scholarly journals The landscape of interactions between cancer polygenic risk scores and somatic alterations in cancer cells

2020 ◽  
Author(s):  
Eduard Porta-Pardo ◽  
Rosalyn Sayaman ◽  
Elad Ziv ◽  
Alfonso Valencia
Keyword(s):  
Predictive Power ◽  
Molecular Subtypes ◽  
Treatment Options ◽  
The Cancer Genome Atlas ◽  
Risk Scores ◽  
Cancer Type ◽  
Clinical Implementation ◽  
Polygenic Risk ◽  
Cancer Subtypes ◽  
Somatic Alterations

Over the last 15 years we have identified hundreds of inherited variants that increase the risk of developing cancer. Polygenic risk scores (PRS) summarize the genetic risk of each individual by accounting for the unique combination of risk alleles in their genome. So far, most studies of PRS in cancer have focused on their predictive value: i.e. to what extent the PRS can predict which individuals will develop a particular cancer type. In parallel, for most cancers, we have identified several subtypes based on their somatic molecular properties. However, little is known about the relationship between the somatic molecular subtypes of cancer and PRS and it is possible that PRS preferentially predict specific cancer subtypes. Since cancer subtypes can have very different outcomes, treatment options and molecular vulnerabilities, answering this question is very important to understand the consequences that widespread PRS use would have in which tumors are detected early. Here we used data from The Cancer Genome Atlas (TCGA) to study the correlation between PRS for different forms of cancer and the landscape of somatic alterations in the tumors developed by each patient. We first validated the predictive power of 8 different PRS in TCGA and describe how PRS for some cancer types are associated with specific molecular subtypes or somatic cancer driver events. Our results highlight important questions that could improve the predictive power of PRS and that need to be answered before their widespread clinical implementation.

scholarly journals DNA Methylation Based Molecular Subtypes Predict Prognosis in Breast Cancer Patients

2021 ◽  
Vol 28 ◽  
pp. 107327482098851
Author(s):  
Zeng-Hong Wu ◽  
Yun Tang ◽  
Yan Zhou
Keyword(s):  
Breast Cancer ◽  
Dna Methylation ◽  
Molecular Subtypes ◽  
Methylation Status ◽  
The Cancer Genome Atlas ◽  
Training Dataset ◽  
Consensus Clustering ◽  
Breast Cancer Patients ◽  
Cancer Subtypes ◽  
Novel Biomarkers

Background: Epigenetic changes are tightly linked to tumorigenesis development and malignant transformation’ However, DNA methylation occurs earlier and is constant during tumorigenesis. It plays an important role in controlling gene expression in cancer cells. Methods: In this study, we determining the prognostic value of molecular subtypes based on DNA methylation status in breast cancer samples obtained from The Cancer Genome Atlas database (TCGA). Results: Seven clusters and 204 corresponding promoter genes were identified based on consensus clustering using 166 CpG sites that significantly influenced survival outcomes. The overall survival (OS) analysis showed a significant prognostic difference among the 7 groups (p<0.05). Finally, a prognostic model was used to estimate the results of patients on the testing set based on the classification findings of a training dataset DNA methylation subgroups. Conclusions: The model was found to be important in the identification of novel biomarkers and could be of help to patients with different breast cancer subtypes when predicting prognosis, clinical diagnosis and management.

scholarly journals CPEM: Accurate cancer type classification based on somatic alterations using an ensemble of a random forest and a deep neural network

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Kanggeun Lee ◽  
Hyoung-oh Jeong ◽  
Semin Lee ◽  
Won-Ki Jeong
Keyword(s):  
Machine Learning ◽  
Prediction Model ◽  
Genomic Data ◽  
The Cancer Genome Atlas ◽  
Cancer Type ◽  
Machine Learning Classifiers ◽  
Learning Classifiers ◽  
Somatic Alterations ◽  
The Impact ◽  
Type Classification

AbstractWith recent advances in DNA sequencing technologies, fast acquisition of large-scale genomic data has become commonplace. For cancer studies, in particular, there is an increasing need for the classification of cancer type based on somatic alterations detected from sequencing analyses. However, the ever-increasing size and complexity of the data make the classification task extremely challenging. In this study, we evaluate the contributions of various input features, such as mutation profiles, mutation rates, mutation spectra and signatures, and somatic copy number alterations that can be derived from genomic data, and further utilize them for accurate cancer type classification. We introduce a novel ensemble of machine learning classifiers, called CPEM (Cancer Predictor using an Ensemble Model), which is tested on 7,002 samples representing over 31 different cancer types collected from The Cancer Genome Atlas (TCGA) database. We first systematically examined the impact of the input features. Features known to be associated with specific cancers had relatively high importance in our initial prediction model. We further investigated various machine learning classifiers and feature selection methods to derive the ensemble-based cancer type prediction model achieving up to 84% classification accuracy in the nested 10-fold cross-validation. Finally, we narrowed down the target cancers to the six most common types and achieved up to 94% accuracy.

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.

Muscle-invasive bladder cancer: Molecular subtypes and response to neoadjuvant chemotherapy.

2017 ◽  
Vol 35 (6_suppl) ◽  
pp. 281-281 ◽  
Author(s):  
Roland Seiler ◽  
Brian Winters ◽  
James Douglas ◽  
Bas W.G. van Rhijn ◽  
Gottfrid Sjödahl ◽  
...  
Keyword(s):  
Molecular Subtypes ◽  
Molecular Subtype ◽  
Treatment Options ◽  
Good Prognosis ◽  
Single Sample ◽  
Clinical Implementation ◽  
Differential Impact ◽  
Muscle Invasive Bladder Cancer ◽  
Muscle Invasive ◽  
The Impact

281 Background: Molecular subtypes of muscle-invasive bladder cancers (MIBC) have recently been discovered based on gene expression. We investigated the impact of different subtyping methods on response to neoadjuvant cisplatin-based chemotherapy (NAC) and developed a single sample model for subtyping. Methods: Transcriptome-wide microarray analysis was conducted on pre-NAC transurethral resection (TUR) specimens of 223 patients with MIBC who received NAC followed by cystectomy at 5 centers. The specimens were classified according to four published methods for molecular subtype (UNC, MDA, TCGA, Lund). Overall survival (OS) for each subtype was compared between NAC patients in this study and non-NAC patients from the provisional TCGA. A genomic classifier (GSC) was trained to predict subtype in a single sample model and validated in independent NAC (2 centers) and non-NAC datasets. Results: The models generated subtype calls similar to previously published ratios. Concordance of a given subtype between the different methods was high. Luminal tumors had the best OS independent of NAC. Patients with tumors classified as UNC basal, MDA basal and TCGA cluster III experienced the greatest improvement in OS after NAC compared to surgery alone. Tumors assigned as UNC claudin-low had the worst OS irrespective of treatment regimen (p=0.005). GSC accurately predicted four classes (luminal, luminal-infiltrated, basal, claudin-low) and the differential impact of a basal subtype on patient OS in NAC (3-yr survival of 75.2%; p=0.001) and non-NAC (3-yr survival of 42.4%; p=0.014) cohorts could be validated. Conclusions: The benefit of NAC varies between molecular subtypes. The good prognosis of luminal/cluster I tumors could not be improved with NAC, which suggests these patients may be managed best with surgery alone. The prognosis of patients with basal tumors improved the most when treated with NAC compared to surgery alone. Poor OS of claudin-low tumors even after NAC implies that these tumors are resistant to cisplatin-based chemotherapy, and these patients should be included in protocols investigating alternative treatment options like immunotherapy. Further validation prior to clinical implementation is needed.

scholarly journals Polygenic Risk Scores for Prediction of Breast Cancer and Breast Cancer Subtypes

2019 ◽  
Vol 104 (1) ◽  
pp. 21-34 ◽  
Author(s):  
Nasim Mavaddat ◽  
Kyriaki Michailidou ◽  
Joe Dennis ◽  
Michael Lush ◽  
Laura Fachal ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Breast Cancer Subtypes ◽  
Risk Scores ◽  
Polygenic Risk ◽  
Cancer Subtypes

scholarly journals GENE-25. GWAS BY MOLECULAR SUBTYPE IDENTIFIED NOVEL RISK LOCI FOR ADULT DIFFUSE GLIOMA

2019 ◽  
Vol 21 (Supplement_6) ◽  
pp. vi102-vi103
Author(s):  
Jeanette Eckel-Passow ◽  
Paul Decker ◽  
Matthew Kosel ◽  
Thomas Kollmeyer ◽  
Annette Molinaro ◽  
...  
Keyword(s):  
Molecular Subtypes ◽  
Molecular Subtype ◽  
Association Studies ◽  
Meta Analysis ◽  
The Cancer Genome Atlas ◽  
Eqtl Analysis ◽  
Diffuse Glioma ◽  
Genome Wide Association Studies ◽  
Germline Variants ◽  
Somatic Alterations

Abstract Genome-wide association studies (GWAS) have revealed that 25 regions in 24 genes are associated with adult diffuse glioma development. These regions were identified by performing GWAS of glioma overall and GWAS by pathology (GBM and nonGBM). Subsequently, these regions have been evaluated for associations with specific molecular subtypes. The 2016 WHO Classification of Tumors of the Central Nervous System utilizes two somatic alterations to molecularly-classify adult diffuse glioma: IDH mutation and 1p/19q codeletion. TERT promoter mutation has also been shown to be associated with age at diagnosis and patient outcome. We hypothesized that germline variants may increase susceptibility to, or interact with, these somatic alterations to accelerate the development of specific molecular subtypes of glioma. To test our hypothesis, we performed a GWAS by glioma molecular subtype – as defined by presence or absence of IDH and TERT somatic mutation and 1p/19q codeletion – utilizing a two-stage design and subsequent meta analysis that included 3001 total glioma cases and 2697 total controls. Data were imputed using the Michigan Server and logistic regression was used, adjusting for age and sex. The Cancer Genome Atlas (TCGA) data were used to perform an expression quantitative trait loci (eQTL) analysis on candidate germline variants. Variants in 2q37 and 7p22 were associated with IDH-mutated glioma (meta analysis p< 5x10-8). The eQTL analyses demonstrated significant associations between 2q37 variants and expression of nearby genes as well as associations between 7p22 variants and nearby genes (p< 0.0001). In conclusion, we identified and validated novel germline variants in two genes that are associated with etiology of IDH-mutated adult diffuse glioma.

scholarly journals Exploring Various Polygenic Risk Scores for Skin Cancer in the Phenomes of the Michigan Genomics Initiative and the UK Biobank with a Visual Catalog:PRSWeb

2018 ◽  
Author(s):  
Lars G. Fritsche ◽  
Lauren J. Beesley ◽  
Peter VandeHaar ◽  
Robert B. Peng ◽  
Maxwell Salvatore ◽  
...  
Keyword(s):  
Skin Cancer ◽  
Cell Carcinoma ◽  
Genetic Risk ◽  
Population Based ◽  
Risk Scores ◽  
Uk Biobank ◽  
Polygenic Risk ◽  
Cancer Subtypes ◽  
Construction Methods ◽  
The Uk

AbstractPolygenic risk scores (PRS) are designed to serve as a single summary measure, condensing information from a large number of genetic variants associated with a disease. They have been used for stratification and prediction of disease risk. The construction of a PRS often depends on the purpose of the study, the available data/summary estimates, and the underlying genetic architecture of a disease. In this paper, we consider several choices for constructing a PRS using summary data obtained from various publicly-available sources including the UK Biobank and evaluate their abilities to predict outcomes derived from electronic health records (EHR). Weexamine the three most common skin cancer subtypes in the USA: basal cellcarcinoma, cutaneous squamous cell carcinoma, and melanoma. The genetic risk profiles of subtypes may consist of both shared and unique elements and we construct PRS to understand the common versus distinct etiology. This study is conducted using data from 30,702 unrelated, genotyped patients of recent European descent from the Michigan Genomics Initiative (MGI), a longitudinal biorepository effort within Michigan Medicine. Using these PRS for various skin cancer subtypes, we conduct a phenome-wide association study (PheWAS) within the MGI data to evaluate their association with secondary traits. PheWAS results are then replicated using population-based UK Biobank data. We develop an accompanying visual catalog calledPRSwebthat provides detailed PheWAS results and allows users to directly compare different PRS construction methods. The results of this study can provide guidance regarding PRS construction in future PRS-PheWAS studies using EHR data involving disease subtypes.Author summaryIn the study of genetically complex diseases, polygenic risk scores synthesize information from multiple genetic risk factors to provide insight into a patient’s risk of developing a disease based on his/her genetic profile. These risk scores can be explored in conjunction with health and disease information available in the electronic medical records. They may be associated with diseases that may be related to or precursors of the underlying disease of interest. Limited work is available guiding risk score construction when the goal is to identify associations across the medical phenome. In this paper, we compare different polygenic risk score construction methods in terms of their relationships with the medical phenome. We further propose methods for using these risk scores to decouple the shared and unique genetic profiles of related diseases and to explore related diseases’ shared and unique secondary associations. Leveraging and harnessing the rich data resources of the Michigan Genomics Initiative, a biorepository effort at Michigan Medicine, and the larger population-based UK Biobank study, we investigated the performance of genetic risk profiling methods for the three most common types of skin cancer: melanoma, basal cell carcinoma and squamous cell carcinoma.

scholarly journals Cancer subtype identification using somatic mutation data

2017 ◽  
Author(s):  
Marieke L. Kuijjer ◽  
Joseph N. Paulson ◽  
Peter Salzman ◽  
Wei Ding ◽  
John Quackenbush
Keyword(s):  
Somatic Mutation ◽  
Treatment Options ◽  
Biological Pathways ◽  
The Cancer Genome Atlas ◽  
Phenotypic Data ◽  
Primary Tumors ◽  
Cancer Subtypes ◽  
Cancer Types ◽  
Mutation Data ◽  
Pan Cancer

BACKGROUNDWith the onset of next generation sequencing technologies, we have made great progress in identifying recurrent mutational drivers of cancer. As cancer tissues are now frequently screened for specific sets of mutations, a large amount of samples has become available for analysis. Classification of patients with similar mutation profiles may help identifying subgroups of patients who might benefit from specific types of treatment. However, classification based on somatic mutations is challenging due to the sparseness and heterogeneity of the data.METHODSHere, we describe a new method to de-sparsify somatic mutation data using biological pathways. We applied this method to 23 cancer types from The Cancer Genome Atlas, including samples from 5, 805 primary tumors.RESULTSWe show that, for most cancer types, de-sparsified mutation data associates with phenotypic data. We identify poor prognostic subtypes in three cancer types, which are associated with mutations in signal transduction pathways for which targeted treatment options are available. We identify subtype-drug associations for 14 additional subtypes. Finally, we perform a pan-cancer subtyping analysis and identify nine pan-cancer subtypes, which associate with mutations in four overarching sets of biological pathways.CONCLUSIONSThis study is an important step towards understanding mutational patterns in cancer.

scholarly journals Publicly available hiPSC lines with extreme polygenic risk scores for modeling schizophrenia

2020 ◽  
Author(s):  
Kristina Rehbach ◽  
Hanwen Zhang ◽  
Debamitra Das ◽  
Sara Abdollahi ◽  
Tim Prorok ◽  
...  
Keyword(s):  
Rare Variants ◽  
Treatment Options ◽  
Induced Pluripotent Stem Cell ◽  
Risk Scores ◽  
Polygenic Risk ◽  
Risk Variants ◽  
Important Challenge ◽  
Cell Type Specific ◽  
Induced Pluripotent ◽  
The Impact

ABSTRACTSchizophrenia (SZ) is a common and debilitating psychiatric disorder with limited effective treatment options. Although highly heritable, risk for this polygenic disorder depends on the complex interplay of hundreds of common and rare variants. Translating the growing list of genetic loci significantly associated with disease into medically actionable information remains an important challenge. Thus, establishing platforms with which to validate the impact of risk variants in cell-type-specific and donor-dependent contexts is critical. Towards this, we selected and characterize a collection of twelve human induced pluripotent stem cell (hiPSC) lines derived from control donors with extremely low and high SZ polygenic risk scores (PRS). These hiPSC lines are publicly available at the California Institute for Regenerative Medicine (CIRM). The suitability of these extreme PRS hiPSCs for CRISPR-based isogenic comparisons of neurons and glia was evaluated across three independent laboratories, identifying 9 out of 12 meeting our criteria. We report a standardized resource of publicly available hiPSCs, with which we collectively commit to conducting future CRISPR-engineering, in order to facilitate comparison and integration of functional validation studies across the field of psychiatric genetics.

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