Translation of the 27-gene immuno-oncology test (IO score) to predict outcomes in immune checkpoint inhibitor treated metastatic urothelial cancer patients.

BACKGROUND: The IO Score is a 27-gene immuno-oncology (IO) classifier that has previously predicted benefit to immune checkpoint inhibitor (ICI) therapy in triple negative breast cancer (TNBC) and non-small cell lung cancer (NSCLC). It generates both a continuous score and a binary result using a defined threshold that is conserved between breast and lung. Herein, we aimed to evaluate the IO Score's binary threshold in ICI-naïve TCGA bladder cancer patients (TCGA-BLCA) and assess its clinical utility in metastatic urothelial cancer (mUC) using the IMvigor210 clinical trial treated with the ICI, atezolizumab. METHODS: We identified a list of tumor immune microenvironment (TIME) related genes expressed across the TCGA breast, lung squamous and lung adenocarcinoma cohorts (TCGA-BRCA, TCGA-LUSQ, and TCGA-LUAD, 939 genes total) and then examined the expression of these 939 genes in TCGA-BLCA, to identify patients as having high inflammatory gene expression. Using this as a test of classification, we assessed the previously established threshold of IO Score. We then evaluated the IO Score with this threshold in the IMvigor210 cohort for its association with overall survival (OS). RESULTS: In TCGA-BLCA, IO Score positive patients had a strong concordance with high inflammatory gene expression (p < 0.0001). Given this concordance, we applied the IO Score to the ICI treated IMvigor210 patients. IO Score positive patients (40%) had a significant Cox proportional hazard ratio (HR) of 0.59 (95% CI 0.45-0.78 p < 0.001) for OS and improved median OS (15.6 versus 7.5 months) compared to IO Score negative patients. The IO Score remained significant in bivariate models combined with all other clinical factors and biomarkers, including PD-L1 protein expression and tumor mutational burden. CONCLUSION: The IMvigor210 results demonstrate the potential for the IO Score as a clinically useful biomarker in mUC. As this is the third tumor type assessed using the same algorithm and threshold, the IO Score may be a promising candidate as a tissue agnostic marker of ICI clinical benefit. The concordance between IO Score and inflammatory gene expression suggests that the classifier is capturing common features of the TIME across cancer types.

A Novel Immunomodulatory 27-Gene Signature to Predict Response to Neoadjuvant Immunochemotherapy for Primary Triple-Negative Breast Cancer.

A precise predictive biomarker for TNBC response to immunochemotherapy is urgently needed. We previously established a 27-gene IO signature for TNBC derived from a previously established 101-gene model for classifying TNBC. Here we report a pilot study to assess the performance of a 27-gene IO signature in predicting the pCR of TNBC to preoperative immunochemotherapy. We obtained RNA sequencing data from the primary tumors of 55 patients with TNBC, who received neoadjuvant immunochemotherapy with the PD-L1 blocker durvalumab. We determined the power and accuracy in predicting pCR for the immunomodulatory (IM) subtype identified by the 101-gene model, the 27-gene IO signature, and PD-L1 expression by immunohistochemistry (IHC). The pCR rate was 45% (25/55). The odds ratios for pCR were as follows: IM subtype by 101-gene model, 3.14 (p = 0.054); 27-gene IO signature, 4.13 (p = 0.012); PD-L1 expression by IHC, 2.63 (p = 0.106); 27-gene IO signature in combination with PD-L1 expression by IHC, 6.53 (p = 0.003). The 27-gene IO signature has the potential to predict the pCR of primary TNBC to neoadjuvant immunochemotherapy. Further analysis in a large cohort is needed.

Rates of immune cell infiltration in patients with triple-negative breast cancer by molecular subtype.

In patients with triple-negative breast cancer (TNBC), tumor-infiltrating lymphocytes (TILs) are associated with improved survival. Lehmann et al. identified 4 molecular subtypes of TNBC [basal-like (BL) 1, BL2, mesenchymal (M), and luminal androgen receptor (LAR)], and an immunomodulatory (IM) gene expression signature indicates the presence of TILs and modifies these subtypes. The association between TNBC subtype and TILs is not known. Also, the association between inflammatory breast cancer (IBC) and the presence of TILs is not known. Therefore, we studied the IM subtype distribution among different TNBC subtypes. We retrospectively analyzed patients with TNBC from the World IBC Consortium dataset. The molecular subtype and the IM signature [positive (IM+) or negative (IM-)] were analyzed. Fisher's exact test was used to analyze the distribution of positivity for the IM signature according to the TNBC molecular subtype and IBC status. There were 88 patients with TNBC in the dataset, and among them 39 patients (44%) had IBC and 49 (56%) had non-IBC. The frequency of IM+ cases differed by TNBC subtype (p = 0.001). The frequency of IM+ cases by subtype was as follows: BL1, 48% (14/29); BL2, 30% (3/10); LAR, 18% (3/17); and M, 0% (0/21) (in 11 patients, the subtype could not be determined). The frequency of IM+ cases did not differ between patients with IBC and non-IBC (23% and 33%, respectively; p = 0.35). In conclusion, the IM signature representing the underlying molecular correlate of TILs in the tumor may differ by TNBC subtype but not by IBC status.

Generation of an algorithm based on minimal gene sets to clinically subtype triple negative breast cancer patients.

BACKGROUND: Recently, a gene expression algorithm, TNBCtype, was developed that can divide triple-negative breast cancer (TNBC) into molecularly-defined subtypes. The algorithm has potential to provide predictive value for TNBC subtype-specific response to various treatments. TNBCtype used in a retrospective analysis of neoadjuvant clinical trial data of TNBC patients demonstrated that TNBC subtype and pathological complete response to neoadjuvant chemotherapy were significantly associated. Herein we describe an expression algorithm reduced to 101 genes with the power to subtype TNBC tumors similar to the original 2188-gene expression algorithm and predict patient outcomes. METHODS: The new classification model was built using the same expression data sets used for the original TNBCtype algorithm. Gene set enrichment followed by shrunken centroid analysis were used for feature reduction, then elastic-net regularized linear modeling was used to identify genes for a centroid model classifying all subtypes, comprised of 101 genes. The predictive capability of both this new "lean" algorithm and the original 2188-gene model were applied to an independent clinical trial cohort of 139 TNBC patients treated initially with neoadjuvant doxorubicin/cyclophosphamide and then randomized to receive either paclitaxel or ixabepilone to determine association of pathologic complete response within the subtypes. RESULTS: The new 101-gene expression model reproduced the classification provided by the 2188-gene algorithm and was highly concordant in the same set of seven TNBC cohorts used to generate the TNBCtype algorithm (87%), as well as in the independent clinical trial cohort (88%), when cases with significant correlations to multiple subtypes were excluded. Clinical responses to both neoadjuvant treatment arms, found BL2 to be significantly associated with poor response (Odds Ratio (OR) =0.12, p=0.03 for the 2188-gene model; OR = 0.23, p < 0.03 for the 101-gene model). Additionally, while the BL1 subtype trended towards significance in the 2188-gene model (OR = 1.91, p = 0.14), the 101-gene model demonstrated significant association with improved response in patients with the BL1 subtype (OR = 3.59, p = 0.02). CONCLUSIONS: These results demonstrate that a model using small gene sets can recapitulate the TNBC subtypes identified by the original 2188-gene model and in the case of standard chemotherapy, the ability to predict therapeutic response.