Project description:DNA methylation-based classification of brain tumors has emerged as a powerful and indispensable diagnostic technique. Initial implementations have used methylation microarrays for data generation, but different sequencing approaches are increasingly used. Most current classifiers, however, rely on a fixed methylation feature space, rendering them incompatible with other platforms, especially different flavors of DNA sequencing. Here, we describe crossNN, a neural network-based machine learning framework which can accurately classify tumor entities using DNA methylation profiles obtained from different platforms and with different epigenome coverage and sequencing depth. It outperforms other deep and conventional machine learning models with respect to diagnostic accuracy and computational requirements while still being fully explainable. We use crossNN to train a pan-cancer classifier to discriminate more than 170 tumor types across all organ sites. Validation in an independent cohort of >5,000 tumors profiled using different microarray and sequencing platforms, including low-pass nanopore and targeted bisulfite sequencing, demonstrates the robustness and scalability of the model with 99.1% and 97.8% precision for brain tumor and pan-cancer model, respectively.
Project description:DNA methylation-based classification of brain tumors has emerged as a powerful and indispensable diagnostic technique. Initial implementations have used methylation microarrays for data generation, but different sequencing approaches are increasingly used. Most current classifiers, however, rely on a fixed methylation feature space, rendering them incompatible with other platforms, especially different flavors of DNA sequencing. Here, we describe crossNN, a neural network-based machine learning framework which can accurately classify tumor entities using DNA methylation profiles obtained from different platforms and with different epigenome coverage and sequencing depth. It outperforms other deep and conventional machine learning models with respect to diagnostic accuracy and computational requirements while still being fully explainable. We use crossNN to train a pan-cancer classifier to discriminate more than 170 tumor types across all organ sites. Validation in an independent cohort of >5,000 tumors profiled using different microarray and sequencing platforms, including low-pass nanopore and targeted bisulfite sequencing, demonstrates the robustness and scalability of the model with 99.1% and 97.8% precision for brain tumor and pan-cancer model, respectively.
Project description:We performed single nuclei RNA-sequencing (snRNA-seq) with matched T cell receptor sequencing (TCR-seq), and pool matched low pass whole genome sequencing (WGS) of 12 treatment-naïve non-small cell lung cancer (NSCLC) primary tumors (PTs) and 31 treatment-naïve NSCLC brain metastases (BMs) . In total, we recovered 277,206 cell transcriptomes in 43 samples.
Project description:We performed single nuclei RNA-sequencing (snRNA-seq) with matched T cell receptor sequencing (TCR-seq), and pool matched low pass whole genome sequencing (WGS) of 12 treatment-naïve non-small cell lung cancer (NSCLC) primary tumors (PTs) and 31 treatment-naïve NSCLC brain metastases (BMs) . In total, we recovered 277,206 cell transcriptomes in 43 samples.
Project description:We performed single nuclei RNA-sequencing (snRNA-seq) with matched T cell receptor sequencing (TCR-seq), pool matched low pass whole genome sequencing (WGS) and single-cell spatial transcriptomics of 12 treatment-naïve non-small cell lung cancer (NSCLC) primary tumors (PTs) and 31 treatment-naïve NSCLC brain metastases (BMs) . In total, we recovered 277,206 cell transcriptomes in 43 samples. We performed matched spatial sequencing using SlideSeq2 on 14 snRNA-seq samples.
Project description:Background: Medulloblastoma (MB) is one of the most prevalent embryonal malignant brain tumors. Current classification organizes these tumors into four molecular groups (WNT-activated, SHH-activated and TP53-wild type, SHH-activated and TP53-mutant, and non-WNT/non-SHH). Recently, a comprehensive classification has been established, identifying numerous subgroups, some of which exhibit a poor prognosis. It is critical to establish effective subgrouping methods for accurate diagnosis and patient’s management that strikes a delicate balance between improving outcomes and minimizing the risk of comorbidities. Methods: We evaluated the ability of Nanopore sequencing to provide clinically relevant methylation and copy number profiles of MB. Nanopore sequencing was applied to an EPIC discovery cohort of frozen MB, benchmarked against the gold standard EPIC array, and validated further evaluated on an integrated diagnosis cohort of MB.