Evolutionary action of mutations reveals antimicrobial resistance genes in Escherichia coli.

Since antibiotic development lags, we search for potential drug targets through directed evolution experiments. A challenge is that many resistance genes hide in a noisy mutational background as mutator clones emerge in the adaptive population. Here, to overcome this noise, we quantify the impact of mutations through evolutionary action (EA). After sequencing ciprofloxacin or colistin resistance strains grown under different mutational regimes, we find that an elevated sum of the evolutionary action of mutations in a gene identifies known resistance drivers. This EA integration approach also suggests new antibiotic resistance genes which are then shown to provide a fitness advantage in competition experiments. Moreover, EA integration analysis of clinical and environmental isolates of antibiotic resistant of E. coli identifies gene drivers of resistance where a standard approach fails. Together these results inform the genetic basis of de novo colistin resistance and support the robust discovery of phenotype-driving genes via the evolutionary action of genetic perturbations in fitness landscapes.

EPIMUTESTR: a nearest neighbor machine learning approach to predict cancer driver genes from the evolutionary action of coding variants.

Discovering rare cancer driver genes is difficult because their mutational frequency is too low for statistical detection by computational methods. EPIMUTESTR is an integrative nearest-neighbor machine learning algorithm that identifies such marginal genes by modeling the fitness of their mutations with the phylogenetic Evolutionary Action (EA) score. Over cohorts of sequenced patients from The Cancer Genome Atlas representing 33 tumor types, EPIMUTESTR detected 214 previously inferred cancer driver genes and 137 new candidates never identified computationally before of which seven genes are supported in the COSMIC Cancer Gene Census. EPIMUTESTR achieved better robustness and specificity than existing methods in a number of benchmark methods and datasets.

A general calculus of fitness landscapes finds genes under selection in cancers.

Genetic variants drive the evolution of traits and diseases. We previously modeled these variants as small displacements in fitness landscapes and estimated their functional impact by differentiating the evolutionary relationship between genotype and phenotype. Conversely, here we integrate these derivatives to identify genes steering specific traits. Over cancer cohorts, integration identified 460 likely tumor-driving genes. Many have literature and experimental support but had eluded prior genomic searches for positive selection in tumors. Beyond providing cancer insights, these results introduce a general calculus of evolution to quantify the genotype-phenotype relationship and discover genes associated with complex traits and diseases.

Integrated Analysis of TP53 Gene and Pathway Alterations in The Cancer Genome Atlas.

The TP53 tumor suppressor gene is frequently mutated in human cancers. An analysis of five data platforms in 10,225 patient samples from 32 cancers reported by The Cancer Genome Atlas (TCGA) enables comprehensive assessment of p53 pathway involvement in these cancers. More than 91% of TP53-mutant cancers exhibit second allele loss by mutation, chromosomal deletion, or copy-neutral loss of heterozygosity. TP53 mutations are associated with enhanced chromosomal instability, including increased amplification of oncogenes and deep deletion of tumor suppressor genes. Tumors with TP53 mutations differ from their non-mutated counterparts in RNA, miRNA, and protein expression patterns, with mutant TP53 tumors displaying enhanced expression of cell cycle progression genes and proteins. A mutant TP53 RNA expression signature shows significant correlation with reduced survival in 11 cancer types. Thus, TP53 mutation has profound effects on tumor cell genomic structure, expression, and clinical outlook.

Comprehensive Genomic Characterization of Parathyroid Cancer Identifies Novel Candidate Driver Mutations and Core Pathways.

CONTEXT: Elucidating the genomic landscape of sporadic parathyroid carcinoma (PC) has been limited by low tumor incidence. OBJECTIVE: Identify driver mutations of sporadic PC and potential actionable pathways. METHODS: Patients undergoing surgical resection for sporadic PC between 1980 and 2016 at MD Anderson Cancer Center were identified. Patients with sporadic PC according to World Health Organization diagnostic criteria and with available formalin-fixed, paraffin-embedded (FFPE) PC tumor tissue were included and their clinical data analyzed to assess extent of disease. Patients with parathyroid tumors of uncertain malignancy or atypical parathyroid neoplasms were excluded. Thirty-one patients meeting diagnostic criteria had available tissue for analysis. FFPE PC tumors were subjected to DNA extraction and next-generation whole-exome sequencing. All variant calls are single-algorithm only. Twenty-nine samples passed quality assurance after DNA extraction. MAIN OUTCOME MEASURES: Somatic or private germline mutations present in sporadic PC and identification of pathways involved in tumorigenesis. RESULTS: We identified 35 genes with considerable mutational load; only eight genes were previously identified in other PC cohorts. These genes mediate critical processes, including chromosome organization, DNA repair, and cell cycle regulations. Gene mutations involved in MAPK signaling and immune response are also heavily implicated. These findings are limited by inherent molecular artifacts in FFPE tissue analysis and the absence of matched germline DNA. Additionally, variant calls are only single algorithm and may include false-positive/negative calls. CONCLUSION: We identified 33 candidate driver genes of sporadic PC, in addition to previously known driver genes CDC73 and MEN1.

Evolutionary Action Score of TP53 Coding Variants Is Predictive of Platinum Response in Head and Neck Cancer Patients.

TP53 is the most frequently altered gene in head and neck squamous cell carcinoma (HNSCC), with mutations occurring in over two thirds of cases; however, the predictive response of these mutations to cisplatin-based therapy remains elusive. In the current study, we evaluate the ability of the Evolutionary Action score of TP53-coding variants (EAp53) to predict the impact of TP53 mutations on response to chemotherapy. The EAp53 approach clearly identifies a subset of high-risk TP53 mutations associated with decreased sensitivity to cisplatin both in vitro and in vivo in preclinical models of HNSCC. Furthermore, EAp53 can predict response to treatment and, more importantly, a survival benefit for a subset of head and neck cancer patients treated with platinum-based therapy. Prospective evaluation of this novel scoring system should enable more precise treatment selection for patients with HNSCC.

Evolutionary Action Score of TP53 Identifies High-Risk Mutations Associated with Decreased Survival and Increased Distant Metastases in Head and Neck Cancer.

TP53 is the most frequently altered gene in head and neck squamous cell carcinoma, with mutations occurring in over two-thirds of cases, but the prognostic significance of these mutations remains elusive. In the current study, we evaluated a novel computational approach termed evolutionary action (EAp53) to stratify patients with tumors harboring TP53 mutations as high or low risk, and validated this system in both in vivo and in vitro models. Patients with high-risk TP53 mutations had the poorest survival outcomes and the shortest time to the development of distant metastases. Tumor cells expressing high-risk TP53 mutations were more invasive and tumorigenic and they exhibited a higher incidence of lung metastases. We also documented an association between the presence of high-risk mutations and decreased expression of TP53 target genes, highlighting key cellular pathways that are likely to be dysregulated by this subset of p53 mutations that confer particularly aggressive tumor behavior. Overall, our work validated EAp53 as a novel computational tool that may be useful in clinical prognosis of tumors harboring p53 mutations.

Wee-1 kinase inhibition overcomes cisplatin resistance associated with high-risk TP53 mutations in head and neck cancer through mitotic arrest followed by senescence.

Although cisplatin has played a role in "standard-of-care" multimodality therapy for patients with advanced squamous cell carcinoma of the head and neck (HNSCC), the rate of treatment failure remains particularly high for patients receiving cisplatin whose tumors have mutations in the TP53 gene. We found that cisplatin treatment of HNSCC cells with mutant TP53 leads to arrest of cells in the G2 phase of the cell cycle, leading us to hypothesize that the wee-1 kinase inhibitor MK-1775 would abrogate the cisplatin-induced G2 block and thereby sensitize isogenic HNSCC cells with mutant TP53 or lacking p53 expression to cisplatin. We tested this hypothesis using clonogenic survival assays, flow cytometry, and in vivo tumor growth delay experiments with an orthotopic nude mouse model of oral tongue cancer. We also used a novel TP53 mutation classification scheme to identify which TP53 mutations are associated with limited tumor responses to cisplatin treatment. Clonogenic survival analyses indicate that nanomolar concentration of MK-1775 sensitizes HNSCC cells with high-risk mutant p53 to cisplatin. Consistent with its ability to chemosensitize, MK-1775 abrogated the cisplatin-induced G2 block in p53-defective cells leading to mitotic arrest associated with a senescence-like phenotype. Furthermore, MK-1775 enhanced the efficacy of cisplatin in vivo in tumors harboring TP53 mutations. These results indicate that HNSCC cells expressing high-risk p53 mutations are significantly sensitized to cisplatin therapy by the selective wee-1 kinase inhibitor, supporting the clinical evaluation of MK-1775 in combination with cisplatin for the treatment of patients with TP53 mutant HNSCC.

Single nucleotide variations: biological impact and theoretical interpretation.

Genome-wide association studies (GWAS) and whole-exome sequencing (WES) generate massive amounts of genomic variant information, and a major challenge is to identify which variations drive disease or contribute to phenotypic traits. Because the majority of known disease-causing mutations are exonic non-synonymous single nucleotide variations (nsSNVs), most studies focus on whether these nsSNVs affect protein function. Computational studies show that the impact of nsSNVs on protein function reflects sequence homology and structural information and predict the impact through statistical methods, machine learning techniques, or models of protein evolution. Here, we review impact prediction methods and discuss their underlying principles, their advantages and limitations, and how they compare to and complement one another. Finally, we present current applications and future directions for these methods in biological research and medical genetics.

Prospective highlights of serum glycoproteins in spontaneous tolerance after orthotopic liver transplantation.

BACKGROUND: We examined the effects and the mechanisms of serum glycoproteins on spontaneous tolerance after orthotopic liver transplantation (OLT) between DA (RT-1(a)) and PVG (RT-1(c)) rats. METHODS: A functional proteome analysis was introduced to investigate differently expressed proteins involved in overcoming major histocompatibility complex (MHC) barriers and lectin blotting was applied to survey the levels of fucosylated proteins. RESULTS: Two-dimensional difference gel electrophoresis (2D-DIGE) coupled with mass spectrometry revealed statistically significant changes in the intensity of 19 proteins at 14 and 60 post-OLT days which respectively corresponded to rejection and tolerance. An interaction network analysis of the identified proteins indicated that the interleukin (IL)-6 signaling pathway might be correlated with the immune events in this model. The peak of IL-6 expression occurred during the recovery period might play a role in the remarkable shifts in the immune response towards spontaneous tolerance. Furthermore, increased levels of IL-6-modulated fucosylation of Igh-6 were also observed during the rejection response while fucosylated hemopexin and haptoglobin were obviously upregulated in the tolerogenic period. CONCLUSIONS: These findings suggest that IL-6 and glycoproteins may play critical roles in this spontaneous tolerogenic DA/PVG OLT model and shed light on prolonging hepatic allograft survival in the future.

Functional proteomic and structural insights into molecular targets related to the growth inhibitory effect of tanshinone IIA on HeLa cells.

Certain antitumor agents have recently been extracted from the roots of Salvia miltiorrhiza Bunge. The diterpene derivative, tanshinone IIA, possesses cytotoxic activity against several human carcinoma cell lines. It also inhibits invasion and metastasis of cancer cells. In the present study, we isolated tanshinone IIA from S. miltiorrhiza, and it exhibited strong growth inhibition against human cervical cancer cells in dose- and time-dependent manners with a 50% cell growth inhibition value of 2.5 microg/mL (8.49 microM). Flow cytometric analysis of cell cycle progression revealed that G(2)/M arrest was initiated after a 24 h exposure to the drug. It also resulted in DNA fragmentation and degradation of poly (ADP-ribose) polymerase indicating that tanshinone IIA may be a potential antitumor agent. Furthermore, we performed a comprehensive proteomic analysis to survey global protein changes induced by tanshinone IIA treatment on HeLa cells. Significant changes in the levels of cytoskeleton proteins as well as stress-associated proteins were observed. Immunoblot analysis and immunofluorescence staining were used to confirm the levels of protein expression. Overexpression of the vimentin rescued these tanshinone IIA-induced events. Computational docking methods indicated that tanshinone IIA could stably bind to the beta-subunit of the microtubule protein. An interaction network analysis of these 12 proteins using MetaCore software suggested that tanshinone IIA treatment regulated the expressions of proteins involved in apoptotic processes, spindle assembly, and p53 activation, including vimentin, Maspin, alpha- and beta-tubulin, and GRP75. Taken together, our results suggest that tanshinone IIA strongly inhibited the growth of cervical cancer cells through interfering in the process of microtubule assembly, leading to G(2)/M phase arrest and sequent apoptosis. The success of this large-scale effort was assessed by a bioinformatics analysis of proteins through predictions of protein domains and possible functional roles. The possible contributions of these proteins to the cytotoxicity of tanshinone IIA provide potential opportunities for the development of cancer therapeutics.