scholarly journals Genotype-Phenotype Relations of the von Hippel-Lindau Tumor Suppressor Inferred from a Large-Scale Analysis of Disease Mutations and Interactors

2018 ◽  
Author(s):  
Giovanni Minervini ◽  
Federica Quaglia ◽  
Francesco Tabaro ◽  
Silvio C.E. Tosatto

AbstractFamiliar cancers represent a privileged point of view for studying the complex cellular events inducing tumor transformation. Von Hippel-Lindau syndrome, a familiar predisposition to develop cancer is a clear example. Here, we present our efforts to decipher the role of von Hippel-Lindau tumor suppressor protein (pVHL) in cancer insurgence. We collected high quality information about both pVHL mutations and interactors to investigate the association between patient phenotypes, mutated protein surface and impaired interactions. Our data suggest that different phenotypes correlate with localized perturbations of the pVHL structure, with specific cell functions associated to different protein surfaces. We propose five different pVHL interfaces to be selectively involved in modulating proteins regulating gene expression, protein homeostasis as well as to address extracellular matrix (ECM) and ciliogenesis associated functions. These data were used to drive molecular docking of pVHL with its interactors and guide Petri net simulations of the most promising alterations. We predict that disruption of pVHL association with certain interactors can trigger tumor transformation, inducing metabolism imbalance and ECM remodeling. Collectively taken, our findings provide novel insights into VHL-associated tumorigenesis. This highly integrated in silico approach may help elucidate novel treatment paradigms for VHL disease.Author summaryCancer is generally caused by a series of mutations accumulating over time in a healthy tissue, which becomes re-programmed to proliferate at the expense of the hosting organism. This process is difficult to follow and understand as events in a multitude of different genes can lead to similar outcomes without apparent cause. The von Hippel-Lindau (VHL) tumor suppressor is one of the few genes harboring a familiar cancer syndrome, i.e. VHL mutations are known to cause a predictable series of events leading cancer in the kidneys and a few selected other tissues. This article describes a large-scale analysis to relate known VHL mutations to specific cancer pathways by looking at the molecular interactions. Different cancer types appear to be caused by mutations changing the surface of specific parts of the VHL protein. By looking at the VHL interactors involved, it is therefore possible to identify other candidate genes for mutations leading to very similar cancer types.

Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 365-365
Author(s):  
Fadi Towfic ◽  
Naser Ansari-Pour ◽  
Maria Ortiz ◽  
Sarah Gooding ◽  
Dan Rozelle ◽  
...  

Introduction Multiple myeloma (MM) is a heterogeneous disease defined by genetic lesions including translocations, chromosomal copy number aberrations (CNA), and mutations. Large-scale analyses of genomic data from newly-diagnosed patients (ndMM) have defined key oncogenic drivers beyond previously known primary events. In relapsed/refractory MM (rrMM), analyses have been completed on small numbers of patient samples for mutational profiles; however, a large-scale analysis of genomic and transcriptomic data has not been performed. In this initial analysis, we describe the rrMM genomic landscape including prevalence of key translocations, oncogenic/tumor suppressor mutational drivers, and chromosomal CNA. Methods We generated whole genome sequencing (WGS) and whole transcriptome expression (RNA-seq) from 485 rrMM patient samples derived from clinical trials: NCT01712789/CC-4047-MM-010 (N=236), NCT02045017/CC-4047-MM-013 (N=17), NCT02773030/CC-220-MM-001 (N=45) and NCT01421524/CC-122-ST-001MM2 (N=10). Somatic single nucleotide variants (SNVs) and indels were derived from WGS from 308 samples using GATK/MuTect2 and annotated using ANNOVAR. Clonal and subclonal CNA were identified using Sclust. Translocations were determined using Manta. Oncogenic/tumor suppressor drivers were identified using cDriver, which utilizes recurrence and functional consequence and cancer cell fraction (CCF) of each mutation. Results Key translocations included: t(11;14) [76/308 (25%)], t(8;14) [79/308 (25%)], t(4;14) [44/308 (14%)], t(14;16) [24/308 (8%)], t(6;14) CCND3 [18/308 (6%)], t(14;20) [17/308 (5.5%)], and t(6;14) [IRF4 17/308 (5.5%)]. Hyperdiploidy was detected in 140/308 (46%) patients. Key chromosomal CNA included del14q 134/308 (44%), del13q 131/308 (43%), del8p 113/308 (37%), del17p 53/308 (17%), amplification of 1q (≥4 copies) 45/308 (15%), and del1q 31/308 (10%). Compared to samples from ndMM patients, we saw an increase of del17p (17% vs. 8%) and t(11;14) (25% vs. 15%) in rrMM. Further, out of the 53 del17p patients, 45 (85%) were high CCF (>0.55) versus 63/107 (59%) reported in ndMM (Thakurta et al, Blood. 2019) Double Hit patients (biallelic inactivation of TP53 or amplification of 1q on a background of ISS3) was detected in 12% patients which is significantly higher than ndMM (6%, p < 0.05) (Walker et al, Leukemia. 2018) We identified a total of 107 driver genes (FDR<0.05). Of these, 23 were known cancer genes according to the COSMIC Cancer Gene Census (CGC) of which 19 were Tier1 such as TP53, DNMT3A and SETD2. Further, 19/107 driver genes were previously identified in ndMM from the Myeloma Genome Project including IRF4, TRAF3, NFKB2 and FGFR3. The top ten ranking driver genes in rrMM were DIS3, FAM46C, IGLL5, KMT2B, TRAF3, SP140, MALRD1, TP53, L1TD1 and PRKCD. Among these, novel driver genes such as IGLL5 (N=19, 6.2%; medianCCF=1; Immunoglobulin Lambda-Like Polypeptide 5) were also identified. We examined if loss-of-function (LOF) and gain-of-function (GOF) variants result in different sets of drivers, thus identifying putative tumor suppressor genes (TSG) and oncogenes (ONC) respectively. We identified a total of 72 and 43 ONCs and TSGs, respectively (FDR < 0.05). The top ten ranking ONCs included driver genes such as UBR4 and IRF4. Among the top ten ranking TSGs were novel driver genes such as TDG and SMARCA4 as well as known ndMM driver genes such as UBR5 and CDKN1B. We confirmed that FGFR3 driver mutations were associated with t(4;14) and IRF4 were associated with t(11;14) (p < 0.05) in our rrMM population. Further analysis will be focused on association of significant mutations with CNA and translocation groups, impact on clinical outcomes in genetic subsets, delineating the effect of multiple lines of therapy on tumor clonality, genetic architecture of resistance patterns, and the role of oncogenic drivers. Conclusions We have established and analyzed the largest molecular rrMM dataset with associated clinical outcome data. These analyses are revealing the evolution of genetic drivers of resistance to therapy and will assist in identification of subsets of poor prognostic groups (eg, Double Hit) and new molecular subsets of rrMM where novel targeted therapies could be developed. Disclosures Towfic: Celgene Corporation: Employment, Equity Ownership. Ansari-Pour:Celgene Corporation: Consultancy. Ortiz:Celgene Corporation: Employment, Equity Ownership. Gooding:Celgene Corporation: Research Funding. Rozelle:Celgene Corporation: Other: Contractor for Celgene. Zadorozhny:Celgene Corporation: Other: Contractor for Celgene. Mavrommatis:Celgene Corporation: Employment, Equity Ownership. Lata:Celgene Corporation: Employment, Equity Ownership. Stong:Celgene Corporation: Employment, Equity Ownership. Kamalakaran:Celgene Corporation: Employment, Equity Ownership. Tsai:Celgene Corporation: Employment, Equity Ownership. Flynt:Celgene Corporation: Employment, Equity Ownership. Thakurta:Celgene: Employment, Equity Ownership.


2021 ◽  
Author(s):  
Mehdi A. Beniddir ◽  
Kyo Bin Kang ◽  
Grégory Genta-Jouve ◽  
Florian Huber ◽  
Simon Rogers ◽  
...  

This review highlights the key computational tools and emerging strategies for metabolite annotation, and discusses how these advances will enable integrated large-scale analysis to accelerate natural product discovery.


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