Hodge, K. Alex

Abstract Background: Although the efficacy of different immunotherapeutic agents varies greatly across tumor types, little is known about the role of different metastatic tumor microenvironments in modulating the expression of immune-checkpoints like PD-L1. Using metastatic breast cancer as a model, this work explored whether metastatic lesions derived from different patients, but colonizing the same host target organ, showed organ-specific immune signatures. Methods: Snap frozen metastatic lesions collected from 30 breast cancer patients enrolled in a prospective phase II multi-omic precision medicine clinical trial (“Side-Out II”) were used for this analysis. Sites of metastasis were: liver (LI) (n=10), skin/chest wall (SC) (n=10), and other lesions (OL) (n=10) [including lymph nodes (LN) (n=4), lung (LU) (n=3), and intra-abdominal lesions (IA) (n=3)]. Pure tumor epithelia were isolated from each specimen via Laser Capture Microdissection (LCM). PD-L1 expression (Antibody Clone E1L3N) was quantitatively measured using the Reverse Phase Protein Microarray (RPPA), a high-throughput, multiplex immunoassay that provides operator-independent quantitative data starting from a small amount of biological material. Proportion of patients with PD-L1 expression above the median and the 75th percentile of the population was compared across metastatic sites. Results: PD-L1 expression had a >5 fold dynamic range across breast cancer-derived metastatic lesions with different distribution across metastatic sites. Specifically, PD-L1 expression of 6/10 LIs, 5/10 OLs (including 2 LU and 3 LN positive lesions), and 4/10 SC metastases was greater than the population median of all metastases combined. LI metastases were the most represented group above the 75th percentile of the population (4/10), followed by OS (2/10) (including 1 LU and 1 LN positive lesions), and SC (1/10). Of the 15 lesions above the population median, 6 were LI (40%), 5 were OS (33%), and 4 were SC (27%); of the 7 metastases above the 75th percentile, 4 were LI (57%), 2 were OS (29%), and 1 was SC (14%). Conclusions: The LCM-RPPA workflow can capture PD-L1 protein expression on a continuous quantitative scale and provide a broader understanding of the dynamic range of expression. Our data suggest that organ specific microenvironments in which metastatic lesions develop may strongly influence overall PD-L1 tumor cell expression. If different metastatic host organs can modulate PD-L1 expression, location of the metastatic lesion should be kept in consideration when selecting patients that are candidates for immunotherapy. Such hypotheses should be further validated in prospective clinical trial where quantitative PD-L1 expression of different metastatic lesions is evaluated along with response to targeted immuno-oncology compounds. Citation Format: Mariaelena Pierobon, K Alex Hodge, Elisa Baldelli, Steven Anthony, Nicholas J. Robert, Donald W. Northfelt, Mohammad J. Jahanzeb, Linda Vocila, Lance A. Liotta, Bryant Dunetz, Emanuel F. Petricoin. PD-L1 expression differs across cancer metastatic sites from breast tumors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3129.

Abstract Background: The efficacy of immunotherapy, including therapeutic strategies capable of modulating innate/adaptive immune resistance, varies greatly across tumor types. As the number of available immunotherapies accelerates, the study of predictive markers by IHC (e.g. PDL1 expression) is under intense investigation. However, staining protocol inconsistency, variation across scoring systems, and subjective interpretation of the immunostaining have produced conflicting results thus far. This work explored the role of Laser Capture Microdissection (LCM) coupled with Reverse Phase Protein Microarray (RPPA) as an alternative high throughput, quantitative, operator independent platform for measuring PDL1 expression across tumor types. Material and Methods: Pure epithelial cells were isolated via LCM from 178 samples including: 72 ovarian cancers (OC), 57 lung adenocarcinomas (LC), 30 metastatic breast cancers (MBC), and 19 pancreatic cancers (PC). PDL1 expression was measured on a continuous scale using quantitative RPPA based analysis. Each tumor type was processed and arrayed independently. Experimental samples and reference standards used for inter-assay normalization were printed in triplicates. Results: PDL1 expression varied greatly across tumor types. LC were characterized by the greatest intra-tumor fold dynamic range (> 35-fold), followed by OC (< 13-fold), MBC (< 4-fold), and PC (< 2-fold). PDL1 expression of 46/57 (80.7%) LC, 17/30 (56.7%) MBC, 6/19 (31.5%) PC, and 20/72 (27.8%) OC was greater than the population median of all tumors combined. Within the LC samples with PDL1 expression equal to the top quartile of the population, 10 (71.4%) were KRAS mutant lesions and 4 (28.6%) were WT tumors. Finally, amongst LC and PC harboring a KRAS mutation, PC showed an overall lower expression of PDL1 with only 2/19 (10.5%) cases been above the population median and none within the top quartile of the population. Conclusions: The LCM-RPPA workflow has the unique ability to capture immune checkpoint expression on a continuous quantitative scale as well as capture its broad dynamic range. Because RPPA is unconstrained by antigen retrieval issues as well as subjectivity of IHC interpretation, this approach may generate more accurate cut-point of therapeutic response prediction. Overall the dynamic range of PDL1 was broader in LC compared to other solid tumors, and LC had a much higher proportion of patients with tumors expressing high levels of PDL1. These quantitative differences may explain therapeutic efficacy of PDL1 inhibition across tumor types. Such speculative hypothesis should be further validated in prospective clinical trials. Finally, these preliminary data suggest that organ specific microenvironments more than specific driving mutations (e.g. KRAS) may strongly influence PDL1 expression in malignant lesions. Citation Format: Elisa Baldelli, Valerie Calvert, K. Alex Hodge, Maria Isabella Sereni, Guido Gambara, Eric B. Haura, Lucio Crino', Bryant Dunetz, Sergio Pecorelli, David J. Perry, Stephen P. Anthony, Nicholas Robert, Donald W. Northfelt, Mohammad Jahanzeb, Emanuel F. Petricoin, Mariaelena Pierobon. Quantitative measurement of PDL1 expression across tumor types using laser capture microdissection and reverse phase protein microarray [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 5656. doi:10.1158/1538-7445.AM2017-5656

Abstract Purpose: Little is known about the molecular signatures associated with specific metastatic sites in breast cancer. Using comprehensive multi-omic molecular profiling, we assessed whether alterations or activation of the PI3K–AKT–mTOR pathway is associated with specific sites of breast cancer metastasis. Experimental Design: Next-generation sequencing–based whole-exome sequencing was coupled with reverse-phase protein microarray (RPPA) functional signaling network analysis to explore the PI3K–AKT–mTOR axis in 32 pretreated breast cancer metastases. RPPA-based signaling data were further validated in an independent cohort of 154 metastatic lesions from breast cancer and 101 unmatched primary breast tumors. The proportion of cases with PI3K–AKT–mTOR genomic alterations or signaling network activation were compared between hepatic and nonhepatic lesions. Results: PIK3CA mutation and activation of AKT (S473) and p70S6K (T389) were detected more frequently among liver metastases than nonhepatic lesions (P < 0.01, P = 0.056, and P = 0.053, respectively). However, PIK3CA mutations alone were insufficient in predicting protein activation (P = 0.32 and P = 0.19 for activated AKT and p70S6K, respectively). RPPA analysis of an independent cohort of 154 tumors confirmed the relationship between pathway activation and hepatic metastasis [AKT (S473), mTOR (S2448), and 4EBP1 (S65); P < 0.01, P = 0.02, and P = 0.01, respectively]. Similar results were also seen between liver metastases and primary breast tumors [AKT (S473) P < 0.01, mTOR (S2448) P < 0.01, 4EBP1 (S65) P = 0.01]. This signature was lost when primary tumors were compared with all metastatic sites combined. Conclusions: Breast cancer patients with liver metastasis may represent a molecularly homogenized cohort with increased incidence of PIK3CA mutations and activation of the PI3K–AKT–mTOR signaling network. Clin Cancer Res; 23(16); 4919–28. ©2017 AACR.