Data Access Committee for the DNA sequencing data included in the study "A generalizable machine learning framework for classifying DNA repair defects using ctDNA exomes”
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ABSTRACT:
PROVIDER: EGAC00001003111 | EGA |
REPOSITORIES: EGA
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Project description:Specific classes of DNA damage repair (DDR) defect can drive sensitivity to emerging therapies for metastatic prostate cancer. However, biomarker approaches based on DDR gene sequencing do not accurately predict DDR deficiency or treatment benefit. Somatic alteration signatures may identify DDR deficiency but historically require whole-genome sequencing of tumour tissue. We assembled whole-exome sequencing data for 155 high ctDNA fraction plasma cell-free DNA and matched leukocyte DNA samples from patients with metastatic prostate or bladder cancer. Labels for DDR gene alterations were established using deep targeted sequencing. Per sample mutation and copy number features were used to train XGBoost ensemble models. Naive somatic features and trinucleotide signatures were associated with specific DDR gene alterations but insufficient to resolve each class. Conversely, XGBoost-derived models showed strong performance including an area under the curve of 0.99, 0.99 and 1.00 for identifying BRCA2, CDK12, and mismatch repair deficiency in metastatic prostate cancer. Our machine learning approach re-classified several samples exhibiting genomic features inconsistent with original labels, identified a metastatic bladder cancer sample with a homozygous BRCA2 copy loss, and outperformed an existing exome-based classifier for BRCA2 deficiency. We present DARC Sign (DnA Repair Classification SIGNatures); a public machine learning tool leveraging clinically-practical liquid biopsy specimens for simultaneously identifying multiple types of metastatic prostate cancer DDR deficiencies. We posit that it will be useful for understanding differential responses to DDR-directed therapies in ongoing clinical trials and may ultimately enable prospective identification of prostate cancers with phenotypic evidence of DDR deficiency.
| S-EPMC10011564 | biostudies-literature
Project description:We present DARC Sign (DnA Repair Classification SIGNatures); a public machine learning tool leveraging clinically-practical liquid biopsy specimens for simultaneously identifying multiple types of metastatic prostate cancer DDR deficiencies. We posit that it will be useful for understanding differential responses to DDR-directed therapies in ongoing clinical trials and may ultimately enable prospective identification of prostate cancers with phenotypic evidence of DDR deficiency.
| EGAS00001007006 | EGA
Project description:We focused on building models that incorporated transcription factor (TF)-DNA interaction data for 12 members of the Auxin Response Factor (ARF) family from soybean as assessed by DNA Affinity Purification and sequencing (DAP-seq).
2023-06-01 | GSE193400 | GEO
Project description:IntroductionThere is an urgent clinical need to accurately predict the risk for disease progression in post-treatment NSCLC patients, yet current ctDNA mutation profiling approaches are limited by low sensitivity. We represent a non-invasive liquid biopsy assay utilizing cfDNA neomer profiling for predicting disease progression in 44 inoperable localized NSCLC patients.MethodsA total of 97 plasma samples were collected at various time points during or post-treatments (TP1: 39, TP2: 33, TP3: 25). cfDNA neomer profiling, generated based on target sequencing data, was used to fit survival support vector machine models for each time point. Leave-one-out cross-validation (LOOCV) was performed to evaluate the models' predictive performances.ResultsOur cfDNA neomer profiling assay showed excellent performance in detecting patients with a high risk for disease progression. At TP1, the high-risk patients detected by our model showed an increased risk of 3.62 times (hazard ratio [HR] = 3.62, p = 0.0026) for disease progression, compared to 3.91 times (HR = 3.91, p = 0.0022) and 4.00 times (HR = 4.00, p = 0.019) for TP2 and TP3. These neomer profiling determined HRs were higher than the ctDNA mutation-based results (HR = 2.08, p = 0.074; HR = 1.49, p = 0.61) at TP1 and TP3. At TP1, the predictive model reached 40% sensitivity at 92.9% specificity, outperforming the mutation-based method (40% sensitivity at 78.6% specificity), while the combination results reached a higher sensitivity (60%). Finally, the longitudinal analysis showed that the combination of neomer and ctDNA mutation-based results could predict disease progression with an excellent sensitivity of 88.9% at 80% specificity.ConclusionIn conclusion, we developed a cfDNA neomer profiling assay for predicting disease progression in inoperable NSCLC patients. This assay showed increased predicting power during and post-treatment compared to the ctDNA mutation-based method, thus illustrating a great clinical potential to guide treatment decisions in inoperable NSCLC patients.Trial registrationClinicalTrials.gov: NCT04014465.
| S-EPMC11499892 | biostudies-literature
Project description:Gene expression profiles were generated from 199 primary breast cancer patients. Samples 1-176 were used in another study, GEO Series GSE22820, and form the training data set in this study. Sample numbers 200-222 form a validation set. This data is used to model a machine learning classifier for Estrogen Receptor Status. RNA was isolated from 199 primary breast cancer patients. A machine learning classifier was built to predict ER status using only three gene features.
2013-01-01 | E-GEOD-29210 | biostudies-arrayexpress
Project description:Identifying transcription factor-DNA interactions using machine learning
| PRJNA796028 | ENA
Project description:We experimented how well various supervised machine learning methods such as decision tree, partial least squares discriminant analysis (PLSDA), support vector machine and random forest perform in classifying endometriosis from the control samples trained on both transcriptomics and methylomics data. The assessment was done from two different perspectives for improving classification performances: (a) implication of three different normalization techniques, and (b) implication of differential analysis using the generalized linear model (GLM). We concluded that an appropriate machine learning diagnostic pipeline for endometriosis should use TMM normalization for transcriptomics data, and quantile or voom normalization for methylomics data, GLM for feature space reduction and classification performance maximization.
2019-07-18 | GSE134052 | GEO
Project description:We experimented how well various supervised machine learning methods such as decision tree, partial least squares discriminant analysis (PLSDA), support vector machine and random forest perform in classifying endometriosis from the control samples trained on both transcriptomics and methylomics data. The assessment was done from two different perspectives for improving classification performances: (a) implication of three different normalization techniques, and (b) implication of differential analysis using the generalized linear model (GLM). We concluded that an appropriate machine learning diagnostic pipeline for endometriosis should use TMM normalization for transcriptomics data, and quantile or voom normalization for methylomics data, GLM for feature space reduction and classification performance maximization.
2019-07-18 | GSE134056 | GEO
Project description:Having accurate maps depicting the locations of residential buildings across a region benefits a range of sectors. This is particularly true for public health programs focused on delivering services at the household level, such as indoor residual spraying with insecticide to help prevent malaria. While open source data from OpenStreetMap (OSM) depicting the locations and shapes of buildings is rapidly improving in terms of quality and completeness globally, even in settings where all buildings have been mapped, information on whether these buildings are residential, commercial or another type is often only available for a small subset. Using OSM building data from Botswana and Swaziland, we identified buildings for which 'type' was indicated, generated via on the ground observations, and classified these into two classes, "sprayable" and "not-sprayable". Ensemble machine learning, using building characteristics such as size, shape and proximity to neighbouring features, was then used to form a model to predict which of these 2 classes every building in these two countries fell into. Results show that an ensemble machine learning approach performed marginally, but statistically, better than the best individual model and that using this ensemble model we were able to correctly classify >86% (using independent test data) of structures correctly as sprayable and not-sprayable across both countries.
| S-EPMC6150517 | biostudies-other
Project description:Patients with carcinoma of unknown primary (CUP) account for 3-5% of all cancer cases. A large number of metastatic cancers require further diagnosis to determine their tissue of origin. However, diagnosis of CUP and identification of its primary site are challenging. Previous studies have suggested that molecular profiling of tissue-specific genes could be useful in inferring the primary tissue of a tumor. The purpose of this study was to evaluate the performance somatic mutations detected in a tumor to identify the cancer tissue of origin. We downloaded the somatic mutation datasets from the International Cancer Genome Consortium project. The random forest algorithm was used to extract features, and a classifier was established based on the logistic regression. Specifically, the somatic mutations of 300 genes were extracted, which are significantly enriched in functions, such as cell-to-cell adhesion. In addition, the prediction accuracy on tissue-of-origin inference for 3,374 cancer samples across 13 cancer types reached 81% in a 10-fold cross-validation. Our method could be useful in the identification of cancer tissue of origin, as well as the diagnosis and treatment of cancers.
| S-EPMC7372518 | biostudies-literature