scholarly journals Prediction of Breast Cancer Treatment–Induced Fatigue by Machine Learning Using Genome-Wide Association Data

2020 ◽  
Vol 4 (5) ◽  
Author(s):  
Sangkyu Lee ◽  
Joseph O Deasy ◽  
Jung Hun Oh ◽  
Antonio Di Meglio ◽  
Agnes Dumas ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Area Under The Curve ◽  
Breast Cancer Treatment ◽  
Nucleotide Polymorphisms ◽  
Cognitive Fatigue ◽  
Single Nucleotide ◽  
False Discovery ◽  
Genome Wide ◽  
Genome Wide Data

Abstract Background We aimed at predicting fatigue after breast cancer treatment using machine learning on clinical covariates and germline genome-wide data. Methods We accessed germline genome-wide data of 2799 early-stage breast cancer patients from the Cancer Toxicity study (NCT01993498). The primary endpoint was defined as scoring zero at diagnosis and higher than quartile 3 at 1 year after primary treatment completion on European Organization for Research and Treatment of Cancer quality-of-life questionnaires for Overall Fatigue and on the multidimensional questionnaire for Physical, Emotional, and Cognitive fatigue. First, we tested univariate associations of each endpoint with clinical variables and genome-wide variants. Then, using preselected clinical (false discovery rate < 0.05) and genomic (P < .001) variables, a multivariable preconditioned random-forest regression model was built and validated on a hold-out subset to predict fatigue. Gene set enrichment analysis identified key biological correlates (MetaCore). All statistical tests were 2-sided. Results Statistically significant clinical associations were found only with Emotional and Cognitive Fatigue, including receipt of chemotherapy, anxiety, and pain. Some single nucleotide polymorphisms had some degree of association (P < .001) with the different fatigue endpoints, although there were no genome-wide statistically significant (P < 5.00 × 10−8) associations. Only for Cognitive Fatigue, the predictive ability of the genomic multivariable model was statistically significantly better than random (area under the curve = 0.59, P = .01) and marginally improved with clinical variables (area under the curve = 0.60, P = .005). Single nucleotide polymorphisms found to be associated (P < .001) with Cognitive Fatigue belonged to genes linked to inflammation (false discovery rate adjusted P = .03), cognitive disorders (P = 1.51 × 10−12), and synaptic transmission (P = 6.28 × 10−8). Conclusions Genomic analyses in this large cohort of breast cancer survivors suggest a possible genetic role for severe Cognitive Fatigue that warrants further exploration.

scholarly journals Machine Learning Methods to Identify Genetic Correlates of Radiation-Associated Contralateral Breast Cancer in the WECARE Study

2019 ◽  
Author(s):  
Sangkyu Lee ◽  
Xiaolin Liang ◽  
Meghan Woods ◽  
Anne S. Reiner ◽  
Duncan Thomas ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Contralateral Breast Cancer ◽  
Area Under The Curve ◽  
Contralateral Breast ◽  
Dependent Manner ◽  
Nucleotide Polymorphisms ◽  
Unilateral Breast Cancer ◽  
Validation Data ◽  
Increased Risk

AbstractThe purpose of this study is to identify germline single nucleotide polymorphisms (SNPs) that optimally predict radiation-associated contralateral breast cancer (RCBC) and to provide new biological insights into the carcinogenic process. Fifty-two women with contralateral breast cancer and 153 women with unilateral breast cancer were identified within the Women’s Environmental Cancer and Radiation Epidemiology (WECARE) Study who were at increased risk of RCBC because they were ≤ 40 years of age at first diagnosis of breast cancer and received a scatter radiation dose > 1 Gy to the contralateral breast. A previously reported algorithm, preconditioned random forest regression, was applied to predict the risk of developing RCBC. The resulting model produced an area under the curve of 0.62 (p=0.04) on hold-out validation data. The biological analysis identified the cyclic AMP-mediated signaling and Ephrin-A as significant biological correlates, which were previously shown to influence cell survival after radiation in an ATM-dependent manner. The key connected genes and proteins that are identified in this analysis were previously identified as relevant to breast cancer, radiation response, or both. In summary, machine learning/bioinformatics methods applied to genome-wide genotyping data have great potential to reveal plausible biological correlates associated with the risk of RCBC.

scholarly journals Analysis of the TP53 Deleterious Single Nucleotide Polymorphisms Impact on Estrogen Receptor Alpha-p53 Interaction: A Machine Learning Approach

2019 ◽  
Vol 20 (12) ◽  
pp. 2962 ◽  
Author(s):  
Kumaraswamy Naidu Chitrala ◽  
Mitzi Nagarkatti ◽  
Prakash Nagarkatti ◽  
Suneetha Yeguvapalli
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Risk Factors ◽  
Estrogen Receptor ◽  
Estrogen Receptor Alpha ◽  
Large Deviation ◽  
The Other ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide ◽  
The Impact

Breast cancer is a leading cancer type and one of the major health issues faced by women around the world. Some of its major risk factors include body mass index, hormone replacement therapy, family history and germline mutations. Of these risk factors, estrogen levels play a crucial role. Among the estrogen receptors, estrogen receptor alpha (ERα) is known to interact with tumor suppressor protein p53 directly thereby repressing its function. Previously, we have studied the impact of deleterious breast cancer-associated non-synonymous single nucleotide polymorphisms (nsnps) rs11540654 (R110P), rs17849781 (P278A) and rs28934874 (P151T) in TP53 gene on the p53 DNA-binding core domain. In the present study, we aimed to analyze the impact of these mutations on p53–ERα interaction. To this end, we, have modelled the full-length structure of human p53 and validated its quality using PROCHECK and subjected it to energy minimization using NOMAD-Ref web server. Three-dimensional structure of ERα activation function-2 (AF-2) domain was downloaded from the protein data bank. Interactions between the modelled native and mutant (R110P, P278A, P151T) p53 with ERα was studied using ZDOCK. Machine learning predictions on the interactions were performed using Weka software. Results from the protein–protein docking showed that the atoms, residues and solvent accessibility surface area (SASA) at the interface was increased in both p53 and ERα for R110P mutation compared to the native complexes indicating that the mutation R110P has more impact on the p53–ERα interaction compared to the other two mutants. Mutations P151T and P278A, on the other hand, showed a large deviation from the native p53-ERα complex in atoms and residues at the surface. Further, results from artificial neural network analysis showed that these structural features are important for predicting the impact of these three mutations on p53–ERα interaction. Overall, these three mutations showed a large deviation in total SASA in both p53 and ERα. In conclusion, results from our study will be crucial in making the decisions for hormone-based therapies against breast cancer.

Candidate-gene association study searching for genetic factors involved in migraine chronification

Cephalalgia ◽  
2014 ◽  
Vol 35 (6) ◽  
pp. 500-507 ◽  
Author(s):  
MA Louter ◽  
J Fernandez-Morales ◽  
B de Vries ◽  
B Winsvold ◽  
V Anttila ◽  
...  
Keyword(s):  
Association Study ◽  
Genetic Factors ◽  
Data Sets ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide ◽  
Candidate Gene Association ◽  
Genome Wide ◽  
Third Stage ◽  
Genome Wide Data ◽  
Two Stages

Introduction Chronic migraine (CM) is at the severe end of the clinical migraine spectrum, but its genetic background is unknown. Our study searched for evidence that genetic factors are involved in the chronification process. Methods We initially selected 144 single-nucleotide polymorphisms (SNPs) from 48 candidate genes, which we tested for association in two stages: The first stage encompassed 262 CM patients, the second investigated 226 patients with high-frequency migraine (HFM). Subsequently, SNPs with p values < 0.05 were forwarded to the replication stage containing 531 patients with CM or HFM. Results Eight SNPs were significantly associated with CM and HFM in the two-stage phase. None survived replication in the third stage. Discussion We present the first comprehensive genetic association study for migraine chronification. There were no significant findings. Future studies may benefit from larger, genome-wide data sets or should use other genetic approaches to identify genetic factors involved in migraine chronification.

scholarly journals Genome-wide SNP Data Reveal an Overestimation of Species Diversity in a Group of Hawkmoths

2019 ◽  
Vol 11 (8) ◽  
pp. 2136-2150 ◽  
Author(s):  
Anna K Hundsdoerfer ◽  
Kyung Min Lee ◽  
Ian J Kitching ◽  
Marko Mutanen
Keyword(s):  
Valid Species ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide ◽  
Contemporary Debate ◽  
Snp Data ◽  
Genome Wide ◽  
Genome Wide Data ◽  
Research Outcome ◽  
Biological Entities ◽  
Hyles Euphorbiae

Abstract The interface between populations and evolving young species continues to generate much contemporary debate in systematics depending on the species concept(s) applied but which ultimately reduces to the fundamental question of “when do nondiscrete entities become distinct, mutually exclusive evolutionary units”? Species are perceived as critical biological entities, and the discovery and naming of new species is perceived by many authors as a major research aim for assessing current biodiversity before much of it becomes extinct. However, less attention is given to determining whether these names represent valid biological entities because this is perceived as both a laborious chore and an undesirable research outcome. The charismatic spurge hawkmoths (Hyles euphorbiae complex, HEC) offer an opportunity to study this less fashionable aspect of systematics. To elucidate this intriguing systematic challenge, we analyzed over 10,000 ddRAD single nucleotide polymorphisms from 62 individuals using coalescent-based and population genomic methodology. These genome-wide data reveal a clear overestimation of (sub)species-level diversity and demonstrate that the HEC taxonomy has been seriously oversplit. We conclude that only one valid species name should be retained for the entire HEC, namely Hyles euphorbiae, and we do not recognize any formal subspecies or other taxonomic subdivisions within it. Although the adoption of genetic tools has frequently revealed morphologically cryptic diversity, the converse, taxonomic oversplitting of species, is generally (and wrongly in our opinion) accepted as rare. Furthermore, taxonomic oversplitting is most likely to have taken place in intensively studied popular and charismatic organisms such as the HEC.

scholarly journals Single-Nucleotide Polymorphisms Promote Dysregulation Activation by Essential Gene Mediated Bio-Molecular Interaction in Breast Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Xue Wang ◽  
Zihui Zhao ◽  
Xueqing Han ◽  
Yutong Zhang ◽  
Yitong Zhang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Single Nucleotide Polymorphisms ◽  
Molecular Mechanism ◽  
Essential Gene ◽  
Expression Profiles ◽  
Machine Learning Algorithms ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide ◽  
Clinical Prognosis

BackgroundBreast cancer (BRCA) is a malignant tumor with a high mortality rate and poor prognosis in patients. However, understanding the molecular mechanism of breast cancer is still a challenge.Materials and MethodsIn this study, we constructed co-expression networks by weighted gene co-expression network analysis (WGCNA). Gene-expression profiles and clinical data were integrated to detect breast cancer survival modules and the leading genes related to prognostic risk. Finally, we introduced machine learning algorithms to build a predictive model aiming to discover potential key biomarkers.ResultsA total of 42 prognostic modules for breast cancer were identified. The nomogram analysis showed that 42 modules had good risk assessment performance. Compared to clinical characteristics, the risk values carried by genes in these modules could be used to classify the high-risk and low-risk groups of patients. Further, we found that 16 genes with significant differential expressions and obvious bridging effects might be considered biological markers related to breast cancer. Single-nucleotide polymorphisms on the CYP24A1 transcript induced RNA structural heterogeneity, which affects the molecular regulation of BRCA. In addition, we found for the first time that ABHD11-AS1 was significantly highly expressed in breast cancer.ConclusionWe integrated clinical prognosis information, RNA sequencing data, and drug targets to construct a breast cancer–related risk module. Through bridging effect measurement and machine learning modeling, we evaluated the risk values of the genes in the modules and identified potential biomarkers for breast cancer. The protocol provides new insight into deciphering the molecular mechanism and theoretical basis of BRCA.

scholarly journals The Impact of Homocysteine on the Risk of Hormone-Related Cancers: A Mendelian Randomization Study

2021 ◽  
Vol 8 ◽  
Author(s):  
Qian He ◽  
Ze Yang ◽  
Yandi Sun ◽  
Zihao Qu ◽  
Xueyao Jia ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Single Nucleotide Polymorphisms ◽  
Mendelian Randomization ◽  
Meta Analysis ◽  
Plasma Homocysteine ◽  
European Ancestry ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide ◽  
Genome Wide ◽  
The Impact

Background: Aberrant homocysteine level is associated with metabolic disorders and DNA damage, which may be involved in the carcinogenesis of hormone-related cancers, but clinical results of observational studies are controversial. In this study, we investigated the causal relationships between plasma homocysteine and breast cancer (BRCA), prostate cancer (PrCa), and renal cell carcinoma (RCC) using Mendelian randomization (MR) analyses.Design and Methods: To investigate the putative causal associations between homocysteine and the aforementioned three types of cancers, a two-sample MR study was employed for the study. The primary strategy for summary data analyses was the inverse-variance-weighted (IVW) approach. In our study, the single-nucleotide polymorphisms (SNPs) excluded confounding factors through Linkage Disequilibrium (LD). Phenoscanner tests were the instrumental variants (IVs), homocysteine was the exposure, and BRCA, PrCa, and RCC were the outcomes. Single-nucleotide polymorphisms associated with homocysteine were extracted from a large genome-wide association study (GWAS) meta-analysis of European participants (n = 44,147). Summary Statistics of BRCA were obtained from the latest and largest GWAS meta-analysis comprising of 82 studies from Breast Cancer Association Consortium (BCAC) studies, including women of European ancestry (133,384 cases and 113,789 controls); we obtained summary-level data from the GWAS meta-analysis of PrCa comprising 79,148 cases and 61,106 controls of European ancestry, and the dataset of RCC was a sex-specific GWAS meta-analysis comprising of two kidney cancer genome-wide scans for men (3,227 cases and 4,916 controls) and women (1,992 cases and 3,095 controls) of European ancestry. The MR-Egger and weight median analyses were applied for the pleiotropy test.Results: The results showed null associations between plasma homocysteine levels and overall BRCA (effect = 0.97, 95% CI: 0.90–1.06, P = 0.543), overall PrCa (effect = 1.01, 95% CI: 0.93–1.11, P = 0.774), RCC in men (effect = 0.99, 95% CI: 0.73–1.34, P = 0.929), and RCC in women (effect = 0.89, 95% CI: 0.61–1.31, P = 0.563).Conclusions: We found no putative causal associations between homocysteine and risk of BRCA, PrCa, and RCC.

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