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Introduction: IMM-1-104, with pan-RAS activity through deep cyclic inhibition MEK, was evaluated in humanized 3D preclinical tumor models displaying diverse MAPK pathway activation events. Based on drug-response, sensitivity and resistance profiles, a biomarker signature for IMM-1-104 was developed in order to project potential therapeutic response of cancer patients found in the AACR Project GENIE (GENIE) database. Experimental Procedures: Humanized 3D preclinical models better predict in vivo tumor responses versus 2D culture and more accurately replicate biology of human tumors. Therefore, the antitumor activity of IMM-1-104 was evaluated in over 130 tumor models spanning 12 distinct histologies in the humanized 3D tumor growth assay (3D-TGA). Cell-based whole exome sequencing readouts were combined with 3D-TGA results to build a pharmacogenomic response algorithm. When applied to the GENIE patient database, resultant tumor-specific response landscapes helped to inform an early pan-RAS clinical trial design for IMM-1-104. Summary of New Data: A machine learning model was developed to predict IMM-1-104 sensitivity using response-associated genes and signaling networks that were identified using 3D-TGA pharmacogenomics data. This model was used to estimate GENIE patient IMM-1-104 response profiles across key solid tumor indications. In addition, mutation constellations from GENIE were compared with those observed in cell lines to identify preclinical models that best resemble real-world patients. This effort was designed to further enrich the translational fidelity of specific tumor models with the goal of translationally identifying patient populations most likely to benefit from IMM-1-104 treatment. Conclusions: The depth of response to IMM-1-104 was evaluated across a panel of diverse 3D-TGA tumor models and led to identification of a biomarker signature for therapeutically addressable MAPK pathway addiction. To translate these findings into a relevant clinical application, a response algorithm was developed and applied to the GENIE database, which has cataloged the molecular profiles of over 100,000 cancer patients. Mutational landscapes of patients within GENIE helped identify preclinical models that better represent patient profiles likely to be encountered in the clinic. This approach could, as a general principle, be applied as a tool for improving biomarker discovery and clinical translation of oncology drugs. Citation Format: Praveen Nair, Sarah Kolitz, Jason Funt, Peter J. King, Kevin D. Fowler, Anna Travesa, Ian Rose, John Brothers, Amy Axel, Scott Barrett, Benjamin J. Zeskind, Brett M. Hall. Humanized 3D tumor models that are mutationally aligned with AACR GENIE patients predict IMM-1-104 activity in RAS-addicted tumors. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4265.

Background: Inappropriate activation of the MAPK pathway, often stemming from mutations in RAS or RAF, represents one of the most common oncogenic events in human cancer. Therefore, MEK1 and MEK2 (MEK), which lie downstream of RAS and RAF but upstream of ERK, have represented an attractive drug target for over two decades. Unfortunately, first generation MEK inhibitors have been limited by pathway reactivation events and serious on-target drug toxicities that restrain clinical utility, especially in patients with RAS mutant tumors. We sought to develop a new approach to MEK inhibition that would be more effective in RAS mutant tumors and with improved tolerability. IMM-1-104 is a novel dual-MEK inhibitor that is designed to disrupt phosphorylation of MEK and subsequently prevent activation of ERK1 and ERK2 (ERK). IMM-1-104’s dual-MEK mechanism resists RAF activation of MEK, and its short drug half-life allows for chronic dosing while maintaining a near-zero drug trough for improved tolerability. Materials & Methods: IMM-1-104 was profiled across a series of preclinical experiments to assess its physicochemical and drug-like properties. Cell-free and cell-based in vitro as well as in vivo characteristics were evaluated, including four independent in vivo pharmacology rodent studies in lung, colon and skin tumor models. Results: IMM-1-104 is a highly selective, orally bioavailable, non-ATP competitive, allosteric dual-MEK inhibitor. At drug exposures up to 1 micromolar (uM) in cell-free and in situ kinome screens, thermodynamic interactions and altered activity levels were observed for MEK1 and MEK2. At higher drug exposures up to 10 uM, IMM-1-104 also thermodynamically interacted with RAF1 (CRAF) and prompted reduction of KSR1 and KSR2 activation markers. IMM-1-104 displayed dual-MEK activity (i.e., RAS mutant tumor cell reductions in both ERK and MEK phosphorylation) across multiple human tumor cell-based models including A549 (KRAS-G12S) lung, A375 (BRAF-V600E) and SK-MEL-2 (NRAS-Q61R) melanoma. Pharmacokinetic studies in rodent models revealed a drug plasma half-life for IMM-1-104 of approximately 1.3 hours. In vivo tumor pharmacology studies in A549 and A375 achieved tumor stasis (i.e., 85% to 95% Tumor Growth Inhibition) with regressions observed in Colon-26 and SK-MEL-2. Conclusions: The dual-MEK mechanism and short half-life of IMM-1-104 has proven to be broadly active and well-tolerated in multiple RAS and RAF mutant preclinical tumor models in rodents. IMM-1-104 drives deep, cyclic inhibition of the MAPK pathway (improving tolerability) while resisting pathway bypass mechanisms (improving activity). Our collective data suggest that RAS and RAF mutant tumor cells are not able to tolerate periods of deep but cyclic MAPK pathway inhibition. Overall, these results are consistent with on and off signaling events that can significantly impact cell fate decisions (i.e. signaling dynamics). Citation Format: Peter J. King, Kevin D. Fowler, Sarah E. Kolitz, Scott Barrett, Benjamin J. Zeskind, Brett M. Hall. IMM-1-104: a novel, oral, selective dual-MEK inhibitor that displays broad antitumor activity and high tolerability across RAS and RAF mutant tumors in vivo [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2021 Oct 7-10. Philadelphia (PA): AACR; Mol Cancer Ther 2021;20(12 Suppl):Abstract nr P252.

We developed a novel dual MEK inhibitor, IMM-1-104, that showed 94% tumor growth inhibition (TGI) in the syngeneic C26 mouse model through 10 days at 150 mg/kg (mpk) BID, with only 3.8% tumor-adjusted body weight loss (BWL) through 24 days. The mechanism differs from that of standard MEK inhibitors (MEKi), and leverages signaling dynamics to decouple efficacy and toxicity. IMM-1-104 achieves deep cyclic inhibition of the pathway rather than constant blockade, and prevents the RAF1 (CRAF) bypass which has hampered standard MEKi. In this study, we examined the transcriptional effects of IMM-1-104 in the C26 model via RNA sequencing. These observations confirmed the pattern of deep cyclic inhibition, demonstrating strong MAPK pathway inhibition in tumor 2h after treatment, and near complete release 12h following treatment. This pattern was observed both after the initial dose as well as following chronic BID dosing (18 days), indicating that deep cyclic inhibition was sustainable across chronic dosing. Filtering the transcriptional signatures to examine effects that followed the pattern of deep cyclic inhibition (strong effect at 2h and release at 12h) revealed effects on mitochondrial and metabolism genes. These genes included Hk2, which was downregulated more than 2-fold after 2h of treatment with 100 mpk IMM-1-104 both after the initial dose and following chronic BID dosing. The effect on Hk2 occurred in tumor but not in muscle tissue from the same mice. These results were independent of adaptive immunity, as they were also observed in nude mice. Top transcriptional effects of IMM-1-104 treatment in tumor also included an increase in expression of Wnt pathway genes at 2h, following either a single dose or chronic BID dosing, at both 25 and 100 mpk. This increase was also observed at 12h following the 100 mpk dose. Activation of Wnt signaling in CRC tumors has previously been reported with standard MEK inhibitors. The transcriptional profile of IMM-1-104 in the C26 model highlighted key aspects of its mechanism related to signaling dynamics, which helps explain its ability to achieve high TGI and tolerability. The profile also highlighted aspects shared with standard MEKi that could shed light on potential resistance pathways. Citation Format: Sarah E. Kolitz, Kevin D. Fowler, Peter J. King, Benjamin J. Zeskind, Brett M. Hall. Transcriptional effects in C26 tumor highlight mechanistic aspects of a novel dual MEK inhibitor, IMM-1-104 [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2021 Oct 7-10. Philadelphia (PA): AACR; Mol Cancer Ther 2021;20(12 Suppl):Abstract nr P254.

Background: Approximately 9 out of 10 patients diagnosed with pancreatic cancer have tumors driven by a single activating mutation in KRAS. These tumors remain unaddressable by current therapies and collectively represent a high unmet clinical need. KRAS is the most commonly mutated form of RAS and represents approximately 85% of all RAS mutant human tumors with HRAS and NRAS accounting for the remaining 15%. Sotorasib (AMG-510), a covalent KRAS-G12C inhibitor, recently received accelerated regulatory approval for KRAS-G12C lung cancer in the United States. While the G12C mutation has been documented in up to 15% of all KRAS mutant tumors, it is rarely observed ( Citation Format: Peter J. King, Amy E. Axel, Kevin D. Fowler, Sarah E. Kolitz, Scott Barrett, Benjamin J. Zeskind, Brett M. Hall. Benchmarking the novel dual-MEK inhibitor, IMM-1-104, head-to-head and in combination with sotorasib (AMG-510) in the MIA PaCa-2 (KRAS-G12C) pancreatic cancer xenograft model [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2021 Oct 7-10. Philadelphia (PA): AACR; Mol Cancer Ther 2021;20(12 Suppl):Abstract nr P240.

Copaxone (glatiramer acetate, GA), a structurally and compositionally complex polypeptide nonbiological drug, is an effective treatment for multiple sclerosis, with a well-established favorable safety profile. The short antigenic polypeptide sequences comprising therapeutically active epitopes in GA cannot be deciphered with state-of-the-art methods; and GA has no measurable pharmacokinetic profile and no validated pharmacodynamic markers. The study reported herein describes the use of orthogonal standard and high-resolution physicochemical and biological tests to characterize GA and a U.S. Food and Drug Administration-approved generic version of GA, Glatopa (USA-FoGA). While similarities were observed with low-resolution or destructive tests, differences between GA and USA-FoGA were measured with high-resolution methods applied to an intact mixture, including variations in surface charge and a unique, high-molecular-weight, hydrophobic polypeptide population observed only in some USA-FoGA lots. Consistent with published reports that modifications in physicochemical attributes alter immune-related processes, genome-wide expression profiles of ex vivo activated splenocytes from mice immunized with either GA or USA-FoGA showed that 7-11% of modulated genes were differentially expressed and enriched for immune-related pathways. Thus, differences between USA-FoGA and GA may include variations in antigenic epitopes that differentially activate immune responses. We propose that the assays reported herein should be considered during the regulatory assessment process for nonbiological complex drugs such as GA.

It is known that differentiation of Th17 cells is promoted by activation of STAT3 and inhibited by activation of STAT1. Although both transcription factors are activated by several cytokines, including IL-6, IL-21, and IL-27, each of these cytokines has a very different effect on Th17 differentiation, ranging from strong induction (IL-6) to strong inhibition (IL-27). To determine the molecular basis for these differences, we measured STAT3 and STAT1 activation profiles for IL-6, IL-21, and IL-27, as well as for cytokine pairs over time. We found that the ratio of activated STAT3/activated STAT1 is crucial in determining whether cytokines promote or inhibit Th17 differentiation. IL-6 and IL-21 induced p-STAT3/p-STAT1 ratios > 1, leading to the promotion of Th17 differentiation, whereas IL-27 or IL-6+IL-27 induced p-STAT3/p-STAT1 ratios < 1, resulting in inhibition of Th17 differentiation. Consistent with these findings, we show that IL-27 induces sufficient p-STAT3 to promote Th17 differentiation in the absence of STAT1. Furthermore, IL-27–induced STAT1-deficient T cells were indistinguishable from bona fide highly proinflammatory Th17 cells because they induced severe experimental autoimmune encephalomyelitis upon adoptive transfer. Our results suggest that the ratio of p-STAT3/p-STAT1 induced by a cytokine or cytokine pairs can be used to predict whether they induce a competent Th17-differentiation program.

Copaxone (glatiramer acetate, GA), a structurally and compositionally complex polypeptide nonbiological drug, is an effective treatment for multiple sclerosis, with a well-established favorable safety profile. The short antigenic polypeptide sequences comprising therapeutically active epitopes in GA cannot be deciphered with state-of-the-art methods; and GA has no measurable pharmacokinetic profile and no validated pharmacodynamic markers. The study reported herein describes the use of orthogonal standard and high-resolution physicochemical and biological tests to characterize GA and a U.S. Food and Drug Administration-approved generic version of GA, Glatopa (USA-FoGA). While similarities were observed with low-resolution or destructive tests, differences between GA and USA-FoGA were measured with high-resolution methods applied to an intact mixture, including variations in surface charge and a unique, high-molecular-weight, hydrophobic polypeptide population observed only in some USA-FoGA lots. Consistent with published reports that modifications in physicochemical attributes alter immune-related processes, genome-wide expression profiles of ex vivo activated splenocytes from mice immunized with either GA or USA-FoGA showed that 7-11% of modulated genes were differentially expressed and enriched for immune-related pathways. Thus, differences between USA-FoGA and GA may include variations in antigenic epitopes that differentially activate immune responses. We propose that the assays reported herein should be considered during the regulatory assessment process for nonbiological complex drugs such as GA.

Pridopidine has demonstrated improvement in Huntington Disease (HD) motor symptoms as measured by secondary endpoints in clinical trials. Originally described as a dopamine stabilizer, this mechanism is insufficient to explain the clinical and preclinical effects of pridopidine. This study therefore explored pridopidine's potential mechanisms of action. The effect of pridopidine versus sham treatment on genome-wide expression profiling in the rat striatum was analysed and compared to the pathological expression profile in Q175 knock-in (Q175 KI) vs Q25 WT mouse models. A broad, unbiased pathway analysis was conducted, followed by testing the enrichment of relevant pathways. Pridopidine upregulated the BDNF pathway (P = 1.73E-10), and its effect on BDNF secretion was sigma 1 receptor (S1R) dependent. Many of the same genes were independently found to be downregulated in Q175 KI mice compared to WT (5.2e-7

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.

Cachexia is a common cause of morbidity across multiple cancer types, yet remains largely under-studied especially in the realm of transcriptomics. An essential component of cancer cachexia is the induced transition of adipocytes from white to brown, which increases energetic inefficiency and contributes to systemic loss of adipose tissue. This is likely caused by the tumor secreting thermo-stimulatory factors into the extracellular space. Here, we integrate publicly-available data to pinpoint actionable gene targets that enable cancer cells to induce fat “browning”. In particular, we identify genes differentially expressed between Lewis lung carcinoma subclones that can induce thermogenesis in primary adipocytes at high or low levels in vitro (GSE57797). We then evaluate whether the expression of these thermo-stimulatory genes predict patient survival in an independent gene expression dataset of primary tumors from advanced stage gastric cancer patients (GSE15459). Finally, we leverage drug gene expression data from CMAP and LINCS to identify molecules that reverse the thermo-stimulatory signature. Our analysis reveals that the thermo-stimulatory signature predicts prognosis in patients with advanced gastric cancer (p=0.005), with the low thermo-stimulatory group exhibiting improved prognosis. The thermo-stimulatory signature is most strongly reversed by expression signatures from inhibitors of MEK (MAP2K1), including pd-0325901, AZD-8330 (ARRY-704), CI-1040, trametinib, and selumetinib. Interestingly, we observe a time-dependent effect of these MEK inhibitors, with shorter durations of exposure consistently reversing thermo-stimulatory signatures more effectively than longer durations of exposure. Analysis of cachexia-related transcriptomic data reveals key marker genes and pathways which can be used to optimize MEK inhibitors for cachexia. Results from this study support the development of MEK inhibitors to treat cachexia, and suggest that careful optimization of pharmacokinetics will be important for success in the clinic. Citation Format: Matthew Ung, Kevin D. Fowler, Jason M. Funt, Renan Escalante-Chong, Gregory Koytiger, Sarah Kolitz, Rebecca L. Kusko, Benjamin Zeskind. Cachexia: 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 1509.

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.

Glatiramer Acetate (GA) has provided safe and effective treatment for multiple sclerosis (MS) patients for two decades. It acts as an antigen, yet the precise mechanism of action remains to be fully elucidated and no validated pharmacokinetic or pharmacodynamic biomarkers exist. In order to better characterize GA’s biological impact, genome-wide expression studies were conducted with a human monocyte (THP-1) cell line. Consistent with previous literature, branded GA upregulated anti-inflammatory markers (e.g. IL10) and modulated multiple immune-related pathways. Despite some similarities, significant differences were observed between expression profiles induced by branded GA and Probioglat, a differently-manufactured glatiramoid purported to be a generic GA. Key results were verified using qRT-PCR. Genes (e.g. CCL5, adj. p −5) critically involved in pro-inflammatory pathways (e.g. response to lipopolysaccharide, adj. p = 8.7 × 10−4) were significantly induced by Probioglat compared with branded GA. Key genes were also tested and confirmed at the protein level and in primary human monocytes. These observations suggest differential biological impact by the two glatiramoids and warrant further investigation.

To generate new insights into the biology of Alzheimer’s Disease (AD), we developed methods to combine and reuse a wide variety of existing data sets in new ways. We first identified genes consistently associated with AD in each of four separate expression studies and confirmed this result using a fifth study. We next developed algorithms to search hundreds of thousands of Gene Expression Omnibus (GEO) data sets, identifying a link between an AD-associated gene (NEUROD6) and gender. We therefore stratified patients by gender along with APOE4 status and analyzed multiple SNP data sets to identify variants associated with AD. SNPs in either the region of NEUROD6 or SNAP25 were significantly associated with AD, in APOE4+ females and APOE4+ males, respectively. We developed algorithms to search Connectivity Map (CMAP) data for medicines that modulate AD-associated genes, identifying hypotheses that warrant further investigation for treating specific AD patient subsets. In contrast to other methods, this approach focused on integrating multiple gene expression datasets across platforms in order to achieve a robust intersection of disease-affected genes and then leveraging these results in combination with genetic studies in order to prioritize potential genes for targeted therapy.

For decades, policies regarding generic medicines have sought to provide patients with economical access to safe and effective drugs, while encouraging the development of new therapies. This balance is becoming more challenging for physicians and regulators as biologics and non-biological complex drugs (NBCDs) such as glatiramer acetate demonstrate remarkable efficacy, because generics for these medicines are more difficult to assess. We sought to develop computational methods that use transcriptional profiles to compare branded medicines to generics, robustly characterizing differences in biological impact. We combined multiple computational methods to determine whether differentially expressed genes result from random variation, or point to consistent differences in biological impact of the generic compared to the branded medicine. We applied these methods to analyze gene expression data from mouse splenocytes exposed to either branded glatiramer acetate or a generic. The computational methods identified extensive evidence that branded glatiramer acetate has a more consistent biological impact across batches than the generic, and has a distinct impact on regulatory T cells and myeloid lineage cells. In summary, we developed a computational pipeline that integrates multiple methods to compare two medicines in an innovative way. This pipeline, and the specific findings distinguishing branded glatiramer acetate from a generic, can help physicians and regulators take appropriate steps to ensure safety and efficacy.

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...

The reason why IL-6 induces a pro-inflammatory response, while IL-10 induces an anti-inflammatory response, despite both cytokines activating the same transcription factor, STAT3, is not well understood. It is known that IL-6 induces a transient STAT3 signal and that IL-10 induces a sustained STAT3 signal due to the STAT3-induced inhibitor SOCS3's ability to bind to the IL-6R and not the IL-10R. We sought to develop a general transcriptional network that is capable of translating sustained signals into one response, while translating transient signals into a second response. The general structure of such a network is that the transcription factor STAT3 can induce both an inflammatory response and an anti-inflammatory response by inducing two different genes. The anti-inflammatory gene can bind to and inhibit the inflammatory gene's production and the inflammatory gene can bind to its own promoter and induce its own transcription in the absence of the signal. One prediction that can be made from such a network is that in SOCS3−/− mice, where IL-6 induces a sustained STAT3 signal, that IL-6 would act as an anti-inflammatory cytokine, which has indeed been observed experimentally in the literature., National Institutes of Health (U.S.). Director's Pioneer Award

DM catalyzes the exchange of peptides bound to Class II major histocompatibility complex (MHC) molecules. Because the dissociation and association components of the overall reaction are difficult to separate, a detailed mechanism of DM catalysis has long resisted elucidation. UV irradiation of DR molecules loaded with a photocleavable peptide (caged Class II MHC molecules) enabled synchronous and verifiable evacuation of the peptide-binding groove and tracking of early binding events in real time by fluorescence polarization. Empty DR molecules generated by photocleavage rapidly bound peptide but quickly resolved into species with substantially slower binding kinetics. DM formed a complex with empty DR molecules that bound peptide with even faster kinetics than empty DR molecules just having lost their peptide cargo. Mathematical models demonstrate that the peptide association rate of DR molecules is substantially higher in the presence of DM. We therefore unequivocally establish that DM contributes directly to peptide association through formation of a peptide-loading complex between DM and empty Class II MHC. This complex rapidly acquires a peptide analogous to the MHC class I peptide-loading complex.

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...