Identification of large-scale genomic variation in cancer genomes using in silico reference models

Tumor elastography and its association with cell-free tumor DNA in the plasma of breast tumor patients: a pilot study

Background

Breast tumor stiffness, which can be objectively and noninvasively evaluated by ultrasound elastography (UE), has been useful for the differentiation of benign and malignant breast lesions and the prediction of clinical outcomes. Liquid biopsy analyses, including cell-free tumor DNA (ctDNA), exhibit great potential for personalized treatment. This study aimed to investigate the correlations between the UE and ctDNA for early breast cancer diagnosis.

Methods

Breast tumor stiffness in 10 patients were assessed by shear wave elastography (SWE), and the ctDNA of eight collected plasma specimens with different tumor stiffness were analyzed by whole-genome sequencing (WGS). Subsequently, the distribution of carcinoma-associated fibroblasts (CAFs) was investigated by detecting the expression levels of alpha-smooth muscle actin (α-SMA) in tissues of breast lesions. We validated the function of discoidin domain receptor 2 (DDR2) in breast tumor CAFs by knockout of fibroblast activation protein (FAP) with different tumor stiffness during cancer progression in vitro and vivo.

Results

The UE estimates of tumor stiffness positively correlated with CAF-rich (α-SMA⁺) tumors (P<0.05). Copy number profiles and percent genome alterations were remarkably different between benign and malignant breast lesions. Somatic genomic alterations or structural variants of DDR2, ANTXRL, TPSG1, and TPSB2 genes were identified in ctDNA of plasma from breast lesions with high SWE values and an increase in the CAF content obtained from clinical samples. Deletion of FAP in breast tumor CAFs by CRISPR/Cas9-mediated gene knockout and decreased tumor stiffness resulted in downregulated expression of DDR2 (P<0.05), which in turn led to decreasing the tumor stiffness and carcinogenesis process in vitro and in vivo.

Conclusions

These results have established proof of principle that WGS analysis of ctDNA could complement current UE approaches to assess tumor stiffness changes for the early diagnosis and prognostic assessment of breast cancer.

Integrative reconstruction of cancer genome karyotypes using InfoGenomeR

Annotation of structural variations (SVs) and base-level karyotyping in cancer cells remains challenging. Here, we present Integrative Framework for Genome Reconstruction (InfoGenomeR)-a graph-based framework that can reconstruct individual SVs into karyotypes based on whole-genome sequencing data, by integrating SVs, total copy number alterations, allele-specific copy numbers, and haplotype information. Using whole-genome sequencing data sets of patients with breast cancer, glioblastoma multiforme, and ovarian cancer, we demonstrate the analytical potential of InfoGenomeR. We identify recurrent derivative chromosomes derived from chromosomes 11 and 17 in breast cancer samples, with homogeneously staining regions for CCND1 and ERBB2, and double minutes and breakage-fusion-bridge cycles in glioblastoma multiforme and ovarian cancer samples, respectively. Moreover, we show that InfoGenomeR can discriminate private and shared SVs between primary and metastatic cancer sites that could contribute to tumour evolution. These findings indicate that InfoGenomeR can guide targeted therapies by unravelling cancer-specific SVs on a genome-wide scale.

Copy number variant analysis using genome-wide mate-pair sequencing

Copy number variation (CNV) is a common form of structural variation detected in human genomes, occurring as both constitutional and somatic events. Cytogenetic techniques like chromosomal microarray (CMA) are widely used in analyzing CNVs. However, CMA techniques cannot resolve the full nature of these structural variations (i.e. the orientation and location of associated breakpoint junctions) and must be combined with other cytogenetic techniques, such as karyotyping or FISH, to do so. This makes the development of a next-generation sequencing (NGS) approach capable of resolving both CNVs and breakpoint junctions desirable. Mate-pair sequencing (MPseq) is a NGS technology designed to find large structural rearrangements across the entire genome. Here we present an algorithm capable of performing copy number analysis from mate-pair sequencing data. The algorithm uses a step-wise procedure involving normalization, segmentation, and classification of the sequencing data. The segmentation technique combines both read depth and discordant mate-pair reads to increase the sensitivity and resolution of CNV calls. The method is particularly suited to MPseq, which is designed to detect breakpoint junctions at high resolution. This allows for the classification step to accurately calculate copy number levels at the relatively low read depth of MPseq. Here we compare results for a series of hematological cancer samples that were tested with CMA and MPseq. We demonstrate comparable sensitivity to the state-of-the-art CMA technology, with the benefit of improved breakpoint resolution. The algorithm provides a powerful analytical tool for the analysis of MPseq results in cancer.