Grant Number:	5R33CA183688-03 Interpret this number
Primary Investigator:	Yeakley, Joanne
Organization:	Biospyder Technologies, Inc.
Project Title:	Rasl-Seq Expression Profiling of Ffpe Tissues
Fiscal Year:	2017

 DESCRIPTION (provided by applicant): This project will develop and validate the application of RASL-Seq, a multiplexed targeted expression profiling technology, to analysis of fixed tumor specimens. The technology detects hundreds of RNA targets at once in cell lysates, using oligo ligation, amplification, and next-generation sequencing for quantitation. This highly multiplexed assay will allow a single, fixed tissue sample-to-answer assay protocol that can be applied to different tumor types, while delivering a simple report tailored to the sample. The technology will also promote clinical research by enabling somatic mutation and gene fusion detection in RNA, as well as measurement of mutations at the level of DNA, and the measurement of variable regions of DNA. By measuring DNA mutations, and gene expression levels on a single platform, potentially in a single assay or two parallel assays, improved information can be provided to the investigator, and in the case of a diagnostic assay, to the patient. It is conceivable that measuring mutations at the level of RNA and not just DNA will not only provide redundancy, but will provide an indication of whether a particular genomic mutation is actually driving the cancer, or if a different mutation is driving it, which will result in improved treatment decisions for patients, by better focusing treatment to likely therapeutic targets. Measurement of somatic mutations may result in better sensitivity than genomic DNA analysis alone, due to the typically larger number of target molecules present per cell. Since the original application, some of the proposed work has been completed, and feasibility for measuring fixed tissue demonstrated, permitting scope to be expanded into DNA measurement. To demonstrate commercial readiness, we will establish the mutation assays, optimize the protocols, introduce additional positive controls and quality assessment steps, assess reproducibility, sensitivity and specificity using matched frozen and FFPE samples, and develop data analysis tools. We will establish performance measures (reproducibility, dynamic range, limit of detection for mutations, and the behavior of controls), and conduct verification and validation studies to establish the positive predictive value and negative predictive value for mutation assays where independent assessment of mutation is available. This program will establish as commercial assays a panel of mutations and a pan-cancer assay of ~1,500 genes and mutations. It will establish a cancer-related gene expression and mutation database qualified by extent of validation from which investigators can select content for customized RASL-Seq assays. If successful, utility will be validated by identifying biomarkers and mutations that differentiate between high grade prostatic intraepithelial neoplasia (HGPN) and invasive prostate cancer (PCa), and potentially by demonstrating that there are biomarkers that differentiate HGPIN in patients without PCa from HGPIN associated morphologically with PCa as a step toward identifying mechanisms leading to progression as well as providing biomarkers that can be used as a diagnostic to identify patients with HGPIN who are likely to progress to PCa. Overall the project will deliver a commercializable technology and specific commercial kit assays that will allow simultaneous measurement of expression and mutations from hundreds of genes with clinical significance for common cancers, all in a single process flow that meets the needs of clinical investigators by starting with FFPE tissue sections and ending with a simple report.




Extraction-free whole transcriptome gene expression analysis of FFPE sections and histology-directed subareas of tissue.
Authors: Trejo C.L. , Babić M. , Imler E. , Gonzalez M. , Bibikov S.I. , Shepard P.J. , VanSteenhouse H.C. , Yeakley J.M. , Seligmann B.E. .
Source: PloS one, 2019; 14(2), p. e0212031.
EPub date: 2019-02-22.
PMID: 30794557
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