From Fresh Frozen Research Articles

Sample Preparation Approach Influences PAM50 Risk of Recurrence Score in Early Breast Cancer.

Simple SummaryThe PAM50 risk of recurrence (ROR) score is predictive of the risk of distant recurrence and the benefits of adjuvant therapy in early breast cancer. The Prosigna assay utilizes RNA from tumor cells selected via macrodissection of formalin-fixed, paraffin-embedded (FFPE) tissue sections to measure the activity of the PAM50 genes. An alternative and widely used extraction method is RNA purification from fresh-frozen (FF) bulk tissue without enriching the tumor cellularity. However, the impact of the RNA preparation approach on ROR scores and subsequent treatment selection has not been systematically evaluated. We compared the different approaches and found high correlation between risk of recurrence scores estimated from macrodissected FFPE tissue and scores estimated from bulk FF tumor tissue. However, important discrepancies were revealed for luminal tumors, which may have consequences for treatment recommendations for these patients.The PAM50 gene expression subtypes and the associated risk of recurrence (ROR) score are used to predict the risk of recurrence and the benefits of adjuvant therapy in early-stage breast cancer. The Prosigna assay includes the PAM50 subtypes along with their clinicopathological features, and is approved for treatment recommendations for adjuvant hormonal therapy and chemotherapy in hormone-receptor-positive early breast cancer. The Prosigna test utilizes RNA extracted from macrodissected tumor cells obtained from formalin-fixed, paraffin-embedded (FFPE) tissue sections. However, RNA extracted from fresh-frozen (FF) bulk tissue without macrodissection is widely used for research purposes, and yields high-quality RNA for downstream analyses. To investigate the impact of the sample preparation approach on ROR scores, we analyzed 94 breast carcinomas included in an observational study that had available gene expression data from macrodissected FFPE tissue and FF bulk tumor tissue, along with the clinically approved Prosigna scores for the node-negative, hormone-receptor-positive, HER2-negative cases (n = 54). ROR scores were calculated in R; the resulting two sets of scores from FFPE and FF samples were compared, and treatment recommendations were evaluated. Overall, ROR scores calculated based on the macrodissected FFPE tissue were consistent with the Prosigna scores. However, analyses from bulk tissue yielded a higher proportion of cases classified as normal-like; these were samples with relatively low tumor cellularity, leading to lower ROR scores. When comparing ROR scores (low, intermediate, and high), discordant cases between the two preparation approaches were revealed among the luminal tumors; the recommended treatment would have changed in a minority of cases.

Single Capture High Throughput Sequencing Assay for Combined V(D)J Clonality Analysis and Oncogene Mutations in the Diagnosis of T and B Lymphoid Malignancies

IntroductionThe diagnosis of B and T cell malignancies relies on the demonstration of B-cell (BCR) or T-cell (TCR) antigen receptor clonality. This can be studied through the analysis of V(D)J rearrangements of BCR and TCR genes by PCR (van Dongen Leukemia 2003) or, more recently, by high-throughput sequencing (HTS). Amplification of a clonal population with a “primers approach” could fail in case of hybridization problems due to too fragmented DNA, somatic mutations or polymorphic variations. Here we evaluated the performance of a HTS capture system for the analysis of B and T-cell clonality in clinical samples from mature T or B malignancies. We further combined this technology to concomitant sequencing of oncogenes of interest.Patients and MethodsDNA was extracted from 58 tumoral samples from fresh/frozen (FF) cells or tissues or formalin-fixed paraffin-embedded tissue (FFPE) (n=19). These samples comprised various T-cell [i.e. 1T-cell prolymphocytic leukemia, 1 T large granular lymphocytic leukemia, 2 Sézary syndrome, 4 peripheral T-cell lymphoma not otherwise specified, 14 angioimmunoblastic T-cell lymphoma] or B-cell [i.e. 14 chronic lymphocytic leukemia, 1 mantle cell lymphoma, 5 diffuse large B-cell lymphoma, 1 grey-zone lymphoma, 13 Hodgkin lymphoma, 1 Poppema, 2 Waldenström and 1 multiple myeloma] malignancies. The Biomed-2 PCR technique was used as standard for assessing the performance of TRG, IGH and IGK clonality analysis. An extensive panel of capture probes was designed (SureSelect XT HS2 DNA system, Agilent Technologies) that covered the variable (V), + diversity (D) and junction (J) segments of the IGH, IGK, TRG, TRB loci and diagnostic/theranostic genes of interest i.e. B2M, BTK, CARD11, CD28, DNMT3A, IDH2, JAK3, PLCG1, PLCG2, ROHA, SOCS1, STAT3, STAT5B, STAT6, TET2, TNFAIP3, TP53. Paired-End sequencing was performed on a MiSeq system (Illumina) in 300, 500 and 600 cycles. Analysis of clonality profiles was performed using Vidjil software and SeqOne.ResultsHTS runs resulted in a median total read count of 1,6M (0.7-2.9) per sample. V(D)J rearrangements were identified with a median of 1503 reads (189-6824) per sample. Five samples were excluded because less than 300 rearranged reads were obtained. The number of rearranged reads and of clonotypes identified are influenced by the number of sequencing cycles (300<500 or 600) but not by the quality of DNA (FFPE vs FF).Analyses of tumoral samples with HTS versus PCR were compared. For the IGH locus (n=47), comparable PCR/HTS clonal (n=22) and polyclonal (PCL, n=20) profiles were identified. One discordant case showed a clonal PCR profile and a PCL HTS profile but the IGK was clonal. For the IGK locus (n=23), 10 clonal and 12 PCL cases were similar with both techniques. One case appeared discordant with a PCL PCR profile but a clonal HTS profile. For the TRG locus (n=31), PCR and HTS profiles were similar in 14 clonal, 5 oligoclonal and 9 PCL cases respectively. Three cases were discordant with oligoclonal PCR profiles but a clonal HTS profile.Overall in the 38 cases of B-cell malignancies, 27 and 11 cases had a concordant B-cell clonal or PCL profile with PCR and HTS. Among PCL cases, only one was discordant with a clonal HTS profile. This case and 3 other PCL cases were Hodgkin lymphomas which all disclosed another mutation (i.e. TP53, TNFAIP3, SOCS1). Of the 20 cases of T-cell malignancies, 14 displayed a clonal TRG profile with PCR and HTS. Among them, 13 showed oncogene mutations that confirmed the oncogenic nature of the clonal proliferation. Among 6 patients with a non-clonal PCR TRG profile, two cases of AITL and T-LGL had a discordant clonal TRG HTS profile and both also had specific mutations (SOCS1, RHOA and STAT3 respectively). Two other AITL samples showed a T-PCL profile with PCR and HTS but also had a mutation/CNV (RHOA, SOCS1).ConclusionA very good performance of B and T cell clonality assessment was obtained here with capture-HTS compared to Biomed-2 PCR. The combined identification of mutation/CNV allowed to confirm the malignant character in cases of clonal or PCL lymphoproliferations, while concomitantly specifying the type of lymphoproliferative disorder. The combined capture-HTS of B and T repertoires and oncogenes of diagnostic or theranostic interest thus appears as an efficient, accurate and useful approach for the diagnosis of mature B and T lymphoid malignancies in clinical practice. DisclosuresNo relevant conflicts of interest to declare.

Overcoming the Challenges of High Quality RNA Extraction from Core Needle Biopsy.

The use of gene expression profiling (GEP) in cancer management is rising, as GEP can be used for disease classification and diagnosis, tailoring treatment to underlying genetic determinants of pharmacological response, monitoring of therapy response, and prognosis. However, the reliability of GEP heavily depends on the input of RNA in sufficient quantity and quality. This highlights the need for standard procedures to ensure best practices for RNA extraction from often small tumor biopsies with variable tissue handling. We optimized an RNA extraction protocol from fresh-frozen (FF) core needle biopsies (CNB) from breast cancer patients and from formalin-fixed paraffin-embedded (FFPE) tissue when FF CNB did not yield sufficient RNA. Methods to avoid ribonucleases andto homogenize or to deparaffinize tissues and the impact of tissue composition on RNA extraction were studied. Additionally, RNA’s compatibility with the nanoString nCounter® technology was studied. This technology platform enables GEP using small RNA fragments. After optimization of the protocol, RNA of high quality and sufficient quantity was obtained from FF CNB in 92% of samples. For the remaining 8% of cases, FFPE material prepared by the pathology department was used for RNA extraction. Both resulting RNA end products are compatible with the nanoString nCounter® technology.

SMIXnorm: Fast and Accurate RNA-Seq Data Normalization for Formalin-Fixed Paraffin-Embedded Samples.

RNA-sequencing (RNA-seq) provides a comprehensive quantification of transcriptomic activities in biological samples. Formalin-Fixed Paraffin-Embedded (FFPE) samples are collected as part of routine clinical procedure, and are the most widely available biological sample format in medical research and patient care. Normalization is an essential step in RNA-seq data analysis. A number of normalization methods, though developed for RNA-seq data from fresh frozen (FF) samples, can be used with FFPE samples as well. The only extant normalization method specifically designed for FFPE RNA-seq data, MIXnorm, which has been shown to outperform the normalization methods, but at the cost of a complex mixture model and a high computational burden. It is therefore important to adapt MIXnorm for simplicity and computational efficiency while maintaining superior performance. Furthermore, it is critical to develop an integrated tool that performs commonly used normalization methods for both FF and FFPE RNA-seq data. We developed a new normalization method for FFPE RNA-seq data, named SMIXnorm, based on a simplified two-component mixture model compared to MIXnorm to facilitate computation. The expression levels of expressed genes are modeled by normal distributions without truncation, and those of non-expressed genes are modeled by zero-inflated Poisson distributions. The maximum likelihood estimates of the model parameters are obtained by a nested Expectation-Maximization algorithm with a less complicated latent variable structure, and closed-form updates are available within each iteration. Real data applications and simulation studies show that SMIXnorm greatly reduces computing time compared to MIXnorm, without sacrificing the performance. More importantly, we developed a web-based tool, RNA-seq Normalization (RSeqNorm), that offers a simple workflow to compute normalized RNA-seq data for both FFPE and FF samples. It includes SMIXnorm and MIXnorm for FFPE RNA-seq data, together with five commonly used normalization methods for FF RNA-seq data. Users can easily upload a raw RNA-seq count matrix and select one of the seven normalization methods to produce a downloadable normalized expression matrix for any downstream analysis. The R package is available at https://github.com/S-YIN/RSEQNORM. The web-based tool, RSeqNorm is available at http://lce.biohpc.swmed.edu/rseqnorm with no restriction to use or redistribute.

In Reply to the Letter to the Editor Regarding “Large Craniotomy Increases the Risk of Minor Perioperative Complications in Revascularization Surgery for Moyamoya Disease”

The 95-gene classifier (95-GC) can classify patients with estrogen receptor (ER)–positive and node-negative breast cancer into those with low and high risk of relapse with an accuracy similar to that of 21-GC (Oncotype DX). Because 95-GC uses RNA from fresh-frozen (FF) tumor tissues, we herein attempted to develop a gene classifier that is applicable to RNA from formalin-fixed paraffin-embedded (FFPE) tumor tissues.25 paired FF and FFPE tumor tissues were subjected to DNA microarray for gene-expression analysis. Of the 95 probes included in the 95-GC, 72 were selected for construction of the gene classifier for FFPE tumor tissues, because the gene expression detected by these 72 probes was well preserved in the FFPE tumor tissues.The 72-GC was constructed with these 72 probes for the training set comprising 549 FF tumor tissues and validated with 434 FF tumor tissues (relapse-free survival at 10 years was 91% for the low-risk and 74% for the high-risk group (P = 3.74 × 10−7). The predictive capability of 72-GC for prognosis was found to be comparable to that of 95-GC. The 25 paired FF and FFPE tumor tissues from each of 25 patients were classified into the same risk group by 72-GC for 23 patients (92% concordance). 72-GC using the FFPE tumor tissues showed that the prognosis for the low-risk group was significantly (P = .007) better than for the high-risk group.72-GC is comparable to 95-GC in terms of accuracy of prognosis prediction, and may be effective for FFPE tumor tissues.

Abstract 466: A novel DNA methylation-based approach for molecular subtyping and improved prognostication of colorectal cancer using formalin-fixed and paraffin-embedded tissue

Abstract Background: Colorectal cancer (CRC) is characterized by marked inter-tumor heterogeneity, both molecularly and clinically. Patient stratification using transcriptional subtyping show promise to resolve the heterogeneity and guide precision medicine. This requires high quality RNA, which can be purified from fresh frozen (FF) tissue, but not from clinically collected formalin-fixed paraffin-embedded (FFPE) tissue. Consequently, transcriptional subtyping is not applicable to most CRC patients. Aim: To establish a DNA methylation-based approach for molecular characterization and subtyping, which is compatible with both FF and FFPE tissue, and use this to improve patient prognostication as compared to histopathological TNM staging. Methods: Using paired RNA expression and DNA methylation profiles (450K array) from 394 CRCs, we identified 200 CpG sites genome-wide, whose methylation levels correlated best with expression for each gene. With this information, we 1) devised an approach, methCORR, that can infer RNA expression in both FF and FFPE samples using only DNA methylation profiles, 2) established a methCORR network that clusters genes according to overlap in the CpG sites associated with their expression level and 3) used the network to determine and characterize biological traits associated with tumor aggressiveness. Results: The methCORR-inferred RNA expression profiles in FF tissue consistently exhibited high correlation to matched RNA-seq. profiles (median Pearson r=0.97; range 0.91-0.98). We found a higher correlation between FF RNA-seq. and FFPE inferred RNA profiles (median r= 0.97; range 0.96-0.98) than between FF and FFPE RNA-seq. profiles (median r= 0.78; range 0.51-0.92). Gene expression profiles were inferred in two FF (n=231, n=203), and two FFPE cohorts (n=113, n=56). Clustering of these profiles identified the same two CRC subtypes independently in all cohorts. Characterization using the methCORR network showed that the two subtypes resembled conventional and serrated CRC. Moreover, methCORR network analysis identified subtype-specific traits that associated strongly with tumor aggressiveness, such as T-cell, fibroblast, and epithelial-mesenchymal transition activity, and thus allowed identification of subtype-specific prognostic biomarkers. These better predicted relapse-free survival (HR=3.22, 95%CI 2.00-5.17) than TNM staging (HR=1.99, 95%CI 1.28-3.08). Finally, we derived four simple and clinically applicable DNA methylation-specific PCR assays for subtyping and prognostication of CRC FFPE samples. Conclusion: We have developed a novel method for characterization, subtyping, and prognostication of CRC, which is compatible with FF and FFPE samples. We envision that application of the methCORR approach to other cancer types will generate similar fruitful results. Citation Format: Trine B. Mattesen, Mads H. Rasmussen, Juan Sandoval, Halit Ongen, Sigrid S. Árnadóttir, Anders H. Madsen, Søren Laurberg, Emmanouil T. Dermitzakis, Manel Esteller, Claus L. Andersen, Jesper B. Bramsen. A novel DNA methylation-based approach for molecular subtyping and improved prognostication of colorectal cancer using formalin-fixed and paraffin-embedded tissue [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 466.

Feasibility of developing reliable gene expression modules from FFPE derived RNA profiled on Affymetrix arrays.

The reliability of differential gene expression analysis on formalin-fixed, paraffin-embedded (FFPE) expression profiles generated using Affymetrix arrays is questionable, due to the high range of percent-present values reported in studies which profiled FFPE samples using this technology. Moreover, the validity of gene-modules derived from external datasets in FFPE microarray expression profiles is unknown. By generating matched gene expression profiles using RNAs derived from fresh-frozen (FF) and FFPE preserved breast tumors with Affymetrix arrays and FF/FFPE RNA specific amplification-and-labeling kits, the reliability of differential expression analysis and the validity of gene modules derived from external datasets were investigated. Specifically, the reliability of differential expression analysis was investigated by developing de-novo ER/HER2 pathway gene-modules from the matched datasets and validating them on external FF/FFPE gene expression datasets using ROC analysis. Spearman's rank correlation coefficient of module scores between matched FFPE/frozen datasets was used to measure the reliability of gene-modules derived from external datasets in FFPE expression profiles. Independent of the array/amplification-kit/sample preservation method used, de-novo ER/HER2 gene-modules derived from all matched datasets showed similar prediction performance in the independent validation (AUC range in FFPE dataset; ER: 0.93–0.95, HER2: 0.85–0.91), except for the de-novo ER/HER2 gene-module derived from the FFPE dataset using the 3'IVT kit (AUC range in FFPE dataset; ER: 0.79–0.81, HER2: 0.78). Among the external gene modules considered, roughly ~50% gene modules showed high concordance between expression profiles derived from matching FF and FFPE RNA. The remaining discordant gene modules between FF and FFPE expression profiles showed high concordance within matching FF datasets and within matching FFPE datasets independently, implying that microarrays still require improved amplification-and-sample-preparation protocols for deriving 100% concordant expression profiles from matching FF and FFPE RNA.

Abstract 2714: Analytical validation of clinical whole genome and transcriptome sequencing of patient derived tumors: clinical application of whole genome sequencing for reporting targetable variants in cancer

Abstract Next Generation DNA Sequencing (NGS) technologies are currently being applied in the clinical setting for the treatment of disease. The goal is to use high-throughput sequencing to identify specific variants within each tumor and recommend personalized treatment approaches or clinical trials tailored to the individual’s disease and genomic profile. These assays are comprised of either predefined sequencing panels, where a handpicked set of clinically significant genes are examined within each patient, or are cancer type specific targeted sequencing protocols or whole exome platforms covering only the coding region of the patient’s genome. Whole genome sequencing allows hypothesis-free interrogation of both coding and non-coding regions of the genome revealing more potential therapeutic options than examining a small set of genes or genomic loci. The protocol eliminates sequence capture related bias observed in whole exome or panel sequencing. The New York Genome Center therefore has performed analytical validation of whole genome and transcriptome sequencing (WGTS) of patient derived tumors and matched normals for the purposes of clinical testing and have devised a clinical reporting strategy of significant driver and therapeutic associated mutations. Many clinical NGS guidelines are directed toward targeted panel or exome sequencing validation. Here, we expanded on New York State’s Department of Health NGS guidelines developing them into novel standards applicable to WGTS for the purposes of clinical test validation. We first sequenced a virtual tumor at very high coverage (300x) and downsampled to determine the optimum depth of sequencing necessary for high confidence somatic variant calling across the entire genome. We then validated whole genome sequencing laboratory protocols for DNA and RNA sequencing on a total of 50 specimens derived from fresh frozen (FF) and formalin-fixed paraffin-embedded (FFPE) tumor samples. We performed a series of experiments to assess the accuracy and reliability of the results based on our laboratory and bioinformatics protocols. We performed our validation on the 50 tumor normal pairs, a subset of which had known genomic profiles. Comparisons were also made for variant calling concordance and reproducibility between matched FF and FFPE tumors. Here, we present our validation results and clinical WGTS standards for depth of sequencing, reproducibility, sensitivity, and present limit of detection analysis for SNV calling, copy number identification and structural variants. RNA sequencing is performed to call fusion or exon skipping events and to confirm the DNA variants. The New York Genome Center WGTS clinical assay is intended to provide a more comprehensive patient variant discovery approach suitable for directed oncological therapeutic applications. Citation Format: Kazimierz O. Wrzeszczynski, Avinash Abhyankar, Vanessa Felice, Esra Dikoglu, Lukasz Kozon, Nicolas Robine, Anne-Katrin Emde, Olca Basturk, Umesh K. Bhanot, Alex Kentsis, Mahesh Mansukhani, Govind Bhagat, Vaidehi Jobanputra. Analytical validation of clinical whole genome and transcriptome sequencing of patient derived tumors: clinical application of whole genome sequencing for reporting targetable variants in cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 2714. doi:10.1158/1538-7445.AM2017-2714

A methodological study of genome-wide DNA methylation analyses using matched archival formalin-fixed paraffin embedded and fresh frozen breast tumors

BackgroundDNA from archival formalin-fixed and paraffin embedded (FFPE) tissue is an invaluable resource for genome-wide methylation studies although concerns about poor quality may limit its use. In this study, we compared DNA methylation profiles of breast tumors using DNA from fresh-frozen (FF) tissues and three types of matched FFPE samples.ResultsFor 9/10 patients, correlation and unsupervised clustering analysis revealed that the FF and FFPE samples were consistently correlated with each other and clustered into distinct subgroups. Greater than 84% of the top 100 loci previously shown to differentiate ER+ and ER– tumors in FF tissues were also FFPE DML. Weighted Correlation Gene Network Analyses (WCGNA) grouped the DML loci into 16 modules in FF tissue, with ~85% of the module membership preserved across tissue types.Materials and MethodsRestored FFPE and matched FF samples were profiled using the Illumina Infinium HumanMethylation450K platform. Methylation levels (β-values) across all loci and the top 100 loci previously shown to differentiate tumors by estrogen receptor status (ER+ or ER−) in a larger FF study, were compared between matched FF and FFPE samples using Pearson's correlation, hierarchical clustering and WCGNA. Positive predictive values and sensitivity levels for detecting differentially methylated loci (DML) in FF samples were calculated in an independent FFPE cohort.ConclusionsFFPE breast tumors samples show lower overall detection of DMLs versus FF, however FFPE and FF DMLs compare favorably. These results support the emerging consensus that the 450K platform can be employed to investigate epigenetics in large sets of archival FFPE tissues.

Abstract 3616: Fix the fixation: effect of formalin fixation on targeted sequencing, variant calling and gene expression

Abstract Tumor biopsies are often Formalin-Fixed and Paraffin-Embedded (FFPE) for histological staining, genetic testing and archival purposes. Formalin treatment preserves tissue by crosslinking proteins, but can lead to mutation of the nucleic acid bases that can be detected by next-generation sequencing (NGS) methods despite being unrelated to the cancer. Studies have shown that certain fixation protocols are compatible with high quality nucleic acid isolation and variant calling by NGS. These studies used tissue fixed with 10% neutral buffered formalin for 24 hours, a standard protocol in the pathology field. In practice, however, FFPE sample handling varies from site-to-site. As we processed FFPE samples from various sites, we found that the quality of the isolated nucleic acids and subsequent sequencing results varied substantially. We hypothesized that this is due to deviations from the standard protocol such as improperly buffered reagents, variation in the fixation time and temperature, microwaving, and varied storage conditions of the samples. To understand the role of formalin fixation on the quality of the isolated nucleic acids and subsequent NGS analyses, we subjected the widely studied cell line NA12878 to various formalin fixation conditions. We then performed an augmented target enrichment and sequencing assay on both the DNA and RNA isolated from fresh frozen (FF) and formalin fixed (FFPE) NA12878 samples. We assessed raw nucleic acid quality, library quality, alignment rate, duplication rate, on-target efficiency and variant concordance between FF and FFPE. We noted substantial negative effects on sequencing library quality associated with sample incubation in unbuffered formalin, at high temperatures, and long periods of time (e.g. 3 days rather than 1 day). These harshly treated samples also showed poor alignment qualities, higher duplication rates, and lower mapping qualities. At the small variant level, they showed an increase in global C&amp;gt;T deamination (CG-&amp;gt;TA, 50% FFPE vs 35% in FF) and oxidation (CG-&amp;gt;AT, 15% FFPE vs 10% FF) rates. Variants caused by formalin fixation were most commonly detected at low (&amp;lt;5%) allele frequencies (AFs). When compared to gold set mutations in NA12878, we observed 80% false negative and 60% false positive variants in harshly treated samples. We are performing a similar analysis with RNA isolated from FF and FFPE NA12878 samples to understand the role of formalin on RNA variant and gene expression assays. Using the results of this study, we intend to provide guidance on optimal fixation conditions for NGS applications. It is crucial to fix tissues in buffered formalin for less than 24hr at room temperature to preserve nucleic acid integrity and amenability for downstream NGS assays. We also demonstrate how a deeper understanding of the effects of formalin can improve variant calling from FFPE tissues, especially at lower AFs where formalin-related errors have the greatest impact. Citation Format: Ravi Alla, Shujun Luo, Elena Helman, Sean M. Boyle, Michael J. Clark, Kirk Scott, Parin Sripakdeevong, Mirian Karbelashvili, Deanna M. Church, Michael Snyder, John West, Richard Chen. Fix the fixation: effect of formalin fixation on targeted sequencing, variant calling and gene expression. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 3616.

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.

Evaluation of Protein Profiles From Treated Xenograft Tumor Models Identifies an Antibody Panel for Formalin-fixed and Paraffin-embedded (FFPE) Tissue Analysis by Reverse Phase Protein Arrays (RPPA)

Reverse phase protein arrays (RPPA) are an established tool for measuring the expression and activation status of multiple proteins in parallel using only very small amounts of tissue. Several studies have demonstrated the value of this technique for signaling pathway analysis using proteins extracted from fresh frozen (FF) tissue in line with validated antibodies for this tissue type; however, formalin fixation and paraffin embedding (FFPE) is the standard method for tissue preservation in the clinical setting. Hence, we performed RPPA to measure profiles for a set of 300 protein markers using matched FF and FFPE tissue specimens to identify which markers performed similarly using the RPPA technique in fixed and unfixed tissues. Protein lysates were prepared from matched FF and FFPE tissue specimens of individual tumors taken from three different xenograft models of human cancer. Materials from both untreated mice and mice treated with either anti-HER3 or bispecific anti-IGF-1R/EGFR monoclonal antibodies were analyzed. Correlations between signals from FF and FFPE tissue samples were investigated. Overall, 60 markers were identified that produced comparable profiles between FF and FFPE tissues, demonstrating significant correlation between the two sample types. The top 25 markers also showed significance after correction for multiple testing. The panel of markers covered several clinically relevant tumor signaling pathways and both phosphorylated and nonphosphorylated proteins were represented. Biologically relevant changes in marker expression were noted when RPPA profiles from treated and untreated xenografts were compared. These data demonstrate that, using appropriately selected antibodies, RPPA analysis from FFPE tissue is well feasible and generates biologically meaningful information. The identified panel of markers that generate similar profiles in matched fixed and unfixed tissue samples may be clinically useful for pharmacodynamic studies of drug effect using FFPE tissues.

Abstract 4842: Pharmacodynamic stratification of metastatic colorectal cancer patients using genomic datasets

Abstract Previously, the mutation status of KRAS was the only validated predictive biomarker for metastatic colorectal cancer (CRC). While KRAS mutated tumors demonstrated resistance to epidermal growth factor (EGFR) inhibitors like cetuximab, KRAS WT and EGFR-expressing tumors were predicted to be responsive. However, KRAS WT metastatic colorectal cancer (CRC) patients have a poor prognosis even with EGFR inhibitor therapy as not all KRAS WT CRCs are responsive to such targeted agents. A gene expression based RAS signature score was developed based on multiple tumor tissue samples to identify RAS activated tumors independent of mutations in the KRAS gene [1, 2]. To further refine this score and define technologies that can be used on FFPE samples isolated in a clinical setting, we analyzed DNA and RNA derived from fifty-five (55) FFPE preserved colorectal cancer tumor biopsies using multiple sequencing, digital and array-based technologies. These samples were selected from a CRC cohort in which the initial gene expression-based RAS signature score was calculated utilizing data compiled from fresh frozen (FF) tumor samples from the same 55 patients. The 55 samples were selected for this study as they had representative samples with high, medium and low RAS signature scores. Transcriptomic analyses (RNA-Seq, Affymetrix microarrays, Nanostring and Targeted RNA-Seq) were performed on all 55 FFPE samples and three new RAS scores were calculated from the gene expression datasets. These RAS scores were based on different gene signatures (1) an 18 gene signature (2) a 13 gene signature, and (3) a 147 gene signature. A significant correlation was identified between RAS scores calculated from the 18 and 13 gene signatures (Correlation coefficient ∼ 0.88 and ∼0.76 respectively, p-value &amp;lt; 0.0001). To further refine gene expression signatures, samples were grouped based upon their mutation status obtained by whole exome sequencing (WES) and targeted DNA sequencing data (Illumina TruSight and LifeTech Cancer Panels). In our sample set, the 18 gene RAS score was found to be dependent on the mutation status of KRAS. Further analysis is being carried out to better understand the relationship between the calculated RAS signature scores and the mutation status of other genes. This analysis will lead to the development of a novel genomic signature for better pharmacodynamic stratification of colorectal carcinoma patients. 1. Loboda A et al. A gene expression signature of RAS pathway dependence predicts response to PI3K and RAS pathway inhibitors and expands the population of RAS pathway activated tumors. 2. BMC Medical Genomics 2010, 3:26Dry JR et al. Transcriptional Pathway Signatures Predict MEK Addiction and Response to Selumetinib (AZD6244). Cancer Res. 2010 Mar 15;70(6):2264-73. Citation Format: Sharon Austin, Fang Yin Lo, Kellie Howard, Mollie McWhorter, Heather Collins, Amanda Leonti, Lindsey Maassel, Christopher Subia, Tuuli Saloranta, Nicole Heying, Leila Ritter, Kerry Deutsch, James Cox, Steven Anderson, Anup Madan, Timothy Yeatman. Pharmacodynamic stratification of metastatic colorectal cancer patients using genomic datasets. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 4842. doi:10.1158/1538-7445.AM2015-4842

Formalin-fixed, paraffin-embedded (FFPE) tissue epigenomics using Infinium HumanMethylation450 BeadChip assays

Current genome-wide methods to detect DNA-methylation in healthy and diseased tissue require high-quality DNA from fresh-frozen (FF) samples. However, well-annotated clinical samples are mostly available as formalin-fixed, paraffin-embedded (FFPE) tissues containing poor-quality DNA. To overcome this limitation, we here aimed to evaluate a DNA restoration protocol for usage with the genome-wide Infinium HumanMethylation450 BeadChip assay (HM-450K). Sixty-six DNA samples from normal colon (n=9) and breast cancer (n=11) were interrogated separately using HM-450K. Analyses included matched FF/FFPE samples and technical duplicates. FFPE DNA was processed with (FFPEr) or without a DNA restoration protocol (Illumina). Differentially methylated genes were finally validated in 24 additional FFPE tissues using nested methylation-specific PCR (MSP). In summary, β-values correlation between FFPEr duplicates was high (ρ=0.9927 (s.d. ±0.0015)). Matched FF/FFPEr correlation was also high (ρ=0.9590 (s.d. ±0.0184)) compared with matched FF/FFPE (ρ=0.8051 (s.d. ±0.1028). Probe detection rate in FFPEr samples (98.37%, s.d. ±0.66) was comparable to FF samples (99.98%, s.d. ±0.019) and substantially lower in FFPE samples (82.31%, s.d. ±18.65). Assay robustness was not decreased by sample archival age up to 10 years. We could also demonstrate no decrease in assay robustness when using 100 ng of DNA input only. Four out of the five selected differentially methylated genes could be validated by MSP. The gene failing validation by PCR showed high variation of CpG β-values in primer-binding sites. In conclusion, by using the FFPE DNA restoration protocol, HM-450K assays provide robust, accurate and reproducible results with FFPE tissue-derived DNA, which are comparable to those obtained with FF tissue. Most importantly, differentially methylated genes can be validated using more sensitive techniques, such as nested MSP, altogether providing an epigenomics platform for molecular pathological epidemiology research on archived samples with limited tissue amount.

Whole-Transcriptome profiling of formalin-fixed, paraffin-embedded renal cell carcinoma by RNA-seq.

BackgroundFormalin-fixed paraffin-embedded (FFPE) tissue samples are routinely archived in the course of patient care and can be linked to clinical outcomes with long-term follow-up. However, FFPE tissues have degraded RNA which poses challenges for analyzing gene expression. Next-generation sequencing (NGS) is rapidly becoming accepted as an effective tool for measuring gene expressions for research and clinical use. However, the feasibility of NGS has not been firmly established when using FFPE tissue.ResultsWe optimized strategies for whole transcriptome sequencing (RNA-seq) using FFPE tissue. Ribosomal RNA (rRNA) was successfully depleted by competitive hybridization using the Ribo-zero™ Kit (Epicentre Biotechnologies), and rRNA sequence content was less than one percent for each library. Gene expression measured by FFPE RNA-seq was compared to two different standards: RNA-seq from fresh frozen (FF) tissue and quantitative PCR (qPCR). Both FF and FFPE tumors were sequenced on an Illumina Genome Analyzer IIX with an average of 10 million reads. The distribution of FPKMs (fragments per kilobase of exon per million fragments mapped) and number of detected genes were similar between FFPE and FF. RNA-seq expressions from FF and FFPE samples from the same renal cell carcinoma (RCC) correlated highly (r = 0.919 for tumor 1 and r = 0.954 for tumor 2). On hierarchical cluster analysis, samples clustered by patient identity rather than method of preservation. TaqMan qPCR of 424 RCC-related genes correlated highly with FFPE RNA-seq expressions (r = 0.775 for FFPE tumor 1, r = 0.803 for FFPE tumor 2). Expression fold changes were considered, to assess biologic relevance of gene expressions. Expression fold changes between FFPE tumors (tumor 1/tumor 2) correlated well when comparing qPCR and RNA-seq (r = 0.890). Expression fold changes between tumors from different risk groups (our high risk RCC/The Cancer Genome Atlas, TCGA, low risk RCC) also correlated well when comparing RNA-seq from FF and FFPE tumors (r = 0.887).ConclusionsFFPE RNA-seq provides reliable genes expression data, comparable to that obtained from fresh frozen tissue. It represents a useful tool for discovery and validation of biomarkers.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2164-15-1087) contains supplementary material, which is available to authorized users.

72-Gene Classifier for Predicting Prognosis of Estrogen Receptor–Positive and Node-Negative Breast Cancer Patients Using Formalin-Fixed, Paraffin-Embedded Tumor Tissues

BackgroundThe 95-gene classifier (95-GC) can classify patients with estrogen receptor (ER)–positive and node-negative breast cancer into those with low and high risk of relapse with an accuracy similar to that of 21-GC (Oncotype DX). Because 95-GC uses RNA from fresh-frozen (FF) tumor tissues, we herein attempted to develop a gene classifier that is applicable to RNA from formalin-fixed paraffin-embedded (FFPE) tumor tissues. Patients and Methods25 paired FF and FFPE tumor tissues were subjected to DNA microarray for gene-expression analysis. Of the 95 probes included in the 95-GC, 72 were selected for construction of the gene classifier for FFPE tumor tissues, because the gene expression detected by these 72 probes was well preserved in the FFPE tumor tissues. ResultsThe 72-GC was constructed with these 72 probes for the training set comprising 549 FF tumor tissues and validated with 434 FF tumor tissues (relapse-free survival at 10 years was 91% for the low-risk and 74% for the high-risk group (P = 3.74 × 10−7). The predictive capability of 72-GC for prognosis was found to be comparable to that of 95-GC. The 25 paired FF and FFPE tumor tissues from each of 25 patients were classified into the same risk group by 72-GC for 23 patients (92% concordance). 72-GC using the FFPE tumor tissues showed that the prognosis for the low-risk group was significantly (P = .007) better than for the high-risk group. Conclusion72-GC is comparable to 95-GC in terms of accuracy of prognosis prediction, and may be effective for FFPE tumor tissues.

Abstract 785: Gene expression profiling identifies two stable clusters of lobular carcinoma in situ

Abstract INTRODUCTION: Historical data suggests that lobular carcinoma in situ (LCIS) is not a precursor to invasive cancer but rather is a marker of increased risk, conferred equally to both breasts. Emerging evidence of genomic similarities between some LCIS and invasive cancers suggests that as subset of LCIS may exhibit precursor potential. The aim of this study was to look for evidence of distinct molecular subtypes of LCIS using cDNA microarray gene expression analysis. METHODS: Mastectomy specimens from 9 patients with classical LCIS only, 74 patients with classical LCIS &amp; synchronous invasive cancer (51 ILC, 16 IDC, 7 mixed) and 5 patients with LCIS + DCIS were selectively sampled using laser capture microdissection. Representative patient matched normal (NL), LCIS and invasive cells were microdissected from fresh frozen (FF) tissue samples. RNA was extracted and hybridized to Affy. HG-U133A 2.0 oligonucleotide arrays. Unsupervised hierarchical clustering of all LCIS samples and matched paired analysis of NL vs LCIS and LCIS vs ILC were performed using the R software (bioconductor package) and data were analyzed with Ingenuity Pathway Analysis filtered for human genes; p 2. RESULTS: Suitable RNA was obtained from 48 FF LCIS samples. Patient matched NL and ILC samples were available for 42 and 17 cases, respectively. Hierarchical cluster analysis of all LCIS (n=48) demonstrated two stable clusters with median co-cluster frequency of 79% and 83%. The NL vs LCIS paired comparison (n=42) showed 306 differentially expressed genes (DEG) in Cluster 1 (FC &amp;gt;2: 3.9% DEG) and 2012 DEG in Cluster 2 (FC &amp;gt;2: 2.5% DEG). There were no common DEGs with FC &amp;gt; 2 between clusters 1 and 2 for the NL vs LCIS comparison. The LCIS vs ILC paired comparison (n=17) showed 87 DEG in Cluster 1 (FC &amp;gt;2: 58.6%) and 339 DEG in Cluster 2 (FC &amp;gt;2: 27.7% DEG). There were 18/87 (21%) shared DEG between Cluster 1 and Cluster 2 for the LCIS vs ILC comparison. The functions of these 18 genes include biologically relevant genes involved in cell adhesion, cell-matrix interaction, cell movement, subcellular organization and cell proliferation. CONCLUSION: Unsupervised hierarchical analysis demonstrated two stable distinct clusters of LCIS. Matched paired analysis of NL vs LCIS suggests that LCIS in cluster 1 is more similar to NL epithelium than LCIS in cluster 2. The absence of overlap in DEG with FC &amp;gt; 2 for this comparison suggests that the initiation of lobular neoplasia may be driven by alternate genes in these two clusters. Matched paired analysis of LCIS vs ILC demonstrates 3.89 times more DEG in cluster 2 than 1, however the proportion of genes with FC &amp;gt;2 was 2.2 times higher in cluster 1, suggesting both quantitative and qualitative differences in these groups. These preliminary analyses support the hypothesis that LCIS is a heterogeneous lesion, further analysis to determine if there is a subtype of LCIS with true precursor potential is underway. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 785.

Molecular risk assessment of BIG 1-98 participants by expression profiling using RNA from archival tissue

BackgroundThe purpose of the work reported here is to test reliable molecular profiles using routinely processed formalin-fixed paraffin-embedded (FFPE) tissues from participants of the clinical trial BIG 1-98 with a median follow-up of 60 months.MethodsRNA from fresh frozen (FF) and FFPE tumor samples of 82 patients were used for quality control, and independent FFPE tissues of 342 postmenopausal participants of BIG 1-98 with ER-positive cancer were analyzed by measuring prospectively selected genes and computing scores representing the functions of the estrogen receptor (eight genes, ER_8), the progesterone receptor (five genes, PGR_5), Her2 (two genes, HER2_2), and proliferation (ten genes, PRO_10) by quantitative reverse transcription PCR (qRT-PCR) on TaqMan Low Density Arrays. Molecular scores were computed for each category and ER_8, PGR_5, HER2_2, and PRO_10 scores were combined into a RISK_25 score.ResultsPearson correlation coefficients between FF- and FFPE-derived scores were at least 0.94 and high concordance was observed between molecular scores and immunohistochemical data. The HER2_2, PGR_5, PRO_10 and RISK_25 scores were significant predictors of disease free-survival (DFS) in univariate Cox proportional hazard regression. PRO_10 and RISK_25 scores predicted DFS in patients with histological grade II breast cancer and in lymph node positive disease. The PRO_10 and PGR_5 scores were independent predictors of DFS in multivariate Cox regression models incorporating clinical risk indicators; PRO_10 outperformed Ki-67 labeling index in multivariate Cox proportional hazard analyses.ConclusionsScores representing the endocrine responsiveness and proliferation status of breast cancers were developed from gene expression analyses based on RNA derived from FFPE tissues. The validation of the molecular scores with tumor samples of participants of the BIG 1-98 trial demonstrates that such scores can serve as independent prognostic factors to estimate disease free survival (DFS) in postmenopausal patients with estrogen receptor positive breast cancer.Trial RegistrationCurrent Controlled Trials: NCT00004205

High-throughput amplicon scanning of the TP53 gene in breast cancer using high-resolution fluorescent melting curve analyses and automatic mutation calling

Identifying mutations in the TP53 gene is important for cancer prognosis, predicting response to therapy, and determining genetic risk. We have developed a high-throughput scanning assay with automatic calling to detect TP53 mutations in DNA from fresh frozen (FF) and formalin-fixed paraffin-embedded (FFPE) tissues. The coding region of the TP53 gene (exons 2-11) was PCR-amplified from breast cancer samples and scanned by high-resolution fluorescent melting curve analyses using a 384-well format in the LightCycler 480 instrument. Mutations were confirmed by direct sequencing. Sensitivity and specificity of scanning and automatic mutation calling was determined for FF tissue (whole genome amplified [WGA] and non-WGA) and FFPE tissue. Thresholds for automatic mutation calling were established for each preparation type. Overall, we confirmed 27 TP53 mutations in 68 primary breast cancers analyzed by high-resolution melting curve scanning and direct sequencing. Using scanning and automatic calling, there was high specificity (>95%) across all DNA preparation methods. Sensitivities ranged from 100% in non-WGA DNA from fresh tissue to 86% in WGA DNA and DNA from formalin-fixed, paraffin-embedded tissue. Scanning could detect mutated DNA at a dilution of 1:200 in a background of wild-type DNA. Mutation scanning by high-resolution fluorescent melting curve analyses can be done in a high-throughput and automated fashion. The TP53 scanning assay can be performed from a variety of specimen types with high sensitivity/specificity and could be used for clinical and research purposes.

Agreement in Breast Cancer Classification between Microarray and Quantitative Reverse Transcription PCR from Fresh-Frozen and Formalin-Fixed, Paraffin-Embedded Tissues

Microarray studies have identified different molecular subtypes of breast cancer with prognostic significance. To transition these classifications into the clinical laboratory, we have developed a real-time quantitative reverse transcription (qRT)-PCR assay to diagnose the biological subtypes of breast cancer from fresh-frozen (FF) and formalin-fixed, paraffin-embedded (FFPE) tissues. We used microarray data from 124 breast samples as a training set for classifying tumors into 4 previously defined molecular subtypes: Luminal, HER2(+)/ER(-), basal-like, and normal-like. We used the training set data in 2 different centroid-based algorithms to predict sample class on 35 breast tumors (test set) procured as FF and FFPE tissues (70 samples). We classified samples on the basis of large and minimized gene sets. We used the minimized gene set in a real-time qRT-PCR assay to predict sample subtype from the FF and FFPE tissues. We evaluated primer set performance between procurement methods by use of several measures of agreement. The centroid-based algorithms were in complete agreement in classification from FFPE tissues by use of qRT-PCR and the minimized "intrinsic" gene set (40 classifiers). There was 94% (33 of 35) concordance between the diagnostic algorithms when comparing subtype classification from FF tissue by use of microarray (large and minimized gene set) and qRT-PCR data. We found that the ratio of the diagonal SD to the dynamic range was the best method for assessing agreement on a gene-by-gene basis. Centroid-based algorithms are robust classifiers for breast cancer subtype assignment across platforms and procurement conditions.