Tumor marker discovery by expression profiling RNA from formalin fixed paraffin embedded tissues.

Clear identification among early-stage cancer patients of those at highest risk of having metastatic disease would be of great benefit in treatment planning and management. Considerable additional benefit would accrue to high-risk patients if their responses to specific therapeutic alternatives could be predicted. Molecular biomarkers in the form of gene expression profiles are proving to be more effective tools for both prognostic and predictive patient stratification than more traditional methods such as patient demographics and histopathology indicators. Such biomarkers must be clinically validated before they can be effectively used to manage patients in clinical studies or clinical practice. This can be most efficiently accomplished by analyzing archived clinical samples with well-characterized clinical outcomes. Doing studies of this type requires reoptimization of traditional molecular expression profiling techniques to analyze RNA from fixed paraffin-embedded tissues. We have modified molecular methods for RNA extraction, RNA quantification, reverse transcription, and quantitative PCR to work optimally in archived clinical samples in order to develop a clinically validated assay for breast cancer prognosis and prediction of patient response to hormonal and chemotherapy.

Fusion transcript discovery in formalin-fixed paraffin-embedded human breast cancer tissues reveals a link to tumor progression.

The identification of gene fusions promises to play an important role in personalized cancer treatment decisions. Many rare gene fusion events have been identified in fresh frozen solid tumors from common cancers employing next-generation sequencing technology. However the ability to detect transcripts from gene fusions in RNA isolated from formalin-fixed paraffin-embedded (FFPE) tumor tissues, which exist in very large sample repositories for which disease outcome is known, is still limited due to the low complexity of FFPE libraries and the lack of appropriate bioinformatics methods. We sought to develop a bioinformatics method, named gFuse, to detect fusion transcripts in FFPE tumor tissues. An integrated, cohort based strategy has been used in gFuse to examine single-end 50 base pair (bp) reads generated from FFPE RNA-Sequencing (RNA-Seq) datasets employing two breast cancer cohorts of 136 and 76 patients. In total, 118 fusion events were detected transcriptome-wide at base-pair resolution across the 212 samples. We selected 77 candidate fusions based on their biological relevance to cancer and supported 61% of these using TaqMan assays. Direct sequencing of 19 of the fusion sequences identified by TaqMan confirmed them. Three unique fused gene pairs were recurrent across the 212 patients with 6, 3, 2 individuals harboring these fusions respectively. We show here that a high frequency of fusion transcripts detected at the whole transcriptome level correlates with poor outcome (P<0.0005) in human breast cancer patients. This study demonstrates the ability to detect fusion transcripts as biomarkers from archival FFPE tissues, and the potential prognostic value of the fusion transcripts detected.

Whole transcriptome RNA-Seq analysis of breast cancer recurrence risk using formalin-fixed paraffin-embedded tumor tissue.

RNA biomarkers discovered by RT-PCR-based gene expression profiling of archival formalin-fixed paraffin-embedded (FFPE) tissue form the basis for widely used clinical diagnostic tests; however, RT-PCR is practically constrained in the number of transcripts that can be interrogated. We have developed and optimized RNA-Seq library chemistry as well as bioinformatics and biostatistical methods for whole transcriptome profiling from FFPE tissue. The chemistry accommodates low RNA inputs and sample multiplexing. These methods both enable rediscovery of RNA biomarkers for disease recurrence risk that were previously identified by RT-PCR analysis of a cohort of 136 patients, and also identify a high percentage of recurrence risk markers that were previously discovered using DNA microarrays in a separate cohort of patients, evidence that this RNA-Seq technology has sufficient precision and sensitivity for biomarker discovery. More than two thousand RNAs are strongly associated with breast cancer recurrence risk in the 136 patient cohort (FDR <10%). Many of these are intronic RNAs for which corresponding exons are not also associated with disease recurrence. A number of the RNAs associated with recurrence risk belong to novel RNA networks. It will be important to test the validity of these novel associations in whole transcriptome RNA-Seq screens of other breast cancer cohorts.

RT-PCR-based gene expression profiling for cancer biomarker discovery from fixed, paraffin-embedded tissues.

A molecular test providing clear identification of individuals at highest risk for developing metastatic disease from among early stage breast cancer patients has proven to be of great benefit in breast cancer treatment planning and therapeutic management. Patients with high risk of disease recurrence can also get an estimate of the magnitude of benefit to be gained by adding chemotherapy to surgery and hormonal therapy. Developing this clinical test was made possible by the availability of technologies capable of identifying molecular biomarkers from the gene expression profiles of preserved surgical specimens. Molecular tests such as the Oncotype DX(®) breast cancer test are proving to be more effective tools for individualized patient stratification and treatment planning than traditional methods such as patient demographic variables and histopathology indicators.Molecular biomarkers must be clinically validated before they can be effectively applied toward patient management in clinical practice. The most effective and efficient means of clinical validation is to use archived surgical specimens annotated with well-characterized clinical outcomes. However, carrying out this type of clinical study requires optimization of traditional molecular expression profiling techniques to analyze RNA from fixed, paraffin-embedded (FPE) tissues. In order to develop our clinically validated breast cancer assay, we modified molecular methods for RNA extraction, RNA quantitation, reverse transcription, and quantitative PCR to work optimally in archived clinical samples. Here, we present an updated description of current best practices for isolating both mRNA and microRNA from FPE tissues for RT-PCR-based expression profiling.

Rt-PCR gene expression profiling of RNA from paraffin-embedded tissues prepared using a range of different fixatives and conditions.

Although RNA is isolated from archival fixed tissues routinely for reverse transcription polymerase chain reaction (RT-PCR) and microarray analyses to identify biomarkers of cancer prognosis and therapeutic response prediction, the sensitivity of these molecular profiling methods to variability in pathology tissue processing has not been described in depth. As increasing numbers of expression analysis studies using fixed archival tumor specimens are reported, it is important to examine how dependent these results are on tissue-processing methods.We carried out a series of studies to systematically evaluate the effects of various tissue-fixation reagents and protocols on RNA quality and RT-PCR gene expression profiles. Human placenta was selected as a model specimen for these studies since it is relatively easily obtained and has proliferative and invasive qualities similar to solid tumors. In addition, each specimen is relatively homogeneous and large enough to provide sufficient tissue to systematically compare a range of fixation conditions and reagents, thereby avoiding the variability inherent in studying collections of tumor tissue specimens. Since anatomical pathology laboratories generally offer hundreds of different tissue-fixation protocols, we focused on fixation reagents and conditions used to process the most common solid tumors for primary cancer diagnosis. Fresh placentas donated under an IRB-approved protocol were collected at delivery and immediately submerged in cold saline for transport to a central pathology laboratory for processing. RNA was extracted from each specimen, quantified, and analyzed for size distribution and analytical performance using a panel of 24 RT-PCR gene expression assays. We found that different tissue-fixation reagents and tissue-processing conditions resulted in widely varying RNA extraction yields and extents of RNA fragmentation. However, the RNA extraction method and RT-PCR assays could be optimized to achieve successful gene expression analysis for nearly all fixation conditions represented in these studies.

Gene expression patterns in formalin-fixed, paraffin-embedded core biopsies predict docetaxel chemosensitivity in breast cancer patients.

Previously, we had identified gene expression patterns that predicted response to neoadjuvant docetaxel. Other studies have validated that a high Recurrence Score (RS) by the 21-gene RT-PCR assay is predictive of worse prognosis but better response to chemotherapy. We investigated whether tumor expression of these 21 genes and other candidate genes can predict response to docetaxel. Core biopsies from 97 patients were obtained before treatment with neoadjuvant docetaxel (4 cycles, 100 mg/m2 q3 weeks). Three 10-microm FFPE sections were submitted for quantitative RT-PCR assays of 192 genes that were selected from our previous work and the literature. Of the 97 patients, 81 (84%) had sufficient invasive cancer, 80 (82%) had sufficient RNA for QRTPCR assay, and 72 (74%) had clinical response data. Mean age was 48.5 years, and the median tumor size was 6 cm. Clinical complete responses (CR) were observed in 12 (17%), partial responses in 41 (57%), stable disease in 17 (24%), and progressive disease in 2 patients (3%). A significant relationship (P<0.05) between gene expression and CR was observed for 14 genes, including CYBA. CR was associated with lower expression of the ER gene group and higher expression of the proliferation gene group from the 21 gene assay. Of note, CR was more likely with a high RS (P=0.008). We have established molecular profiles of sensitivity to docetaxel. RT-PCR technology provides a potential platform for a predictive test of docetaxel chemosensitivity using small amounts of routinely processed material.

Analytical validation of the Oncotype DX genomic diagnostic test for recurrence prognosis and therapeutic response prediction in node-negative, estrogen receptor-positive breast cancer.

BACKGROUND: Oncotype DX is a clinically validated, high-complexity, multianalyte reverse transcription-PCR genomic test that predicts the likelihood of breast cancer recurrence in early-stage, node-negative, estrogen receptor-positive breast cancer. The Recurrence Score (RS) provides a more accurate, reproducible measure of breast cancer aggressiveness and therapeutic responsiveness than standard measures. Individualized patient management requires strict performance criteria for clinical laboratory tests. We therefore investigated the analytical performance of the assay. METHODS: Assays used a pooled RNA sample from fixed paraffin-embedded tissues to evaluate the analytical performance of a 21-gene panel with respect to amplification efficiency, precision, linearity, and dynamic range, as well as limits of detection and quantification. Performance variables were estimated from assays carried out with sample dilutions. In addition, individual patient samples were used to test the optimized assay for reproducibility and sources of imprecision. RESULTS: Assay results defined acceptable operational performance ranges, including an estimated maximum deviation from linearity of <1 cycle threshold (C(T)) units over a > or =2000-fold range of RNA concentrations, with a mean quantification bias of 0.3% and CVs of 3.2%-5.7%. An analysis of study design showed that assay imprecision contributed by instrument, operator, reagent, and day-to-day baseline variation was low, with SDs of <0.5 C(T). CONCLUSION: The analytical and operational performance specifications defined for the Oncotype DX assay allow the reporting of quantitative RS values for individual patients with an SD within 2 RS units on a 100-unit scale.