Frequent FGFR1 hotspot alterations in driver-unknown low-grade glioma and mixed neuronal-glial tumors

Purpose Low-grade gliomas (LGG) and mixed neuronal-glial tumors (MNGT) show frequent MAPK pathway alterations. Oncogenic fibroblast growth factor receptor 1 (FGFR1) tyrosinase kinase domain has been reported in brain tumors of various histologies. We sought to determine the frequency of FGFR1 hotspot mutations N546 and K656 in driver-unknown LGG/MNGT and examined FGFR1 immunohistochemistry as a potential tool to detect those alterations. Methods We analyzed 476 LGG/MNGT tumors for KIAA-1549-BRAF fusion, IDH1/2, TERT promotor, NF1, H3F3A and the remaining cases for FGFR1 mutation frequency and correlated FGFR1 immunohistochemistry in 106 cases. Results 368 of 476 LGG/MNGT tumors contained non-FGFR1 alterations. We identified 9 FGFR1 p.N546K and 4 FGFR1 p.K656E mutations among the 108 remaining driver-unknown samples. Five tumors were classified as dysembryoplastic neuroepithelial tumor (DNT), 4 as pilocytic astrocytoma (PA) and 3 as rosette-forming glioneuronal tumor (RGNT). FGFR1 mutations were associated with oligodendroglia-like cells, but not with age or tumor location. FGFR1 immunohistochemical expression was observed in 92 cases. FGFR1 immunoreactivity score was higher in PA and DNT compared to diffuse astrocytoma, but no correlation between FGFR1 mutation in tumors and FGFR1 expression level was observed. Conclusion FGFR1 hotspot mutations are the fifth most prevailing alteration in LGG/MNGT. Performing FGFR1 sequencing analysis in driver-unknown low-grade brain tumors could yield up to 12% FGFR1 N546/K656 mutant cases.

A Saturation-Mutagenesis Analysis of the Interplay Between Stability and Activation in Ras

Cancer mutations in Ras occur predominantly at three hotspots: Gly 12, Gly 13, and Gln 61. Previously, we reported that deep mutagenesis of H?Ras using a bacterial assay identified many other activating mutations (Bandaru et al., 2017). We now show that the results of saturation mutagenesis of H?Ras in mammalian Ba/F3 cells correlate well with results of bacterial experiments in which H-Ras or K-Ras are co-expressed with a GTPase?activating protein (GAP). The prominent cancer hotspots are not dominant in the Ba/F3 data. We used the bacterial system to mutagenize Ras constructs of different stabilities and discovered a feature that distinguishes the cancer hotspots. While mutations at the cancer hotspots activate Ras regardless of construct stability, mutations at lower-frequency sites (e.g., at Val 14 or Asp 119) can be activating or deleterious, depending on the stability of the Ras construct. We characterized the dynamics of three non-hotspot activating Ras mutants by using NMR to monitor hydrogen?deuterium exchange (HDX). These mutations result in global increases in HDX rates, consistent with the destabilization of Ras. An explanation for these observations is that mutations that destabilize Ras increase nucleotide dissociation rates, enabling activation by spontaneous nucleotide exchange. A further stability decrease can lead to insufficient levels of folded Ras – and subsequent loss of function. In contrast, the cancer hotspot mutations are mechanism-based activators of Ras that interfere directly with the action of GAPs. Our results demonstrate the importance of GAP surveillance and protein stability in determining the sensitivity of Ras to mutational activation.

A full-proteome, interaction-specific characterization of mutational hotspots across human cancers

Rapid accumulation of cancer genomic data has led to the identification of an increasing number of mutational hotspots with uncharacterized significance. Here we present a biologically informed computational framework that characterizes the functional relevance of all 1107 published mutational hotspots identified in approximately 25,000 tumor samples across 41 cancer types in the context of a human 3D interactome network, in which the interface of each interaction is mapped at residue resolution. Hotspots reside in network hub proteins and are enriched on protein interaction interfaces, suggesting that alteration of specific protein–protein interactions is critical for the oncogenicity of many hotspot mutations. Our framework enables, for the first time, systematic identification of specific protein interactions affected by hotspot mutations at the full proteome scale. Furthermore, by constructing a hotspot-affected network that connects all hotspot-affected interactions throughout the whole-human interactome, we uncover genome-wide relationships among hotspots and implicate novel cancer proteins that do not harbor hotspot mutations themselves. Moreover, applying our network-based framework to specific cancer types identifies clinically significant hotspots that can be used for prognosis and therapy targets. Overall, we show that our framework bridges the gap between the statistical significance of mutational hotspots and their biological and clinical significance in human cancers.

Hotspot Mutations in SARS-CoV-2

Since its emergence in Wuhan, China, severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has spread very rapidly around the world, resulting in a global pandemic. Though the vaccination process has started, the number of COVID-affected patients is still quite large. Hence, an analysis of hotspot mutations of the different evolving virus strains needs to be carried out. In this regard, multiple sequence alignment of 71,038 SARS-CoV-2 genomes of 98 countries over the period from January 2020 to June 2021 is performed using MAFFT followed by phylogenetic analysis in order to visualize the virus evolution. These steps resulted in the identification of hotspot mutations as deletions and substitutions in the coding regions based on entropy greater than or equal to 0.3, leading to a total of 45 unique hotspot mutations. Moreover, 10,286 Indian sequences are considered from 71,038 global SARS-CoV-2 sequences as a demonstrative example that gives 52 unique hotspot mutations. Furthermore, the evolution of the hotspot mutations along with the mutations in variants of concern is visualized, and their characteristics are discussed as well. Also, for all the non-synonymous substitutions (missense mutations), the functional consequences of amino acid changes in the respective protein structures are calculated using PolyPhen-2 and I-Mutant 2.0. In addition to this, SSIPe is used to report the binding affinity between the receptor-binding domain of Spike protein and human ACE2 protein by considering L452R, T478K, E484Q, and N501Y hotspot mutations in that region.