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CD38-targeted antibody, daratumumab, is approved for the treatment of multiple myeloma (MM). Phase 1/2 studies GEN501/SIRIUS revealed a novel immunomodulatory mechanism of action (MOA) of daratumumab that enhanced the immune response, reducing natural killer (NK) cells without affecting efficacy or safety. We further evaluated daratumumab’s effects on immune cells in whole blood samples of relapsed/refractory MM patients from both treatment arms of the phase 3 POLLUX study (lenalidomide/dexamethasone [Rd] or daratumumab plus Rd [D-Rd]) at baseline (D-Rd, 40; Rd, 45) and after 2 months on treatment (D-Rd, 31; Rd, 33) using cytometry by time-of-flight. We confirmed previous reports of NK cell reduction with D-Rd. Persisting NK cells were phenotypically distinct, with increased expression of HLA-DR, CD69, CD127, and CD27. The proportion of T cells increased preferentially in deep responders to D-Rd, with a higher proportion of CD8+ versus CD4+ T cells. The expansion of CD8+ T cells correlated with clonality, indicating generation of adaptive immune response with D-Rd. D-Rd resulted in a higher proportion of effector memory T cells versus Rd. D-Rd reduced immunosuppressive CD38+ regulatory T cells. This study confirms daratumumab’s immunomodulatory MOA in combination with immunomodulatory drugs and provides further insight into immune cell changes and activation status following daratumumab-based therapy.

The vast majority of hearing loss, the most common sensory impairment, and vertigo, which commonly causes falls, both reflect underlying dysfunction of inner ear cells. Perilymph sampling can thus provide molecular cues to hearing and balance disorders. While such "liquid biopsy" of the inner ear is not yet in routine clinical practice, previous studies have uncovered alterations in perilymph in patients with certain types of hearing loss. However, the proteome of perilymph from patients with intact hearing has been unknown. Furthermore, no complete characterization of perilymph from patients with vestibular dysfunction has been reported. Here, using liquid-chromatography with tandem mass spectrometry, we analyzed samples of normal perilymph collected from three patients with skull base meningiomas and intact hearing. We identified 228 proteins that were common across the samples, establishing a greatly expanded proteome of the previously inferred normal human perilymph. Further comparison to perilymph obtained from three patients with vestibular dysfunction with drop attacks due to Meniere's disease showed 38 proteins with significantly differential abundance. The abundance of four protein candidates with previously unknown roles in inner ear biology was validated in murine cochleae by immunohistochemistry and in situ hybridization: AACT, HGFAC, EFEMP1, and TGFBI. Together, these results motivate future work in characterizing the normal human perilymph and identifying biomarkers of inner ear disease.

Five-year survival rates for patients whose cancer has metastasized are dramatically lower than for those with localized disease. However, few therapeutic development efforts specifically focus on inhibiting the process of metastasis. Here we perform a systematic pan-cancer analysis of RNA-seq data from The Cancer Genome Atlas to identify targetable gene expression changes in primary tumors that confer metastatic potential. In 4,844 patients comprising 13 different cancer types, we construct a gene expression signature that captures consistent pan-cancer alterations that differ between primary tumors associated with node-negative disease with no recorded distant metastasis (N0 not M1) and primary tumors from patients with node-positive disease (N1, N2, or N3). In order to identify potential therapeutic targets, we query this metastasis signature against drug-induced transcriptomic signatures from CMAP and LINCS. Among the top candidates is Chembl410456, which modulates the TGFβ and Wnt/β-catenin signaling pathways, among others. Genes upregulated in metastatic primary tumors and downregulated by Chembl410456 include SERPINE1 and TWIST1. We observe that low SERPINE1 and TWIST1 expression also associate with improved prognosis in multiple cancers, including colon adenocarcinoma (p=0.007 and p=0.014, respectively). Other top-ranked drugs also target the TGFβ signaling pathway. To summarize, we leverage public RNA-seq data to identify a pan-cancer metastatic signature that can be interrogated to systematically identify potential therapeutics that target metastasis. Building on this, we pinpoint antimetastasis drug candidates including several with mechanisms of action involving modulation of TGFβ and/or Wnt/β-catenin pathways. Citation Format: Matthew Ung, Jason M. Funt, Andrew C. Lysaght, Jenny Zhang, Renan Escalante-Chong, Gregory Koytiger, Sarah Kolitz, Rebecca Kusko, Benjamin Zeskind, Kevin D. Fowler. Metastasis: Leveraging transcriptomics to identify potential therapeutics [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 5177.

Introduction: Targeting metastasis has the potential to reduce the lethality of cancer, instead making it a manageable disease. While metastasis has been studied extensively in individual forms of cancer, less attention has been paid to aspects of metastatic progression conserved across cancers. Here, we identify mechanisms consistently associated with metastasis in primary tumors from 4106 patients across 12 different cancers via comparison of primary-site tumors with no lymph node involvement to those with staging in other tissues or lymph nodes. Methods: Leveraging RNA-seq data collected by The Cancer Genome Atlas (TCGA), we identify genes differentially expressed between primary tumors from patients with primary-site, node-negative disease (M0, N0) compared to primary tumors from patients with node-positive disease (N1, N2, or N3) and/or distant metastases (M1). Differentially expressed genes were first determined within each cancer type, to reduce tissue- or disease-specific effects. Subsequently via meta-analysis, we combine these results to identify commonality across 12 different types of cancer. Results: Twenty-six genes passed a threshold of adj. P < 0.05 in both the meta-analysis and individually in at least three types of cancer, while another 45 passed a threshold of adj. P < 0.05 in both the meta-analysis and individually in at least two types of cancer. These 71 genes differing most consistently between primary tumors that have metastasized and those that have not include both genes previously known to be associated with metastasis, and genes for which this is the first report of an association with metastasis. The ranked group of 71 genes represents a prioritized list of genes that warrant further consideration as targets for inhibiting metastasis. Conclusions: Metastasis is a universal problem in cancer. In the pan-cancer analysis described here, we have identified signatures that distinguish primary tumors that ultimately metastasize from those that do not. A pan-cancer analysis has identified 71 genes most consistently associated with metastasis across different types of cancer. The fact that these genes are conserved across cancer types means that they may be more associated with fundamental underlying mechanisms of metastasis and therefore represent more robust intervention points, and also suggests that therapies targeting these mechanisms will be more broadly applicable to a wider set of patients. Citation Format: Kevin D. Fowler, Jason M. Funt, Sarah Kolitz, Jenny Zhang, Matthew Ung, Renan Escalante-Chong, Andrew C. Lysaght, Gregory Koytiger, Yoonjeong Cha, Benjamin Zeskind. A meta-metastasis analysis identifies pan-cancer markers and therapeutic targets [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2017 Oct 26-30; Philadelphia, PA. Philadelphia (PA): AACR; Mol Cancer Ther 2018;17(1 Suppl):Abstract nr B027.

Cancer cell lines represent the front line of new compound testing, and results from these experiments often decide which compounds go on for further testing. Genomic context plays a critical role in drug response and now genomic data for tumors and cell lines are widely available. However, cell lines are often chosen based on ease of access, literature prevalence, and ease of culture. We combined gene expression and CNV/mutation profiling from four pancreatic cancer tumor datasets (GSE21501, GSE28735, ICGC, TCGA,) and three pancreatic cancer cell line datasets (Klijn et al, Collisson et al, and CCLE) to identify which cell lines best match patient tumors. CNV comparison revealed that popular cell lines do not always have the best CNV correlation with tumors: when comparing pancreatic cancer tumors to cell lines, the citations of the top five cell lines by CNV correlation were less than 10% of the pancreatic cancer cell line total. Next we filtered for driver mutations including SMAD4 and CDKN2A using mutation scoring algorithms and clustered tumors and cell lines. We found that many cell lines with few citation counts clustered readily amongst tumors (such as L33). Leveraging the hypothesis that different hits in the same pathway can have a similar downstream effect, we combined CNV, expression and mutation data and clustered cell lines together with tumors based on overall aberrations in MSigDB cancer pathways. L33 and YAPC clustered near tumors while the majority of other cell lines clustered together. To identify coexpressed gene clusters, we ran WGCNA individually in all seven datasets and discovered modules consistent in cell line and tumor datasets using iGraph. One of the most interesting modules (interferon regulated genes) is expressed highly in the majority of tumors profiled. About half of cell lines also express this module highly, suggesting that they may be more ideal models for high interferon expression tumors than other cell lines. Here we present evidence demonstrating that certain cell lines mimic pancreatic tumor genomes more closely while others represent patterns of genomic features not commonly observed in vivo. We also show that certain biologically relevant tumor subtypes may be better represented by some cell lines than others. Our analysis highlights the emerging role of genomics in advancing the clinical success of therapeutic trials. Citation Format: Yoonjeong Cha, Adam Labradorf, Joseph Perez-Rogers, Brian Haas, Andrew Lysaght, Brian Weiner, Fadi Towfic, Kevin Fowler, Benjamin Zeskind, Sarah Kolitz, Badri Vardarajan, Maxim Artyomov, Rebecca L. Kusko. Leveraging transcriptomic and genomic data to better select models for preclinical oncology therapeutic development to identify cell lines most similar to patient tumors. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 789.

e15730Background: Pancreatic cancer has an estimated 5-year survival rate of only 5-6% and is often diagnosed after metastasis has occurred. Gemcitabine or gemcitabine combination therapies are fre...

Cell lines used for pre-clinical testing of oncology compounds are not always chosen based on how well they models patient tumors. Instead they are often chosen based on availability and literature prevalence. The advent of high throughput genomic profiling demonstrates a causative relationship between genomic features and drug response, suggesting that cancer drug discovery could be accelerated by using genomics as a criteria to find ideal cell lines for a given cancer type. The overall oncology clinical trial success rate is dismally low, especially in pancreatic cancer. Pancreatic cancer has a five year survival of 5-6% and is predicted to be the second leading cause of cancer by 2030 with a dearth of promising medicines currently in trials. In order to forecast optimal cell lines for drug testing in pancreatic cancer, we leveraged gene expression, mutations, and copy number variation (CNV) data to compare tumors from The Cancer Genome Atlas (TCGA) to cell lines in Cancer Cell Line Encyclopedia (CCLE). To approximate cell line usage, the number of hits for each cell line in PubMed and Google Scholar were combined. Less than 20% of queried pancreatic cancer cell lines represented more than 88% of the total search hits, demonstrating a robust bias towards certain cell lines. We calculated the CNV correlation between each cell line and each tumor. The cell lines that were popular in literature, such as DAN-G (24% of citations), were often ranked worst by CNV correlation with tumors while some cell lines which were rarely cited such as L33 had among the highest CNV correlation. Next, we filtered mutation data using publicly available mutation scoring algorithms to select the most cancer driving mutations. Hierarchical clustering was applied to the tumor samples and cell lines together based on the presence or absence of the top scoring mutations in order to pinpoint cell lines with mutational spectra similar to tumors. In support of observations made in CNV data, popular cell lines such as DAN-G clustered with other cell lines and L33 clustered predominantly amongst tumor samples, providing further evidence that L33 may be an ideal cell line for modeling pancreatic cancer drug response. In order to leverage all available data types, the selected CNVs and mutations were combined into a pathway level event matrix based on the number of relevant mutations or CNVs within a given pathway and then clustered. Unsurprisingly these results show that most cell lines are much more similar to each other than to tumors. However, a few cell lines (including L33) cluster with tumor samples. Overall our results demonstrate that comprehensively L33 shows the best similarity to pancreatic cancer tumors. We believe that selecting preclinical screening methods that best match relevant tumor biology and genomic drivers could help accelerate the development of new medicines for a variety of cancers. Citation Format: Yoonjeong Cha, Andrew Lysaght, Brian Weiner, Sarah Kolitz, Fadi Towfic, Kevin Fowler, Badri Vardarajan, Maxim Artyomov, Benjamin Zeskind, Rebecca Kusko. Leveraging genomics to optimize models for accelerated pancreatic cancer drug development. [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2015 Nov 5-9; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2015;14(12 Suppl 2):Abstract nr A78.

Currently, pancreatic cancer has an estimated 5-year survival rate of only 5-6%. The projection that pancreatic cancer will be the second leading cause of cancer related death by 2020 compounded by the numerous clinical trial failures precipitates the need for novel approaches to accelerate progress in new medicine development. Cell lines used for screening pre-clinical compounds prior to animal models and human testing are usually chosen based on ease of access and literature prevalence. However, the constellation of genomic derangements in cell lines commonly used for in vitro studies may not be representative of pancreatic cancer. In this study, we leveraged copy number variation (CNV) and targeted sequencing data from The Cancer Genome Atlas (TCGA) and the Cancer Cell Line Encyclopedia (CCLE) to predict optimal cell lines that mirror pancreatic cancer genomes most closely. We calculated the frequency of each CCLE pancreatic cancer cell line in literature and compared this to how well each cell line recapitulates the pancreatic cancer population. Unsurprisingly, we observed that CCLE pancreatic cancer cell lines overall have more frequent CNVs and mutations than TCGA pancreatic cancer tumors. This observation is likely due to inherent genomic instability of cell lines and underscores the importance of using low passage cells. Next, we directly compared the median per gene CNV values in TCGA pancreatic cancer tumors and pancreatic cancer cell lines in CCLE. Contrary to our expectation, the top five cell lines by CNV correlation with TCGA pancreatic tumors represented only 6% out of all literature search hits for all CCLE pancreatic cancer cell lines, indicating the availability of more optimal cell lines from a genomics perspective. Additionally, we leveraged targeted sequencing data to compare the most frequent mutations with medium to high Mutation Assessor scores in TCGA pancreatic cancer tumors to CCLE pancreatic cancer cell lines. The seven most common mutations by this method in TCGA pancreatic cancer tumors were: KRAS, TP53, MYH8, TAOK2, PCDH15, ATRX, and CDKN2A. Using hierarchical clustering based on the presence or absence of these 7 mutations in pancreatic cancer CCLE cell lines and TCGA tumors, we showed that some cell lines readily clustered amongst TCGA tumors (such as BXPC3), while others occupied discrete branches of the dendrogram exclusive of most TCGA tumors such as PK1 and PANC1. This implies that while some cell line mimic pancreatic tumor mutations closely, others represent mutation constellations not commonly observed in patients. It is possible to apply this method to other cancer types, given consideration for potentially different cancer biology. In summary, our work reports that many popular pancreatic cancer cell lines harbor distinct genomic aberration profiles from pancreatic cancer tumors and highlights the emerging role of genomics in advancing the clinical success of therapeutic trials. Citation Format: Yoonjeong Cha, Andrew Lysaght, Rain Cui, Brian Weiner, Sarah Kolitz, Fadi Towfic, Jason Funt, Kevin Fowler, Badri Vardarajan, Maxim Artyomov, Benjamin Zeskind, Rebecca Kusko. Improving pancreatic cancer drug discovery by leveraging genomics to select better in vitro models. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 4741. doi:10.1158/1538-7445.AM2015-4741

e15268 Background: Despite substantial research efforts in pancreatic cancer the estimated 5-year survival rate remains 5-6%. The lack of early detection methods compounded by a startlingly low clinical trial success rate precipitates the need for innovative approaches to pancreatic cancer drug development. Choice of cell lines used for pre-clinical compound screening is usually based on ease of culture, popularity, and availability. However, we have observed that the constellation of pathway derangements in commonly used pancreatic cancer cell lines may not optimally model pancreatic cancer tumors. Methods: With this motivation, we leveraged gene expression (GE), copy number variation (CNV) and targeted sequencing data from The Cancer Genome Atlas (TCGA) and the Cancer Cell Line Encyclopedia (CCLE) to predict cell lines that best model pancreatic tumor genomic architecture. Inspired by the hypothesis that a disease relevant CNV should impact GE, we used an eQTL-like approach independently in pancreatic c...