At Golden Helix, our community of customers and researchers is at the core of our commitment to advancing genomic research and precision medicine. We are thrilled to highlight some recent customer publications that have made innovative use of our VarSeq software in their research. These studies, which span across diverse fields including cardiology and oncology, illustrate the versatile applications of VarSeq in analyzing and interpreting genetic data. Not only do they represent breakthroughs in their respective fields, but they also underscore the instrumental role that VarSeq plays in propelling cutting-edge research.
CASE SERIES OF DILATED CARDIOMYOPATHY STUDIED WITH WHOLE GENOME SEQUENCING
The explanation of the genetic aspects of dilatative cardiomyopathy (DCM) is important for at least two major reasons: 1. there is a phenotypic and clinical overlap between the various cardiomyopathies with DCM – thus it might be suggested DCM may be an end phenotype; 2. genetic predisposition to DCM might render the myocardium susceptible to various exogenous factors such as infections, alcohol, toxins, etc.
We share the results from the Next Gene sequencing of 10 patients with DCM. They were of various ages, with the inclusion requirement to be without coronary or hypertensive disease, and clinical manifestations of the DCM from yearly age. Bioinformatics analysis of the data was done with the appropriate protocol on a Dragen server, as well as VarSeq (Goldenhelix) for all NGS data.
This case series of Caucasian patients with DCM presented with mutations in TTN, TTN-AS1, SCN5A, RYR2, DSG2, DSG2-AS1, FLNC, LAMA4, ACTN2, GATA6, DMD genes. All these genes are responsible for structural sarcomere, nucleus proteins or ion transporters. They are common also in arrhythmogenic right ventricular dysplasia, hypertrophic cardiomyopathy, muscular dystrophy and Brugada syndrome. Omim search found them as VUS or weak pathogenic and only a mutation in TTN as likely pathogenic. Nevertheless, the cumulation of data for the genetic aspects of cardiomyopathies is important. The echocardiographic ejection fraction of the patients varied with the medical treatment and could not be used as guiding in their risk assessment.
As far as the ejection fraction and left ventricular morphology are modifiable factors, we need a more specific way to assess the inherent risk in our cardiac patients. NGS might provide the necessary information, but we need to accumulate clinical and genetic data to make specific cardiomyopathy risk profiles. These risk profiles should be based on genetic analysis and not on ejection fraction or other morphological criteria, as we have cardiomyopathies’ overlap.
Optimizing precision medicine for breast cancer brain metastases with functional drug response assessment.
The development of novel therapies for brain metastases is an unmet need. Brain metastases may have unique molecular features that could be explored as therapeutic targets. A better understanding of the drug sensitivity of live cells coupled to molecular analyses will lead to a rational prioritization of therapeutic candidates. We evaluated the molecular profiles of twelve breast cancer brain metastases
(BCBM) and matched primary breast tumors to identify potential therapeutic targets. We established six novel patient-derived xenograft (PDX) from BCBM from patients undergoing clinically indicated surgical resection of BCBM and used the PDXs as a drug screening platform to interrogate potential molecular targets. Many of the alterations were conserved in brain metastases compared to the matched primary. We observed differential expressions in the immune-related and metabolism pathways. The PDXs from BCBM captured the potentially targetable molecular alterations in the source brain metastases tumor. The alterations in the PI3K pathway were the most predictive for drug efficacy in the PDXs. The PDXs were also treated with a panel of over 350 drugs and demonstrated high sensitivity to HDAC and proteasome inhibitors. Our study revealed significant differences between the paired BCBM and primary breast tumors with the pathways involved in metabolisms and immune functions. While molecular-targeted drug therapy based on genomic profiling of tumors is currently evaluated in clinical trials for patients with brain metastases, a functional precision medicine strategy may complement such an approach by expanding potential therapeutic options, even for BCBM without known targetable molecular alterations.
Molecular Analysis of Recurrent Translocations in Mucoepidermoid Carcinoma
Head and neck cancers include a diverse group of malignancies, and pathogenesis is driven by different recurring somatic mutations. In head and neck squamous cell carcinoma (HNSCC), these mutations include single nucleotide variants of several different genes, as well as HPV viral integration. By contrast, many salivary gland tumors are characterized by genomic translocations, resulting in frequent gene fusions. For example, mucoepidermoid carcinomas (MEC) have prevalent CRTC1-MAML2 fusions, while hyalinizing clear cell carcinomas (HCCC) have prevalent EWSR1-ATF1 fusions. Exploring the molecular phenotypes caused by these driver mutations and others will better explain the mechanisms of tumorigenesis and growth in salivary gland tumors, and is therefore necessary to identify potential targets for future patient treatments. In my thesis, I investigate the hypothesis that driver mutations, such as CRTC1-MAML2 and EWSR1-ATF1, alter transcription regulation in salivary gland tumors, varying based on tumor type, fusion status, and grade. In this thesis, I begin by using molecular techniques to differentiate two salivary gland tumors, MEC and HCCC, which are difficult to differentiate by standard histopathology approaches. Using RNA sequencing (RNAseq), I identify a 354 gene signature that differentiates both malignancies. These genes are significantly enriched for an ATF1 binding motif, consistent with the EWSR1-ATF1 fusion found in HCCC. These differentially expressed genes include IGF1R, SGK1, and SGK3, which are elevated in HCCC tumors. This, and other differentially expressed genes in this signature, describe examples of differing molecular pathology between MEC and HCCC. I then seek to further understand the genetic underpinning of MEC. Within MEC tumors, the most common somatic translocation forms the CRTC1-MAML2 fusion. I map the CRTC1-MAML2 breakpoint in four MEC-derived cell lines, via long-read sequencing. I also identify a series of genomic translocations leading to this fusion and uncover a TERT promoter rearrangement in NCI-H292. Subsequent TERT break apart FISH reveals TERT copy number increase and translocation events in all four cell lines tested. These experiments reveal complex genomic rearrangement leading to CRTC1-MAML2 formation and a novel TERT driver mutation. Thus, I discover and validate TERT as a novel MEC driver. While the CRTC1-MAML2 fusion is the most common MEC driver mutation, patients with CRTC1-MAML2, or less commonly CRTC3-MAML2, positive tumors have a better prognosis. Therefore, using RNAseq on 48 MEC tumors, I identify gene expression patterns associated with tumor CRTC1/3-MAML2 fusion status and grade. Gene expression signatures associated with fusion status are enriched for gene sets involving cellular respiration, including oxidative phosphorylation and the electron transport chain. Moreover, changes to T and B cell infiltration are associated with MAML2 fusion status and grade, respectively. Therefore, I perform spatial RNA sequencing to measure the effect of CRTC1-MAML2 activity throughout the MEC tumor microenvironment. I identify spatial overlap between CRTC1-MAML2 associated gene expression and many other transcripts, including VEGFA and CTNNB1. These data suggest that CRTC1-MAML2-associated gene expression affect a variety of biological processes throughout the tumor microenvironment. Overall, these data describe a pattern of gene regulation dependent on tumor type, fusion status, and grade. These gene expression changes, coupled with novel driver mutations, such as TERT translocation, affect multiple cancer phenotypes throughout the tumor microenvironment. These biological processes play a role in the molecular etiology of HCCC and MEC tumors, uncovering several pathways which are opportunities to advance targeted therapies, which may improve the survival of MEC and HCCC patients.
The mutational repertoire of uterine sarcomas and carcinosarcomas in a Brazilian cohort: A preliminary study
The present study aimed to contribute to the catalog of genetic mutations involved in the carcinogenic processes of uterine sarcomas (USs) and carcinosarcomas (UCSs), which may assist in the accurate diagnosis of, and selection of treatment regimens for, these conditions.
We performed gene-targeted next-generation sequencing (NGS) of 409 cancer-related genes in 15 US (7 uterine leiomyosarcoma [ULMS], 7 endometrial stromal sarcoma [ESS], 1 adenosarcoma [ADS]), 5 UCS, and 3 uterine leiomyoma (ULM) samples. Quality, frequency, and functional filters were applied to select putative somatic variants.
Among the 23 samples evaluated in this study, 42 loss-of-function (LOF) mutations and 111 missense mutations were detected, with a total of 153 mutations. Among them, 66 mutations were observed in the Catalogue of Somatic Mutations in Cancer (COSMIC) database. TP53 (48%), ATM (22%), and PIK3CA (17%) were the most frequently mutated genes. With respect to specific tumor subtypes, ESS showed mutations in the PDE4DIP, IGTA10, and DST genes, UCS exhibited mutations in ERBB4, and ULMS showed exclusive alterations in NOTCH2 and HER2. Mutations in the KMT2A gene were observed exclusively in ULM and ULMS. In silico pathway analyses demonstrated that many genes mutated in ULMS and ESS have functions associated with the cellular response to hypoxia and cellular response to peptide hormone stimulus. In UCS and ADS, genes with most alterations have functions associated with phosphatidylinositol kinase activity and glycerophospholipid metabolic process.
This preliminary study observed pathogenic mutations in US and UCS samples. Further studies with a larger cohort and functional analyses will foster the development of a precision medicine-based approach for the treatment of US and UCS.
Reading about how VarSeq software has been employed in these studies to unveil crucial insights is both inspiring and affirming. It demonstrates the immeasurable value of the collaborative spirit within the scientific community, and how, together, we are forging ahead to solve complex healthcare challenges. We remain steadfast in our commitment to providing robust and versatile software solutions for genomic data analysis. To learn more about VarSeq, visit our site here!