Whole-genome gene expression profiling of formalin-fixed, paraffin-embedded tissue samples

Differential expression of genes controlling lymphocyte differentiation and migration in two distinct endotypes of type 1 diabetes

AIMS

Morphological studies of pancreas samples obtained from young people with recent-onset type 1 diabetes have revealed distinct patterns of immune cell infiltration of the pancreatic islets suggestive of two age-associated type 1 diabetes endotypes that differ by inflammatory responses and rates of disease progression. The objective of this study was to investigate whether these proposed disease endotypes are associated with pathological differences in immune cell activation and cytokine secretion by applying multiplexed gene expression analysis to pancreatic tissue from recent-onset type 1 diabetes cases.

METHODS

RNA was extracted from samples of fixed, paraffin-embedded pancreas tissue from type 1 diabetes cases characterised by endotype and from controls without diabetes. Expression levels of 750 genes associated with autoimmune inflammation were determined by hybridisation to a panel of capture and reporter probes and these were counted as a measure of gene expression. Normalised counts were analysed for differences in expression between 29 type 1 diabetes cases and 7 controls without diabetes, and between the two type 1 diabetes endotypes.

RESULTS

Ten inflammation-associated genes, including INS, were significantly under-expressed in both endotypes and 48 genes were more highly expressed. A different set of 13 genes associated with the development, activation and migration of lymphocytes was uniquely overexpressed in the pancreas of people developing diabetes at younger age.

CONCLUSIONS

The results provide evidence that histologically defined type 1 diabetes endotypes differ in their immunopathology and identify inflammatory pathways specifically involved in disease developing at a young age, essential for a better understanding of disease heterogeneity.

Clinical and pathological findings in neurolymphomatosis: Preliminary association with gene expression profiles in sural nerves

Although inflammation appears to play a role in neurolymphomatosis (NL), the mechanisms leading to degeneration in the peripheral nervous system are poorly understood. The purpose of this exploratory study was to identify molecular pathways underlying NL pathogenesis, combining clinical and neuropathological investigation with gene expression (GE) studies. We characterized the clinical and pathological features of eight patients with NL. We further analysed GE changes in sural nerve biopsies obtained from a subgroup of NL patients (n=3) and thirteen patients with inflammatory neuropathies as neuropathic controls. Based on the neuropathic symptoms and signs, NL patients were classified into three forms of neuropathy: chronic symmetrical sensorimotor polyneuropathy (SMPN, n=3), multiple mononeuropathy (MN, n=4) and acute motor-sensory axonal neuropathy (AMSAN, n=1). Predominantly diffuse malignant cells infiltration of epineurium was present in chronic SMPN, whereas endoneurial perivascular cells invasion was observed in MN. In contrast, diffuse endoneurium malignant cells localization occurred in AMSAN. We identified alterations in the expression of 1266 genes, with 115 up-regulated and 1151 down-regulated genes, which were mainly associated with ribosomal proteins (RP) and olfactory receptors (OR) signaling pathways, respectively. Among the top up-regulated genes were actin alpha 1 skeletal muscle (ACTA1) and desmin (DES). Similarly, in NL nerves ACTA1, DES and several RPs were highly expressed, associated with endothelial cells and pericytes abnormalities. Peripheral nerve involvement may be due to conversion towards a more aggressive phenotype, potentially explaining the poor prognosis. The candidate genes reported in this study may be a source of clinical biomarkers for NL.

A gentle introduction to understanding preclinical data for cancer pharmaco-omic modeling

A central goal of precision oncology is to administer an optimal drug treatment to each cancer patient. A common preclinical approach to tackle this problem has been to characterize the tumors of patients at the molecular and drug response levels, and employ the resulting datasets for predictive in silico modeling (mostly using machine learning). Understanding how and why the different variants of these datasets are generated is an important component of this process. This review focuses on providing such introduction aimed at scientists with little previous exposure to this research area.

An Integrated Transcriptomic Approach to Identify Molecular Markers of Calcineurin Inhibitor Nephrotoxicity in Pediatric Kidney Transplant Recipients

Calcineurin inhibitors are highly efficacious immunosuppressive agents used in pediatric kidney transplantation. However, calcineurin inhibitor nephrotoxicity (CNIT) has been associated with the development of chronic renal allograft dysfunction and decreased graft survival. This study evaluated 37 formalin-fixed paraffin-embedded biopsies from pediatric kidney transplant recipients using gene expression profiling. Normal allograft samples (n = 12) served as negative controls and were compared to biopsies exhibiting CNIT (n = 11). The remaining samples served as positive controls to validate CNIT marker specificity and were characterized by other common causes of graft failure such as acute rejection (n = 7) and interstitial fibrosis/tubular atrophy (n = 7). MiRNA profiles served as the platform for data integration. Oxidative phosphorylation and mitochondrial dysfunction were the top molecular pathways associated with overexpressed genes in CNIT samples. Decreased ATP synthesis was identified as a significant biological function in CNIT, while key toxicology pathways included NRF2-mediated oxidative stress response and increased permeability transition of mitochondria. An integrative analysis demonstrated a panel of 13 significant miRNAs and their 33 CNIT-specific gene targets involved with mitochondrial activity and function. We also identified a candidate panel of miRNAs/genes, which may serve as future molecular markers for CNIT diagnosis as well as potential therapeutic targets.

Formalin-fixed paraffin-embedded renal biopsy tissues: an underexploited biospecimen resource for gene expression profiling in IgA nephropathy

Primary IgA nephropathy (IgAN) diagnosis is based on IgA-dominant glomerular deposits and histological scoring is done on formalin-fixed paraffin embedded tissue (FFPE) sections using the Oxford classification. Our aim was to use this underexploited resource to extract RNA and identify genes that characterize active (endocapillary–extracapillary proliferations) and chronic (tubulo-interstitial) renal lesions in total renal cortex. RNA was extracted from archival FFPE renal biopsies of 52 IgAN patients, 22 non-IgAN and normal renal tissue of 7 kidney living donors (KLD) as controls. Genome-wide gene expression profiles were obtained and biomarker identification was carried out comparing gene expression signatures a subset of IgAN patients with active (N = 8), and chronic (N = 12) renal lesions versus non-IgAN and KLD. Bioinformatic analysis identified transcripts for active (DEFA4,TNFAIP6,FAR2) and chronic (LTB,CXCL6, ITGAX) renal lesions that were validated by RT-PCR and IHC. Finally, two of them (TNFAIP6 for active and CXCL6 for chronic) were confirmed in the urine of an independent cohort of IgAN patients compared with non-IgAN patients and controls. We have integrated transcriptomics with histomorphological scores, identified specific gene expression changes using the invaluable repository of archival renal biopsies and discovered two urinary biomarkers that may be used for specific clinical decision making.

Challenges in Cancer Biomarker Discovery Exemplified by the Identification of Diagnostic MicroRNAs in Prostate Tissues

Identification and clinical translation of routinely tested biomarkers require a complex and multistep workflow. Here, we described a confirmatory process estimating the utility of previously identified candidate tissue miRNAs for diagnosis of prostate cancer (PCa). RNA was isolated from formalin-fixed paraffin-embedded (FFPE) prostate tissue surgically resected from 44 patients with PCa and 24 patients with benign prostate hyperplasia (BPH). Of the 92 RNA samples obtained, 68 represented 42 malignant (PCa) areas and 26 represented nonmalignant (PCa 0%) areas of the prostate tissue sections. The levels of miR-32-5p, miR-183-5p, miR-141-5p, miR-187-3p, miR-375, miR-663b, miR-615-3p, miR-205-5p, miR-221-3p, and miR-222-3p were evaluated using Exiqon chemistry. Five (miR-32-5p, miR-141-5p, miR-187-3p, miR-375, and miR-615-3p), one (miR-32-5p), and two (miR-32-5p and miR-141-5p) miRNAs discriminated between BPH and areas of cancer-bearing prostate tissue harboring different numbers of cancer cells (PCa 15–70%, PCa 2–10%, and PCA 0%, respectively), with an area under the receiver operating characteristics curve (AUC-ROC) > 0.9. Only miRNA 32-5p discriminated BPH specimens from sections of cancer-bearing prostate tissue with a low percentage, a high percentage, or no dysplastic cells. miR-32-5p could be considered as potential diagnostic biomarker discriminating BPH from noncancerous areas within cancer-bearing prostate tissue. However, further clinical studies are warranted to confirm its diagnostic utility.

Identification of a Gene-Related Risk Signature in Melanoma Patients Using Bioinformatic Profiling

Introduction

Gene signature has been used to predict prognosis in melanoma patients. Meanwhile, the efficacy of immunotherapy was correlated with particular genes expression or mutation. In this study, we systematically explored the gene expression pattern in the melanoma-immune microenvironment and its relationship with prognosis.

Methods

A cohort of 122 melanoma cases with whole-genome microarray expression data were enrolled from the Gene Expression Omnibus (GEO) database. The findings were validated using The Cancer Genome Atlas (TCGA) database. A principal component analysis (PCA), gene set enrichment analysis (GSEA), and gene oncology (GO) analysis were performed to explore the bioinformatic implications.

Results

Different gene expression patterns were identified according to the clinical stage. All eligible gene sets were analyzed, and the 8 genes (GPR87, KIT, SH3GL3, PVRL1, ATP1B1, CDAN1, FAU, and TNFSF14) with the greatest prognostic impact on melanoma. A gene-related risk signature was developed to distinguish patients with a high or low risk of an unfavorable outcome, and this signature was validated using the TCGA database. Furthermore, the prognostic significance of the signature between the classified subgroups was verified as an independent prognostic predictor of melanoma. Additionally, the low-risk melanoma patients presented an enhanced immune phenotype compared to that of the high-risk gene signature patients.

Conclusions

The gene pattern differences in melanoma were profiled, and a gene signature that could independently predict melanoma patients with a high risk of poor survival was established, highlighting the relationship between prognosis and the local immune response.

RCRnorm: An integrated system of random-coefficient hierarchical regression models for normalizing NanoString nCounter data

Formalin-fixed paraffin-embedded (FFPE) samples have great potential for biomarker discovery, retrospective studies, and diagnosis or prognosis of diseases. Their application, however, is hindered by the unsatisfactory performance of traditional gene expression profiling techniques on damaged RNAs. NanoString nCounter platform is well suited for profiling of FFPE samples and measures gene expression with high sensitivity, which may greatly facilitate realization of scientific and clinical values of FFPE samples. However, methodological development for normalization, a critical step when analyzing this type of data, is far behind. Existing methods designed for the platform use information from different types of internal controls separately and rely on an overly-simplified assumption that expression of housekeeping genes is constant across samples for global scaling. Thus, these methods are not optimized for the nCounter system, not mentioning that they were not developed for FFPE samples. We construct an integrated system of random-coefficient hierarchical regression models to capture main patterns and characteristics observed from NanoString data of FFPE samples, and develop a Bayesian approach to estimate parameters and normalize gene expression across samples. Our method, labeled RCRnorm, incorporates information from all aspects of the experimental design and simultaneously removes biases from various sources. It eliminates the unrealistic assumption on housekeeping genes and offers great interpretability. Furthermore, it is applicable to freshly frozen or like samples that can be generally viewed as a reduced case of FFPE samples. Simulation and applications showed the superior performance of RCRnorm.

Robust transcriptional tumor signatures applicable to both formalin-fixed paraffin-embedded and fresh-frozen samples

Formalin-fixed paraffin-embedded (FFPE) samples represent a valuable resource for clinical researches. However, FFPE samples are usually considered an unreliable source for gene expression analysis due to the partial RNA degradation. In this study, through comparing gene expression profiles between FFPE samples and paired fresh-frozen (FF) samples for three cancer types, we firstly showed that expression measurements of thousands of genes had at least two-fold change in FFPE samples compared with paired FF samples. Therefore, for a transcriptional signature based on risk scores summarized from the expression levels of the signature genes, the risk score thresholds trained from FFPE (or FF) samples could not be applied to FF (or FFPE) samples. On the other hand, we found that more than 90% of the relative expression orderings (REOs) of gene pairs in the FF samples were maintained in their paired FFPE samples and largely unaffected by the storage time. The result suggested that the REOs of gene pairs were highly robust against partial RNA degradation in FFPE samples. Finally, as a case study, we developed a REOs-based signature to distinguish liver cirrhosis from hepatocellular carcinoma (HCC) using FFPE samples. The signature was validated in four datasets of FFPE samples and eight datasets of FF samples. In conclusion, the valuable FFPE samples can be fully exploited to identify REOs-based diagnostic and prognostic signatures which could be robustly applicable to both FF samples and FFPE samples with degraded RNA.

Gene expression modules in primary breast cancers as risk factors for organotropic patterns of first metastatic spread: a case control study

BACKGROUND

Metastases from primary breast cancers can involve single or multiple organs at metastatic disease diagnosis. Molecular risk factors for particular patterns of metastastic spread in a clinical population are limited.

METHODS

A case-control design including 1357 primary breast cancers was used to study three distinct clinical patterns of metastasis, which occur within the first six months of metastatic disease: bone and visceral metasynchronous spread, bone-only, and visceral-only metastasis. Whole-genome expression profiles were obtained using whole genome (WG)-DASL assays from formalin-fixed paraffin-embedded (FFPE) samples. A systematic protocol was developed for handling FFPE samples together with stringent data quality controls to identify robust expression profiling data. A panel of published and novel gene sets were tested for association with these specific patterns of metastatic spread and odds ratios (ORs) were calculated.

RESULTS

Metasynchronous metastasis to bone and viscera was found in all intrinsic breast cancer subtypes, while immunohistochemically (IHC)-defined receptor status and specific IntClust subgroups were risk factors for visceral-only or bone-only first metastases. Among gene modules, those related to proliferation increased the risk of metasynchronous metastasis (OR (95% CI) = 2.3 (1.1-4.8)) and visceral-only first metastasis (OR (95% CI) = 2.5 (1.2-5.1)) but not bone-only metastasis (OR (95% CI) = 0.97 (0.56-1.7)). A 21-gene module (BV) was identified in estrogen-receptor-positive breast cancers with metasynchronous metastasis to bone and viscera (area under the curve = 0.77), and its expression increased the risk of bone and visceral metasynchronous spread in this population. BV was further orthogonally validated with NanoString nCounter in primary breast cancers, and was reproducible in their matched lymph nodes metastases and an external cohort.

CONCLUSION

This case-control study of WG-DASL global expression profiles from FFPE tumour samples, after careful quality control and RNA selection, revealed that gene modules in the primary tumour have differing risks for clinical patterns of metasynchronous first metastases. Moreover, a novel gene module was identified as a putative risk factor for metasynchronous bone and visceral first metastatic spread, with potential implications for disease monitoring and treatment planning.

Unraveling gene expression profiles in peripheral motor nerve from amyotrophic lateral sclerosis patients: insights into pathogenesis

The aim of the present study is to investigate the molecular pathways underlying amyotrophic lateral sclerosis (ALS) pathogenesis within the peripheral nervous system. We analyzed gene expression changes in human motor nerve diagnostic biopsies obtained from eight ALS patients and seven patients affected by motor neuropathy as controls. An integrated transcriptomics and system biology approach was employed. We identified alterations in the expression of 815 genes, with 529 up-regulated and 286 down-regulated in ALS patients. Up-regulated genes clustered around biological process involving RNA processing and protein metabolisms. We observed a significant enrichment of up-regulated small nucleolar RNA transcripts (p = 2.68*10-11) and genes related to endoplasmic reticulum unfolded protein response and chaperone activity. We found a significant down-regulation in ALS of genes related to the glutamate metabolism. Interestingly, a network analysis highlighted HDAC2, belonging to the histone deacetylase family, as the most interacting node. While so far gene expression studies in human ALS have been performed in postmortem tissues, here specimens were obtained from biopsy at an early phase of the disease, making these results new in the field of ALS research and therefore appealing for gene discovery studies.

Virus-encoded microRNA contributes to the molecular profile of EBV-positive Burkitt lymphomas

Burkitt lymphoma (BL) is an aggressive neoplasm characterized by consistent morphology and phenotype, typical clinical behavior and distinctive molecular profile. The latter is mostly driven by the MYC over-expression associated with the characteristic translocation (8;14) (q24; q32) or with variant lesions. Additional genetic events can contribute to Burkitt Lymphoma pathobiology and retain clinical significance. A pathogenetic role for Epstein-Barr virus infection in Burkitt lymphomagenesis has been suggested; however, the exact function of the virus is largely unknown.

In this study, we investigated the molecular profiles (genes and microRNAs) of Epstein-Barr virus-positive and -negative BL, to identify specific patterns relying on the differential expression and role of Epstein-Barr virus-encoded microRNAs.

First, we found significant differences in the expression of viral microRNAs and in selected target genes. Among others, we identified LIN28B, CGNL1, GCET2, MRAS, PLCD4, SEL1L, SXX1, and the tyrosine kinases encoding STK10/STK33, all provided with potential pathogenetic significance. GCET2, also validated by immunohistochemistry, appeared to be a useful marker for distinguishing EBV-positive and EBV-negative cases. Further, we provided solid evidences that the EBV-encoded microRNAs (e.g. BART6) significantly mold the transcriptional landscape of Burkitt Lymphoma clones.

In conclusion, our data indicated significant differences in the transcriptional profiles of EBV-positive and EBV-negative BL and highlight the role of virus encoded miRNA.

Gene expression panel predicts metastatic-lethal prostate cancer outcomes in men diagnosed with clinically localized prostate cancer

Prognostic biomarkers are needed to distinguish patients with clinically localized prostate cancer (PCa) who are at high risk of metastatic progression. The tumor transcriptome may reveal its aggressiveness potential and have utility for predicting adverse patient outcomes. Genomewide gene expression levels were measured in primary tumor samples of 383 patients in a population‐based discovery cohort, and from an independent clinical validation dataset of 78 patients. Patients were followed for ≥ 5 years after radical prostatectomy to ascertain outcomes. Area under the receiver‐operating characteristic curve (AUC), partial AUC (pAUC, 95% specificity), and P‐value criteria were used to detect and validate the differentially expressed transcripts. Twenty‐three differentially expressed transcripts in patients with metastatic‐lethal compared with nonrecurrent PCa were validated (P < 0.05; false discovery rate < 0.20) in the independent dataset. The addition of each validated transcript to a model with Gleason score showed that 17 transcripts significantly improved the AUC (range: 0.83–0.88; all P‐values < 0.05). These differentially expressed mRNAs represent genes with diverse cellular functions related to tumor aggressiveness. This study validated 23 gene transcripts for predicting metastatic‐lethal PCa in patients surgically treated for clinically localized disease. Several of these mRNA biomarkers have clinical potential for identifying the subset of PCa patients with more aggressive tumors who would benefit from closer monitoring and adjuvant therapy.

Novel Gene Expression Signature Predictive of Clinical Recurrence After Radical Prostatectomy in Early Stage Prostate Cancer Patients

BACKGROUND

Current clinical tools have limited accuracy in differentiating patients with localized prostate cancer who are at risk of recurrence from patients with indolent disease. We aimed to identify a gene expression signature that jointly with clinical variables could improve upon the prediction of clinical recurrence after RP for patients with stage T2 PCa.

METHODS

The study population includes consented patients who underwent a radical retropubic prostatectomy (RP) and bilateral pelvic lymph node dissection at the University of Southern California in the PSA-era (1988-2008). We used a nested case-control study of 187 organ-confined patients (pT2N0M0): 154 with no recurrence ("controls") and 33 with clinical recurrence ("cases"). RNA was obtained from laser capture microdissected malignant glands representative of the overall Gleason score of each patient. Whole genome gene expression profiles (29,000 transcripts) were obtained using the Whole Genome DASL HT platform (Illumina, Inc). A gene expression signature of PCa clinical recurrence was identified using stability selection with elastic net regularized logistic regression. Three existing datasets generated with the Affymetrix Human Exon 1.0ST array were used for validation: Mayo Clinic (MC, n = 545), Memorial Sloan Kettering Cancer Center (SKCC, n = 150), and Erasmus Medical Center (EMC, n = 48). The areas under the ROC curve (AUCs) were obtained using repeated fivefold cross-validation.

RESULTS

A 28-gene expression signature was identified that jointly with key clinical variables (age, Gleason score, pre-operative PSA level, and operation year) was predictive of clinical recurrence (AUC of clinical variables only was 0.67, AUC of clinical variables, and 28-gene signature was 0.99). The AUC of this gene signature fitted in each of the external datasets jointly with clinical variables was 0.75 (0.72-0.77) (MC), 0.90 (0.86-0.94) (MSKCC), and 0.82 (0.74-0.91) (EMC), whereas the AUC for clinical variables only in each dataset was 0.72 (0.70-0.74), 0.86 (0.82-0.91), and 0.76 (0.67-0.85), respectively.

CONCLUSIONS

We report a novel gene-expression based classifier identified using agnostic approaches from whole genome expression profiles that can improve upon the accuracy of clinical indicators to stratify early stage localized patients at risk of clinical recurrence after RP. Prostate 76:1239-1256, 2016. © 2016 Wiley Periodicals, Inc.

Reliable gene expression profiling of formalin-fixed paraffin-embedded breast cancer tissue (FFPE) using cDNA-mediated annealing, extension, selection, and ligation whole-genome (DASL WG) assay

BACKGROUND

The difficulties in using formalin-fixed and paraffin-embedded (FFPE) tumour specimens for molecular marker studies have hampered progress in translational cancer research. The cDNA-mediated, annealing, selection, extension, and ligation (DASL) assay is a platform for gene expression profiling from FFPE tissue and hence could allow analysis of large collections of tissue with associated clinical data from existing archives, therefore facilitating the development of novel biomarkers.

METHOD

RNA isolated from matched fresh frozen (FF) and FFPE cancer specimens was profiled using both the DASL whole-genome (WG) platform, and Illumina BeadArray's, and results were compared. Samples utilized were obtained from the breast cancer tumour bank held at the Cambridge University Hospitals NHS Foundation Trust.

RESULTS

The number of reliably detected probes was comparable between the DASL and BeadArray platforms, indicating that the source of RNA did not result in a significant difference in the detection rates (Mean probes- 17114 in FFPE & 17400 in FF). There was a significant degree of correlation between replicates within the FF and FFPE sample sets (r (2) = 0.96-0.98) as well as between the two platforms (DASL vs. BeadArray r (2) = range 0.83-0.89). Hierarchical clustering using the most informative probes showed that replicate and matched samples were grouped into the same sub-cluster, regardless of whether RNA was derived from FF or FFPE tissue.

CONCLUSION

Both FF and FFPE material generated reproducible gene expression profiles, although there was more noise in profiles from FFPE specimens. We have shown that the DASL WG platform is suitable for profiling formalin-fixed paraffin-embedded samples, but robust bioinformatics analysis is required.

Normalization of gene expression measurement of tissue samples obtained by transurethral resection of bladder tumors

Background

Sample processing is a crucial step for all types of genomic studies. A major challenge for researchers is to understand and predict how RNA quality affects the identification of transcriptional differences (by introducing either false-positive or false-negative errors). Nanotechnologies help improve the quality and quantity control for gene expression studies.

Patients and methods

The study was performed on 14 tumor and matched normal pairs of tissue from patients with bladder urothelial carcinomas. We assessed the RNA quantity by using the NanoDrop spectrophotometer and the quality by nano-microfluidic capillary electrophoresis technology provided by Agilent 2100 Bioanalyzer. We evaluated the amplification status of three housekeeping genes and one small nuclear RNA gene using the ViiA 7 platform, with specific primers.

Results

Every step of the sample handling protocol, which begins with sample harvest and ends with the data analysis, is of utmost importance due to the fact that it is time consuming, labor intensive, and highly expensive. High temperature of the surgical procedure does not affect the small nucleic acid sequences in comparison with the mRNA.

Conclusion

Gene expression is clearly affected by the RNA quality, but less affected in the case of small nuclear RNAs. We proved that the high-temperature, highly invasive transurethral resection of bladder tumor procedure damages the tissue and affects the integrity of the RNA from biological specimens.

A Comparison of Fresh Frozen vs. Formalin-Fixed, Paraffin-Embedded Specimens of Canine Mammary Tumors via Branched-DNA Assay

Mammary neoplasms are the tumors most affecting female dogs and women. Formalin-fixed, paraffin-embedded (FFPE) tissues are an invaluable source of archived biological material. Fresh frozen (FF) tissue is considered ideal for gene expression analysis. However, strategies based on FFPE material offer several advantages. Branched-DNA assays permit a reliable and fast workflow when analyzing gene expression. The aim of this study was to assess the comparability of the branched-DNA assay when analyzing certain gene expression patterns between FF and FFPE samples in canine mammary tumors. RNA was isolated from 109 FFPE samples and from 93 FF samples of different canine mammary tissues. Sixteen (16) target genes (Tp53; Myc; HMGA1; Pik3ca; Mcl1; MAPK3; FOXO3; PTEN; GATA4; PFDN5; HMGB1; MAPK1; BRCA2; BRCA1; HMGA2; and Her2) were analyzed via branched-DNA assay (b-DNA). ACTB, GAPDH, and HPRT1 were used as data normalizers. Overall, the relative gene expression of the two different origins of samples showed an agreement of 63%. Still, care should be taken, as FFPE specimens showed lower expression of the analyzed targets when compared to FF samples. The fact that the gene expression in FFPE proved to be lower than in FF specimens is likely to have been caused by the effect of storage time. ACTB had the best performance as a data normalizer.

Characterization of gene expression profiling of mouse tissues obtained during the postmortem interval

Attempts to establish a tissue bank from autopsy samples have led to uncovering of the secrets of many diseases. Here, we examined the length of time that the RNA from postmortem tissues is available for microarray analysis and reported the gene expression profile for up- and down-regulated genes during the postmortem interval. We extracted RNA from fresh-frozen (FF) and formalin-fixed paraffin-embedded (FFPE) brains and livers of three different groups of mice: 1) mice immediately after death, 2) mice that were stored at room temperature for 3h after death, and 3) mice that were stored at 4°C for 18h after death, as this storage resembles the human autopsy process in Japan. The RNA quality of the brain and the liver was maintained up to 18h during the postmortem interval. Based on the microarray analysis, we selected genes that were altered by >1.3-fold or <0.77-fold and classified these genes using hierarchical cluster analysis following DAVID gene ontology analysis. These studies revealed that cytoskeleton-related genes were enriched in the set of up-regulated genes, while serine protease inhibitors were enriched in the set of down-regulated genes. Interestingly, although the RNA quality was maintained due to high RNA integrity number (RIN) values, up-regulated genes were not validated by quantitative PCR, suggesting that these genes may become fragmented or modified by an unknown mechanism. Taken together, our findings suggest that under typical autopsy conditions, gene expression profiles that reflect disease pathology can be examined by understanding comprehensive recognition of postmortem fluctuation of gene expression.

A Method to Correlate mRNA Expression Datasets Obtained from Fresh Frozen and Formalin-Fixed, Paraffin-Embedded Tissue Samples: A Matter of Thresholds

Background

Gene expression profiling of tumors is a successful tool for the discovery of new cancer biomarkers and potential targets for the development of new therapeutic strategies. Reliable profiling is preferably performed on fresh frozen (FF) tissues in which the quality of nucleic acids is better preserved than in formalin-fixed paraffin-embedded (FFPE) material. However, since snap-freezing of biopsy materials is often not part of daily routine in pathology laboratories, one may have to rely on archival FFPE material. Procedures to retrieve the RNAs from FFPE materials have been developed and therefore, datasets obtained from FFPE and FF materials need to be made compatible to ensure reliable comparisons are possible.

Aim

To develop an efficient method to compare gene expression profiles obtained from FFPE and FF samples using the same platform.

Methods

Twenty-six FFPE-FF sample pairs of the same tumors representing various cancer types, and two FFPE-FF sample pairs of breast cancer cell lines, were included. Total RNA was extracted and gene expression profiling was carried out using Illumina’s Whole-Genome cDNA-mediated Annealing, Selection, extension and Ligation (WG-DASL) V3 arrays, enabling the simultaneous detection of 24,526 mRNA transcripts. A sample exclusion criterion was created based on the expression of 11 stably expressed reference genes. Pearson correlation at the probe level was calculated for paired FFPE-FF, and three cut-off values were chosen. Spearman correlation coefficients between the matched FFPE and FF samples were calculated for three probe lists with varying levels of significance and compared to the correlation based on all measured probes. Unsupervised hierarchical cluster analysis was performed to verify performance of the included probe lists to compare matched FPPE-FF samples.

Results

Twenty-seven FFPE-FF pairs passed the sample exclusion criterion. From the profiles of 27 FFPE and FF matched samples, the best correlating probes were identified for various levels of significance (Pearson P<0.01, n = 1,432; P<0.05, n = 2,530; and P<0.10, n = 3,351 probes). Unsupervised hierarchical clustering of the 27 pairs using the resulting probes yielded 25, 21, and 19 correctly clustered pairs, respectively, compared to 1 pair when all probes were used.

Conclusion

The proposed method enables comparison of gene expression profiles of FFPE and/or FF origin measured on the same platform.

Molecular prognostic prediction in liver cirrhosis

The natural history of cirrhosis varies and therefore prognostic prediction is critical given the sizable patient population. A variety of clinical prognostic indicators have been developed and enable patient risk stratification although their performance is somewhat limited especially within relatively earlier stage of disease. Molecular prognostic indicators are expected to refine the prediction, and potentially link a subset of patients with molecular targeted interventions that counteract poor prognosis. Here we overview clinical and molecular prognostic indicators in the literature, and discuss critical issues to successfully define, evaluate, and deploy prognostic indicators as clinical scores or tests. The use of liver biopsy has been diminishing due to sampling variability on fibrosis assessment and emergence of imaging- or lab test-based fibrosis assessment methods. However, recent rapid developments of genomics technologies and selective molecular targeted agents has highlighted the need for biopsy tissue specimen to explore and establish molecular information-guided personalized/stratified clinical care, and eventually achieve “precision medicine”.

Expression of cell cycle-regulated genes and prostate cancer prognosis in a population-based cohort

BACKGROUND

Prostate cancer (PCa) is clinically and biologically heterogeneous, making it difficult to predict at detection whether it will take an indolent or aggressive disease course. Cell cycle-regulated genes may be more highly expressed in actively dividing cells, with transcript levels reflecting tumor growth rate. Here, we evaluated expression of cell cycle genes in relation to PCa outcomes in a population-based cohort.

METHODS

Gene expression data were generated from tumor tissues obtained at radical prostatectomy for 383 population-based patients (12.3-years average follow-up). The overall mean and individual transcript levels of 30 selected cell cycle genes was compared between patients with no evidence of recurrence (73%) and those who recurred (27%) or died (7%) from PCa.

RESULTS

The multivariate adjusted hazard ratio (HR) for a change from the 25th to 75th percentile of mean gene expression level (range 8.02-10.05) was 1.25 (95%CI 0.96-1.63; P = 0.10) for PCa recurrence risk, and did not vary substantially by Gleason score, TMPRSS2-ERG fusion status, or family history of PCa. For lethal PCa, the HR for a change (25th to 75th percentile) in mean gene expression level was 2.04 (95%CI 1.26-3.31; P = 0.004), adjusted for clinicopathological variables. The ROC curve for mean gene expression level alone (AUC = 0.740) did not perform as well as clinicopathological variables alone (AUC = 0.803) for predicting lethal PCa, and the addition of mean gene expression to clinicopathological variables did not substantially improve prediction (AUC = 0.827; P = 0.18). Higher TK1 expression was strongly associated with both recurrent (P = 6.7 × 10(-5)) and lethal (P = 6.4 × 10(-6)) PCa.

CONCLUSIONS

Mean expression level for 30 selected cell cycle-regulated genes was unrelated to recurrence risk, but was associated with a twofold increase in risk of lethal PCa. However, gene expression had less discriminatory accuracy than clinical variables alone for predicting lethal events. Transcript levels for several genes in the panel were significantly overexpressed in lethal versus non-recurrent PCa.

Cancer biomarker discovery and validation

With the emergence of genomic profiling technologies and selective molecular targeted therapies, biomarkers play an increasingly important role in the clinical management of cancer patients. Single gene/protein or multi-gene "signature"-based assays have been introduced to measure specific molecular pathway deregulations that guide therapeutic decision-making as predictive biomarkers. Genome-based prognostic biomarkers are also available for several cancer types for potential incorporation into clinical prognostic staging systems or practice guidelines. However, there is still a large gap between initial biomarker discovery studies and their clinical translation due to the challenges in the process of cancer biomarker development. In this review we summarize the steps of biomarker development, highlight key issues in successful validation and implementation, and overview representative examples in the oncology field. We also discuss regulatory issues and future perspectives in the era of big data analysis and precision medicine.

Revealing the Molecular Portrait of Triple Negative Breast Tumors in an Understudied Population through Omics Analysis of Formalin-Fixed and Paraffin-Embedded Tissues

Triple negative breast cancer (TNBC), defined by the lack of expression of the estrogen receptor, progesterone receptor and human epidermal receptor 2, is an aggressive form of breast cancer that is more prevalent in certain populations, in particular in low- and middle-income regions. The detailed molecular features of TNBC in these regions remain unexplored as samples are mostly accessible as formalin-fixed paraffin embedded (FFPE) archived tissues, a challenging material for advanced genomic and transcriptomic studies. Using dedicated reagents and analysis pipelines, we performed whole exome sequencing and miRNA and mRNA profiling of 12 FFPE tumor tissues collected from pathological archives in Mexico. Sequencing analyses of the tumor tissues and their blood pairs identified TP53 and RB1 genes as the most frequently mutated genes, with a somatic mutation load of 1.7 mutations/exome Mb on average. Transcriptional analyses revealed an overexpression of growth-promoting signals (EGFR, PDGFR, VEGF, PIK3CA, FOXM1), a repression of cell cycle control pathways (TP53, RB1), a deregulation of DNA-repair pathways, and alterations in epigenetic modifiers through miRNA:mRNA network de-regulation. The molecular programs identified were typical of those described in basal-like tumors in other populations. This work demonstrates the feasibility of using archived clinical samples for advanced integrated genomics analyses. It thus opens up opportunities for investigating molecular features of tumors from regions where only FFPE tissues are available, allowing retrospective studies on the search for treatment strategies or on the exploration of the geographic diversity of breast cancer.

Independent replication of a melanoma subtype gene signature and evaluation of its prognostic value and biological correlates in a population cohort

Development and validation of robust molecular biomarkers has so far been limited in melanoma research. In this paper we used a large population-based cohort to replicate two published gene signatures for melanoma classification. We assessed the signatures prognostic value and explored their biological significance by correlating them with factors known to be associated with survival (vitamin D) or etiological routes (nevi, sun sensitivity and telomere length). Genomewide microarray gene expressions were profiled in 300 archived tumors (224 primaries, 76 secondaries). The two gene signatures classified up to 96% of our samples and showed strong correlation with melanoma specific survival (P=3 x 10(-4)), Breslow thickness (P=5 x 10(-10)), ulceration (P=9.x10-8) and mitotic rate (P=3 x 10(-7)), adding prognostic value over AJCC stage (adjusted hazard ratio 1.79, 95%CI 1.13-2.83), as previously reported. Furthermore, molecular subtypes were associated with season-adjusted serum vitamin D at diagnosis (P=0.04) and genetically predicted telomere length (P=0.03). Specifically, molecular high-grade tumors were more frequent in patients with lower vitamin D levels whereas high immune tumors came from patients with predicted shorter telomeres. Our data confirm the utility of molecular biomarkers in melanoma prognostic estimation using tiny archived specimens and shed light on biological mechanisms likely to impact on cancer initiation and progression.

Isolating Viral and Host RNA Sequences from Archival Material and Production of cDNA Libraries for High-Throughput DNA Sequencing

The vast majority of surgical biopsy and post-mortem tissue samples are formalin-fixed and paraffin-embedded (FFPE), but this process leads to RNA degradation that limits gene expression analysis. As an example, the viral RNA genome of the 1918 pandemic influenza A virus was previously determined in a 9-year effort by overlapping RT-PCR from post-mortem samples. Using the protocols described here, the full genome of the 1918 virus was determined at high coverage in one high-throughput sequencing run of a cDNA library derived from total RNA of a 1918 FFPE sample after duplex-specific nuclease treatments. This basic methodological approach should assist in the analysis of FFPE tissue samples isolated over the past century from a variety of infectious diseases.

Accuracy of Molecular Data Generated with FFPE Biospecimens: Lessons from the Literature

Formalin-fixed and paraffin-embedded (FFPE) tissue biospecimens are a valuable resource for molecular cancer research. Although much can be gained from their use, it remains unclear whether the genomic and expression profiles obtained from FFPE biospecimens accurately reflect the physiologic condition of the patient from which they were procured, or if such profiles are confounded by biologic effects from formalin fixation and processing. To assess the physiologic accuracy of genomic and expression data generated with FFPE specimens, we surveyed the literature for articles investigating genomic and expression endpoints in case-matched FFPE and fresh or frozen human biospecimens using the National Cancer Institute's Biospecimen Research Database (http://biospecimens.cancer.gov/brd). Results of the survey revealed that the level of concordance between differentially preserved biospecimens varied among analytical parameters and platforms but also among reports, genes/transcripts of interest, and tumor status. The identified analytical techniques and parameters that resulted in strong correlations between FFPE and frozen biospecimens may provide guidance when optimizing molecular protocols for FFPE use; however, discrepancies reported for similar assays also illustrate the importance of validating protocols optimized for use with FFPE specimens with a case-matched fresh or frozen cohort for each platform, gene or transcript, and FFPE processing regime. On the basis of evidence published to date, validation of analytical parameters with a properly handled frozen cohort is necessary to ensure a high degree of concordance and confidence in the results obtained with FFPE biospecimens.

DNA methylation and RNA expression profiles in lung adenocarcinomas of never-smokers

Lung cancer occurs in never-smokers. Epigenetic changes in lung cancer potentially represent important diagnostic, prognostic, and therapeutic targets. We compared DNA methylation profiles of 28 adenocarcinomas of the lungs of never-smokers with paired adjacent nonmalignant lung tissue. We correlated differential methylation changes with gene expression changes from the same 28 sample pairs. Using principal component analysis, we observed a distinct separation in methylation profiles between tumor and adjacent nonmalignant lung tissue. Tumors were generally hypomethylated compared with adjacent nonmalignant tissue. Of 1,906 CpG sites differentially methylated between tumor and nonmalignant tissue, 1,198 were within classically defined CpG islands where tumors were hypermethylated compared with nonmalignant tissue. A total of 708 sites were outside CpG islands where tumors were hypomethylated compared with nonmalignant tissue. There were significant differences in expression of 351 genes (23%) of the 1,522 genes matched to the differentially methylated CpG sites. Genes that were not significantly differentially expressed and were hypermethylated within CpG sites were enriched for homeobox genes. These results suggest that the methylation profiles of lung adenocarcinomas of never-smokers and adjacent nonmalignant lung tissue are significantly different. Despite the differential methylation of homeobox genes, no significant changes in expression of these genes were detected.

Differential Gene Expression Landscape of Co-Existing Cervical Pre-Cancer Lesions Using RNA-seq

Genetic changes occurring in different stages of pre-cancer lesions reflect causal events initiating and promoting the progression to cancer. Co-existing pre-cancerous lesions including low- and high-grade squamous intraepithelial lesion (LGSIL and HGSIL), and adjacent “normal” cervical epithelium from six formalin-fixed paraffin-embedded samples were selected. Tissues from these 18 samples were isolated using laser-capture microdissection, RNA was extracted and sequenced. RNA-sequencing generated 2.4 billion raw reads in 18 samples, of which ~50.1% mapped to known and annotated genes in the human genome. There were 40 genes up-regulated and 3 down-regulated (normal to LGSIL) in at least one-third of the sample pairs (same direction and FDR p < 0.05) including S100A7 and KLK6. Previous studies have shown that S110A7 and KLK7 are up-regulated in several other cancers, whereas CCL18, CFTR, and SLC6A14, also differentially expressed in two samples, are up-regulated specifically in cervical cancer. These differentially expressed genes in normal to LGSIL progression were enriched in pathways related to epithelial cell differentiation, keratinocyte differentiation, peptidase, and extracellular activities. In progression from LGSIL to HGSIL, two genes were up-regulated and five down-regulated in at least two samples. Further investigations using co-existing samples, which account for all internal confounders, will provide insights to better understand progression of cervical pre-cancer.

Gene expression profiling of circulating tumor cells in breast cancer

BACKGROUND

Determining the transcriptional profile of circulating tumor cells (CTCs) may allow the acquisition of clinically relevant information while overcoming tumor heterogeneity-related biases associated with use of tissue samples for biomarker assessment. However, such molecular characterization is challenging because CTCs are rare and outnumbered by blood cells.

METHODS

Here, we describe a technical protocol to measure the expression of >29 000 genes in CTCs captured from whole blood with magnetic beads linked with antibodies against epithelial cell adhesion molecule (EpCAM) and the carcinoma-associated mucin, MUC1, designed to be used for CTC characterization in clinical samples. Low numbers of cells (5-200) from the MCF7 and MDA-MB-468 breast cancer cell lines were spiked in healthy donor blood samples and isolated with the AdnaTest EMT-1/Stem CellSelect kit. Gene expression profiles (GEPs) were obtained with the WG-DASL HT assay and compared with GEPs obtained from RNA isolated from cultured cell lines and unspiked samples.

RESULTS

GEPs from samples containing 25 or more spiked cells correlated (r = 0.95) with cognate 100-ng RNA input samples, clustered separately from blood control samples, and allowed MCF7 and MDA-MB-468 cells to be distinguished. GEPs with comparable technical quality were also obtained in a preliminary series of clinical samples.

CONCLUSIONS

Our approach allows technically reliable GEPs to be obtained from isolated CTCs for the acquisition of biologically useful information. It is reproducible and suitable for application in prospective studies to assess the clinical utility of CTC GEPs, provided that >25 CTCs can be isolated.

Pathogenetic and diagnostic significance of microRNA deregulation in peripheral T-cell lymphoma not otherwise specified

Peripheral T-cell lymphomas not otherwise specified (PTCLs/NOS) are rare and aggressive tumours whose molecular pathogenesis and diagnosis are still challenging. The microRNA (miRNA) profile of 23 PTCLs/NOS was generated and compared with that of normal T-lymphocytes (CD4+, CD8+, naive, activated). The differentially expressed miRNA signature was compared with the gene expression profile (GEP) of the same neoplasms. The obtained gene patterns were tested in an independent cohort of PTCLs/NOS. The miRNA profile of PTCLs/NOS then was compared with that of 10 angioimmunoblastic T-cell lymphomas (AITLs), 6 anaplastic large-cell lymphomas (ALCLs)/ALK+ and 6 ALCLs/ALK−. Differentially expressed miRNAs were validated in an independent set of 20 PTCLs/NOS, 20 AITLs, 19 ALCLs/ALK− and 15 ALCLs/ALK+. Two hundred and thirty-six miRNAs were found to differentiate PTCLs/NOS from activated T-lymphocytes. To assess which miRNAs impacted on GEP, a multistep analysis was performed, which identified all miRNAs inversely correlated to different potential target genes. One of the most discriminant miRNAs was selected and its expression was found to affect the global GEP of the tumours. Moreover, two sets of miRNAs were identified distinguishing PTCL/NOS from AITL and ALCL/ALK−, respectively. The diagnostic accuracy of this tool was very high (83.54%) and its prognostic value validated.

Prognostic and therapeutic relevance of molecular subtypes in high-grade serous ovarian cancer

Molecular classification of high-grade serous ovarian cancer (HGSOC) using transcriptional profiling has proven to be complex and difficult to validate across studies. We determined gene expression profiles of 174 well-annotated HGSOCs and demonstrate prognostic significance of the prespecified TCGA Network gene signatures. Furthermore, we confirm the presence of four HGSOC transcriptional subtypes using a de novo classification. Survival differed statistically significantly between de novo subtypes (log rank, P = .006) and was the best for the immunoreactive-like subtype, but statistically significantly worse for the proliferative- or mesenchymal-like subtypes (adjusted hazard ratio = 1.89, 95% confidence interval = 1.18 to 3.02, P = .008, and adjusted hazard ratio = 2.45, 95% confidence interval = 1.43 to 4.18, P = .001, respectively). More prognostic information was provided by the de novo than the TCGA classification (Likelihood Ratio tests, P = .003 and P = .04, respectively). All statistical tests were two-sided. These findings were replicated in an external data set of 185 HGSOCs and confirm the presence of four prognostically relevant molecular subtypes that have the potential to guide therapy decisions.

Coextraction and PCR Based Analysis of Nucleic Acids From Formalin-Fixed Paraffin-Embedded Specimens

BACKGROUND

Retrospective studies of archived human specimens, with known clinical follow-up, are used to identify predictive and prognostic molecular markers of disease. Due to biochemical differences, however, formalin-fixed paraffin embedded (FFPE) DNA and RNA have generally been extracted separately from either different tissue sections or from the same section by dividing the digested tissue. Our optimized co-extraction approach provides the option of collecting DNA, which would otherwise be discarded or degraded, for additional or subsequent studies because of the high importance and less availability of clinical FFPE specimen.

METHODS

Coextraction of DNA and RNA from a single gastric cancer FFPE specimen was optimized by using TRIzol and purifying DNA from the lower aqueous and RNA from the upper organic phases. The protocol involves modification of incubation period for 30 min with proteinase K in glycin-tris-ethylenediamine tetra acetic acid buffer before adding TRIzol.

RESULTS

All samples tested successfully performed semiquantitative gene expression by reverse transcriptase PCR. The quantity and quality of DNA from FFPE samples was high which resulted in successful PCR amplification. The isolated DNA also aided in detection of Helicobacter pylori by amplifying the ribosomal 16S gene in a multiplex PCR reaction along with cagA.

CONCLUSION

These results show that the RNA/DNA isolated by this method can be used for easy clinical diagnosis of disease-related gene expression as well as mutation and pathogen detection from a homogenous population of tumor cells.

Prognostic and therapeutic relevance of molecular subtypes in high-grade serous ovarian cancer

Molecular classification of high-grade serous ovarian cancer (HGSOC) using transcriptional profiling has proven to be complex and difficult to validate across studies. We determined gene expression profiles of 174 well-annotated HGSOCs and demonstrate prognostic significance of the prespecified TCGA Network gene signatures. Furthermore, we confirm the presence of four HGSOC transcriptional subtypes using a de novo classification. Survival differed statistically significantly between de novo subtypes (log rank, P = .006) and was the best for the immunoreactive-like subtype, but statistically significantly worse for the proliferative- or mesenchymal-like subtypes (adjusted hazard ratio = 1.89, 95% confidence interval = 1.18 to 3.02, P = .008, and adjusted hazard ratio = 2.45, 95% confidence interval = 1.43 to 4.18, P = .001, respectively). More prognostic information was provided by the de novo than the TCGA classification (Likelihood Ratio tests, P = .003 and P = .04, respectively). All statistical tests were two-sided. These findings were replicated in an external data set of 185 HGSOCs and confirm the presence of four prognostically relevant molecular subtypes that have the potential to guide therapy decisions.

Prognostic and therapeutic relevance of molecular subtypes in high-grade serous ovarian cancer

Molecular classification of high-grade serous ovarian cancer (HGSOC) using transcriptional profiling has proven to be complex and difficult to validate across studies. We determined gene expression profiles of 174 well-annotated HGSOCs and demonstrate prognostic significance of the prespecified TCGA Network gene signatures. Furthermore, we confirm the presence of four HGSOC transcriptional subtypes using a de novo classification. Survival differed statistically significantly between de novo subtypes (log rank, P = .006) and was the best for the immunoreactive-like subtype, but statistically significantly worse for the proliferative- or mesenchymal-like subtypes (adjusted hazard ratio = 1.89, 95% confidence interval = 1.18 to 3.02, P = .008, and adjusted hazard ratio = 2.45, 95% confidence interval = 1.43 to 4.18, P = .001, respectively). More prognostic information was provided by the de novo than the TCGA classification (Likelihood Ratio tests, P = .003 and P = .04, respectively). All statistical tests were two-sided. These findings were replicated in an external data set of 185 HGSOCs and confirm the presence of four prognostically relevant molecular subtypes that have the potential to guide therapy decisions.

Prognostic and therapeutic relevance of molecular subtypes in high-grade serous ovarian cancer

Molecular classification of high-grade serous ovarian cancer (HGSOC) using transcriptional profiling has proven to be complex and difficult to validate across studies. We determined gene expression profiles of 174 well-annotated HGSOCs and demonstrate prognostic significance of the prespecified TCGA Network gene signatures. Furthermore, we confirm the presence of four HGSOC transcriptional subtypes using a de novo classification. Survival differed statistically significantly between de novo subtypes (log rank, P = .006) and was the best for the immunoreactive-like subtype, but statistically significantly worse for the proliferative- or mesenchymal-like subtypes (adjusted hazard ratio = 1.89, 95% confidence interval = 1.18 to 3.02, P = .008, and adjusted hazard ratio = 2.45, 95% confidence interval = 1.43 to 4.18, P = .001, respectively). More prognostic information was provided by the de novo than the TCGA classification (Likelihood Ratio tests, P = .003 and P = .04, respectively). All statistical tests were two-sided. These findings were replicated in an external data set of 185 HGSOCs and confirm the presence of four prognostically relevant molecular subtypes that have the potential to guide therapy decisions.

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.

Gene expression profiles in granuloma tissue reveal novel diagnostic markers in sarcoidosis

Sarcoidosis is an immune-mediated multisystem disease characterized by the formation of non-caseating granulomas. The pathogenesis of sarcoidosis is unclear, with proposed infectious or environmental antigens triggering an aberrant immune response in susceptible hosts. Multiple pro-inflammatory signaling pathways have been implicated in mediating macrophage activation and granuloma formation in sarcoidosis, including IFN-γ/STAT-1, IL-6/STAT-3, and NF-κB. It is difficult to distinguish sarcoidosis from other granulomatous diseases or assess disease severity and treatment response with histopathology alone. Therefore, development of improved diagnostic tools is imperative. Herein, we describe an efficient and reliable technique to classify granulomatous disease through selected gene expression and identify novel genes and cytokine pathways contributing to the pathogenesis of sarcoidosis. We quantified the expression of twenty selected mRNAs extracted from formalin-fixed paraffin embedded (FFPE) tissue (n = 38) of normal lung, suture granulomas, sarcoid granulomas, and fungal granulomas. Utilizing quantitative real-time RT-PCR we analyzed the expression of several genes, including IL-6, COX-2, MCP-1, IFN-γ, T-bet, IRF-1, Nox2, IL-33, and eotaxin-1 and revealed differential regulation between suture, sarcoidosis, and fungal granulomas. This is the first study demonstrating that quantification of target gene expression in FFPE tissue biopsies is a potentially effective diagnostic and research tool in sarcoidosis.

A study of gene expression markers for predictive significance for bevacizumab benefit in patients with metastatic colon cancer: a translational research study of the Hellenic Cooperative Oncology Group (HeCOG)

Background

Bevacizumab, an antibody neutralizing Vascular Endothelial Growth Factor (VEGF), is licensed for the management of patients with advanced colon cancer. However, tumor biomarkers identifying the molecular tumor subsets most amenable to angiogenesis modulation are lacking.

Methods

We profiled expession of 24526 genes by means of whole genome 24 K DASL (c-DNA-mediated, Annealing, Selection and Ligation) arrays, (Illumina, CA) in 16 bevacizumab-treated patients with advanced colon cancer (Test set). Genes with correlation to 8-month Progression-free status were studied by means of qPCR in two independent colon cancer cohorts: 49 patients treated with bevacizumab + chemotherapy (Bevacizumab qPCR set) and 72 patients treated with chemotherapy only (Control qPCR set). Endpoints were best tumor response before metastasectomy (ORR) and progression-free survival (PFS).

Results

Five genes were significantly correlated to 8-month progression-free status in the Test set: overexpression of KLF12 and downregulation of AGR2, ALDH6A1, MCM5, TFF2. In the two independent datasets, irinotecan- or oxaliplatin-based chemotherapy was administered as first-line treatment and metastasectomies were subsequently applied in 8-14% of patients. No prognostically significant gene classifier encompassing all five genes could be validated in the Bevacizumab or Control qPCR sets. The complex gene expression profile of all-low tumor (ALDH6A1 + TFF2 + MCM5) was strongly associated with ORR in the Bevacizumab qPCR set (ORR 85.7%, p = 0.007), but not in the Control set (ORR 36.4%, p = 0.747). The Odds Ratio for response for the all-low tumor (ALDH6A1 + TFF2 + MCM5) profile versus any other ALDH6A1 + TFF2 + MCM5 profile was 15 (p = 0.018) in the Bevacizumab qPCR set but only 0.72 (p = 0.63) in the Control set. The tumor expression profile of (KLF12-high + TFF2-low) was significantly associated with PFS only in the Bevacizumab qPCR set: bevacizumab-treated patients with (KLF12-high + TFF2-low) tumors had superior PFS (median 14 months, 95% CI 2-21) compared to patients with any other (KLF12 + TFF2) expression profile (median PFS 7 months, 95% CI 5-10, p = 0.021). The Hazard Ratio for disease progression for (KLF12-high + TFF2-low) versus any other KLF12 + TFF2 expression profile was 2.92 (p = 0.03) in the Validation and 1.29 (p = 0.39) in the Control set.

Conclusions

Our «three-stage» hypothesis-generating study failed to validate the prognostic significance of a five-gene classifier in mCRC patients. Exploratory analyses suggest two gene signatures that are potentially associated with bevazicumab benefit in patients with advanced colon cancer.

Transcriptome profiling of archived sectioned formalin-fixed paraffin-embedded (AS-FFPE) tissue for disease classification

Background

Archived tissues from previously completed prospective trials represent invaluable resource for biomarker development. However, such specimens are often stored as sections on glass slides, in which RNA is severely degraded due to prolonged air exposure. We evaluated whether a proportion of archived sectioned formalin-fixed paraffin-embedded (AS-FFPE) tissues yield transcriptome profiles comparable to freshly cut (FC) FFPE tissues, which can be used for retrospective class prediction analysis.

Methods

Genome-wide transcriptome profiles of 6 to 7-year-old AS-FFPE tissue sections (generated from 5 to 16-year-old blocks) of 83 hepatocellular carcinoma (HCC) and 47 liver cirrhosis samples were generated by using whole-genome DASL assay (Illumina) and digital transcript counting (nCounter) assay (NanoString), and gene signature-based prediction of HCC subclasses and prognosis was compared with previously generated FC-FFPE profiles from the same tissue blocks.

Results

RNA quality and assay reproducibility of AS-FFPE RNA were comparable to intermediate to poor quality FC-FFPE samples (RNA Integrity Number: up to 2.50, R-square for technical replicates: up to 0.93). Analyzable transcriptome profiles were obtained in 64 (77%) HCC and 36 (77%) cirrhosis samples. Statistically more confident predictions based on random resampling-based method (nearest template prediction) were obtained in 37 (58%) HCC and 13 (36%) cirrhosis samples. Predictions made in FC-FFPE profiles were reproduced in 36 (97%) HCC and 11 (85%) cirrhosis AS-FFPE profiles. nCounter assay was tested in 24 cirrhosis samples, which yielded confident prediction in 15 samples (63%), of which 10 samples (67%) showed concordant predictions with FC-FFPE profiles.

Conclusions

AS-FFPE tissues yielded poorer quality RNA and transcriptome profiles compared to FC-FFPE tissues. Statistically more confident class prediction was feasible in 37 of 83 HCC samples and 13 of 47 cirrhosis samples. These results suggest that AS-FFPE tissues can be regarded as a resource for retrospective transcriptome-based class prediction analysis when they are the only available materials.

Regulation of adipose oestrogen output by mechanical stress

Adipose stromal cells are the primary source of local oestrogens in adipose tissue, aberrant production of which promotes oestrogen receptor-positive breast cancer. Here we show that extracellular matrix compliance and cell contractility are two opposing determinants for oestrogen output of adipose stromal cells. Using synthetic extracellular matrix and elastomeric micropost arrays with tunable rigidity, we find that increasing matrix compliance induces transcription of aromatase, a rate-limiting enzyme in oestrogen biosynthesis. This mechanical cue is transduced sequentially by discoidin domain receptor 1, c-Jun N-terminal kinase 1, and phosphorylated JunB, which binds to and activates two breast cancer-associated aromatase promoters. In contrast, elevated cell contractility due to actin stress fibre formation dampens aromatase transcription. Mechanically stimulated stromal oestrogen production enhances oestrogen-dependent transcription in oestrogen receptor-positive tumour cells and promotes their growth. This novel mechanotransduction pathway underlies communications between extracellular matrix, stromal hormone output, and cancer cell growth within the same microenvironment.

Molecular Signatures of Recurrent Hepatocellular Carcinoma Secondary to Hepatitis C Virus following Liver Transplantation

Chronic hepatitis C virus (HCV) induced hepatocellular carcinoma (HCC) is a primary indication for liver transplantation (LT). In western countries, the estimated rate of HCC recurrence following LT is between 15% and 20% and is a major cause of mortality. Currently, there is no standard method to treat patients who are at high risk for HCC recurrence. The aim of this study was to investigate the molecular signatures underlying HCC recurrence that may lead to future studies on gene regulation contributing to new therapeutic options. Two groups of patients were selected, one including patients with HCV who developed HCC recurrence (HCC-R) ≤3 years from LT and the second group including patients with HCV who did not have recurrent HCC (HCC-NR). Microarray analysis containing more than 29,000 known genes was performed on formalin-fixed-paraffin-embedded (FFPE) liver tissue from explanted livers. Gene expression profiling revealed 194 differentially regulated genes between the two groups. These genes belonged to cellular networks including cell cycle G1/S checkpoint regulators, RAN signaling, chronic myeloid leukemia signaling, molecular mechanisms of cancer, FXR/RXR activation and hepatic cholestasis. A subset of molecular signatures associated with HCC recurrence was found. The expression levels of these genes were validated by quantitative PCR analysis.

Feasibility of RNA and DNA extraction from fresh pipelle and archival endometrial tissues for use in gene expression and SNP arrays

Identifying molecular markers of endometrial hyperplasia (neoplasia) progression is critical to cancer prevention. To assess RNA and DNA quantity and quality from routinely collected endometrial samples and evaluate the performance of RNA- and DNA-based arrays across endometrial tissue types, we collected fresh frozen (FF) Pipelle, FF curettage, and formalin-fixed paraffin-embedded (FFPE) hysterectomy specimens (benign indications) from eight women. Additionally, neoplastic and uninvolved tissues from 24 FFPE archival hysterectomy specimens with endometrial hyperplasias and carcinomas were assessed. RNA was extracted from 15 of 16 FF and 51 of 51 FFPE samples, with yields >1.2 μg for 13/15 (87%) FF and 50/51 (98%) FFPE samples. Extracted RNA was of high quality; all samples performed successfully on the Illumina whole-genome cDNA-mediated annealing, selection, extension, and ligation (WG-DASL) array and performance did not vary by tissue type. While DNA quantity from FFPE samples was excellent, quality was not sufficient for successful performance on the Affymetrix SNP Array 6.0. In conclusion, FF Pipelle samples, which are minimally invasive, yielded excellent quantity and quality of RNA for gene expression arrays (similar to FF curettage) and should be considered for use in genomic studies. FFPE-derived DNA should be evaluated on new rapidly evolving sequencing platforms.

CXCR4 pathway associated with family history of melanoma

PURPOSE

Genetic predisposition plays a major role in the etiology of melanoma, but known genetic markers only account for a limited fraction of family-history-associated melanoma cases. Expression microarrays have offered the opportunity to identify further genomic profiles correlated with family history of melanoma. We aimed to distinguish mRNA expression signatures between melanoma cases with and without a family history of melanoma.

METHODS

Based on the Nurses' Health Study, family history was defined as having one or more first-degree family members diagnosed with melanoma. Melanoma diagnosis was confirmed by reviewing pathology reports, and tumor blocks were collected by mail from across the USA. Genomic interrogation was accomplished through evaluating expression profiling of formalin-fixed paraffin-embedded tissues from 78 primary cutaneous invasive melanoma cases, on either a 6K or whole-genome (24K) Illumina gene chip. Gene set enrichment analysis was performed for each batch to determine the differentially enriched pathways and key contributing genes.

RESULTS

The CXC chemokine receptor 4 (CXCR4) pathway was consistently up-regulated within cases of familial melanoma in both platforms. Leading edge analysis showed four genes from the CXCR4 pathway, including MAPK1, PLCG1, CRK, and PTK2, were among the core members that contributed to the enrichment of this pathway. There was no association between the enrichment of CXCR4 pathway and NRAS, BRAF mutation, or Breslow thickness of the primary melanoma cases.

CONCLUSIONS

We found that the CXCR4 pathway might constitute a novel susceptibility pathway associated with family history of melanoma in first-degree relatives.

Yin Yang gene expression ratio signature for lung cancer prognosis

Many studies have established gene expression-based prognostic signatures for lung cancer. All of these signatures were built from training data sets by learning the correlation of gene expression with the patients' survival time. They require all new sample data to be normalized to the training data, ultimately resulting in common problems of low reproducibility and impracticality. To overcome these problems, we propose a new signature model which does not involve data training. We hypothesize that the imbalance of two opposing effects in lung cancer cells, represented by Yin and Yang genes, determines a patient's prognosis. We selected the Yin and Yang genes by comparing expression data from normal lung and lung cancer tissue samples using both unsupervised clustering and pathways analyses. We calculated the Yin and Yang gene expression mean ratio (YMR) as patient risk scores. Thirty-one Yin and thirty-two Yang genes were identified and selected for the signature development. In normal lung tissues, the YMR is less than 1.0; in lung cancer cases, the YMR is greater than 1.0. The YMR was tested for lung cancer prognosis prediction in four independent data sets and it significantly stratified patients into high- and low-risk survival groups (p = 0.02, HR = 2.72; p = 0.01, HR = 2.70; p = 0.007, HR = 2.73; p = 0.005, HR = 2.63). It also showed prediction of the chemotherapy outcomes for stage II & III. In multivariate analysis, the YMR risk factor was more successful at predicting clinical outcomes than other commonly used clinical factors, with the exception of tumor stage. The YMR can be measured in an individual patient in the clinic independent of gene expression platform. This study provided a novel insight into the biology of lung cancer and shed light on the clinical applicability.

Molecular profiling improves classification and prognostication of nodal peripheral T-cell lymphomas: results of a phase III diagnostic accuracy study

PURPOSE

The differential diagnosis among the commonest peripheral T-cell lymphomas (PTCLs; ie, PTCL not otherwise specified [NOS], angioimmunoblastic T-cell lymphoma [AITL], and anaplastic large-cell lymphoma [ALCL]) is difficult, with the morphologic and phenotypic features largely overlapping. We performed a phase III diagnostic accuracy study to test the ability of gene expression profiles (GEPs; index test) to identify PTCL subtype.

METHODS

We studied 244 PTCLs, including 158 PTCLs NOS, 63 AITLs, and 23 ALK-negative ALCLs. The GEP-based classification method was established on a support vector machine algorithm, and the reference standard was an expert pathologic diagnosis according to WHO classification.

RESULTS

First, we identified molecular signatures (molecular classifier [MC]) discriminating either AITL and ALK-negative ALCL from PTCL NOS in a training set. Of note, the MC was developed in formalin-fixed paraffin-embedded (FFPE) samples and validated in both FFPE and frozen tissues. Second, we found that the overall accuracy of the MC was remarkable: 98% to 77% for AITL and 98% to 93% for ALK-negative ALCL in test and validation sets of patient cases, respectively. Furthermore, we found that the MC significantly improved the prognostic stratification of patients with PTCL. Particularly, it enhanced the distinction of ALK-negative ALCL from PTCL NOS, especially from some CD30+ PTCL NOS with uncertain morphology. Finally, MC discriminated some T-follicular helper (Tfh) PTCL NOS from AITL, providing further evidence that a group of PTCLs NOS shares a Tfh derivation with but is distinct from AITL.

CONCLUSION

Our findings support the usage of an MC as additional tool in the diagnostic workup of nodal PTCL.