Brett Wallden

Analytical validation of the PAM50-based Prosigna Breast Cancer Prognostic Gene Signature Assay and nCounter Analysis System using formalin-fixed paraffin-embedded breast tumor specimens

BackgroundNanoString’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.MethodsAnalytical 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.ResultsThe 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).ConclusionsThe analytical performance of NanoString’s Prosigna assay has been validated using FFPE breast tumor specimens across multiple clinical testing laboratories.

Development and verification of the PAM50-based Prosigna breast cancer gene signature assay.

BackgroundThe 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.Methods514 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.ResultsThe 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.ConclusionsThe 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 tumors 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.

PAM50 breast cancer intrinsic subtypes and effect of gemcitabine in advanced breast cancer patients.

Abstract Background. In vitro studies suggest basal breast cancers are more sensitive to gemcitabine relative to other intrinsic subtypes. The main objective of this study was to use specimens from a randomized clinical trial to evaluate whether the basal-like subtype identifies patients with advanced breast cancer who benefit from gemcitabine plus docetaxel (GD) compared to single agent docetaxel (D). Material and methods. From patients randomly assigned to GD or D, RNA was isolated from archival formalin-fixed, paraffin-embedded primary breast tumor tissue and used for PAM50 intrinsic subtyping by NanoString nCounter. Statistical analyses were prespecified as a formal prospective-retrospective clinical trial correlative study. Using time to progression (TTP) as primary endpoint, overall survival (OS) and response rate as secondary endpoints, relationships between subtypes and outcome after chemotherapy were analyzed by the Kaplan-Meier method, and Cox proportional hazards regression models. Data analysis was performed independently by the Danish Breast Cancer Cooperative Group (DBCG) statistical core and all statistical tests were two-sided. Results. RNA from 270 patients was evaluable; 84 patients (31%) were classified as luminal A, 97 (36%) luminal B, 43 (16%) basal-like, and 46 (17%) as HER2-enriched. PAM50 intrinsic subtype was a significant independent prognostic factor for both TTP (p = 0.014) and OS (p = 0.0003). Response rate was not different by subtype, and PAM50 was not a predictor of TTP by treatment arm. PAM50 was however a highly significant predictor of OS following GD compared to D (pinteraction = 0.0016). Patients with a basal-like subtype had a significant reduction in OS events [hazard ratio (HR) = 0.29, 95% confidence interval (CI) = 0.15–0.57; pinteraction = 0.0006]. Conclusion. A significantly improved and clinically important prolongation of survival was seen from the addition of gemcitabine to docetaxel in advanced basal-like breast cancer patients.

Pan-cancer adaptive immune resistance as defined by the Tumor Inflammation Signature (TIS): results from The Cancer Genome Atlas (TCGA)

The Tumor Inflammation Signature (TIS) is an investigational use only (IUO) 18-gene signature that measures a pre-existing but suppressed adaptive immune response within tumors. The TIS has been shown to enrich for patients who respond to the anti-PD1 agent pembrolizumab. To explore this immune phenotype within and across tumor types, we applied the TIS algorithm to over 9000 tumor gene expression profiles downloaded from The Cancer Genome Atlas (TCGA). As expected based on prior evidence, tumors with known clinical sensitivity to anti-programmed cell death protein 1 (PD-1) blockade had higher average TIS scores. Furthermore, TIS scores were more variable within than between tumor types, and within each tumor type a subset of patients with elevated scores was identifiable although with different prevalence associated with each tumor type, the latter consistent with the observed clinical responsiveness to anti PD-1 blockade. Notably, TIS scores only minimally correlated with mutation load in most tumors and ranking tumors by median TIS score showed differing association to clinical sensitivity to PD-1/PD-1 ligand 1 (PD-L1) blockade than ranking of the same tumors by mutation load. The expression patterns of the TIS algorithm genes were conserved across tumor types yet appeared to be minimally prognostic in most cancers, consistent with the TIS score serving as a pan-cancer measurement of the inflamed tumor phenotype. Characterization of the prevalence and variability of TIS will lead to increased understanding of the immune status of untreated tumors and may lead to improved indication selection for testing immunotherapy agents.

Abstract LB-327: Analytical performance of the nCounter analysis system for gene expression cancer signatures

Background : The nCounter Analysis System is a platform for performing highly multiplexed, digital quantification of hundreds of different nucleic acid species in a single reaction. The system is being developed for use as a platform for in vitro diagnostic applications. The current study aimed to evaluate the analytical performance of the system by implementing a 50-gene signature used for determining the intrinsic subtype and prognostic risk of a breast cancer tumor from formalin-fixed, paraffin-embedded (FFPE) tissue samples. The gene expression profiles can be used to divide breast cancer into four intrinsic subtypes: Basal-like, Luminal A, Luminal B, and HER2 enriched. A normal-like subtype identifies tumor specimens contaminated with a high percentage of normal tissue. A prognostic Risk-Of-Relapse (ROR) score is also calculated. Methods : The nCounter assay is performed by direct, multiplexed hybridization of molecular barcodes to target mRNAs. For this study, a CodeSet containing probes for 50 classification genes and 8 normalizing genes was developed. The intrinsic subtyping algorithm was trained by supervised hierarchical clustering of data from 538 samples. Prototypical centroids for tumor subtypes were chosen as statistically significant clusters, while the normal-like centroid was trained from reduction mammoplasty samples. All samples were correlated to the five prototypical centroids and assigned the subtype with the largest positive correlation. The analytical precision of the assay was measured by testing the same sample across replicates. The precision of the subtyping test was determined by assaying 40 different samples across 9 different combinations of reagent lots. The performance of the assay was also evaluated with varying input levels of RNA. Results : The precision of the nCounter assay was found to be driven by Poisson noise in the digital measurements at low expression levels. When samples were subtyped across multiple reagent lots, the correlations to each of the five subtypes were narrowly distributed. One sample varied in the categorical subtype call between Luminal B and HER2-enriched across all lots, however, this sample was similarly correlated between both Luminal B and HER2-enriched across all lot combinations. Not surprisingly, the variability for the ROR was minimal, even for the case where the categorical subtype call changed. Finally, the subtype call and ROR score were stable across a 10x range of RNA input. Conclusions : The NanoString nCounter system has the sensitivity and precision to be implemented as a distributed platform for multiplexed gene expression profiling of tumors. The sensitivity and direct digital detection without the need for enzymatic amplification make the technology compatible with FFPE tissue samples. The precision should make the technology robust under the many varied conditions seen in testing labs. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr LB-327. doi:10.1158/1538-7445.AM2011-LB-327