Identifying predictors of glioma evolution from longitudinal sequencing.

Clonal evolution drives cancer progression and therapeutic resistance. Recent studies have revealed divergent longitudinal trajectories in gliomas, but early molecular features steering posttreatment cancer evolution remain unclear. Here, we collected sequencing and clinical data of initial-recurrent tumor pairs from 544 adult diffuse gliomas and performed multivariate analysis to identify early molecular predictors of tumor evolution in three diffuse glioma subtypes. We found that CDKN2A deletion at initial diagnosis preceded tumor necrosis and microvascular proliferation that occur at later stages of IDH-mutant glioma. Ki67 expression at diagnosis was positively correlated with acquiring hypermutation at recurrence in the IDH-wild-type glioma. In all glioma subtypes, MYC gain or MYC-target activation at diagnosis was associated with treatment-induced hypermutation at recurrence. To predict glioma evolution, we constructed CELLO2 (Cancer EvoLution for LOngitudinal data version 2), a machine learning model integrating features at diagnosis to forecast hypermutation and progression after treatment. CELLO2 successfully stratified patients into subgroups with distinct prognoses and identified a high-risk patient group featured by MYC gain with worse post-progression survival, from the low-grade IDH-mutant-noncodel subtype. We then performed chronic temozolomide-induction experiments in glioma cell lines and isogenic patient-derived gliomaspheres and demonstrated that MYC drives temozolomide resistance by promoting hypermutation. Mechanistically, we demonstrated that, by binding to open chromatin and transcriptionally active genomic regions, c-MYC increases the vulnerability of key mismatch repair genes to treatment-induced mutagenesis, thus triggering hypermutation. This study reveals early predictors of cancer evolution under therapy and provides a resource for precision oncology targeting cancer dynamics in diffuse gliomas.

Tumor-associated monocytes promote mesenchymal transformation through EGFR signaling in glioma.

The role of brain immune compartments in glioma evolution remains elusive. We profile immune cells in glioma microenvironment and the matched peripheral blood from 11 patients. Glioblastoma exhibits specific infiltration of blood-originated monocytes expressing epidermal growth factor receptor (EGFR) ligands EREG and AREG, coined as tumor-associated monocytes (TAMo). TAMo infiltration is mutually exclusive with EGFR alterations (p = 0.019), while co-occurring with mesenchymal subtype (p = 4.7 × 10-7) and marking worse prognosis (p = 0.004 and 0.032 in two cohorts). Evolutionary analysis of initial-recurrent glioma pairs and single-cell study of a multi-centric glioblastoma reveal association between elevated TAMo and glioma mesenchymal transformation. Further analyses identify FOSL2 as a TAMo master regulator and demonstrates that FOSL2-EREG/AREG-EGFR signaling axis promotes glioma invasion in vitro. Collectively, we identify TAMo in tumor microenvironment and reveal its driving role in activating EGFR signaling to shape glioma evolution.

Machine learning of genomic features in organotropic metastases stratifies progression risk of primary tumors.

Metastatic cancer is associated with poor patient prognosis but its spatiotemporal behavior remains unpredictable at early stage. Here we develop MetaNet, a computational framework that integrates clinical and sequencing data from 32,176 primary and metastatic cancer cases, to assess metastatic risks of primary tumors. MetaNet achieves high accuracy in distinguishing the metastasis from the primary in breast and prostate cancers. From the prediction, we identify Metastasis-Featuring Primary (MFP) tumors, a subset of primary tumors with genomic features enriched in metastasis and demonstrate their higher metastatic risk and shorter disease-free survival. In addition, we identify genomic alterations associated with organ-specific metastases and employ them to stratify patients into various risk groups with propensities toward different metastatic organs. This organotropic stratification method achieves better prognostic value than the standard histological grading system in prostate cancer, especially in the identification of Bone-MFP and Liver-MFP subtypes, with potential in informing organ-specific examinations in follow-ups.

De Novo Germline and Somatic Variants Convergently Promote Endothelial-to-Mesenchymal Transition in Simplex Brain Arteriovenous Malformation.

[Figure: see text].

Alternative Splicing of Three Genes Encoding Mechanotransduction-Complex Proteins in Auditory Hair Cells.

The mechanotransduction (MT) complex in auditory hair cells converts the mechanical stimulation of sound waves into neural signals. Recently, the MT complex has been suggested to contain at least four distinct integral membrane proteins: protocadherin 15 (PCDH15), transmembrane channel-like protein 1 (TMC1), lipoma HMGIC fusion partner-like 5 (LHFPL5), and transmembrane inner ear protein (TMIE). However, the composition, function, and regulation of the MT-complex proteins remain incompletely investigated. Here, we report previously undescribed splicing isoforms of TMC1, LHFPL5, and TMIE. We identified four alternative splicing events for the genes encoding these three proteins by analyzing RNA-seq libraries of auditory hair cells from adult mice [over postnatal day (P)28], and we then verified the alternative splicing events by using RT-PCR and Sanger sequencing. Moreover, we examined the tissue-specific distribution, developmental expression patterns, and tonotopic gradient of the splicing isoforms by performing semiquantitative and quantitative real-time PCR (qRT-PCR), and we found that the alternative splicing of TMC1 and LHFPL5 is cochlear-specific and occurs in both neonatal and adult mouse cochleae. Our findings not only reveal the potential complexity of the MT-complex composition, but also provide critical insights for guiding future research on the function, regulation, and trafficking of TMC1, LHFPL5, and TMIE and on the clinical diagnosis of hearing loss related to aberrant splicing of these three key genes in hearing.

Identifying Transcription Error-Enriched Genomic Loci Using Nuclear Run-on Circular-Sequencing Coupled with Background Error Modeling.

RNA polymerase transcribes certain genomic loci with higher errors rates. These transcription error-enriched genomic loci (TEELs) have implications in disease. Current deep-sequencing methods cannot distinguish TEELs from post-transcriptional modifications, stochastic transcription errors, and technical noise, impeding efforts to elucidate the mechanisms linking TEELs to disease. Here, we describe background error model-coupled precision nuclear run-on circular-sequencing (EmPC-seq) to discern genomic regions enriched for transcription misincorporations. EmPC-seq innovatively combines a nuclear run-on assay for capturing nascent RNA before post-transcriptional modifications, a circular-sequencing step that sequences the same nascent RNA molecules multiple times to improve accuracy, and a statistical model for distinguishing error-enriched regions among stochastic polymerase errors. Applying EmPC-seq to the ribosomal RNA transcriptome, we show that TEELs of RNA polymerase I are not randomly distributed but clustered together, with higher error frequencies at nascent transcript 3' ends. Our study establishes a reliable method of identifying TEELs with nucleotide precision, which can help elucidate their molecular origins.

Mutational Landscape of Secondary Glioblastoma Guides MET-Targeted Trial in Brain Tumor.

Low-grade gliomas almost invariably progress into secondary glioblastoma (sGBM) with limited therapeutic option and poorly understood mechanism. By studying the mutational landscape of 188 sGBMs, we find significant enrichment of TP53 mutations, somatic hypermutation, MET-exon-14-skipping (METex14), PTPRZ1-MET (ZM) fusions, and MET amplification. Strikingly, METex14 frequently co-occurs with ZM fusion and is present in ∼14% of cases with significantly worse prognosis. Subsequent studies show that METex14 promotes glioma progression by prolonging MET activity. Furthermore, we describe a MET kinase inhibitor, PLB-1001, that demonstrates remarkable potency in selectively inhibiting MET-altered tumor cells in preclinical models. Importantly, this compound also shows blood-brain barrier permeability and is subsequently applied in a phase I clinical trial that enrolls MET-altered chemo-resistant glioma patients. Encouragingly, PLB-1001 achieves partial response in at least two advanced sGBM patients with rarely significant side effects, underscoring the clinical potential for precisely treating gliomas using this therapy.

AptRank: an adaptive PageRank model for protein function prediction on bi-relational graphs.

MOTIVATION: Diffusion-based network models are widely used for protein function prediction using protein network data and have been shown to outperform neighborhood-based and module-based methods. Recent studies have shown that integrating the hierarchical structure of the Gene Ontology (GO) data dramatically improves prediction accuracy. However, previous methods usually either used the GO hierarchy to refine the prediction results of multiple classifiers, or flattened the hierarchy into a function-function similarity kernel. No study has taken the GO hierarchy into account together with the protein network as a two-layer network model. RESULTS: We first construct a Bi-relational graph (Birg) model comprised of both protein-protein association and function-function hierarchical networks. We then propose two diffusion-based methods, BirgRank and AptRank, both of which use PageRank to diffuse information on this two-layer graph model. BirgRank is a direct application of traditional PageRank with fixed decay parameters. In contrast, AptRank utilizes an adaptive diffusion mechanism to improve the performance of BirgRank. We evaluate the ability of both methods to predict protein function on yeast, fly and human protein datasets, and compare with four previous methods: GeneMANIA, TMC, ProteinRank and clusDCA. We design four different validation strategies: missing function prediction, de novo function prediction, guided function prediction and newly discovered function prediction to comprehensively evaluate predictability of all six methods. We find that both BirgRank and AptRank outperform the previous methods, especially in missing function prediction when using only 10% of the data for training. AVAILABILITY AND IMPLEMENTATION: The MATLAB code is available at https://github.rcac.purdue.edu/mgribsko/aptrank . CONTACT: gribskov@purdue.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Assessment of subnetwork detection methods for breast cancer.

Subnetwork detection is often used with differential expression analysis to identify modules or pathways associated with a disease or condition. Many computational methods are available for subnetwork analysis. Here, we compare the results of eight methods: simulated annealing-based jActiveModules, greedy search-based jActiveModules, DEGAS, BioNet, NetBox, ClustEx, OptDis, and NetWalker. These methods represent distinctly different computational strategies and are among the most widely used. Each of these methods was used to analyze gene expression data consisting of paired tumor and normal samples from 50 breast cancer patients. While the number of genes/proteins and protein interactions detected by the eight methods vary widely, a core set of 60 genes and 50 interactions was found to be shared by the subnetworks identified by five or more of the methods. Within the core set, 12 genes were found to be known breast cancer genes.