Christopher P. Vellano

Characterization of Human Cancer Cell Lines by Reverse-phase Protein Arrays

Cancer cell lines are major model systems for mechanistic investigation and drug development. However, protein expression data linked to high-quality DNA, RNA, and drug-screening data have not been available across a large number of cancer cell lines. Using reverse-phase protein arrays, we measured expression levels of ∼230 key cancer-related proteins in >650 independent cell lines, many of which have publically available genomic, transcriptomic, and drug-screening data. Our dataset recapitulates the effects of mutated pathways on protein expression observed in patient samples, and demonstrates that proteins and particularly phosphoproteins provide information for predicting drug sensitivity that is not available from the corresponding mRNAs. We also developed a user-friendly bioinformatic resource, MCLP, to help serve the biomedical research community.

Autotaxin, a lysophospholipase D with pleomorphic effects in oncogenesis and cancer progression

The ectonucleotide pyrophosphatase/phosphodiesterase type 2, more commonly known as autotaxin (ATX), is an ecto-lysophospholipase D encoded by the human ENNP2 gene. ATX is expressed in multiple tissues and participates in numerous key physiologic and pathologic processes, including neural development, obesity, inflammation, and oncogenesis, through the generation of the bioactive lipid, lysophosphatidic acid. Overwhelming evidence indicates that altered ATX activity leads to oncogenesis and cancer progression through the modulation of multiple hallmarks of cancer pathobiology. Here, we review the structural and catalytic characteristics of the ectoenzyme, how its expression and maturation processes are regulated, and how the systemic integration of its pleomorphic effects on cells and tissues may contribute to cancer initiation, progression, and therapy. Additionally, the up-to-date spectrum of the most frequent ATX genomic alterations from The Cancer Genome Atlas project is reported for a subset of cancers.

Rational combination therapy with PARP and MEK inhibitors capitalizes on therapeutic liabilities in RAS mutant cancers

The combination of PARP and MEK inhibitors is effective against RAS and RAF mutant tumors. No escape for KRAS mutant tumors Alterations in one of the RAS proteins, such as KRAS, are among the most common oncogenic mutations and also among the most difficult to treat. RAS mutant tumors are usually resistant to PARP inhibitors, one of the newest classes of anticancer therapeutics, and many other chemotherapy types. However, Sun et al. have discovered that inhibition of MEK or ERK (proteins in the RAS pathway) can reverse PARP inhibitor resistance in KRAS mutant tumors. MEK and PARP inhibitors are clinically approved drugs that provide a readily translatable therapeutic combination, and the authors have demonstrated its effectiveness in mouse models of aggressive tumors such as ovarian and pancreatic cancer. Mutant RAS has remained recalcitrant to targeted therapy efforts. We demonstrate that combined treatment with poly(adenosine diphosphate–ribose) polymerase (PARP) inhibitors and mitogen-activated protein kinase (MAPK) kinase (MEK) inhibitors evokes unanticipated, synergistic cytotoxic effects in vitro and in vivo in multiple RAS mutant tumor models across tumor lineages where RAS mutations are prevalent. The effects of PARP and MEK inhibitor combinations are independent of BRCA1/2 and p53 mutation status, suggesting that the synergistic activity is likely to be generalizable. Synergistic activity of PARP and MEK inhibitor combinations in RAS mutant tumors is associated with (i) induction of BIM-mediated apoptosis, (ii) decrease in expression of components of the homologous recombination DNA repair pathway, (iii) decrease in homologous recombination DNA damage repair capacity, (iv) decrease in DNA damage checkpoint activity, (v) increase in PARP inhibitor–induced DNA damage, (vi) decrease in vascularity that could increase PARP inhibitor efficacy by inducing hypoxia, and (vii) elevated PARP1 protein, which increases trapping activity of PARP inhibitors. Mechanistically, enforced expression of FOXO3a, which is a target of the RAS/MAPK pathway, was sufficient to recapitulate the functional consequences of MEK inhibitors including synergy with PARP inhibitors. Thus, the ability of mutant RAS to suppress FOXO3a and its reversal by MEK inhibitors accounts, at least in part, for the synergy of PARP and MEK inhibitors in RAS mutant tumors. The rational combination of PARP and MEK inhibitors warrants clinical investigation in patients with RAS mutant tumors where there are few effective therapeutic options.

Improved prediction of PARP inhibitor response and identification of synergizing agents through use of a novel gene expression signature generation algorithm

Despite rapid advancement in generation of large-scale microarray gene expression datasets, robust multigene expression signatures that are capable of guiding the use of specific therapies have not been routinely implemented into clinical care. We have developed an iterative resampling analysis to predict sensitivity algorithm to generate gene expression sensitivity profiles that predict patient responses to specific therapies. The resultant signatures have a robust capacity to accurately predict drug sensitivity as well as the identification of synergistic combinations. Here, we apply this approach to predict response to PARP inhibitors, and show it can greatly outperforms current clinical biomarkers, including BRCA1/2 mutation status, accurately identifying PARP inhibitor-sensitive cancer cell lines, primary patient-derived tumor cells, and patient-derived xenografts. These signatures were also capable of predicting patient response, as shown by applying a cisplatin sensitivity signature to ovarian cancer patients. We additionally demonstrate how these drug-sensitivity signatures can be applied to identify novel synergizing agents to improve drug efficacy. Tailoring therapeutic interventions to improve patient prognosis is of utmost importance, and our drug sensitivity prediction signatures may prove highly beneficial for patient management.Personalized medicine: Signature-guided cancer therapyPersonalized cancer therapy is one of the holy grails of oncology, as the ability to determine what treatment would best benefit a patient would serve not only to improve outcomes, but also mitigate side effects from less effective treatments. Here, we develop algorithms to predict what patients will respond to a given therapeutic modality, as well as ways to specifically target any observed phenotype, by integrating large scale data sets that profile cancer cell line gene expression and sensitivity to hundreds of drugs. Furthermore, we show how these gene expression signatures can be used to predict novel synergizing agents to further enhance the efficacy of these therapeutics. Taken together, this work stands to advance the era of personalized medicine by enabling precision medicine approaches in the clinic.

Systematic Functional Annotation of Somatic Mutations in Cancer

The functional impact of the vast majority of cancer somatic mutations remains unknown, representing a critical knowledge gap for implementing precision oncology. Here, we report the development of a moderate-throughput functional genomic platform consisting of efficient mutant generation, sensitive viability assays using two growth factor-dependent cell models, and functional proteomic profiling of signaling effects for select aberrations. We apply the platform to annotate >1,000 genomic aberrations, including gene amplifications, point mutations, indels, and gene fusions, potentially doubling the number of driver mutations characterized in clinically actionable genes. Further, the platform is sufficiently sensitive to identify weak drivers. Our data are accessible through a user-friendly, public data portal. Our study will facilitate biomarker discovery, prediction algorithm improvement, and drug development.

Implementation of a Multiplex and Quantitative Proteomics Platform for Assessing Protein Lysates Using DNA-Barcoded Antibodies

Molecular analysis of tumors forms the basis for personalized cancer medicine and increasingly guides patient selection for targeted therapy. Future opportunities for personalized medicine are highlighted by the measurement of protein expression levels via immunohistochemistry, protein arrays, and other approaches; however, sample type, sample quantity, batch effects, and “time to result” are limiting factors for clinical application. Here, we present a development pipeline for a novel multiplexed DNA-labeled antibody platform which digitally quantifies protein expression from lysate samples. We implemented a rigorous validation process for each antibody and show that the platform is amenable to multiple protocols covering nitrocellulose and plate-based methods. Results are highly reproducible across technical and biological replicates, and there are no observed “batch effects” which are common for most multiplex molecular assays. Tests from basal and perturbed cancer cell lines indicate that this platform is comparable to orthogonal proteomic assays such as Reverse-Phase Protein Array, and applicable to measuring the pharmacodynamic effects of clinically-relevant cancer therapeutics. Furthermore, we demonstrate the potential clinical utility of the platform with protein profiling from breast cancer patient samples to identify molecular subtypes. Together, these findings highlight the potential of this platform for enhancing our understanding of cancer biology in a clinical translation setting.

A murine preclinical syngeneic transplantation model for breast cancer precision medicine

This work presents a new preclinical platform for implementation of selected targeted therapeutics for breast cancer patients. We previously demonstrated that altered activity of lysophosphatidic acid in murine mammary glands promotes tumorigenesis. We have now established and characterized a heterogeneous collection of mouse-derived syngeneic transplants (MDSTs) as preclinical platforms for the assessment of personalized pharmacological therapies. Detailed molecular and phenotypic analyses revealed that MDSTs are the most heterogeneous group of genetically engineered mouse models (GEMMs) of breast cancer yet observed. Response of MDSTs to trametinib, a mitogen-activated protein kinase (MAPK) kinase inhibitor, correlated with RAS/MAPK signaling activity, as expected from studies in xenografts and clinical trials providing validation of the utility of the model. Sensitivity of MDSTs to talazoparib, a poly(adenosine 5′-diphosphate–ribose) polymerase (PARP) inhibitor, was predicted by PARP1 protein levels and by a new PARP sensitivity predictor (PSP) score developed from integrated analysis of drug sensitivity data of human cell lines. PSP score–based classification of The Cancer Genome Atlas breast cancer suggested that a subset of patients with limited therapeutic options would be expected to benefit from PARP-targeted drugs. These results indicate that MDSTs are useful models for studies of targeted therapies, and propose novel potential biomarkers for identification of breast cancer patients likely to benefit from personalized pharmacological treatments.

Abstract 2441: NanoString 3D Biology™ technology: simultaneous digital counting of DNA, RNA and protein

Introduction: Development of improved cancer diagnostics and therapeutics requires detailed understanding of the genomic, transcriptomic, and proteomic profiles in the tumor microenvironment. Current technologies can excel at measuring a single analyte, but it remains challenging to simultaneously collect high-throughput DNA, RNA, and protein data from small samples. We have developed an approach that uses optical barcodes to simultaneously profile DNA, RNA, and protein from as little as 5ng DNA, 25ng RNA, and 250ng protein or just 2 5µm FFPE slides, and simplifies data analysis by generating digital counts for each analyte. Methods: The approach uses paired capture and reporter oligonucleotide probes and optical barcodes to enumerate up to 800 targets. The platform was initially developed to measure RNA, and we have adapted it to measure DNA single nucleotide variants (SNVs), proteins, and phospho-proteins. SNVs are detected by direct hybridization of sequence discriminating probes to the wild-type and mutant sequence of interest. Proteins are detected via binding of oligonucleotide-conjugated antibodies. Results: Combinations of DNA, RNA, and protein in biological and experimental contexts. SNV probes are able to detect variant alleles down to 5% abundance within a wild type population and can discriminate variants within mutation hotspots. It was >96% accurate at identifying variants from samples displaying a range of allele frequencies and DNA integrity when benchmarked against next-generation sequencing. Protein detection has been developed for cell surface, cytosolic, and nuclear proteins, as well as phospho-proteins. It was validated against flow cytometry, western blot, and mass spectrometry using cell lines with ectopic target expression and primary cells. To demonstrate concurrent measurement of DNA, RNA, and protein from a single system, BRAFWT or BRAFV600E cell lines were treated with the BRAFV600E inhibitor vemurafenib and the MEK inhibitor trametinib. We measured the allele usage at the BRAFV600 locus, as well as BRAFV600E dependent changes in mRNA expression, protein expression and protein phosphorylation in a single experiment. Conclusions: 3D Biology has several advantages over other analytical approaches. Direct, single-molecule digital counting allows detection over a broad dynamic range with high reproducibility, often over 98% concordance between technical replicates. The simultaneous interrogation of DNA, RNA, and protein maximizes the amount of data obtained from precious samples and minimizes instrumentation demands by leveraging a single detection platform. The 3D Biology approach allows holistic, digital analysis of biological samples with high specificity and precision. This technology is currently available for research use, but may also have clinical application in the future. Citation Format: Chris Lausted, Yong Zhou, Jinho Lee, Christopher Vellano, Karina A. Eterovic, Ping Song, Lin-ya Tang, Gloria Fawcett, Tae-Beom Kim, Ken Chen, Gary Geiss, Gavin Meredith, Qian Mei, Gokhan Demirkan, Dwayne Dunaway, Dae Kim, P. Martin Ross, Elizabeth Manrao, Nathan Elliott, Sarah Warren, Christina Bailey, Chung-Ying Huang, Joseph Beechem, Gordon Mills, Leroy Hood. NanoString 3D Biology™ technology: simultaneous digital counting of DNA, RNA and protein [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 2441. doi:10.1158/1538-7445.AM2017-2441