Luciane T. Kagohara

PD-L1 Expression in the Merkel Cell Carcinoma Microenvironment: Association with Inflammation, Merkel Cell Polyomavirus, and Overall Survival

Using paraffin-embedded specimens from 49 patients diagnosed with various stages of Merkel cell carcinoma (MCC), Lipson and colleagues found PD-L1 expression in approximately 50% of these rare tumors. PD-L1+ carcinomas were invariably associated with immune infiltrates and the presence of Merkel cell polyomavirus DNA. These findings suggest that an endogenous immune response, perhaps directed in part to MCC-related antigen, promotes PD-L1 expression in the tumor microenvironment and provide a rationale for investigating therapies blocking PD-1/PD-L1 for patients with MCC. Merkel cell carcinoma (MCC) is a lethal, virus-associated cancer that lacks effective therapies for advanced disease. Agents blocking the PD-1/PD-L1 pathway have shown objective, durable tumor regressions in patients with advanced solid malignancies and efficacy has been linked to PD-L1 expression in the tumor microenvironment. To investigate whether MCC might be a target for PD-1/PD-L1 blockade, we examined MCC PD-L1 expression, its association with tumor-infiltrating lymphocytes (TIL), Merkel cell polyomavirus (MCPyV), and overall survival. Sixty-seven MCC specimens from 49 patients were assessed with immunohistochemistry for PD-L1 expression by tumor cells and TILs, and immune infiltrates were characterized phenotypically. Tumor cell and TIL PD-L1 expression were observed in 49% and 55% of patients, respectively. In specimens with PD-L1(+) tumor cells, 97% (28/29) showed a geographic association with immune infiltrates. Among specimens with moderate-severe TIL intensities, 100% (29/29) showed PD-L1 expression by tumor cells. Significant associations were also observed between the presence of MCPyV DNA, a brisk inflammatory response, and tumor cell PD-L1 expression: MCPyV(-) tumor cells were uniformly PD-L1(-). Taken together, these findings suggest that a local tumor-specific and potentially MCPyV-specific immune response drives tumor PD-L1 expression, similar to previous observations in melanoma and head and neck squamous cell carcinomas. In multivariate analyses, PD-L1(-) MCCs were independently associated with worse overall survival [HR 3.12; 95% confidence interval, 1.28–7.61; P = 0.012]. These findings suggest that an endogenous immune response promotes PD-L1 expression in the MCC microenvironment when MCPyV is present, and provide a rationale for investigating therapies blocking PD-1/PD-L1 for patients with MCC. Cancer Immunol Res; 1(1); 54–63. ©2013 AACR.

Targeted sequencing reveals clonal genetic changes in the progression of early lung neoplasms and paired circulating DNA

Lungs resected for adenocarcinomas often harbour minute discrete foci of cytologically atypical pneumocyte proliferations designated as atypical adenomatous hyperplasia (AAH). Evidence suggests that AAH represents an initial step in the progression to adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA) and fully invasive adenocarcinoma. Despite efforts to identify predictive markers of malignant transformation, alterations driving this progression are poorly understood. Here we perform targeted next-generation sequencing on multifocal AAHs and different zones of histologic progression within AISs and MIAs. Multiregion sequencing demonstrated different genetic drivers within the same tumour and reveal that clonal expansion is an early event of tumorigenesis. We find that KRAS, TP53 and EGFR mutations are indicators of malignant transition. Utilizing droplet digital PCR, we find alterations associated with early neoplasms in paired circulating DNA. This study provides insight into the heterogeneity of clonal events in the progression of early lung neoplasia and demonstrates that these events can be detected even before neoplasms have invaded and acquired malignant potential.

Global and gene-specific DNA methylation pattern discriminates cholecystitis from gallbladder cancer patients in Chile

Aim The aim of the study was to evaluate the use of global and gene-specific DNA methylation changes as potential biomarkers for gallbladder cancer (GBC) in a cohort from Chile. Material & methods DNA methylation was analyzed through an ELISA-based technique and quantitative methylation-specific PCR. Results Global DNA Methylation Index (p = 0.02) and promoter methylation of SSBP2 (p = 0.01) and ESR1 (p = 0.05) were significantly different in GBC when compared with cholecystitis. Receiver curve operator analysis revealed promoter methylation of APC, CDKN2A, ESR1, PGP9.5 and SSBP2, together with the Global DNA Methylation Index, had 71% sensitivity, 95% specificity, a 0.97 area under the curve and a positive predictive value of 90%. Conclusion Global and gene-specific DNA methylation may be useful biomarkers for GBC clinical assessment.

Epigenetic regulation of gene expression in cancer: techniques, resources and analysis

Abstract Cancer is a complex disease, driven by aberrant activity in numerous signaling pathways in even individual malignant cells. Epigenetic changes are critical mediators of these functional changes that drive and maintain the malignant phenotype. Changes in DNA methylation, histone acetylation and methylation, noncoding RNAs, posttranslational modifications are all epigenetic drivers in cancer, independent of changes in the DNA sequence. These epigenetic alterations were once thought to be crucial only for the malignant phenotype maintenance. Now, epigenetic alterations are also recognized as critical for disrupting essential pathways that protect the cells from uncontrolled growth, longer survival and establishment in distant sites from the original tissue. In this review, we focus on DNA methylation and chromatin structure in cancer. The precise functional role of these alterations is an area of active research using emerging high-throughput approaches and bioinformatics analysis tools. Therefore, this review also describes these high-throughput measurement technologies, public domain databases for high-throughput epigenetic data in tumors and model systems and bioinformatics algorithms for their analysis. Advances in bioinformatics data that combine these epigenetic data with genomics data are essential to infer the function of specific epigenetic alterations in cancer. These integrative algorithms are also a focus of this review. Future studies using these emerging technologies will elucidate how alterations in the cancer epigenome cooperate with genetic aberrations during tumor initiation and progression. This deeper understanding is essential to future studies with epigenetics biomarkers and precision medicine using emerging epigenetic therapies.

Evaluation of O6-methylguanine-DNA methyltransferase by immunohistochemistry: best clinical and research practices.

O6-Methyguanine-DNA methyltransferase (MGMT) repairs DNA damage and acts as a tumor suppressor in normal cells by preventing DNA mutations. Several antibodies against MGMT are used for immunohistochemical assessment of this marker and no universal standard is adopted. We evaluated the immunohistochemical expression of MGMT with 5 commercially available primary antibodies in 59 invasive breast carcinomas. A tissue microarray was constructed using 59 invasive breast carcinoma samples. Five primary antibodies against MGMT were used for immunohistochemistry, including clones MT3.1, SPM287, and MT23.2. Heat-induced antigen retrieval and polymer-based immunohistochemistry were performed. Stains were analyzed by microscopy and automated digital slide technology. qRT-PCR was performed for all tumors. Clone SPM287 had the highest sensitivity (p<0.001), and clone MT3.1 had the lowest sensitivity (p<0.001). Clone MT23.2 generated higher levels of cytoplasmic staining, which was not observed with the other antibodies (p<0.001). Fifty-six samples (94.9%) showed hypoexpression of MGMT compared with normal breast, as measured by qRT-PCR (p<0.001). Only clone SPM287 correlated significantly with the qRT-PCR results (p=0.027). Antibody clone SPM287 is the most sensitive and specific antibody for the immunohistochemical evaluation of MGMT, rendering it a reliable and effective reagent for research and clinical practice in breast cancer.

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.

Comparative mutational landscape analysis of patient-derived tumour xenografts

Background:Screening of patients for cancer-driving mutations is now used for cancer prognosis, remission scoring and treatment selection. Although recently emerged targeted next-generation sequencing-based approaches offer promising diagnostic capabilities, there are still limitations. There is a pressing clinical need for a well-validated, rapid, cost-effective mutation profiling system in patient specimens. Given their speed and cost-effectiveness, quantitative PCR mutation detection techniques are well suited for the clinical environment. The qBiomarker mutation PCR array has high sensitivity and shorter turnaround times compared with other methods. However, a direct comparison with existing viable alternatives are required to assess its true potential and limitations.Methods:In this study, we evaluated a panel of 117 patient-derived tumour xenografts by the qBiomarker array and compared with other methods for mutation detection, including Ion AmpliSeq sequencing, whole-exome sequencing and droplet digital PCR.Results:Our broad analysis demonstrates that the qBiomarker’s performance is on par with that of other labour-intensive and expensive methods of cancer mutation detection of frequently altered cancer-associated genes, and provides a foundation for supporting its consideration as an option for molecular diagnostics.Conclusions:This large-scale direct comparison and validation of currently available mutation detection approaches is extremely relevant for the current scenario of precision medicine and will lead to informed choice of screening methodologies, especially in lower budget conditions or time frame limitations.

Untangling The Gene-Epigenome Networks: Timing Of Epigenetic Regulation Of Gene Expression In Acquired Cetuximab Resistance

Widespread characterization of the genomic landscape of tumors has enabled precision treatment strategies. Despite significant advances in development of targeted therapies, patients with tumors sensitive to inhibitors often acquire resistance and succumb to their tumors. The timing and function of gene regulation responsible for resistance remain unknown. Clinical gains from the use of the anti-EGFR antibody, cetuximab, in head and neck squamous cell carcinoma (HNSCC) lead to FDA approval. However, cetuximab is not curative for HNSCC patients and a significant proportion acquire resistance to the treatment. A comprehensive characterization of the mechanisms resulting in acquired cetuximab resistance is critical to delineate new strategies to overcome resistance. To this end, we developed a novel time course analysis to study the in vitro progression of the molecular mechanisms resulting in acquired cetuximab resistance in HNSCC. Specifically, we collected multiple experimentally equivalent cultures over several generations in order to measure changes in gene expression, DNA methylation, and proliferation as resistance developed. This new long-term treatment protocol models the progression of acquired therapeutic resistance, including controls for clonal selection unrelated to treatment. The epigenetic regulation of FGFR1 expression emerged as the dominant mechanism of acquired therapeutic resistance in this system and was confirmed in primary tumors from The Cancer Genome Atlas (TCGA). The association of FGFR1 overexpression with cetuximab resistance is consistent with previous studies. Even in a subset of cetuximab stable resistant clones presenting substantial epigenetic heterogeneity, FGFR1 up-regulation in response to loss of promoter methylation emerged as a key regulator of resistance corroborating our pooled time course epigenetics data. Therefore, we hypothesize that alternative molecular mechanisms giving rise to EGFR inhibitor resistance could be overcome with the introduction of combined FGFR1 inhibition. Taken together, our time course profiling of DNA methylation and gene expression data provide a significant contribution to the characterization of mechanisms involved in acquired cetuximab resistance in HNSCC and provides new insights to alternative options for targeted therapy in this tumor type.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 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 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.

Combining mathematical and statistical modeling to simulate time course bulk and single cell gene expression data in cancer with CancerInSilico

Motivation 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. Results 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 cell-based mathematical model, implemented for an off-lattice, cell-center Monte Carlo mathematical model. We also adapt this model to simulate the impact of growth suppression by targeted therapeutics in cancer and benchmark simulations against bulk in vitro experimental data. Sensitivity to parameters is evaluated and used to predict the relative impact of variation in cellular growth parameters and cell types on tumor heterogeneity in therapeutic response. Availability and Implementation CancerInSilico is implemented in an R/Bioconductor package by the same name. Applications presented are available from https://github.com/FertigLab/CancerInSilico-Figures.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/

The Genome-Wide Molecular Landscape of HPV-Driven and HPV-Negative Head and Neck Squamous Cell Carcinoma

Recent advances in sequencing technology have enabled unprecedented genome-wide characterization of head and neck squamous cell carcinoma (HNSCC). Integrated analyses of publicly available multiplatform high-throughput data have uncovered the vast genomic, epigenetic, and transcriptional diversity of HNSCC. Recognition of human papillomavirus (HPV) involvement in HNSCC carcinogenesis has resulted in the categorization of two HNSCC subtypes (HPV-driven and HPV-negative) with distinct etiologies, molecular properties, clinical features, and prognostic outcomes. Differences in the molecular landscapes of HPV-driven and HPV-negative HNSCC occur genome-wide and encompass changes in genomic, epigenetic, and transcriptional landscapes. Even within each subtype, HNSCC tumors have substantial inter-tumor and intra-tumor molecular heterogeneity. Improving the understanding of the underlying biological function of these complex molecular landscapes through emerging cross-platform genomic analyses is essential to developing more effective diagnostic and therapeutic strategies for HNSCC.