Machine Learning Models Predict the Primary Sites of Head and Neck Squamous Cell Carcinoma Metastases Based on DNA Methylation
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PROVIDER: PRJNA721669 | ENA |
REPOSITORIES: ENA
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Project description:In head and neck squamous cell cancers (HNSCs) that present as metastases with an unknown primary (HNSC-CUPs), the identification of a primary tumor improves therapy options and increases patient survival. However, the currently available diagnostic methods are laborious and do not offer a sufficient detection rate. Predictive machine learning models based on DNA methylation profiles have recently emerged as a promising technique for tumor classification. We applied this technique to HNSC to develop a tool that can improve the diagnostic workup for HNSC-CUPs. On a reference cohort of 405 primary HNSC samples, we developed four classifiers based on different machine learning models (random forest (RF), neural network (NN), elastic net penalized logistic regression (LOGREG), support vector machine (SVM)) that predict the primary site of HNSC tumors from their DNA methylation profile. The classifiers achieved high classification accuracies (RF=83%, NN=88%, LOGREG=SVM=89%) on an independent cohort of 64 HNSC metastases. Further, the NN, LOGREG, and SVM models significantly outperformed p16 status as a marker for an origin in the oropharynx. In conclusion, the DNA methylation profiles of HNSC metastases are characteristic for their primary sites and the classifiers developed in this study, which are made available to the scientific community, can provide valuable information to guide the diagnostic workup of HNSC-CUP.
2021-12-08 | GSE171994 | GEO
Project description:Head and neck squamous cell carcinoma (HNSC) patients are at risk of suffering from both pulmonary metastases or a second squamous cell carcinoma of the lung (LUSC). Differentiating pulmonary metastases from primary lung cancers is of high clinical importance, but not possible in most cases with current diagnostics. To address this, we performed DNA methylation profiling of primary tumors and trained three different machine learning methods to distinguish metastatic HNSC from primary LUSC. We developed an artificial neural network that correctly classi- fied 96.4% of the cases in a validation cohort of 279 patients with HNSC and LUSC as well as normal lung controls, outperforming support vector machines (95.7%) and random forests (87.8%). Prediction accuracies of more than 99% were achieved for 92.1% (neural network), 90% (support vector machine), and 43% (random forest) of these cases by applying thresholds to the resulting probability scores and excluding samples with low confidence. As independent clinical validation of the approach, we analyzed a series of 51 patients with a history of HNSC and a second lung tumor, demonstrating the correct classifications based on clinicopathological properties. In summary, our approach may facilitate the reliable diagnostic differentiation of pulmonary metastases of HNSC from primary LUSC to guide therapeutic decisions.
2019-09-10 | GSE124052 | GEO
Project description:Methylation profiling and machine learning distinguishes primary lung squamous cell carcinomas from head and neck metastases
| PRJNA510619 | ENA
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:The Natural History of Lymph Node Metastases in Head and Neck Cancer
| PRJNA937382 | ENA
Project description:The Natural History of Lymph Node Metastases in Head and Neck Cancer
| PRJNA937383 | ENA
Project description:Background: Human papillomavirus has been shown to have a causal role in the development of head and neck squamous cell carcinoma and represents a distinct and well-defined pathology. While HPV-positive HNSCC is associated with a better response to treatment and prognosis, a subset of patients do not respond favorably to current standard of care thus suffering unnecessary morbidity and delay to receive effective therapy. Methods: RNA from nineteen patients with HPV-positive HNSCC was subjected to gene expression analysis using Affymetrix microarrays. HPV-status was confirmed by detection of HPV16 E7 with RT-PCR. Results: In addition to specific genetic biomarkers (including LCE3D, KRTDAP, HMOX1, KRT19, MDK, TSPAN1), differentially expressed genes were highly represented in the cell processes of genomic stability, cell cycle, and DNA damage. Conclusions: This pilot study suggests possible biomarkers that predict response to chemoradiation therapy. These data can potentially lead to an assay that can be used clinically to predict HPV-positive HNSCC patients that will not benefit from chemoradiation, thus helping clinicians to lower morbidity and get selected patients to surgery faster. HPV-positive head and neck squamous cell carcinoma (HNSCC) has a good prognosis with a large percentage of patients responding to therapy. However, a certain percentage of patients do not respond. Gene expression data from Affymetrix Human Exon 1.0ST microarrays was utilized to compare patients that responded to therapy with those that did not respond.
2014-12-19 | E-GEOD-40020 | biostudies-arrayexpress
Project description:Background: Human papillomavirus has been shown to have a causal role in the development of head and neck squamous cell carcinoma and represents a distinct and well-defined pathology. While HPV-positive HNSCC is associated with a better response to treatment and prognosis, a subset of patients do not respond favorably to current standard of care thus suffering unnecessary morbidity and delay to receive effective therapy. Methods: RNA from nineteen patients with HPV-positive HNSCC was subjected to gene expression analysis using Affymetrix microarrays. HPV-status was confirmed by detection of HPV16 E7 with RT-PCR. Results: In addition to specific genetic biomarkers (including LCE3D, KRTDAP, HMOX1, KRT19, MDK, TSPAN1), differentially expressed genes were highly represented in the cell processes of genomic stability, cell cycle, and DNA damage. Conclusions: This pilot study suggests possible biomarkers that predict response to chemoradiation therapy. These data can potentially lead to an assay that can be used clinically to predict HPV-positive HNSCC patients that will not benefit from chemoradiation, thus helping clinicians to lower morbidity and get selected patients to surgery faster.
2014-12-19 | GSE40020 | GEO
Project description:Background. The unknown tissue of origin in head and neck cancer of unknown primary (hnCUP) leads to invasive diagnostic procedures and unspecific and potentially inefficient treatment options for patients. The most common histological subtype, squamous cell carcinoma, can stem from various tumor primary sites, including the oral cavity, oropharynx, larynx, head and neck skin, lungs, and esophagus. DNA methylation profiles are highly tissue-specific and have been successfully used to classify tissue origin. We therefore developed a support vector machine (SVM) classifier trained with publicly available DNA methylation profiles of commonly cervically metastasizing squamous cell carcinomas (n = 1,103) in order to identify the primary tissue of origin of our own cohort of squamous cell hnCUP patient’s samples (n = 28). Methylation analysis was performed with Infinium MethylationEPIC v1.0 BeadChip by Illumina. Results. The SVM algorithm achieved the highest overall accuracy of tested classifiers, with 87%. Squamous cell hnCUP samples on DNA methylation level resembled squamous cell carcinomas commonly metastasizing into cervical lymph nodes. The most frequently predicted cancer localization was the oral cavity in 11 cases (39%), followed by the oropharynx and larynx (both 7, 25%), skin (2, 7%), and esophagus (1, 4%). These frequencies concord with the expected distribution of lymph node metastases in epidemiological studies. Conclusions. On DNA methylation level, hnCUP is comparable to primary tumor tissue cancer types that commonly metastasize to cervical lymph nodes. Our SVM-based classifier can accurately predict these cancers’ tissues of origin and could significantly reduce the invasiveness of hnCUP diagnostics and enable a more precise therapy after clinical validation.
2024-03-04 | GSE256413 | GEO
Project description:Head and neck squamous cell carcinoma (HNSCC) is a heterogeneous group of malignancies arising from different sites and the 8th most common type of cancer in the world. While the advent of new techniques and multimodality therapy have improved prognosis of locally HNSCC, the appearance of distant metastases (DM) remains a challenge both for prognosis and treatment. To date, the genomic landscape of dissemination of HNSCC has to be further studied, in order to predict whether a patient will develop distant metastases. Therefore, knowing that the risk of disease spread to DM is directly correlated to initial staging, it suggested the hypothesis that the development of DM could be linked to the biologic characteristics of the primary tumor. The aim of our work is to compare gene expression profile of primary tumors of patients who develop and not develop DM. Additionally, we studied the expression profile of matched distant metastasis.
2020-05-21 | GSE136037 | GEO