3566 Background: Clinical biomarker studies are often hindered by the availability of tissue specimens of sufficient quality and quantity. While RNA-Seq is often considered the gold standard for measuring mRNA expression levels in cancer tissue, it typically requires multiple formalin-fixed paraffin-embedded (FFPE) tissue sections to extract a sufficient amount of quality RNA for subsequent gene expression profiling analysis. The HTG EdgeSeq technology is a gene expression profiling platform that combines quantitative nuclease protection assay technology with next-generation sequencing detection. Unlike RNA-Seq, the HTG EdgeSeq technology does not require RNA extraction, and can use small amounts of tissue material, typically several mm2, to generate reproducible gene expression profiles. Methods: This study compares the performance of RNA-Seq and HTG's profiling panel, the HTG EdgeSeq Precision Immuno-Oncology Panel (PIP), which is designed to measure expression levels of 1,392 genes focused on tumor/immune interaction. Approximately 1,200 samples from three tumor indications (gastric cancer, colorectal cancer and ovarian cancer) were tested using both technologies. Results: Up to four FFPE slides were used for RNA extraction to support RNA-Seq testing; out of the 1,202 samples processed, 1,099 generated extracted RNA of sufficient quality and quantity (as measured by RNA concentration, RIN score and %DV200) to proceed to sequencing, which resulted in a pass rate of 91.4% for RNA-Seq. The HTG EdgeSeq PIP panel resulted in a pass rate of 97.3% (samples passing QC metrics) when the same 1,200 samples were tested, and required only a single FFPE section owing to the small sample requirement. The t-SNE (a non-linear dimensionality reduction method) analysis of the common 1,358 genes revealed similar clustering of the three cancer indications between the two methods. Correlations across individual genes by sample resulted in the mean Spearman correlation coefficient of 0.73 (95% confidence interval of 0.61 - 0.80). Additionally, gene-wise comparisons across all samples were also evaluated. Conclusions: These data demonstrate that HTG EdgeSeq gene expression panels can be used as a competitive alternative to RNA-Seq, generating equivalent gene expression results, while offering the added benefits of a small sample size requirement, lack of RNA extraction bias, and fully automated data analysis pipeline.
BackgroundTumor microenvironment (TME)-targeting agents such as anti-angiogenic therapies and check-point inhibitors (CPIs), have shown both promise and variability in effectiveness depending on the tumor type. For immune-targeting agents like CPIs, efforts to identify features or biomarkers that predispose responding patients include but are not limited to genomic stability, tumor mutation burden, and PD-L1 expression. Oncologie is developing a RNA-based platform that identifies subsets of patients based on multiple aspects of the biological processes (dominant biology) existing within the tumor microenvironment.MethodsRNA data from publicly available sources including microarray, RNASeq exome and whole RNA were analyzed with respect to gene signatures that describe four different microenvironmental phenotypes. Phenotypes were then evaluated for relationships to clinical efficacy endpoints. From these RNA signatures and driven by machine learning methodologies, drug-specific algorithms were developed and applied to retrospectively to clinical data. Comparative analyses were explored between gene signatures, commonly used biomarkers (eg. presence of microsatellite DNA, expression levels of PD-L1, etc) and within-patient metadata to better understand better how this approach can be utilized in prospective clinical studies.ResultsAttributes in RNA expression identified using Oncologie’s platform have retrospectively characterized responders to CPIs or anti-angiogenic drugs, demonstrating a relationship between clinical response and biomarker positive and negative patient populations. Exploratory data summarizing the use of the this platform demonstrates its utility for enriching response to both immune- and angiogenesis-targeting drugs. Relative expression changes between archival and fresh biopsies demonstrate changes in the TME with time and/or following targeted therapy. Lastly, cross-tumor comparisons support a tumor-agnostic utility of this approach. Detailed comparisons of this biomarker approach relative to other available biomarkers will be presented for standard of care drugs and those in the Oncologie pipeline based on retrospective analyses.ConclusionsRNA based descriptors of biology may be a useful approach to enrich for response to targeted therapies whose mechanism of action is to modify the TME biology.
The most utilized targeted therapies in colorectal cancer (CRC) are focused on EGFR inhibition and anti-angiogenesis. In the ~5% of patients with microsatellite instability (MSI-H) or high tumor mutational burden (TMB), checkpoint inhibitors (CPIs) have been approved. Oncxerna has developed an RNA expression-based approach to characterize the ‘dominant' biology of a patient's tumor microenvironment with the diagnostic hypothesis to prospectively pair those patients with therapies and known mechanism of action that directly target these biologies. We developed an RNA-based gene expression panel (TME Panel-1) and machine learning (ML) algorithms to prospectively predict a patient's response to anti-angiogenesis or immune modulators, such as CPIs. In this study, we explore the potential of the TME Panel-1 to identify dominant biologies present in colorectal cancer specimens procured from the Wood-Hudson Cancer Research Lab. Total RNA expression counts from FFPE slides were analyzed with the ML algorithms and used to assign each sample into one of four subgroups. The respective prevalence of the subgroups are similar to those observed in gastric cancer and ovarian cancer samples, suggesting that the TME-Panel 1 has potential to be used to develop pan-tumor diagnostics. We will present these results, correlations with clinical outcomes and other relevant biomarkers for CRC. In summary, we conclude that RNA-based descriptors of biology may be a useful approach to enrich for better response to targeted therapies whose mechanism of action is to modify the TME biology. Citation Format: Kristen Strand-Tibbitts, Kerry Culm-Merdek, Valerie Chamberlain Santps, Laura Benjamin, Julia Carter, Larry Douglass, Roman Luštrik, Robert Cvitkovič, Luka Ausec, Rafael Rosengarten. Development of an RNA based diagnostic panel to the tumor microenvironment to match cancer therapies for colorectal cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 348.
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