Signatures of Discriminative Copy Number Aberrations in 31 Cancer Subtypes

Abstract

Copy number aberrations (CNA) are one of the most important classes of genomic mutations related to oncogenetic effects. In the past three decades, a vast amount of CNA data has been generated by molecular-cytogenetic and genome sequencing based methods. While this data has been instrumental in the identification of cancer-related genes and promoted research into the relation between CNA and histo-pathologically defined cancer types, the heterogeneity of source data and derived CNV profiles pose great challenges for data integration and comparative analysis. Furthermore, a majority of existing studies has been focused on the association of CNA to pre-selected “driver” genes with limited application to rare drivers and other genomic elements.In this study, we developed a bioinformatic pipeline to integrate a collection of 44,988 high-quality CNA profiles of high diversity. Using a hybrid model of neural networks and attention algorithm, we generated the CNA signatures of 31 cancer subtypes, depicting the uniqueness of their respective CNA landscapes. Finally, we constructed a multi-label classifier to identify the cancer type and the organ of origin from copy number profiling data. The investigation of the signatures suggested common patterns, not only of physiologically related cancer types but also of clinico-pathologically distant cancer types such as different cancers originating from the neural crest. Further experiments of classification models confirmed the effectiveness of the signatures in distinguishing different cancer types and demonstrated their potential in tumor classification.

Longitudinal Copy-Number Alteration Analysis in Plasma Cell-Free DNA of Neuroendocrine Neoplasms is a Novel Specific Biomarker for Diagnosis, Prognosis, and Follow-up

“…Since the detected CNAs have a specific pattern in PNENs, we hypothesized that the detected CNA pattern could distinguish PNENs from the more common PAADs. Classification of tumor types based on CNA patterns has been previously done for various tumor types, but not yet for PNENs and PAADs ( 27 ). To investigate this, a classification model was first generated and validated on a tissue dataset of PNENs and PAADs, and afterwards tested in a PNEN and PAAD cfDNA dataset.…”

“…Interestingly, classification of tumors based on their tissue CNA patterns has been performed for other tumor types, as CNA patterns were found to differ between different tumor types ( 27 ). However, besides a study in patients with lung cancer, classification based on plasma cfDNA CNA patterns remains unexplored ( 9 ).…”

The Progenetix oncogenomic resource in 2021

“…Alternatively, users can also upload their own data for single or multiple samples to visualize genome-wide CNA ( Figure 4C ). In addition, a list of studies and cohorts can be selected in the navigation menu, including arrayMap (probe-specific arrays from published studies ( 5 )), diffuse intrinsic pontine glioma cohort ( 53 ) and the ‘cancer signature’ cohort ( 54 ). All the functionalities and provided services are detailed in the documentation pages at info.progenetix.org, which invite request submission through the GitHub ‘issues’ tracker.…”