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-tRNASec, 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.

Background: Mdm2, the principal ubiquitin ligase for the tumor suppressor p53, also ubiquitinates itself, but the consequences are unclear. Results: Auto-ubiquitination enhances Mdm2 binding to ubiquitin-conjugating enzymes (E2s) and its ability to ubiquitinate p53. Conclusion: Increased E2 recruitment by auto-ubiquitinated Mdm2 may enable processivity of substrate ubiquitination. Significance: Auto-ubiquitination may be a general mechanism for the activation of ubiquitin ligases.

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 immunohistochemistry in 130 newly diagnosed and 43 recurrent HNSCC patients. Correlative statistical analysis with clinicopathological 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 vivo. Results Our analysis revealed that SMAD4 loss was associated with an aggressive, HPV-negative, cetuximab-resistant phenotype. We found a signature of pro-survival and anti-apoptotic 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 a 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.

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.

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.

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. To determine the dynamics of these molecular changes, we obtained high throughput omics data weekly during the development of cetuximab resistance in a head and neck cancer in vitro model. RESULTS:An unsupervised algorithm, CoGAPS, was used to quantify the evolving transcriptional and epigenetic changes. Applying a PatternMarker statistic to the results from CoGAPS enabled novel heatmapbased 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 resistance. These genes include FGFR1, which was associated with EGFR inhibitor resistance previously. Thus, integrated omics analysis distinguishes the timing of molecular drivers of resistance. Our findings provide a relevant towards better understanding of the time course progression of changes resulting in acquired resistance to targeted therapies. This is an important contribution to the development of alternative treatment strategies that would introduce new drugs before the resistant phenotype develops.

BackgroundTargeted 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.MethodsTo 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.ResultsCoGAPS 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.ConclusionsGenes 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.Electronic supplementary materialThe online version of this article (10.1186/s13073-018-0545-2) contains supplementary material, which is available to authorized users.

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.