Search

Somatic genomic copy-number alterations can lead to transcriptional activation or inactivation of tumor driver or suppressor genes, contributing to the malignant properties of cancer cells. Selection for such events may manifest as recurrent amplifications or deletions of size-limited (focal) regions. While methods have been developed to identify such focal regions, finding the exact targeted genes remains a challenge. Algorithms are also available that integrate copy number and RNA expression data, to aid in identifying individual targeted genes, but specificity is lacking. Here, we describe FocalScan, a tool designed to simultaneously uncover patterns of focal copy number alteration and coordinated expression change, thus combining both principles. The method outputs a ranking of tentative cancer drivers or suppressors. FocalScan works with RNA-seq data, and unlike other tools it can scan the genome unaided by a gene annotation, enabling identification of novel putatively functional elements including lncRNAs. Application on a breast cancer data set suggests considerably better performance than other DNA/RNA integration tools., (© The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Purpose: Cancer of unknown primary is a group of metastatic tumors in which the standard diagnostic workup fails to identify the site of origin of the tumor. The potential impact of precision oncology on this group of patients is large, because actionable driver mutations and a correct diagnosis could provide treatment options otherwise not available for patients with these fatal cancers. This study investigated if comprehensive genomic analyses could provide information on the origin of the tumor. Patients and Methods: Here we describe a patient whose tumor was misdiagnosed at least three times. Next-generation sequencing, a patient-derived xenograft mouse model, and bioinformatics were used to identify an actionable mutation, predict resistance development to the targeted therapy, and correctly diagnose the origin of the tumor. Transcriptomic classification was benchmarked using The Cancer Genome Atlas (TCGA). Results: Despite the lack of a known primary tumor site and the absence of diagnostic immunohistochemical markers, the origin of the patient’s tumor was established using the novel bioinformatic workflow. This included a mutational signature analysis of the sequenced metastases and comparison of their transcriptomic profiles to a pan-cancer panel of tumors from TCGA. We further discuss the strengths and limitations of the latter approaches in the context of three potentially incorrectly diagnosed TCGA lung tumors. Conclusion: Comprehensive genomic analyses can provide information on the origin of tumors in patients with cancer of unknown primary. [ABSTRACT FROM AUTHOR]