Naeem Dowidar

BACKGROUND: The four intrinsic subtypes of breast cancer, defined by differential expression of 50 genes (PAM50), have been shown to be predictive of risk of recurrence and benefit of hormonal therapy and chemotherapy. Here we describe the development of Prosigna™, a PAM50-based subtype classifier and risk model on the NanoString nCounter Dx Analysis System intended for decentralized testing in clinical laboratories. METHODS: 514 formalin-fixed, paraffin-embedded (FFPE) breast cancer patient samples were used to train prototypical centroids for each of the intrinsic subtypes of breast cancer on the NanoString platform. Hierarchical cluster analysis of gene expression data was used to identify the prototypical centroids defined in previous PAM50 algorithm training exercises. 304 FFPE patient samples from a well annotated clinical cohort in the absence of adjuvant systemic therapy were then used to train a subtype-based risk model (i.e. Prosigna ROR score). 232 samples from a tamoxifen-treated patient cohort were used to verify the prognostic accuracy of the algorithm prior to initiating clinical validation studies. RESULTS: The gene expression profiles of each of the four Prosigna subtype centroids were consistent with those previously published using the PCR-based PAM50 method. Similar to previously published classifiers, tumor samples classified as Luminal A by Prosigna had the best prognosis compared to samples classified as one of the three higher-risk tumor subtypes. The Prosigna Risk of Recurrence (ROR) score model was verified to be significantly associated with prognosis as a continuous variable and to add significant information over both commonly available IHC markers and Adjuvant! Online. CONCLUSIONS: The results from the training and verification data sets show that the FDA-cleared and CE marked Prosigna test provides an accurate estimate of the risk of distant recurrence in hormone receptor positive breast cancer and is also capable of identifying a tumor's intrinsic subtype that is consistent with the previously published PCR-based PAM50 assay. Subsequent analytical and clinical validation studies confirm the clinical accuracy and technical precision of the Prosigna PAM50 assay in a decentralized setting.

BACKGROUND: NanoString's Prosigna™ Breast Cancer Prognostic Gene Signature Assay is based on the PAM50 gene expression signature. The test outputs a risk of recurrence (ROR) score, risk category, and intrinsic subtype (Luminal A/B, HER2-enriched, Basal-like). The studies described here were designed to validate the analytical performance of the test on the nCounter Analysis System across multiple laboratories. METHODS: Analytical precision was measured by testing five breast tumor RNA samples across 3 sites. Reproducibility was measured by testing replicate tissue sections from 43 FFPE breast tumor blocks across 3 sites following independent pathology review at each site. The RNA input range was validated by comparing assay results at the extremes of the specified range to the nominal RNA input level. Interference was evaluated by including non-tumor tissue into the test. RESULTS: The measured standard deviation (SD) was less than 1 ROR unit within the analytical precision study and the measured total SD was 2.9 ROR units within the reproducibility study. The ROR scores for RNA inputs at the extremes of the range were the same as those at the nominal input level. Assay results were stable in the presence of moderate amounts of surrounding non-tumor tissue (<70% by area). CONCLUSIONS: The analytical performance of NanoString's Prosigna assay has been validated using FFPE breast tumor specimens across multiple clinical testing laboratories.

RNA levels of flagellar genes in eight different genetic backgrounds were compared to that of the wild type by DNA microarray analysis. Cluster analysis identified new, potential flagellar genes, three putative methyl-accepting chemotaxis proteins, STM3138 (McpA), STM3152 (McpB), and STM3216(McpC), and a CheV homolog, STM2314, in Salmonella, that are not found in Escherichia coli. Isolation and characterization of Mud-lac insertions in cheV, mcpB, mcpC, and the previously uncharacterized aer locus of S. enterica serovar Typhimurium revealed them to be controlled by sigma28-dependent flagellar class 3 promoters. In addition, the srfABC operon previously isolated as an SsrB-regulated operon clustered with the flagellar class 2 operon and was determined to be under FlhDC control. The previously unclassified fliB gene, encoding flagellin methylase, clustered as a class 2 gene, which was verified using reporter fusions, and the fliB transcriptional start site was identified by primer extension analysis. RNA levels of all flagellar genes were elevated in flgM or fliT null strains. RNA levels of class 3 flagellar genes were elevated in a fliS null strain, while deletion of the fliY, fliZ, or flk gene did not affect flagellar RNA levels relative to those of the wild type. The cafA (RNase G) and yhjH genes clustered with flagellar class 3 transcribed genes. Null alleles in cheV, mcpA, mcpB, mcpC, and srfB did not affect motility, while deletion of yhjH did result in reduced motility compared to that of the wild type.

3034 Background: Pembrolizumab is a humanized anti-PD1 antibody that is FDA approved for use in patients with advanced melanoma and in selected patients with metastatic non-small-cell lung cancer. It has also shown clinical activity in a number of other tumor types in clinical trials, but there is need for a precise and accurate test that can identify patients most likely to benefit from therapy. Several immune-related gene expression (Gx) signatures in formalin fixed, paraffin embedded (FFPE) tissue were previously reported to enrich for responders to pembrolizumab across different tumor types. We have developed a clinical trial assay, referred to here as the anti-PD1 Gx test, based on genes repeatedly found to be associated with improved response to pembrolizumab in a number of cancers. Here we describe the development and analytical performance of the anti-PD1 Gx test in multiple tumor types. Methods: The anti-PD1 Predictor Score (PS) algorithm was trained using RNA from FFPE specimens from all cancer types in the KEYNOTE-012 trial and several cancer types in the KEYNOTE-028 trial (anal canal, biliary tract, colorectal, esophageal, and ovarian) to determine the final genes and weightings. Analytical precision from RNA, reproducibility from multiple tissue blocks, impact of intra-tumor heterogeneity, and sensitivity to RNA input amount were measured across operators using samples from multiple tumor types. The robustness of the assay was evaluated with the inclusion of adjacent non-tumor tissue. Results: The total standard deviation in anti-PD1 PS was < 5% of the score range with random error being the major source of variance. The assay was robust across the specified RNA input range and against the inclusion of non-tumor tissue. The major source of variability in Gx across multiple tumor types was associated with the tumors’ immune Gx signature rather than intra-tumor variability or even tumor type. Conclusions: The NanoString anti-PD1 Gx test is a robust assay, which profiles immune related Gx across multiple cancer types. The assay is well suited to clinical applications and its ability to identify responders to anti-PD1 therapy is being investigated in multiple indications in several studies.