Analytical performance of an immunoprofiling assay based on RNA models

As immuno-oncology drugs grow more popular in the treatment of cancer, better methods are needed to quantify the tumor immune cell component to determine which patients are most likely to benefit from treatment. Methods such as flow cytometry can accurately assess the composition of infiltrating immune cells; however, they show limited use in formalin-fixed, paraffin-embedded (FFPE) specimens. This article describes a novel hybrid-capture RNA sequencing assay, ImmunoPrism, that estimates the relative percentage abundance of eight immune cell types in FFPE solid tumors. Immune health expression models were generated using machine learning methods and used to uniquely identify each immune cell type using the most discriminatively expressed genes. The analytical performance of the assay was assessed using 101 libraries from 40 FFPE and 32 fresh-frozen samples. With defined samples, ImmunoPrism had a precision of ±2.72%, a total error of 2.75%, and a strong correlation (

T Cell Subtype Profiling measures exhaustion and predicts anti-PD-1 response

Anti-PD-1 therapy can provide long, durable benefit to a fraction of patients. The on-label PD-L1 test, however, does not accurately predict response. To build a better biomarker, we created a method called T Cell Subtype Profiling (TCSP) that characterizes the abundance of T cell subtypes (TCSs) in FFPE specimens using five RNA models. These TCS RNA models are created using functional methods, and robustly discriminate between naïve, activated, exhausted, effector memory, and central memory TCSs, without the reliance on non-specific, classical markers. TCSP is analytically valid and corroborates associations between TCSs and clinical outcomes. Multianalyte biomarkers based on TCS estimates predicted response to anti-PD-1 therapy in three different cancers and outperformed the indicated PD-L1 test, as well as Tumor Mutational Burden. Given the utility of TCSP, we investigated the abundance of TCSs in TCGA cancers and created a portal to enable researchers to discover other TCSP-based biomarkers

Prognostic Significance of Immune Cell Infiltration in Muscle-invasive Bladder Cancer Treated with Definitive Chemoradiation: A Secondary Analysis of RTOG 0524 and RTOG 0712

Chemoradiation therapy (CRT) is a treatment for muscle-invasive bladder cancer (MIBC). Using a novel transcriptomic profiling panel, we validated prognostic immune biomarkers to CRT using 70 pretreatment tumor samples from prospective trials of MIBC (NRG/RTOG 0524 and 0712). Disease-free survival (DFS) and overall survival (OS) were estimated via the Kaplan-Meier method and stratified by genes correlated with immune cell activation. Cox proportional-hazards models were used to assess group differences. Clustering of gene expression profiles revealed that the cluster with high immune cell content was associated with longer DFS (hazard ratio [HR] 0.53, 95% confidence interval [CI] 0.26-1.10; p = 0.071) and OS (HR 0.48, 95% CI 0.24-0.97; p = 0.040) than the cluster with low immune cell content. Higher expression of T-cell infiltration genes (CD8A and ICOS) was associated with longer DFS (HR 0.40, 95% CI 0.21-0.75; p = 0.005) and OS (HR 0.49, 95% CI 0.25-0.94; p = 0.033). Higher IDO1 expression (IFNγ signature) was also associated with longer DFS (HR 0.44, 95% CI 0.24-0.88; p = 0.021) and OS (HR 0.49, 95% CI 0.24-0.99; p = 0.048). These findings should be validated in prospective CRT trials that include biomarkers, particularly for trials incorporating immunotherapy for MIBC. PATIENT SUMMARY: We analyzed patient samples from two clinical trials (NRG/RTOG 0524 and 0712) of chemoradiation for muscle-invasive bladder cancer using a novel method to assess immune cells in the tumor microenvironment. Higher expression of genes associated with immune activation and high overall immune-cell content were associated with better disease-free survival and overall survival for patients treated with chemoradiation

Multidimensional biomarker predicts disease control in response to immunotherapy in recurrent or metastatic head and neck squamous-cell carcinoma.

PURPOSE: Anti-PD-1 therapy provides clinical benefit in 40-50% of patients with relapsed and/or metastatic head and neck squamous cell carcinoma (RM-HNSCC). Selection of anti- PD-1 therapy is typically based on patient PD-L1 immunohistochemistry (IHC) which has low specificity for predicting disease control. Therefore, there is a critical need for a clinical biomarker that will predict clinical benefit to anti-PD-1 treatment with high specificity. METHODS: Clinical treatment and outcomes data for 103 RM-HNSCC patients were paired with RNA-sequencing data from formalin-fixed patient samples. Using logistic regression methods, we developed a novel biomarker classifier based on expression patterns in the tumor immune microenvironment to predict disease control with monotherapy PD-1 inhibitors (pembrolizumab and nivolumab). The performance of the biomarker was internally validated using out-of-bag methods. RESULTS: The biomarker significantly predicted disease control (65% in predicted non-progressors vs. 17% in predicted progressors, p < 0.001) and was significantly correlated with overall survival (OS; p = 0.004). In addition, the biomarker outperformed PD-L1 IHC across numerous metrics including sensitivity (0.79 vs 0.64, respectively; p = 0.005) and specificity (0.70 vs 0.61, respectively; p = 0.009). CONCLUSION: This novel assay uses tumor immune microenvironment expression data to predict disease control and OS with high sensitivity and specificity in patients with RM-HNSCC treated with anti-PD-1 monotherapy

Multidimensional biomarker predicts disease control in response to immunotherapy in recurrent or metastatic head and neck squamous-cell carcinoma.

PURPOSE: Anti-PD-1 therapy provides clinical benefit in 40-50% of patients with relapsed and/or metastatic head and neck squamous cell carcinoma (RM-HNSCC). Selection of anti- PD-1 therapy is typically based on patient PD-L1 immunohistochemistry (IHC) which has low specificity for predicting disease control. Therefore, there is a critical need for a clinical biomarker that will predict clinical benefit to anti-PD-1 treatment with high specificity. METHODS: Clinical treatment and outcomes data for 103 RM-HNSCC patients were paired with RNA-sequencing data from formalin-fixed patient samples. Using logistic regression methods, we developed a novel biomarker classifier based on expression patterns in the tumor immune microenvironment to predict disease control with monotherapy PD-1 inhibitors (pembrolizumab and nivolumab). The performance of the biomarker was internally validated using out-of-bag methods. RESULTS: The biomarker significantly predicted disease control (65% in predicted non-progressors vs. 17% in predicted progressors, p \u3c 0.001) and was significantly correlated with overall survival (OS; p = 0.004). In addition, the biomarker outperformed PD-L1 IHC across numerous metrics including sensitivity (0.79 vs 0.64, respectively; p = 0.005) and specificity (0.70 vs 0.61, respectively; p = 0.009). CONCLUSION: This novel assay uses tumor immune microenvironment expression data to predict disease control and OS with high sensitivity and specificity in patients with RM-HNSCC treated with anti-PD-1 monotherapy