Craig April

Combining Clinical, Pathology, and Gene Expression Data to Predict Recurrence of Hepatocellular Carcinoma

BACKGROUND & AIMS In approximately 70% of patients with hepatocellular carcinoma (HCC) treated by resection or ablation, disease recurs within 5 years. Although gene expression signatures have been associated with outcome, there is no method to predict recurrence based on combined clinical, pathology, and genomic data (from tumor and cirrhotic tissue). We evaluated gene expression signatures associated with outcome in a large cohort of patients with early stage (Barcelona-Clinic Liver Cancer 0/A), single-nodule HCC and heterogeneity of signatures within tumor tissues. METHODS We assessed 287 HCC patients undergoing resection and tested genome-wide expression platforms using tumor (n = 287) and adjacent nontumor, cirrhotic tissue (n = 226). We evaluated gene expression signatures with reported prognostic ability generated from tumor or cirrhotic tissue in 18 and 4 reports, respectively. In 15 additional patients, we profiled samples from the center and periphery of the tumor, to determine stability of signatures. Data analysis included Cox modeling and random survival forests to identify independent predictors of tumor recurrence. RESULTS Gene expression signatures that were associated with aggressive HCC were clustered, as well as those associated with tumors of progenitor cell origin and those from nontumor, adjacent, cirrhotic tissues. On multivariate analysis, the tumor-associated signature G3-proliferation (hazard ratio [HR], 1.75; P = .003) and an adjacent poor-survival signature (HR, 1.74; P = .004) were independent predictors of HCC recurrence, along with satellites (HR, 1.66; P = .04). Samples from different sites in the same tumor nodule were reproducibly classified. CONCLUSIONS We developed a composite prognostic model for HCC recurrence, based on gene expression patterns in tumor and adjacent tissues. These signatures predict early and overall recurrence in patients with HCC, and complement findings from clinical and pathology analyses.

Age-Dependent Brain Gene Expression and Copy Number Anomalies in Autism Suggest Distinct Pathological Processes at Young Versus Mature Ages

Autism is a highly heritable neurodevelopmental disorder, yet the genetic underpinnings of the disorder are largely unknown. Aberrant brain overgrowth is a well-replicated observation in the autism literature; but association, linkage, and expression studies have not identified genetic factors that explain this trajectory. Few studies have had sufficient statistical power to investigate whole-genome gene expression and genotypic variation in the autistic brain, especially in regions that display the greatest growth abnormality. Previous functional genomic studies have identified possible alterations in transcript levels of genes related to neurodevelopment and immune function. Thus, there is a need for genetic studies involving key brain regions to replicate these findings and solidify the role of particular functional pathways in autism pathogenesis. We therefore sought to identify abnormal brain gene expression patterns via whole-genome analysis of mRNA levels and copy number variations (CNVs) in autistic and control postmortem brain samples. We focused on prefrontal cortex tissue where excess neuron numbers and cortical overgrowth are pronounced in the majority of autism cases. We found evidence for dysregulation in pathways governing cell number, cortical patterning, and differentiation in young autistic prefrontal cortex. In contrast, adult autistic prefrontal cortex showed dysregulation of signaling and repair pathways. Genes regulating cell cycle also exhibited autism-specific CNVs in DNA derived from prefrontal cortex, and these genes were significantly associated with autism in genome-wide association study datasets. Our results suggest that CNVs and age-dependent gene expression changes in autism may reflect distinct pathological processes in the developing versus the mature autistic prefrontal cortex. Our results raise the hypothesis that genetic dysregulation in the developing brain leads to abnormal regional patterning, excess prefrontal neurons, cortical overgrowth, and neural dysfunction in autism.

Whole-Genome Gene Expression Profiling of Formalin-Fixed, Paraffin-Embedded Tissue Samples

Background We have developed a gene expression assay (Whole-Genome DASL®), capable of generating whole-genome gene expression profiles from degraded samples such as formalin-fixed, paraffin-embedded (FFPE) specimens. Methodology/Principal Findings We demonstrated a similar level of sensitivity in gene detection between matched fresh-frozen (FF) and FFPE samples, with the number and overlap of probes detected in the FFPE samples being approximately 88% and 95% of that in the corresponding FF samples, respectively; 74% of the differentially expressed probes overlapped between the FF and FFPE pairs. The WG-DASL assay is also able to detect 1.3–1.5 and 1.5–2 -fold changes in intact and FFPE samples, respectively. The dynamic range for the assay is ∼3 logs. Comparing the WG-DASL assay with an in vitro transcription-based labeling method yielded fold-change correlations of R2 ∼0.83, while fold-change comparisons with quantitative RT-PCR assays yielded R2∼0.86 and R2∼0.55 for intact and FFPE samples, respectively. Additionally, the WG-DASL assay yielded high self-correlations (R2>0.98) with low intact RNA inputs ranging from 1 ng to 100 ng; reproducible expression profiles were also obtained with 250 pg total RNA (R2∼0.92), with ∼71% of the probes detected in 100 ng total RNA also detected at the 250 pg level. When FFPE samples were assayed, 1 ng total RNA yielded self-correlations of R2∼0.80, while still maintaining a correlation of R2∼0.75 with standard FFPE inputs (200 ng). Conclusions/Significance Taken together, these results show that WG-DASL assay provides a reliable platform for genome-wide expression profiling in archived materials. It also possesses utility within clinical settings where only limited quantities of samples may be available (e.g. microdissected material) or when minimally invasive procedures are performed (e.g. biopsied specimens).

Gene-expression signature of vascular invasion in hepatocellular carcinoma

BACKGROUND & AIMS Vascular invasion is a major predictor of tumor recurrence after surgical treatments for hepatocellular carcinoma (HCC). While macroscopic vascular invasion can be detected by radiological techniques, pre-operative detection of microscopic vascular invasion, which complicates 30-40% of patients with early tumors, remains elusive. METHODS A total of 214 patients with hepatocellular carcinoma who underwent resection were included in the study. By using genome-wide gene-expression profiling of 79 hepatitis C-related hepatocellular carcinoma samples (training set), a gene-expression signature associated with vascular invasion was defined. The signature was validated in formalin-fixed paraffin-embedded tissues obtained from an independent set of 135 patients with various etiologies. RESULTS A 35-gene signature of vascular invasion was defined in the training set, predicting vascular invasion with an accuracy of 69%. The signature was independently associated with the presence of vascular invasion (OR 3.38, 95% CI 1.48-7.71, p=0.003) along with tumor size (diameter greater than 3 cm, OR 2.66, 95% CI 1.17-6.05, p=0.02). In the validation set, the signature discarded the presence of vascular invasion with a negative predictive value of 0.77, and significantly improved the diagnostic power of tumor size alone (p=0.045). CONCLUSIONS The assessment of a gene-expression signature obtained from resected biopsied tumor specimens improved the diagnosis of vascular invasion beyond clinical variable-based prediction. The signature may aid in candidate selection for liver transplantation, and guide the design of clinical trials with experimental adjuvant therapies.

CDK6 Levels Regulate Quiescence Exit in Human Hematopoietic Stem Cells

Summary Regulated blood production is achieved through the hierarchical organization of dormant hematopoietic stem cell (HSC) subsets that differ in self-renewal potential and division frequency, with long-term (LT)-HSCs dividing the least. The molecular mechanisms underlying this variability in HSC division kinetics are unknown. We report here that quiescence exit kinetics are differentially regulated within human HSC subsets through the expression level of CDK6. LT-HSCs lack CDK6 protein. Short-term (ST)-HSCs are also quiescent but contain high CDK6 protein levels that permit rapid cell cycle entry upon mitogenic stimulation. Enforced CDK6 expression in LT-HSCs shortens quiescence exit and confers competitive advantage without impacting function. Computational modeling suggests that this independent control of quiescence exit kinetics inherently limits LT-HSC divisions and preserves the HSC pool to ensure lifelong hematopoiesis. Thus, differential expression of CDK6 underlies heterogeneity in stem cell quiescence states that functionally regulates this highly regenerative system.

Angiogenic mRNA and microRNA gene expression signature predicts a novel subtype of serous ovarian cancer.

Ovarian cancer is the fifth leading cause of cancer death for women in the U.S. and the seventh most fatal worldwide. Although ovarian cancer is notable for its initial sensitivity to platinum-based therapies, the vast majority of patients eventually develop recurrent cancer and succumb to increasingly platinum-resistant disease. Modern, targeted cancer drugs intervene in cell signaling, and identifying key disease mechanisms and pathways would greatly advance our treatment abilities. In order to shed light on the molecular diversity of ovarian cancer, we performed comprehensive transcriptional profiling on 129 advanced stage, high grade serous ovarian cancers. We implemented a, re-sampling based version of the ISIS class discovery algorithm (rISIS: robust ISIS) and applied it to the entire set of ovarian cancer transcriptional profiles. rISIS identified a previously undescribed patient stratification, further supported by micro-RNA expression profiles, and gene set enrichment analysis found strong biological support for the stratification by extracellular matrix, cell adhesion, and angiogenesis genes. The corresponding “angiogenesis signature” was validated in ten published independent ovarian cancer gene expression datasets and is significantly associated with overall survival. The subtypes we have defined are of potential translational interest as they may be relevant for identifying patients who may benefit from the addition of anti-angiogenic therapies that are now being tested in clinical trials.

Analysis of gene expression in stage I serous tumors identifies critical pathways altered in ovarian cancer

OBJECTIVE Despite recent advances in the conceptual understanding of the pathogenesis of ovarian cancer, it remains the foremost cause of death from gynecologic malignancies in developed countries. The main reason for such a high rate of mortality is the lack of sensitive and specific biomarkers and imaging techniques for early detection of ovarian cancer. Additional biological insights into early-stage ovarian carcinogenesis are needed to help speed the development of markers for early detection of ovarian cancer. The objective of this study was to characterize differentially expressed genes in high-grade stage I serous carcinoma of the ovary. METHODS We analyzed gene expression in macrodissected formalin-fixed, paraffin-embedded samples from 5 high-grade stage I serous carcinomas and 5 stage I borderline tumors of the ovary using the Illumina Whole Genome DASL assay (cDNA-mediated annealing, selection, extension, and ligation) corresponding to 24,000 genes. Significance Analysis of Microarrays was performed to determine differentially expressed genes in stage I serous carcinoma, and class prediction analysis was performed to determine the predictive value of differentially expressed gene sets to correctly classify serous carcinoma from borderline tumors in 3 independent data sets. Altered transcription factor pathways and biological pathways unique to stage I serous carcinoma were identified through class comparison of differentially expressed genes. RESULTS Unsupervised cluster analysis of gene expression correctly classified stage I serous carcinomas from serous borderline tumors. Supervised analysis identified several known, as well as novel, genes differentially expressed in stage I ovarian cancer. Using a differentially expressed gene set, class comparison prediction analysis correctly identified serous carcinomas from serous borderline tumors in 3 independent data sets at over 80% accuracy, sensitivity, and specificity. Pathway analysis demonstrated the significance of p53 and E2F pathways in serous carcinogenesis and significant involvements of cell cycle and immune response pathways in stage I serous epithelial ovarian cancer. CONCLUSION We have identified differentially expressed genes associated with the carcinogenesis of high-grade stage I serous EOC. Furthermore, integrative analysis of biological and transcription pathway data contributed to the confirmation of important biological pathways and discovery of additional unique genes and pathways that may have potential importance in ovarian pathogenesis and biomarker development.

MicroRNA paraffin-based studies in osteosarcoma reveal reproducible independent prognostic profiles at 14q32

BackgroundAlthough microRNAs (miRNAs) are implicated in osteosarcoma biology and chemoresponse, miRNA prognostic models are still needed, particularly because prognosis is imperfectly correlated with chemoresponse. Formalin-fixed, paraffin-embedded tissue is a necessary resource for biomarker studies in this malignancy with limited frozen tissue availability.MethodsWe performed miRNA and mRNA microarray formalin-fixed, paraffin-embedded assays in 65 osteosarcoma biopsy and 26 paired post-chemotherapy resection specimens and used the only publicly available miRNA dataset, generated independently by another group, to externally validate our strongest findings (n = 29). We used supervised principal components analysis and logistic regression for survival and chemoresponse, and miRNA activity and target gene set analysis to study miRNA regulatory activity.ResultsSeveral miRNA-based models with as few as five miRNAs were prognostic independently of pathologically assessed chemoresponse (median recurrence-free survival: 59 months versus not-yet-reached; adjusted hazards ratio = 2.90; P = 0.036). The independent dataset supported the reproducibility of recurrence and survival findings. The prognostic value of the profile was independent of confounding by known prognostic variables, including chemoresponse, tumor location and metastasis at diagnosis. Model performance improved when chemoresponse was added as a covariate (median recurrence-free survival: 59 months versus not-yet-reached; hazard ratio = 3.91; P = 0.002). Most prognostic miRNAs were located at 14q32 - a locus already linked to osteosarcoma - and their gene targets display deregulation patterns associated with outcome. We also identified miRNA profiles predictive of chemoresponse (75% to 80% accuracy), which did not overlap with prognostic profiles.ConclusionsFormalin-fixed, paraffin-embedded tissue-derived miRNA patterns are a powerful prognostic tool for risk-stratified osteosarcoma management strategies. Combined miRNA and mRNA analysis supports a possible role of the 14q32 locus in osteosarcoma progression and outcome. Our study creates a paradigm for formalin-fixed, paraffin-embedded-based miRNA biomarker studies in cancer.

A Panel of CpG Methylation Sites Distinguishes Human Embryonic Stem Cells and Induced Pluripotent Stem Cells

Summary Whether human induced pluripotent stem cells (hiPSCs) are epigenetically identical to human embryonic stem cells (hESCs) has been debated in the stem cell field. In this study, we analyzed DNA methylation patterns in a large number of hiPSCs (n = 114) and hESCs (n = 155), and identified a panel of 82 CpG methylation sites that can distinguish hiPSCs from hESCs with high accuracy. We show that 12 out of the 82 CpG sites were subject to hypermethylation in part by DNMT3B. Notably, DNMT3B contributes directly to aberrant hypermethylation and silencing of the signature gene, TCERG1L. Overall, we conclude that DNMT3B is involved in a wave of de novo methylation during reprogramming, a portion of which contributes to the unique hiPSC methylation signature. These 82 CpG methylation sites may be useful as biomarkers to distinguish between hiPSCs and hESCs.

Reduced lymphoid lineage priming promotes human hematopoietic stem cell expansion.

The hematopoietic system sustains regeneration throughout life by balancing self-renewal and differentiation. To stay poised for mature blood production, hematopoietic stem cells (HSCs) maintain low-level expression of lineage-associated genes, a process termed lineage priming. Here, we modulated expression levels of Inhibitor of DNA binding (ID) proteins to ask whether lineage priming affects self-renewal of human HSCs. We found that lentiviral overexpression of ID proteins in cord blood HSCs biases myeloerythroid commitment at the expense of lymphoid differentiation. Conversely, reducing ID2 expression levels increases lymphoid potential. Mechanistically, ID2 inhibits the transcription factor E47 to attenuate B-lymphoid priming in HSCs and progenitors. Strikingly, ID2 overexpression also results in a 10-fold expansion of HSCs in serial limiting dilution assays, indicating that early lymphoid transcription factors antagonize human HSC self-renewal. The relationship between lineage priming and self-renewal can be exploited to increase expansion of transplantable human HSCs and points to broader implications for other stem cell populations.