Fast and sensitive validation of fusion transcripts in whole-genome sequencing data

Comparative Whole-Genome Sequencing Analysis of In-situ and Invasive Acral Lentiginous Melanoma: Markedly Increased Copy Number Gains of GAB2 , PAK1 , UCP2 , and CCND1 are Associated with Melanoma Invasion

Acral lentiginous melanoma (ALM) is the most common subtype of acral melanoma. Even though recent genetic studies are reported in acral melanomas, the genetic differences between in-situ and invasive ALM remain unclear. We aimed to analyze specific genetic changes in ALM and compare genetic differences between in-situ and invasive lesions to identify genetic changes associated with the pathogenesis and progression of ALM. We performed whole genome sequencing of 71 tissue samples from 29 patients with ALM. Comparative analyses were performed, pairing in-situ ALMs with normal tissues and, furthermore, invasive ALMs with normal and in-situ tissues. Among 21 patients with in-situ ALMs, 3 patients (14.3%) had SMIM14 , SLC9B1 , FRG1 , FAM205A , ESRRA , and ESPN mutations, and copy number (CN) gains were identified in only 2 patients (9.5%). Comparing 13 invasive ALMs with in-situ tissues, CN gains were identified in GAB2 in 8 patients (61.5%), PAK1 in 6 patients (46.2%), and UCP2 and CCND1 in 5 patients (38.5%). Structural variants were frequent in in-situ and invasive ALM lesions. Both in-situ and invasive ALMs had very low frequencies of common driver mutations. Structural variants were common in both in-situ and invasive ALMs. Invasive ALMs had markedly increased CN gains, such as GAB2 , PAK1 , UCP2 , and CCND1 , compared with in-situ lesions. These results suggest that they are associated with melanoma invasion.

Intra-prostatic tumour evolution, steps in metastatic spread and histogenomic associations revealed by integration of multi-region whole-genome sequencing with histopathological features

BACKGROUND

Extension of prostate cancer beyond the primary site by local invasion or nodal metastasis is associated with poor prognosis. Despite significant research on tumour evolution in prostate cancer metastasis, the emergence and evolution of cancer clones at this early stage of expansion and spread are poorly understood. We aimed to delineate the routes of evolution and cancer spread within the prostate and to seminal vesicles and lymph nodes, linking these to histological features that are used in diagnostic risk stratification.

METHODS

We performed whole-genome sequencing on 42 prostate cancer samples from the prostate, seminal vesicles and lymph nodes of five treatment-naive patients with locally advanced disease. We spatially mapped the clonal composition of cancer across the prostate and the routes of spread of cancer cells within the prostate and to seminal vesicles and lymph nodes in each individual by analysing a total of > 19,000 copy number corrected single nucleotide variants.

RESULTS

In each patient, we identified sample locations corresponding to the earliest part of the malignancy. In patient 10, we mapped the spread of cancer from the apex of the prostate to the seminal vesicles and identified specific genomic changes associated with the transformation of adenocarcinoma to amphicrine morphology during this spread. Furthermore, we show that the lymph node metastases in this patient arose from specific cancer clones found at the base of the prostate and the seminal vesicles. In patient 15, we observed increased mutational burden, altered mutational signatures and histological changes associated with whole genome duplication. In all patients in whom histological heterogeneity was observed (4/5), we found that the distinct morphologies were located on separate branches of their respective evolutionary trees.

CONCLUSIONS

Our results link histological transformation with specific genomic alterations and phylogenetic branching. These findings have implications for diagnosis and risk stratification, in addition to providing a rationale for further studies to characterise the genetic changes causally linked to morphological transformation. Our study demonstrates the value of integrating multi-region sequencing with histopathological data to understand tumour evolution and identify mechanisms of prostate cancer spread.

Improved detection of clinically relevant fusion transcripts in cancer by machine learning classification

BACKGROUND

Genomic rearrangements in cancer cells can create fusion genes that encode chimeric proteins or alter the expression of coding and non-coding RNAs. In some cancer types, fusions involving specific kinases are used as targets for therapy. Fusion genes can be detected by whole genome sequencing (WGS) and targeted fusion panels, but RNA sequencing (RNA-Seq) has the advantageous capability of broadly detecting expressed fusion transcripts.

RESULTS

We developed a pipeline for validation of fusion transcripts identified in RNA-Seq data using matched WGS data from The Cancer Genome Atlas (TCGA) and applied it to 910 tumors from 11 different cancer types. This resulted in 4237 validated gene fusions, 3049 of them with at least one identified genomic breakpoint. Utilizing validated fusions as true positive events, we trained a machine learning classifier to predict true and false positive fusion transcripts from RNA-Seq data. The final precision and recall metrics of the classifier were 0.74 and 0.71, respectively, in an independent dataset of 249 breast tumors. Application of this classifier to all samples with RNA-Seq data from these cancer types vastly extended the number of likely true positive fusion transcripts and identified many potentially targetable kinase fusions. Further analysis of the validated gene fusions suggested that many are created by intrachromosomal amplification events with microhomology-mediated non-homologous end-joining.

CONCLUSIONS

A classifier trained on validated fusion events increased the accuracy of fusion transcript identification in samples without WGS data. This allowed the analysis to be extended to all samples with RNA-Seq data, facilitating studies of tumor biology and increasing the number of detected kinase fusions. Machine learning could thus be used in identification of clinically relevant fusion events for targeted therapy. The large dataset of validated gene fusions generated here presents a useful resource for development and evaluation of fusion transcript detection algorithms.