighted epigenetic-related pathways shared by all BC cell lines and revealed potential miRNA targets. Quantitative evaluation of BC patient samples and GETx/TCGA-BRCA datasets confirmed MYB and EZH2 as potential targets from BC miRNome. Moreover, overall survival was impacted by EZH2 expression. The expression of 15 miRNAs, selected according to aggressiveness of BC subtypes, was confirmed in TCGA-BRCA dataset. Of these miRNAs, miRNA-mRNA interaction prediction revealed 7 novel or underexplored miRNAs in BC: miR-1271-5p, miR-130a-5p, and miR-134 as MYB regulators and miR-138-5p, miR-455-3p, miR-487a, and miR-487b as EZH2 regulators. Herein, we report a novel molecular miRNA signature for BC and identify potential miRNA/mRNAs involved in disease subtypes.

scholarly journals Genomic characterization of meningiomas.

2012 ◽  
Vol 30 (15_suppl) ◽  
pp. 2020-2020
Author(s):  
Priscilla Kaliopi Brastianos ◽  
Peleg Horowitz ◽  
Sandro Santagata ◽  
Robert T. Jones ◽  
Aaron McKenna ◽  
...  
Keyword(s):  
Statistical Significance ◽  
Point Mutations ◽  
Suppressor Gene ◽  
Genetic Alterations ◽  
Chromosome 1 ◽  
Tumor Type ◽  
Driver Genes ◽  
Genetic Changes ◽  
Fresh Frozen ◽  
Copy Number Changes

2020 Background: Understanding the genetic alterations in cancer has lead to groundbreaking discoveries in targeted therapies. Meningiomas are among the most common primary brain tumors, with approximately 18,000 new cases diagnosed annually. Though certain genes have been associated with the development of meningiomas, most notably the tumor suppressor gene neurofibromatosis 2 (NF2), the genetic changes that drive meningiomas remain poorly understood. Our objective was to comprehensively characterize the somatic genetic alterations of meningiomas to gain insight into the molecular pathways that drive this disease. Methods: Fresh frozen specimens and paired blood were collected from 16 consented patients. DNA was extracted from regions of high tumor purity determined by evaluation of H&E slides. Whole-genome sequencing from 10 tumor-normal pairs and whole-exome sequencing from 6 tumor-normal pairs was carried out. We performed an unbiased screen for point mutations, insertions-deletions, rearrangements and copy-number changes across the exomes and genomes. Recurrent (potential driver) events were then analyzed with additional algorithms for statistical significance. Results: Alterations in the NF2 gene were present in 9 of 16 patients. Multiple novel rearrangements and recurrent non-NF2 mutations were also identified in the cohort. Massive genomic rearrangement termed chromothripsis was observed in chromosome 1 in one sample, which has never previously been described in meningiomas, and represents a potentially new mechanism of malignant transformation in this tumor type. Conclusions: While NF2 mutations appear to drive a majority of these tumors, our analysis has uncovered additional potential driver genes in meningiomas, particularly in those tumors negative for NF2 alterations. To our knowledge, this is the first study to comprehensively characterize the totality of somatic genetic alterations in meningiomas, and brings us closer to the development of new therapeutic targets for this disease.

scholarly journals Identifying cancer type specific oncogenes and tumor suppressors using limited size data

2016 ◽  
Vol 14 (06) ◽  
pp. 1650031 ◽  
Author(s):  
Ana B. Pavel ◽  
Cristian I. Vasile
Keyword(s):  
Tumor Suppressors ◽  
Molecular Mechanisms ◽  
Lung Squamous Cell Carcinoma ◽  
The Cancer Genome Atlas ◽  
Driver Mutations ◽  
Cancer Type ◽  
Multiple Cancer ◽  
Driver Genes ◽  
Cancer Subtypes ◽  
Cancer Types

Cancer is a complex and heterogeneous genetic disease. Different mutations and dysregulated molecular mechanisms alter the pathways that lead to cell proliferation. In this paper, we explore a method which classifies genes into oncogenes (ONGs) and tumor suppressors. We optimize this method to identify specific (ONGs) and tumor suppressors for breast cancer, lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC) and colon adenocarcinoma (COAD), using data from the cancer genome atlas (TCGA). A set of genes were previously classified as ONGs and tumor suppressors across multiple cancer types (Science 2013). Each gene was assigned an ONG score and a tumor suppressor score based on the frequency of its driver mutations across all variants from the catalogue of somatic mutations in cancer (COSMIC). We evaluate and optimize this approach within different cancer types from TCGA. We are able to determine known driver genes for each of the four cancer types. After establishing the baseline parameters for each cancer type, we identify new driver genes for each cancer type, and the molecular pathways that are highly affected by them. Our methodology is general and can be applied to different cancer subtypes to identify specific driver genes and improve personalized therapy.

scholarly journals Whole-Genome Analysis of De Novo Somatic Point Mutations Reveals Novel Mutational Biomarkers in Pancreatic Cancer

Cancers ◽  
2021 ◽  
Vol 13 (17) ◽  
pp. 4376
Author(s):  
Amin Ghareyazi ◽  
Amir Mohseni ◽  
Hamed Dashti ◽  
Amin Beheshti ◽  
Abdollah Dehzangi ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Signaling Pathways ◽  
Molecular Mechanisms ◽  
De Novo ◽  
Treatment Options ◽  
Point Mutations ◽  
Growth Factor Receptor ◽  
Causative Mutation ◽  
Whole Genome Analysis ◽  
Cancer Subtypes

It is now known that at least 10% of samples with pancreatic cancers (PC) contain a causative mutation in the known susceptibility genes, suggesting the importance of identifying cancer-associated genes that carry the causative mutations in high-risk individuals for early detection of PC. In this study, we develop a statistical pipeline using a new concept, called gene-motif, that utilizes both mutated genes and mutational processes to identify 4211 3-nucleotide PC-associated gene-motifs within 203 significantly mutated genes in PC. Using these gene-motifs as distinguishable features for pancreatic cancer subtyping results in identifying five PC subtypes with distinguishable phenotypes and genotypes. Our comprehensive biological characterization reveals that these PC subtypes are associated with different molecular mechanisms including unique cancer related signaling pathways, in which for most of the subtypes targeted treatment options are currently available. Some of the pathways we identified in all five PC subtypes, including cell cycle and the Axon guidance pathway are frequently seen and mutated in cancer. We also identified Protein kinase C, EGFR (epidermal growth factor receptor) signaling pathway and P53 signaling pathways as potential targets for treatment of the PC subtypes. Altogether, our results uncover the importance of considering both the mutation type and mutated genes in the identification of cancer subtypes and biomarkers.

scholarly journals Molecular mechanisms of cancer metastasis via the lymphatic versus the blood vessels

2021 ◽  
Author(s):  
Stanley P. Leong ◽  
Kamila Naxerova ◽  
Laura Keller ◽  
Klaus Pantel ◽  
Marlys Witte
Keyword(s):  
Lymph Node ◽  
Cancer Cells ◽  
Blood Vessels ◽  
Cancer Progression ◽  
Molecular Mechanisms ◽  
Cancer Metastasis ◽  
Distant Metastases ◽  
Nodal Basin ◽  
Cancer Types

AbstractCancer metastasis is the process by which primary cancer cells invade through the lymphatic or blood vessels to distant sites. The molecular mechanisms by which cancer cells spread either through the lymphatic versus blood vessels or both are not well established. Two major developments have helped us to understand the process more clearly. First, the development of the sentinel lymph node (SLN) concept which is well established in melanoma and breast cancer. The SLN is the first lymph node in the draining nodal basin to receive cancer cells. Patients with a negative SLN biopsy show a significantly lower incidence of distant metastasis, suggesting that the SLN may be the major gateway for cancer metastasis in these cancer types. Second, the discovery and characterization of several biomarkers including VEGF-C, LYVE-1, Podoplanin and Prox-1 have opened new vistas in the understanding of the induction of lymphangiogenesis by cancer cells. Cancer cells must complete multiple steps to invade the lymphatic system, some of which may be enabled by the evolution of new traits during cancer progression. Thus, cancer cells may spread initially through the main gateway of the SLN, from which evolving cancer clones can invade the blood vessels to distant sites. Cancer cells may also enter the blood vessels directly, bypassing the SLN to establish distant metastases. Future studies need to pinpoint the molecules that are used by cancer cells at different stages of metastasis via different routes so that specific therapies can be targeted against these molecules, with the goal of stopping or preventing cancer metastasis.

Survey and comparative assessments of computational multi-omics integrative methods with multiple regulatory networks identifying distinct tumor compositions across pan-cancer data sets

2020 ◽  
Author(s):  
Zhuohui Wei ◽  
Yue Zhang ◽  
Wanlin Weng ◽  
Jiazhou Chen ◽  
Hongmin Cai
Keyword(s):  
Molecular Mechanisms ◽  
Genomic Data ◽  
Low Rank ◽  
Integrated Analysis ◽  
Data Sets ◽  
Omics Data ◽  
Data Types ◽  
Cancer Data ◽  
Cancer Types ◽  
Pan Cancer

Abstract The significance of pan-cancer categories has recently been recognized as widespread in cancer research. Pan-cancer categorizes a cancer based on its molecular pathology rather than an organ. The molecular similarities among multi-omics data found in different cancer types can play several roles in both biological processes and therapeutic developments. Therefore, an integrated analysis for various genomic data is frequently used to reveal novel genetic and molecular mechanisms. However, a variety of algorithms for multi-omics clustering have been proposed in different fields. The comparison of different computational clustering methods in pan-cancer analysis performance remains unclear. To increase the utilization of current integrative methods in pan-cancer analysis, we first provide an overview of five popular computational integrative tools: similarity network fusion, integrative clustering of multiple genomic data types (iCluster), cancer integration via multi-kernel learning (CIMLR), perturbation clustering for data integration and disease subtyping (PINS) and low-rank clustering (LRACluster). Then, a priori interactions in multi-omics data were incorporated to detect prominent molecular patterns in pan-cancer data sets. Finally, we present comparative assessments of these methods, with discussion over key issues in applying these algorithms. We found that all five methods can identify distinct tumor compositions. The pan-cancer samples can be reclassified into several groups by different proportions. Interestingly, each method can classify the tumors into categories that are different from original cancer types or subtypes, especially for ovarian serous cystadenocarcinoma (OV) and breast invasive carcinoma (BRCA) tumors. In addition, all clusters of the five computational methods show notable prognostic values. Furthermore, both the 9 recurrent differential genes and the 15 common pathway characteristics were identified across all the methods. The results and discussion can help the community select appropriate integrative tools according to different research tasks or aims in pan-cancer analysis.

scholarly journals Metastasis and cancer stem cell markers identify two colorectal cancer subtypes with different molecular and clinical features

2020 ◽  
Author(s):  
Burair A. Alsaihati ◽  
Shaying Zhao
Keyword(s):  
Colorectal Cancer ◽  
Tumor Heterogeneity ◽  
Molecular Subtypes ◽  
The United States ◽  
Lymphatic Invasion ◽  
Stem Cell Markers ◽  
Lethal Outcome ◽  
Cancer Subtypes ◽  
Cancer Stem Cell Markers ◽  
Cancer Types

AbstractColorectal cancer (CRC) is among the top prevalent cancer types with lethal outcome in the United States and worldwide. CRC inter-tumor heterogeneity highlights the importance of identifying molecular markers for meaningful classification and prognosis. The recently published Consensus Molecular Subtypes (CMS) represent a widely used molecular subtyping system of CRC. However, our analyses indicate that clear heterogeneity still exists in some CMSs. In this work, we demonstrate that both CMS2 and CMS4 are composed of two molecularly distinct subtypes. We named them S1 and S2, short for subtype 1 and subtype 2. Our results indicate that the two subtypes also differ clinically. Notably, S2 exhibits more frequent lymphatic invasion across CRCs and more frequent metastasis events within CMS2 patients.

scholarly journals Pan-Cancer Integrative Analysis of Whole-Genome De novo Somatic Point Mutations Reveals 17 Cancer Types

2021 ◽  
Author(s):  
Amirreza Kazemi ◽  
Amin Ghareyazi ◽  
Kimia Hamidieh ◽  
Hamed Dashti ◽  
Maedeh Tahaei ◽  
...  
Keyword(s):  
Cancer Patients ◽  
Molecular Mechanisms ◽  
High Throughput Sequencing ◽  
Specific Treatment ◽  
Cancer Drug ◽  
Cancer Type ◽  
Cancer Subtypes ◽  
Genotypic Analysis ◽  
Cancer Types ◽  
Pan Cancer

The advent of high throughput sequencing has enabled researchers to systematically evaluate the genetic variations in cancer, resulting in identifying many cancer-associated genes. Although cancers in the same tissue are widely categorized in the same group, they demonstrate many differences concerning their mutational profiles. Hence there is no “silver bullet” for the treatment of a cancer type. This reveals the importance of developing a pipeline to identify cancer-associated genes accurately and re-classify patients with similar mutational profiles. Classification of cancer patients with similar mutational profiles may help discover subtypes of cancer patients who might benefit from specific treatment types. In this study, we propose a new machine learning pipeline to identify protein-coding genes mutated in a significant portion of samples to identify cancer subtypes. We applied our pipeline to 12270 samples collected from the International Cancer Genome Consortium (ICGC), covering 19 cancer types. Here we identified 17 different cancer subtypes. Comprehensive phenotypic and genotypic analysis indicates distinguishable properties, including unique cancer-related signaling pathways, in which, for most of them, targeted treatment options are currently available. This new subtyping approach offers a novel opportunity for cancer drug development based on the mutational profile of patients. We also comprehensive study the causes of mutations among samples in each subtype by mining the mutational signatures, which provides important insight into their active molecular mechanisms. Some of the pathways we identified in most subtypes, including the cell cycle and the Axon guidance pathways, are frequently observed in cancer disease. Interestingly, we also identified several mutated genes and different rates of mutation in multiple cancer subtypes. In addition, our study on “gene-motif” suggests the importance of considering both the context of the mutations and mutational processes in identifying cancer-associated genes. The source codes for our proposed clustering pipeline and analysis are publicly available at: https://github.com/bcb-sut/Pan-Cancer.

scholarly journals NEMO: cancer subtyping by integration of partial multi-omic data

2019 ◽  
Vol 35 (18) ◽  
pp. 3348-3356 ◽  
Author(s):  
Nimrod Rappoport ◽  
Ron Shamir
Keyword(s):  
State Of The Art ◽  
Clustering Algorithms ◽  
Supplementary Information ◽  
Data Imputation ◽  
Full Data ◽  
Cancer Subtypes ◽  
Partial Data ◽  
Omic Data ◽  
Novel Algorithm

Abstract Motivation Cancer subtypes were usually defined based on molecular characterization of single omic data. Increasingly, measurements of multiple omic profiles for the same cohort are available. Defining cancer subtypes using multi-omic data may improve our understanding of cancer, and suggest more precise treatment for patients. Results We present NEMO (NEighborhood based Multi-Omics clustering), a novel algorithm for multi-omics clustering. Importantly, NEMO can be applied to partial datasets in which some patients have data for only a subset of the omics, without performing data imputation. In extensive testing on ten cancer datasets spanning 3168 patients, NEMO achieved results comparable to the best of nine state-of-the-art multi-omics clustering algorithms on full data and showed an improvement on partial data. On some of the partial data tests, PVC, a multi-view algorithm, performed better, but it is limited to two omics and to positive partial data. Finally, we demonstrate the advantage of NEMO in detailed analysis of partial data of AML patients. NEMO is fast and much simpler than existing multi-omics clustering algorithms, and avoids iterative optimization. Availability and implementation Code for NEMO and for reproducing all NEMO results in this paper is in github: https://github.com/Shamir-Lab/NEMO. Supplementary information Supplementary data are available at Bioinformatics online.

Sign in / Sign up

Export Citation Format

Share Document