CancerInSilico: An R/Bioconductor package for combining mathematical and statistical modeling to simulate time course bulk and single cell gene expression data in cancer.

Bioinformatics techniques to analyze time course bulk and single cell omics data are advancing. The absence of a known ground truth of the dynamics of molecular changes challenges benchmarking their performance on real data. Realistic simulated time-course datasets are essential to assess the performance of time course bioinformatics algorithms. We develop an R/Bioconductor package, CancerInSilico, to simulate bulk and single cell transcriptional data from a known ground truth obtained from mathematical models of cellular systems. This package contains a general R infrastructure for running cell-based models and simulating gene expression data based on the model states. We show how to use this package to simulate a gene expression data set and consequently benchmark analysis methods on this data set with a known ground truth. The package is freely available via Bioconductor: http://bioconductor.org/packages/CancerInSilico/.

Auto-ubiquitination of Mdm2 enhances its substrate ubiquitin ligase activity.

The RING domain E3 ubiquitin ligase Mdm2 is the master regulator of the tumor suppressor p53. It targets p53 for proteasomal degradation, restraining the potent activity of p53 and enabling cell survival and proliferation. Like most E3 ligases, Mdm2 can also ubiquitinate itself. How Mdm2 auto-ubiquitination may influence its substrate ubiquitin ligase activity is undefined. Here we show that auto-ubiquitination of Mdm2 is an activating event. Mdm2 that has been conjugated to polyubiquitin chains, but not to single ubiquitins, exhibits substantially enhanced activity to polyubiquitinate p53. Mechanistically, auto-ubiquitination of Mdm2 facilitates the recruitment of the E2 ubiquitin-conjugating enzyme. This occurs through noncovalent interactions between the ubiquitin chains on Mdm2 and the ubiquitin binding domain on E2s. Mutations that diminish the noncovalent interactions render auto-ubiquitination unable to stimulate Mdm2 substrate E3 activity. These results suggest a model in which polyubiquitin chains on an E3 increase the local concentration of E2 enzymes and permit the processivity of substrate ubiquitination. They also support the notion that autocatalysis may be a prevalent mode for turning on the activity of latent enzymes.

Model-informed experimental design recommendations for distinguishing intrinsic and acquired targeted therapeutic resistance in head and neck cancer.

The promise of precision medicine has been limited by the pervasive resistance to many targeted therapies for cancer. Inferring the timing (i.e., pre-existing or acquired) and mechanism (i.e., drug-induced) of such resistance is crucial for designing effective new therapeutics. This paper studies cetuximab resistance in head and neck squamous cell carcinoma (HNSCC) using tumor volume data obtained from patient-derived tumor xenografts. We ask if resistance mechanisms can be determined from this data alone, and if not, what data would be needed to deduce the underlying mode(s) of resistance. To answer these questions, we propose a family of mathematical models, with each member of the family assuming a different timing and mechanism of resistance. We present a method for fitting these models to individual volumetric data, and utilize model selection and parameter sensitivity analyses to ask: which member(s) of the family of models best describes HNSCC response to cetuximab, and what does that tell us about the timing and mechanisms driving resistance? We find that along with time-course volumetric data to a single dose of cetuximab, the initial resistance fraction and, in some instances, dose escalation volumetric data are required to distinguish among the family of models and thereby infer the mechanisms of resistance. These findings can inform future experimental design so that we can best leverage the synergy of wet laboratory experimentation and mathematical modeling in the study of novel targeted cancer therapeutics.

Integrated time course omics analysis distinguishes immediate therapeutic response from acquired resistance.

BACKGROUND: Targeted therapies specifically act by blocking the activity of proteins that are encoded by genes critical for tumorigenesis. However, most cancers acquire resistance and long-term disease remission is rarely observed. Understanding the time course of molecular changes responsible for the development of acquired resistance could enable optimization of patients' treatment options. Clinically, acquired therapeutic resistance can only be studied at a single time point in resistant tumors. METHODS: To determine the dynamics of these molecular changes, we obtained high throughput omics data (RNA-sequencing and DNA methylation) weekly during the development of cetuximab resistance in a head and neck cancer in vitro model. The CoGAPS unsupervised algorithm was used to determine the dynamics of the molecular changes associated with resistance during the time course of resistance development. RESULTS: CoGAPS was used to quantify the evolving transcriptional and epigenetic changes. Applying a PatternMarker statistic to the results from CoGAPS enabled novel heatmap-based visualization of the dynamics in these time course omics data. We demonstrate that transcriptional changes result from immediate therapeutic response or resistance, whereas epigenetic alterations only occur with resistance. Integrated analysis demonstrates delayed onset of changes in DNA methylation relative to transcription, suggesting that resistance is stabilized epigenetically. CONCLUSIONS: Genes with epigenetic alterations associated with resistance that have concordant expression changes are hypothesized to stabilize the resistant phenotype. These genes include FGFR1, which was associated with EGFR inhibitors resistance previously. Thus, integrated omics analysis distinguishes the timing of molecular drivers of resistance. This understanding of the time course progression of molecular changes in acquired resistance is important for the development of alternative treatment strategies that would introduce appropriate selection of new drugs to treat cancer before the resistant phenotype develops.

SMAD4 Loss Is Associated with Cetuximab Resistance and Induction of MAPK/JNK Activation in Head and Neck Cancer Cells.

Purpose: We previously demonstrated an association between decreased SMAD4 expression and cetuximab resistance in head and neck squamous cell carcinoma (HNSCC). The purpose of this study was to further elucidate the clinical relevance of SMAD4 loss in HNSCC.Experimental Design: SMAD4 expression was assessed by IHC in 130 newly diagnosed and 43 patients with recurrent HNSCC. Correlative statistical analysis with clinicopathologic data was also performed. OncoFinder, a bioinformatics tool, was used to analyze molecular signaling in TCGA tumors with low or high SMAD4 mRNA levels. The role of SMAD4 was investigated by shRNA knockdown and gene reconstitution of HPV-negative HNSCC cell lines in vitro and in vivoResults: Our analysis revealed that SMAD4 loss was associated with an aggressive, HPV-negative, cetuximab-resistant phenotype. We found a signature of prosurvival and antiapoptotic pathways that were commonly dysregulated in SMAD4-low cases derived from TCGA-HNSCC dataset and an independent oral cavity squamous cell carcinoma (OSCC) cohort obtained from GEO. We show that SMAD4 depletion in an HNSCC cell line induces cetuximab resistance and results in worse survival in an orthotopic mouse model in vivo We implicate JNK and MAPK activation as mediators of cetuximab resistance and provide the foundation for the concomitant EGFR and JNK/MAPK inhibition as a potential strategy for overcoming cetuximab resistance in HNSCCs with SMAD4 loss.Conclusions: Our study demonstrates that loss of SMAD4 expression is a signature characterizing the cetuximab-resistant phenotype and suggests that SMAD4 expression may be a determinant of sensitivity/resistance to EGFR/MAPK or EGFR/JNK inhibition in HPV-negative HNSCC tumors. Clin Cancer Res; 23(17); 5162-75. ©2017 AACR.

CoGAPS matrix factorization algorithm identifies transcriptional changes in AP-2alpha target genes in feedback from therapeutic inhibition of the EGFR network.

Patients with oncogene driven tumors are treated with targeted therapeutics including EGFR inhibitors. Genomic data from The Cancer Genome Atlas (TCGA) demonstrates molecular alterations to EGFR, MAPK, and PI3K pathways in previously untreated tumors. Therefore, this study uses bioinformatics algorithms to delineate interactions resulting from EGFR inhibitor use in cancer cells with these genetic alterations. We modify the HaCaT keratinocyte cell line model to simulate cancer cells with constitutive activation of EGFR, HRAS, and PI3K in a controlled genetic background. We then measure gene expression after treating modified HaCaT cells with gefitinib, afatinib, and cetuximab. The CoGAPS algorithm distinguishes a gene expression signature associated with the anticipated silencing of the EGFR network. It also infers a feedback signature with EGFR gene expression itself increasing in cells that are responsive to EGFR inhibitors. This feedback signature has increased expression of several growth factor receptors regulated by the AP-2 family of transcription factors. The gene expression signatures for AP-2alpha are further correlated with sensitivity to cetuximab treatment in HNSCC cell lines and changes in EGFR expression in HNSCC tumors with low CDKN2A gene expression. In addition, the AP-2alpha gene expression signatures are also associated with inhibition of MEK, PI3K, and mTOR pathways in the Library of Integrated Network-Based Cellular Signatures (LINCS) data. These results suggest that AP-2 transcription factors are activated as feedback from EGFR network inhibition and may mediate EGFR inhibitor resistance.

Decreased SMAD4 expression is associated with induction of epithelial-to-mesenchymal transition and cetuximab resistance in head and neck squamous cell carcinoma.

Epidermal growth factor receptor (EGFR) is frequently overexpressed in head and neck squamous cell carcinoma (HNSCC) and cetuximab, a monoclonal antibody targeting this receptor, is widely used to treat these patients. In the following investigation, we examined the role of SMAD4 down-regulation in mediating epithelial-to-mesenchymal transition (EMT) and cetuximab resistance in HNSCC. We determined that SMAD4 downregulation was significantly associated with increased cell motility, increased expression of vimentin, and cetuximab resistance in HNSCC cell lines. In the HNSCC genomic dataset obtained from The Cancer Genome Atlas, SMAD4 was altered in 20/279 (7%) of HNSCC via homozygous deletion, and nonsense, missense, and silent mutations. When SMAD4 expression was compared with respect to human papillomavirus (HPV) status, HPV-positive tumors had higher expression compared to HPV-negative tumors. Furthermore, higher SMAD4 expression also correlated with higher CDKN2A (p16) expression. Our data suggest that SMAD4 down-regulation plays an important role in the induction of EMT and cetuximab resistance. Patients with higher SMAD4 expression may benefit from cetuximab use in the clinic.

A novel protein domain induces high affinity selenocysteine insertion sequence binding and elongation factor recruitment.

Selenocysteine (Sec) is incorporated at UGA codons in mRNAs possessing a Sec insertion sequence (SECIS) element in their 3'-untranslated region. At least three additional factors are necessary for Sec incorporation: SECIS-binding protein 2 (SBP2), Sec-tRNA(Sec), and a Sec-specific translation elongation factor (eEFSec). The C-terminal half of SBP2 is sufficient to promote Sec incorporation in vitro, which is carried out by the concerted action of a novel Sec incorporation domain and an L7Ae RNA-binding domain. Using alanine scanning mutagenesis, we show that two distinct regions of the Sec incorporation domain are required for Sec incorporation. Physical separation of the Sec incorporation and RNA-binding domains revealed that they are able to function in trans and established a novel role of the Sec incorporation domain in promoting SECIS and eEFSec binding to the SBP2 RNA-binding domain. We propose a model in which SECIS binding induces a conformational change in SBP2 that recruits eEFSec, which in concert with the Sec incorporation domain gains access to the ribosomal A site.