AACR 2018
Scientific Posters

Below you will find a summary for each of the 8 posters presented by Q2 Solutions | EA Genomics scientists and collaborators at AACR 2018. Download each poster to learn more.

  1. Tumor Mutational Burden: Guidelines for derivation and robustness of measurement” presented by Natalie Mola and colleagues described a new method for calculating tumor mutation burden (TMB) that reduces false positives in TMB estimates.

  2. Dan Su and co-authors illustrated in the poster “Optimization of an RNA sequencing method for low-quantity degraded samples”, an RNA access method to further improve the competitiveness of our service.

  3. Wendell Jones and collaborators from Levine Cancer Institute-Carolinas HealthCare Center discussed “Long-Term Disease-Free Survival in High Grade Serous Ovarian Cancer (HGSOC) is Dependent on Immune Microenvironment Characteristics of Primary Tumor” and showed that immune cell gene expression signatures could be used predictively and prognostically.

  4. Effect of tumor purity on somatic mutation detection using next generation sequencing technology: A benchmarking study” co-authored by Wendell Jones and collaborators from the SEQC2 (Sequencing Quality Control Consortium) discussed optimal methods for variant detection and structural variation in tumors and normal cells.

  5. Sensitive detection of MET exon 14 skipping by RT-qPCR and next generation sequencing”, by Mukund Patel and colleagues illustrated the development of an RNA-based RT-qPCR assay that detects the challenging MET exon 14 skipping with excellent analytical sensitivity.

  6. Jeff Jasper and co-authors described development of an RNA-based fusion detection algorithm to improve identification of cancer-associated rearrangements in RNA sequencing data in their poster “STAR-SEQR: Accurate fusion detection and support for fusion neoantigen applications.”

  7. Effects of sequencing parameters and panel size on mutational burden calculations” presented by Natalie Mola and colleagues illustrated that Tumor Mutational Burden (TMB) could be accurately calculated using only a small panel of genes (Q2 Solutions Comprehensive Cancer Panel, QCCP) rather than whole exome sequencing.

  8. Yiqing Tian and colleagues presented a machine learning approach and preliminary data to call TMB from tumor only samples rather than requiring both tumor and normal in the poster “Method for normal-independent calculation of TMB.”