Digital pathology to evaluate PD-L1 IHC scoring as a predictor of outcome with second-line avelumab treatment in patients with non-small cell lung cancer (NSCLC)

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Background: Assessment of programmed death ligand-1 (PD-L1) protein expression using immunohistochemistry (IHC)-based tests is currently the only approved biomarker guiding treatment of non-small cell lung cancer (NSCLC) with checkpoint inhibitors. Robust scoring guidelines and suitable cut-offs should be defined specifically for each PD-L1 IHC assay and are critical for appropriate treatment decisions. Methods: We retrospectively applied a novel digital pathology (DP) solution that mimics the conventional tumor proportion scoring (TPS) of PD-L1. The exploratory DP solution was developed and validated using samples from 340 patients enrolled in the first- and second-line (1L and 2L) NSCLC cohorts of the avelumab phase 1 JAVELIN Solid Tumor trial (NCT01772004) and 792 patients with NSCLC enrolled in the avelumab phase 3 JAVELIN Lung 200 trial (NCT02395172). Efficacy analyses were conducted for overall survival (OS) and progression-free survival (PFS) using the full analysis set with evaluable imaging data (n = 136 and n = 544, respectively). Results: Comparison of DP and conventional, semiquantitative pathologist scoring resulted in a high correlation overall (Spearman correlation coefficient, 0.86), with comparable performance for prediction of outcome to treatment with avelumab in 2L NSCLC. Consistent with conventional scoring, median OS and median PFS in avelumab-treated patients increased with higher PD-L1 expression cut-offs: in patients with ≥1%, ≥50%, and ≥80% PD-L1 expression on tumors cells, median OS was 10.0 months (95% CI: 8.6-14.3), 13.8 months (95% CI: 9.6-20.4) and 18.5 months (95% CI: 9.6-not estimable), respectively; median PFS, 3.1 months (95% CI: 2.7-4.9), 5.5 months (95% CI: 2.8-8.3) and 5.6 months (95% CI: 2.8-9.9). Conclusions: Our results demonstrate the technical feasibility, robustness, and utility of DP in scoring PD-L1 IHC in clinical trial samples, achieving comparable performance to conventional, semiquantitative pathologist scoring. Furthermore, our study supports the manual pathologist scoring algorithm (TPS scoring) in NSCLC and the selection of higher cut-offs for the PD-L1 IHC Ab clone 73-10.

Activation of tumor suppressor protein PP2A inhibits KRAS-driven tumor growth

Targeted cancer therapies, which act on specific cancer-associated molecular targets, are predominantly inhibitors of oncogenic kinases. While these drugs have achieved some clinical success, the inactivation of kinase signaling via stimulation of endogenous phosphatases has received minimal attention as an alternative targeted approach. Here, we have demonstrated that activation of the tumor suppressor protein phosphatase 2A (PP2A), a negative regulator of multiple oncogenic signaling proteins, is a promising therapeutic approach for the treatment of cancers. Our group previously developed a series of orally bioavailable small molecule activators of PP2A, termed SMAPs. We now report that SMAP treatment inhibited the growth of KRAS-mutant lung cancers in mouse xenografts and transgenic models. Mechanistically, we found that SMAPs act by binding to the PP2A Aα scaffold subunit to drive conformational changes in PP2A. These results show that PP2A can be activated in cancer cells to inhibit proliferation. Our strategy of reactivating endogenous PP2A may be applicable to the treatment of other diseases and represents an advancement toward the development of small molecule activators of tumor suppressor proteins.

Abstract B1-43: Cancer drug response networks built for comparative cancer pharmacogenomics identifies combination therapies for repositioning

We present an integrative framework for classifying drug response: CAncer Drug Response nEtworks (CADREs). We built CADREs for drugs with differential sensitivity profiles in cancer cell lines by integrating baseline copy number, gene expression, and drug sensitivity data for the cell lines. The nodes in the network are drugs and the edges are weighted according to the number of shared copy number variations, differentially expressed genes, and dysregulated pathways present in sensitive cell lines at baseline. These baseline features were mapped into pathways to generate a Pathway CADRE, and were also integrated into a differential gene expression and copy number Feature CADRE. CADREs were built for breast, ovarian, and endometrial cancers individually and combined to assess shared and unique features of drug sensitivity. Analyzing CADRE clusters associated multiple gene features and pathways with sensitivity to frontline therapies used in combination in these cancers. We identified four clusters of breast cancer drugs whose effectiveness is driven by a shared core of pathways. Compounds that were used in combination in clinical trials tended to not share an edge or are completely disconnected in networks. Specifically, ten such drug pairs in our breast CADREs have been found to work in combination in breast cancer and an additional five have been used in combination in other cancers, representing opportunities for repositioning these combinations in breast cancer. Examining highly interconnected CADRE nodes gave us an understanding for compounds that might work best together.

Citation Format: Douglas Brubaker, Elena Svenson, Smith Kai, Maya Monroe, Christopher Ryu, Goutham Narla, Mark R. Chance, Gurkan Bebek. Cancer drug response networks built for comparative cancer pharmacogenomics identifies combination therapies for repositioning. [abstract]. In: Proceedings of the AACR Special Conference on Computational and Systems Biology of Cancer; Feb 8-11 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 2):Abstract nr B1-43.