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Assessment of the degree of synergy between cisplatin and MK-1775 in HN30 (wild-type TP53) and HN31 (mutant TP53) HNSCC cells respectively using the Chou and Talalay method.

Determination of drug synergy by the combination-index and isobologram analyses using the CalcuSyn software.

The Wee-1 inhibitor, MK-1775 Synergizes with cisplatin in vitro in established HNSCC cell lines with known TP53 mutational status; Dual phospho-H3 staining and PI flow cytometric analysis (FACS); The synergy between cisplatin and MK-1775 in HNSCC is not mediated by apoptosis.

Estimating the effects of variants found in disease driver genes opens the door to personalized therapeutic opportunities. Clinical associations and laboratory experiments can only characterize a tiny fraction of all the available variants, leaving the majority as variants of unknown significance (VUS). In silico methods bridge this gap by providing instant estimates on a large scale, most often based on the numerous genetic differences between species. Despite concerns that these methods may lack reliability in individual subjects, their numerous practical applications over cohorts suggest they are already helpful and have a role to play in genome interpretation when used at the proper scale and context. In this review, we aim to gain insights into the training and validation of these variant effect predicting methods and illustrate representative types of experimental and clinical applications. Objective performance assessments using various datasets that are not yet published indicate the strengths and limitations of each method. These show that cautious use of in silico variant impact predictors is essential for addressing genome interpretation challenges.

Supplemental Methods. Description of additional methods and procedures used in the study.

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. Cancer Res; 75(7); 1205–15. ©2015 AACR.

Supplementary Table 1: TP53 mutations scored and stratified by EAp53 and Poeta classification in the platinum based induction chemotherapy cohort.

Supplementary Figure 3: Effect of cisplatin therapy on tumor growth and survival in an orthotopic nude mouse model of oral tongue cancer harboring TP53 mutations presented as area under the growth curve (AUC).

Supplemental Table S1. Summary of patient data and prognostic data for both the test and validation datasets.

Supplementary Figure 2: TP53 knockdown illustrates both loss and gain of function in vitro in HNSCC cells.

Supplementary Table 2: Univariate analysis of overall survival.

Supplemental Figure S2. Cellular proliferation is independent of TP53 status.

Supplementary Figure 1: Expression of p53 target genes is impaired following cisplatin treatment in HNSCC harboring mutant TP53.

Supplementary Table 3: Cox proportional hazard regression afterward backward selection and adjusting for EA score for overall survival.

Supplementary Table 4: Expression of genes associated with loss and gain of function phenotypes for ordinal regression analysis shown in Figure 3B and 3F.

Methods and Materials. Quantitative Reverse Transcription PCR (RT-qPCR) Analyses; mRNA Expression Arrays; Orthotopic Nude Mouse Model of Oral Cavity Cancer; Patient Cohort and TP53 Sequencing;Supplementary References;Footnotes.

Legends for Supplemental Figures S1-S4.

Supplementary Figure Legends. Expression of p53 target genes is impaired following cisplatin treatment in HNSCC harboring mutant TP53; TP53 knockdown illustrates both loss and gain of function in vitro in HNSCC cells;Effect of cisplatin therapy on tumor growth and survival in an orthotopic nude mouse model of oral tongue cancer harboring TP53 mutations presented as area under the growth curve (AUC).

Purpose We characterize the clinical and molecular phenotypes of six unrelated individuals with intellectual disability and autism spectrum disorder who carry heterozygous missense variants of the PRKAR1B gene, which encodes the R1β subunit of the cyclic AMP-dependent protein kinase A (PKA). Methods Variants of PRKAR1B were identified by single- or trio-exome analysis. We contacted the families and physicians of the six individuals to collect phenotypic information, performed in vitro analyses of the identified PRKAR1B-variants, and investigated PRKAR1B expression during embryonic development. Results Recent studies of large patient cohorts with neurodevelopmental disorders found significant enrichment of de novo missense variants in PRKAR1B. In our cohort, de novo origin of the PRKAR1B variants could be confirmed in five of six individuals, and four carried the same heterozygous de novo variant c.1003C>T (p.Arg335Trp; NM_001164760). Global developmental delay, autism spectrum disorder, and apraxia/dyspraxia have been reported in all six, and reduced pain sensitivity was found in three individuals carrying the c.1003C>T variant. PRKAR1B expression in the brain was demonstrated during human embryonal development. Additionally, in vitro analyses revealed altered basal PKA activity in cells transfected with variant-harboring PRKAR1B expression constructs. Conclusion Our study provides strong evidence for a PRKAR1B-related neurodevelopmental disorder.

11 pags., 6figs., 3 pags., Our understanding of enzymes with high substrate ambiguity remains limited because their large active sites allow substrate docking freedom to an extent that seems incompatible with stereospecificity. One possibility is that some of these enzymes evolved a set of evolutionarily fitted sequence positions that stringently allow switching substrate ambiguity and chiral specificity. To explore this hypothesis, we targeted for mutation a serine ester hydrolase (EH) that exhibits an impressive 71-substrate repertoire but is not stereospecific (e.e. 50%). We used structural actions and the computational evolutionary trace method to explore specificity-swapping sequence positions and hypothesized that position I244 was critical. Driven by evolutionary action analysis, this position was substituted to leucine, which together with isoleucine appears to be the amino acid most commonly present in the closest homologous sequences (max. identity, ca. 67.1%), and to phenylalanine, which appears in distant homologues. While the I244L mutation did not have any functional consequences, the I244F mutation allowed the esterase to maintain a remarkable 53-substrate range while gaining stereospecificity properties (e.e. 99.99%). These data support the possibility that some enzymes evolve sequence positions that control the substrate scope and stereospecificity. Such residues, which can be evolutionarily screened, may serve as starting points for further designing substrate-ambiguous, yet chiral-specific, enzymes that are greatly appreciated in biotechnology and synthetic chemistry., MF acknowledges the grant ‘INMARE’ from the EuropeanUnion’s Horizon 2020 (grant agreement no. 634486), the grantsPCIN-2017-078 (within the Marine Biotechnology ERA-NET) and BIO2017-85522-R from the Ministerio de Economía, Industria y Competitividad, Agencia Estatal de Investigación (AEI), Fondo Eur-opeo de Desarrollo Regional (FEDER) and the European Union (EU),and the grant 2020AEP061 from the Agencia Estatal CSIC. J.S-A.acknowledges grant PID2019-105838RB-C33 from the Ministeriode Ciencia e Innovación, Agencia Estatal de Investigación (AEI),Fondo Europeo de Desarrollo Regional (FEDER) and the EuropeanUnion (EU). P.N.G. acknowledges the support of the Era-Net IB Pro-ject MetaCat funded through UK Biotechnology and BiologicalSciences Research Council (BBSRC), grant No. BB/M029085/1, andthe Centre for Environmental Biotechnology Project, co-fundedby European Regional Development Fund (ERDF) via the WelshGovernment (WEFO); R.B. acknowledges the Supercomputing Wales project, co-funded by ERDF via WEFO. OL and PK were sup-ported by the National Institutes of Health (NIH) grants 5R01AG061105, 5R01GM066099, and 5R01GM079656. C. Coscolínthanks the Ministerio de Economía y Competitividad and FEDER fora PhD fellowship (Grant BES-2015-073829). Staff of the Synchrotron Radiation Source at Alba (Barcelona, Spain) for assistance at the BL13-XALOC beamline

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.

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.

We present a Chemistry and Structure Screen Integrated Efficiently (CASSIE) approach (named for Greek prophet Cassandra) to design inhibitors for cancer biology and pathogenesis. CASSIE provides an effective path to target master keys to control the repair-replication interface for cancer cells and SARS CoV-2 pathogenesis as exemplified here by specific targeting of Poly(ADP-ribose) glycohydrolase (PARG) and ADP-ribose glycohydrolase ARH3 macrodomains plus SARS CoV-2 nonstructural protein 3 (Nsp3) Macrodomain 1 (Mac1) and Nsp15 nuclease. As opposed to the classical massive effort employing libraries with large numbers of compounds against single proteins, we make inhibitor design for multiple targets efficient. Our compact, chemically diverse, 5000 compound Goldilocks (GL) library has an intermediate number of compounds sized between fragments and drugs with predicted favorable ADME (absorption, distribution, metabolism, and excretion) and toxicological profiles. Amalgamating our core GL library with an approved drug (AD) library, we employ a combined GLAD library virtual screen, enabling an effective and efficient design cycle of ranked computer docking, top hit biophysical and cell validations, and defined bound structures using human proteins or their avatars. As new drug design is increasingly pathway directed as well as molecular and mechanism based, our CASSIE approach facilitates testing multiple related targets by efficiently turning a set of interacting drug discovery problems into a tractable medicinal chemistry engineering problem of optimizing affinity and ADME properties based upon early co-crystal structures. Optimization efforts are made efficient by a computationally-focused iterative chemistry and structure screen. Thus, we herein describe and apply CASSIE to define prototypic, specific inhibitors for PARG vs distinct inhibitors for the related macrodomains of ARH3 and SARS CoV-2 Nsp3 plus the SARS CoV-2 Nsp15 RNA nuclease.

Simple Summary This study aimed to explore the potential role of PPARγ–DNMT1 interaction through PPAR-responsive elements (PPREs), which we have found to be enriched with Alu repeats. Apart from protein–protein interactions and co-expression in multiple cancer types, we exclusively described a prognostic role for PPARγ in uveal melanoma (UM). Abstract Background: PPARγ (peroxisome proliferator-activated receptor gamma) is involved in the pathology of numerous diseases, including UM and other types of cancer. Emerging evidence suggests that an interaction between PPARγ and DNMTs (DNA methyltransferase) plays a role in cancer that is yet to be defined. Methods: The configuration of the repeating elements was performed with CAP3 and MAFFT, and the structural modelling was conducted with HDOCK. An evolutionary action scores algorithm was used to identify oncogenic variants. A systematic bioinformatic appraisal of PPARγ and DNMT1 was performed across 29 tumor types and UM available in The Cancer Genome Atlas (TCGA). Results: PPAR-responsive elements (PPREs) enriched with Alu repeats are associated with different genomic regions, particularly the promotor region of DNMT1. PPARγ–DNMT1 co-expression is significantly associated with several cancers. C-terminals of PPARγ and DNMT1 appear to be the potential protein–protein interaction sites where disease-specific mutations may directly impair the respective protein functions. Furthermore, PPARγ expression could be identified as an additional prognostic marker for UM. Conclusions: We hypothesize that the function of PPARγ requires an additional contribution of Alu repeats which may directly influence the DNMT1 network. Regarding UM, PPARγ appears to be an additional discriminatory prognostic marker, in particular in disomy 3 tumors.

Genotype-phenotype relationships shape health and population fitness but remain difficult to predict and interpret. Here, we apply an evolutionary action method to de novo missense variants in whole-exome sequences of individuals with autism spectrum disorder (ASD) to unravel genes and pathways connected to ASD. Evolutionary action predicts the impact of missense variants on protein function by measuring the fitness effect based on phylogenetic distances and substitution odds in homologous gene sequences. By examining de novo missense variants in 2384 individuals with ASD (probands) compared to matched siblings without ASD, we found missense variants in 398 genes representing 23 pathways that were biased toward higher evolutionary action scores than expected by random chance; these pathways were involved in axonogenesis, synaptic transmission, and neurodevelopment. The predicted fitness impact of de novo and inherited missense variants in candidate genes correlated with the IQ of individuals with ASD, even for new gene candidates. Taking an evolutionary action method, we detected those missense variants most likely to contribute to ASD pathogenesis and elucidated their phenotypic impact. This approach could be applied to integrate missense variants across a patient cohort to identify genes contributing to a shared phenotype in other complex diseases.

This paper reports the evaluation of predictions for the ``CALM1'' challenge in the fifth round of the Critical Assessment of Genome Interpretation held in 2018. In the challenge, the participants were asked to predict effects on yeast growth caused by missense variants of human calmodulin, a highly conserved protein in eukaryotic cells sensing calcium concentration. The performance of predictors implementing different algorithms and methods is similar. Most predictors are able to identify the deleterious or tolerated variants with modest accuracy, with a baseline predictor based purely on sequence conservation slightly outperforming the submitted predictions. Nevertheless, we think that the accuracy of predictions remains far from satisfactory, and the field awaits substantial improvements. The most poorly predicted variants in this round surround functional CALM1 sites that bind calcium or peptide, which suggests that better incorporation of structural analysis may help improve predictions.

Whole-genome sequencing (WGS) holds great potential as a diagnostic test. However, the majority of patients currently undergoing WGS lack a molecular diagnosis, largely due to the vast number of undiscovered disease genes and our inability to assess the pathogenicity of most genomic variants. The CAGI SickKids challenges attempted to address this knowledge gap by assessing state-of-the-art methods for clinical phenotype prediction from genomes. CAGI4 and CAGI5 participants were provided with WGS data and clinical descriptions of 25 and 24 undiagnosed patients from the SickKids Genome Clinic Project, respectively. Predictors were asked to identify primary and secondary causal variants. In addition, for CAGI5, groups had to match each genome to one of three disorder categories (neurologic, ophthalmologic, and connective), and separately to each patient. The performance of matching genomes to categories was no better than random but two groups performed significantly better than chance in matching genomes to patients. Two of the ten variants proposed by two groups in CAGI4 were deemed to be diagnostic, and several proposed pathogenic variants in CAGI5 are good candidates for phenotype expansion. We discuss implications for improving in silico assessment of genomic variants and identifying new disease genes.

Many computational approaches estimate the effect of coding variants, but their predictions often disagree with each other. These contradictions confound users and raise questions regarding reliability. Performance assessments can indicate the expected accuracy for each method and highlight advantages and limitations. The Critical Assessment of Genome Interpretation (CAGI) community aims to organize objective and systematic assessments: They challenge predictors on unpublished experimental and clinical data and assign independent assessors to evaluate the submissions. We participated in CAGI experiments as predictors, using the Evolutionary Action (EA) method to estimate the fitness effect of coding mutations. EA is untrained, uses homology information, and relies on a formal equation: The fitness effect equals the functional sensitivity to residue changes multiplied by the magnitude of the substitution. In previous CAGI experiments (between 2011 and 2016), our submissions aimed to predict the protein activity of single mutants. In 2018 (CAGI5), we also submitted predictions regarding clinical associations, folding stability, and matching genomic data with phenotype. For all these diverse challenges, we used EA to predict the fitness effect of variants, adjusted to specifically address each question. Our submissions had consistently good performance, suggesting that EA predicts reliably the effects of genetic variants.

OBJECTIVE: To assess the impact of somatic gene mutations on survival among patients undergoing resection of colorectal liver metastases (CLM). SUMMARY BACKGROUND DATA: Patients undergoing CLM resection have heterogeneous outcomes, and accurate risk stratification is necessary to optimize patient selection for surgery. METHODS: Next-generation sequencing of 50 cancer-related genes was performed from primary tumors and/or liver metastases in 401 patients undergoing CLM resection. Missense TP53 mutations were classified by the Evolutionary Action Score (EAp53), a novel approach that dichotomizes mutations as low or high risk. RESULTS: The most frequent somatic gene mutations were TP53 (65.6%), followed by KRAS (48.1%) and APC (47.4%). Double mutation in RAS/TP53, identified in 31.4% of patients, was correlated with primary tumor location in the right colon (P = 0.006). On multivariable analysis, RAS/TP53 double mutation was an independent predictor of shorter overall survival (hazard ratio, 2.62; 95% confidence interval 1.41 to 4.87; P = 0.002). In patients with co-mutated RAS, EAp53 high risk mutations were associated with shorter 5-year overall survival of 12.2%, compared with 55.7% for TP53 wild-type (P < 0.001). The negative prognostic effects of RAS and TP53 mutations were limited to tumors harboring mutations in both genes. CONCLUSIONS: Concomitant RAS and TP53 mutations are associated with decreased survival after CLM resection. A high EAp53 predicts a subset of patients with worse prognosis. These preliminary analyses suggest that surgical resection of liver metastases should be carefully considered in this subset of patients.

An increasing number of mitochondrial diseases are found to be caused by pathogenic variants in nuclear encoded mitochondrial aminoacyl-tRNA synthetases. FARS2 encodes mitochondrial phenylalanyl-tRNA synthetase (mtPheRS) which transfers phenylalanine to its cognate tRNA in mitochondria. Since the first case was reported in 2012, a total of 21 subjects with FARS2 deficiency have been reported to date with a spectrum of disease severity that falls between two phenotypes; early onset epileptic encephalopathy and a less severe phenotype characterized by spastic paraplegia. In this report, we present an additional 15 individuals from 12 families who are mostly Arabs homozygous for the pathogenic variant Y144C, which is associated with the more severe early onset phenotype. The total number of unique pathogenic FARS2 variants known to date is 21 including three different partial gene deletions reported in four individuals. Except for the large deletions, all variants but two (one in-frame deletion of one amino acid and one splice-site variant) are missense. All large deletions and the single splice-site variant are in trans with a missense variant. This suggests that complete loss of function may be incompatible with life. In this report, we also review structural, functional, and evolutionary significance of select FARS2 pathogenic variants reported here.

Motivation Since the first recognized case of COVID-19, more than 100 million people have been infected worldwide. Global efforts in drug and vaccine development to fight the disease have yielded vaccines and drug candidates to cure COVID-19. However, the spread of SARS-CoV-2 variants threatens the continued efficacy of these treatments. In order to address this, we interrogate the evolutionary history of the entire SARS-CoV-2 proteome to identify evolutionarily conserved functional sites that can inform the search for treatments with broader coverage across the coronavirus family. Results Combining coronavirus family sequence information with the mutations observed in the current COVID-19 outbreak, we systematically and comprehensively define evolutionarily stable sites that may provide useful drug and vaccine targets and which are less likely to be compromised by the emergence of new virus strains. Several experimentally validated effective drugs interact with these proposed target sites. In addition, the same evolutionary information can prioritize cross reactive antigens that are useful in directing multi-epitope vaccine strategies to illicit broadly neutralizing immune responses to the betacoronavirus family. Although the results are focused on SARS-CoV-2, these approaches stem from evolutionary principles that are agnostic to the organism or infective agent. Availability and implementation The results of this work are made interactively available at http://cov.lichtargelab.org. Supplementary information Supplementary data are available at Bioinformatics online.

Since the first recognized case of COVID-19, more than 100 million people have been infected worldwide. Global efforts in drug and vaccine development to fight the disease have yielded vaccines and drug candidates to cure COVID-19. However, the spread of SARS-CoV-2 variants threatens the continued efficacy of these treatments. In order to address this, we interrogate the evolutionary history of the entire SARS-CoV-2 proteome to identify evolutionarily conserved functional sites that can inform the search for treatments with broader coverage across the coronavirus family. Combining this information with the mutations observed in the current COVID-19 outbreak, we systematically and comprehensively define evolutionarily stable sites that may provide useful drug and vaccine targets and which are less likely to be compromised by the emergence of new virus strains. Several experimentally-validated effective drugs interact with these proposed target sites. In addition, the same evolutionary information can prioritize cross reactive antigens that are useful in directing multi-epitope vaccine strategies to illicit broadly neutralizing immune responses to the betacoronavirus family. Although the results are focused on SARS-CoV-2, these approaches stem from evolutionary principles that are agnostic to the organism or infective agent.