Brett Wallden

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.

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.

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.

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.

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.