Ultra High-plex Spatial Proteogenomic Investigation of Giant Cell Glioblastoma Multiforme Immune Infiltrates Reveals Distinct Protein and RNA Expression Profiles

A deeper understanding of complex biological processes, including tumor development and immune response, requires ultra high-plex, spatial interrogation of multiple "omes". Here we present the development and implementation of a novel spatial proteogenomic (SPG) assay on the GeoMx Digital Spatial Profiler platform with next-generation sequencing readout that enables ultra high-plex digital quantitation of proteins (>100-plex) and RNA (whole transcriptome, >18,000-plex) from a single formalin-fixed paraffin-embedded (FFPE) sample. This study highlighted the high concordance, R > 0.85 and <15% change in sensitivity between the SPG assay and the single-analyte assays on various cell lines and tissues from human and mouse. Furthermore, we demonstrate that the SPG assay was reproducible across multiple users. When used in conjunction with advanced cellular neighborhood segmentation, distinct immune or tumor RNA and protein targets were spatially resolved within individual cell subpopulations in human colorectal cancer and non-small cell lung cancer. We used the SPG assay to interrogate 23 different glioblastoma multiforme (GBM) samples across four pathologies. The study revealed distinct clustering of both RNA and protein based on pathology and anatomic location. The in-depth investigation of giant cell glioblastoma multiforme (gcGBM) revealed distinct protein and RNA expression profiles compared with that of the more common GBM. More importantly, the use of spatial proteogenomics allowed simultaneous interrogation of critical protein posttranslational modifications alongside whole transcriptomic profiles within the same distinct cellular neighborhoods.

Significance

We describe ultra high-plex spatial proteogenomics; profiling whole transcriptome and high-plex proteomics on a single FFPE tissue section with spatial resolution. Investigation of gcGBM versus GBM revealed distinct protein and RNA expression profiles.

Abstract 4617: A complete pipeline for high-plex spatial proteomic profiling and analysis on the cosmxtm spatial molecular imager and atomtm spatial informatics platform

Detecting and analyzing large numbers of proteins using whole-slide imaging is critical for a comprehensive picture of immune response to cancer. Many existing approaches for high-plex proteomics face issues around simplicity, speed, scalability, and big data analysis. Here, we present an integrated workflow from sample preparation through downstream analysis that addresses many key concerns around high plex proteomics. The CosMx Spatial Molecular Imager (SMI) and AtoMx Spatial Informatics Platform (SIP) comprise of a turnkey, end-to-end workflow that efficiently handles highly multiplex protein analysis at plex sizes exceeding 110 targets. We demonstrate an extension of our commercially available 64-plex human immuno-oncology panel to higher numbers of targets and show how the cloud computing-enabled AtoMx SIP allows flexible construction of analytic pipelines for cell typing and spatial analyses.

The CosMx protein assay uses antibodies conjugated with oligonucleotides, which are detected using universal, multi-analyte CosMx readout reagents. The CosMx Human Immuno-oncology panel was optimized to comprehensively profile lymphoid and stromal lineages within the tumor microenvironment as well as markers of cancer signaling and progression. Each CosMx SMI antibody was validated on multi-organ FFPE tissue microarrays covering prevalent solid tumor types with matched controls, and 52 human FFPE cell lines, including overexpression lines for key targets such as GITR, CD278, PD-L1, and PD-1. CosMx SMI uses a deep learning algorithm to segment whole cells and a semi-supervised algorithm to classify cell types. The AtoMx SIP provides full analysis support, including a whole-slide image viewer, and methods for performing built-in or fully customizable analyses for cell typing, ligand-receptor analysis, neighborhood analysis and spatial differential expression.

Within the cancer sample profiled, we performed in-depth single-cell proteomic profiling across different cell populations. We detected TLS, characterized TLS maturation, and identified immune interactions with the tumor microenvironment. The CosMx SMI assay profiled the composition and spatial organization of infiltrating immune cells within and around the tumor microenvironment. We found that markers of T cell activation and exhaustion varied across the tumor landscape.

CosMx SMI is a high-plex spatial multi-omics platform that enables detection of more than 110 proteins at subcellular resolution in real-world FFPE tissues. The extensibility of the CosMx protein assay to large numbers of protein targets and our flexible, scalable bioinformatic platform provides a straightforward and robust solution for comprehensive immune phenotyping with full spatial context.

FOR RESEARCH USE ONLY. Not for use in diagnostic procedures.

Citation Format: Tien Phan-Everson, Zachary Lewis, Giang Ong, Yan Liang, Emily Brown, Liuliu Pan, Aster Wardhani, Mithra Korukonda, Carl Brown, Dwayne Dunaway, Edward Zhao, Dan McGuire, Sangsoon Woo, Alyssa Rosenbloom, Brian Filanoski, Rhonda Meredith, Kan Chantranuvatana, Brian Birditt, Hye Son Yi, Erin Piazza, Jason Reeves, John Lyssand, Vik Devgan, Michael Rhodes, Gary Geiss, Joseph Beechem. A complete pipeline for high-plex spatial proteomic profiling and analysis on the cosmxtm spatial molecular imager and atomtm spatial informatics platform. [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 4617.

Abstract 3484: Development of a custom high-plex GeoMx digital spatial profiler breast cancer protein biomarker assay

The goal of this study was to develop a high-plex assay to simultaneously quantitate 27 established and novel breast cancer (BC)-related, immune protein and phosphoprotein biomarkers using the GeoMx® Digital Spatial Profiler (DSP). The custom assay performance was compared to standard clinical BC biomarker assays (e.g.ER, PR, HER2, Ki67) across the spectrum of BC subtypes and in multiple laboratories. Commercially available antibodies to 27 BC-related protein biomarkers, including ER, PR, HER2, Ki-67, AR, immune-related (e.g. PD-L1) and several cell cycle/proliferation markers were oligonucleotide-tagged, and verified by immunohistochemistry for performance against untagged antibodies. The tagged antibodies were combined with 3 isotype controls and 2 housekeeping proteins into a custom BC high-plex assay for DSP. Confirmation of target specificity was done on a custom tissue microarray (TMA) (Run control) composed of (un)treated cancer cell lines and normal tissues. For clinical BC samples, each biopsy had 4x600 µm regions of interest segmented into pan-Cytokeratin+ tumoral epithelium and pan-Cytokeratin- adjacent stroma segments. With targeted UV light, oligonucleotides were collected from each segment sequentially and quantitated with nCounter. Raw counts were geomean normalized for analysis. Intra-site and inter-site assay reproducibility was assessed with the TMA and serial FFPE sections from BC biopsies. Quantitation of proteins showed high reproducibility within sites (e.g. Run 1 v. Run 2, p&gt;0.05 for all protein targets) and the high-plex custom assay showed good concordance between 3 participating laboratories. GeoMx DSP-derived protein data correlated well with orthogonal methodologies performed on a sample subset including immunohistochemistry, reverse phase protein array, DNA and RNA-sequencing. For example, DSP protein counts for p53 protein were higher in tumors with TP53 point mutation than those with truncating TP53 alterations. Expanded sample testing is underway with 120 well-characterized breast cancer biopsies of various histologies, immune subtypes (e.g. TIL scores) and clinical biomarker phenotypes (e.g. ER+/HER2-/Ki-67 high BC; ER+/HER2-/Ki-67 low BC; HER2+ BC; ER-/PR-/HER2- BC).Our preliminary data demonstrate that this custom high-plex BC assay can quantitate protein biomarkers across a wide dynamic range with high intra-lab and inter-lab reproducibility. The assay requirement of a single 5-µm tissue section facilitates complex biomarker profiling in biopsies with limited material. The custom assay alone or in combination with other targeted DSP protein modules can simultaneously interrogate the biomarker profile, immune-based and other drug target profile, and immune microenvironment of BC specimens, providing a novel approach for actionable tumor subtyping. For research use only. Not for use in diagnostic procedures.

Citation Format: Christopher Corless, Amber Bridgeman, Jinho Lee, Gary Geiss, Sarah Warren, Sarah Church, Joseph M. Beechem, E Aubrey Thompson, Jodi Carter. Development of a custom high-plex GeoMx digital spatial profiler breast cancer protein biomarker assay [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3484.

Abstract 2029: Multiomic analysis of whole transcriptome and high plex protein assays on a single FFPE slide

The GeoMx࣪ Digital Spatial Profiler (DSP) enables high-plex, high-throughput spatial profiling and quantification from a single slide for either protein or RNA. To fully understand the interplay between RNA and protein in tissue, we have developed a novel multi analyte assay that allows for the profiling of both analytes from the area of interest (AOI) on a single slide. Here we describe the development and performance of the multi analyte assay on cell pellet array (CPA) and various tissues including tonsil and colorectal cancer (CRC) using a high plex GeoMx Protein Panel and the GeoMx Human Whole Transcriptome Atlas (WTA). We successfully identified conditions to detect RNA and protein simultaneously, with comparable sensitivity and specificity to the single analyte conditions. The data accurately matched cell lines in an unbiased correlation analysis between GeoMx WTA and the entire CCLE RNAseq database. Furthermore, the assay performance was maintained in tissue samples. Tonsil and colorectal cancer biopsies were profiled for RNA (whole transcriptome) and protein from the same AOI, allowing simultaneous measurements of transcriptional and translational regulation of key biological pathways. This multi-analyte profiling enables deeper characterization of precious biological samples that are available in limited quantities.

Citation Format: Shilah Bonnett, Nicholas Confuorto, Kit Fuhrman, Giang Ong, Alyssa Rosenbloom, John Lyssand, Gary Geiss, Joseph Beechem. Multiomic analysis of whole transcriptome and high plex protein assays on a single FFPE slide [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2029.

Abstract 3878: Subcellular characterization of over 100 proteins in FFPE tumor biopsies with CosMx Spatial Molecular Imager

The spatial interactions between the immune system and tumor cells greatly influence antitumoral immunity. Characterization of immune cell composition and infiltration within the tumor niche informs prognosis, drug delivery efficiency, and therapeutic efficacy. However, few methods exist to query large numbers of immune biomarkers at subcellular spatial resolution. The CosMx™ Spatial Molecular Imager is the first platform to demonstrate simultaneous single-cell and subcellular detection of over 100 proteins on standard, biobanked, FFPE tissue samples. This high-plex protein panel detects key drivers of cancer progression and immune cell activation states. Here, we apply the CosMx 100-plex immuno-oncology assay on a set of breast cancer biopsies and demonstrate its quantitative and spatial capabilities. Key to CosMx protein technology is an antibody-oligonucleotide-conjugate 64-bit encoding method, not a cyclic exchange method. The encoding scheme is enabled by a 20nm hybridization-based optical barcode. The CosMx system uses a fully automated, cyclic microfluidics imaging system, high-resolution optics and 3D capability. The raw cyclic encoded 4-color tissue images are decoded using a robust automated decoding algorithm that detects protein sub-cellular localization and quantifies expression level. CosMx SMI produces protein localization maps for each target, which characterizes tissue microenvironment heterogeneity while providing spatial information. Additionally, accurate segmentation of individual cells enables spatial single-cell protein expression analysis, facilitating further mining and analyses of cellular subpopulations. The CosMx protein assay reagents were validated on multi-organ FFPE tissue microarrays and 35 human FFPE cell lines, including overexpression lines for key targets and cellular activation states, such as GITR, CD278, PD-L1, and PD-1. Benchmarking to multiple orthogonal datasets (e.g., the Human Protein Atlas, Cancer Cell Line Encyclopedia, and low-plex IHC) demonstrates that the assay is highly sensitive and specific. CosMx SMI protein assay can be coupled with SMI’s 1000-plex RNA-detection assay; together, such a multi-omics platform can generate an unprecedented information-rich view of spatial biology that could usher in novel discoveries about health and disease. FOR RESEARCH USE ONLY. Not for use in diagnostic procedures.

Citation Format: Zachary R. Lewis, Tien Phan-Everson, Gary Geiss, Mithra Korukonda, Ruchir Bhatt, Carl Brown, Dwayne Dunaway, Joseph Phan, Alyssa Rosenbloom, Brian Filanoski, Rhonda Meredith, Kan Chantranuvatana, Yan Liang, Emily Brown, Brian Birditt, Giang Ong, Hye Son Yi, Erin Piazza, Vikram Devgan, Nicole Ortogero, Patrick Danaher, Sarah Warren, Michael Rhodes, Joseph Beechem. Subcellular characterization of over 100 proteins in FFPE tumor biopsies with CosMx Spatial Molecular Imager [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3878.

Inter- and intra-tumor heterogeneity of metastatic prostate cancer determined by digital spatial gene expression profiling

Metastatic prostate cancer (mPC) comprises a spectrum of diverse phenotypes. However, the extent of inter- and intra-tumor heterogeneity is not established. Here we use digital spatial profiling (DSP) technology to quantitate transcript and protein abundance in spatially-distinct regions of mPCs. By assessing multiple discrete areas across multiple metastases, we find a high level of intra-patient homogeneity with respect to tumor phenotype. However, there are notable exceptions including tumors comprised of regions with high and low androgen receptor (AR) and neuroendocrine activity. While the vast majority of metastases examined are devoid of significant inflammatory infiltrates and lack PD1, PD-L1 and CTLA4, the B7-H3/CD276 immune checkpoint protein is highly expressed, particularly in mPCs with high AR activity. Our results demonstrate the utility of DSP for accurately classifying tumor phenotype, assessing tumor heterogeneity, and identifying aspects of tumor biology involving the immunological composition of metastases.

The inter- and intra-tumor heterogeneity of metastatic prostate cancer (mPC) is underexplored. Here the authors use Digital Spatial Profiling to study gene and protein expression heterogeneity in 27 mPC patients, finding variation in associated pathways and potential immunotherapy targets.

MEK activation modulates glycolysis and supports suppressive myeloid cells in TNBC

Triple-negative breast cancers (TNBCs) are heterogeneous and aggressive, with high mortality rates. TNBCs frequently respond to chemotherapy, yet many patients develop chemoresistance. The molecular basis and roles for tumor cell-stromal crosstalk in establishing chemoresistance are complex and largely unclear. Here we report molecular studies of paired TNBC patient-derived xenografts (PDXs) established before and after the development of chemoresistance. Interestingly, the chemoresistant model acquired a distinct KRASQ61R mutation that activates K-Ras. The chemoresistant KRAS-mutant model showed gene expression and proteomic changes indicative of altered tumor cell metabolism. Specifically, KRAS-mutant PDXs exhibited increased redox ratios and decreased activation of AMPK, a protein involved in responding to metabolic homeostasis. Additionally, the chemoresistant model exhibited increased immunosuppression, including expression of CXCL1 and CXCL2, cytokines responsible for recruiting immunosuppressive leukocytes to tumors. Notably, chemoresistant KRAS-mutant tumors harbored increased numbers of granulocytic myeloid-derived suppressor cells (gMDSCs). Interestingly, previously established Ras/MAPK-associated gene expression signatures correlated with myeloid/neutrophil-recruiting CXCL1/2 expression and negatively with T cell-recruiting chemokines (CXCL9/10/11) across patients with TNBC, even in the absence of KRAS mutations. MEK inhibition induced tumor suppression in mice while reversing metabolic and immunosuppressive phenotypes, including chemokine production and gMDSC tumor recruitment in the chemoresistant KRAS-mutant tumors. These results suggest that Ras/MAPK pathway inhibitors may be effective in some breast cancer patients to reverse Ras/MAPK-driven tumor metabolism and immunosuppression, particularly in the setting of chemoresistance.

Multiplex digital spatial profiling of proteins and RNA in fixed tissue

Digital Spatial Profiling (DSP) is a method for highly multiplex spatial profiling of proteins or RNAs suitable for use on formalin-fixed, paraffin-embedded (FFPE) samples. The approach relies on (1) multiplexed readout of proteins or RNAs using oligonucleotide tags; (2) oligonucleotide tags attached to affinity reagents (antibodies or RNA probes) through a photocleavable (PC) linker; and (3) photocleaving light projected onto the tissue sample to release PC oligonucleotides in any spatial pattern across a region of interest (ROI) covering 1 to ~5,000 cells. DSP is capable of single-cell sensitivity within an ROI using the antibody readout, with RNA detection feasible down to ~600 individual mRNA transcripts. We show spatial profiling of up to 44 proteins and 96 genes (928 RNA probes) in lymphoid, colorectal tumor and autoimmune tissues by using the nCounter system and 1,412 genes (4,998 RNA probes) by using next-generation sequencing (NGS). DSP may be used to profile not only proteins and RNAs in biobanked samples but also immune markers in patient samples, with potential prognostic and predictive potential for clinical decision-making.

Abstract 1511: MEK activation modulates immunosuppressive MDSCs and metabolic phenotypes in TNBC

Triple-negative breast cancers (TNBCs) are highly heterogeneous and aggressive, with high mortality rates. Although TNBC is typically more responsive to chemotherapy than other breast cancer subtypes, many patients develop chemo-resistance. The molecular processes between tumor and stromal cells involved in developing chemo-resistance are under-explored. Here we report studies of paired TNBC patient-derived xenografts (PDX) established before and after chemo-resistance. Despite significant genetic similarities, the chemo-resistant PDX model harbored a rare constitutively-active KRASQ61R mutation which was not present in the chemo-naive PDX. Further analysis demonstrated that the chemo-resistant KRAS-mutant model exhibited altered gene expression changes including increased expression of CXCR2-ligands CXCL1 and CXCL2, which are responsible for recruiting immune cells to tumors. These expression patterns were largely inhibited in vivo by MEK inhibitor (MEKi) treatment. Moreover, in breast cancer cell lines, CXCL1, CXCL2, and granulocyte macrophage-colony stimulating factor (CSF2, stimulates granulocyte and macrophage differentiation from hematopoietic precursor cells, including immunosuppressive myeloid cells) transcripts were also downregulated by MEKi. Notably, chemo-resistant KRAS-mutant tumors harbored increased Gr1+ and Arginase-1+ cells, consistent with recruitment of immunosuppressive M2-like macrophages and/or myeloid-derived suppressor cells (MDSCs), which was inhibited by MEKi. Further experiments demonstrate that CD45+CD11b+Ly6G+ MDSC accumulation in tumors can be inhibited by MEKi treatment alone, or by CXCR2 inhibition, suggesting that the effects of MEK inhibition on MDSC recruitment are CXCL1/2-dependent. Confirming the translational relevance of these findings, in &gt;200 murine and &gt;1000 human breast tumors, Ras/MAPK transcriptional activity correlated with myeloid-recruiting CXCL1/2 expression and negatively with T-cell recruiting chemokines (CXCL9/10/11), even in the absence of activating KRAS mutations. The association with Ras/MAPK activity was also confirmed using immunofluorescence to quantify MHC-II-low myeloid cells in 80 post-chemotherapy TNBC tumors. Importantly, MEKi and chemotherapy combination treatment reversed immunosuppressive cell accumulation and metabolic phenotypes exemplified by altered optical redox ratios (NAD(P)H/FAD) in the chemo-resistant KRAS mutant tumors, resulting in tumor growth suppression in mice. MEKi treatment also reduced redox ratios in 3D cultures of breast cancer cell lines further suggesting that MEK inhibition targets multiple oncogenic processes in breast cancer. These results suggest that Ras/MAPK pathway inhibitors may be effective in some breast cancer patients to reverse Ras/MAPK-driven tumor metabolism and immunosuppression, particularly in the setting of chemo-resistant disease.

Citation Format: Derek A. Franklin, Joe T. Sharick, Paula I. Ericsson-Gonzalez, Violeta Sanchez, Phillip T. Dean, Susan R. Opalenik, Stefano Cairo, Jean-Gabriel Judde, Michael T. Lewis, Jenny C. Chang, Melinda E. Sanders, Rebecca S. Cook, Melissa C. Skala, Jennifer Bordeaux, Jehovana Orozco Bender, Christine Vaupel, Gary Geiss, Douglas Hinerfeld, Justin M. Balko. MEK activation modulates immunosuppressive MDSCs and metabolic phenotypes in TNBC [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1511.

Abstract 1609: Simple and rapid high-plex analysis of RNA and protein from low-frequency sorted T cells

The immune system is increasingly becoming a direct target for cancer therapeutics. These cancer immunotherapy efforts require deep biologic characterization to better understand and characterize rare or low-frequency immune cells, necessitating next-generation methods to expand or complement current flow cytometry or similar methods. Recent advances in flow and mass cytometry have resulted in an improved understanding of immune system heterogeneity. These technologies, however, remain limited in the number of parameters and types of analytes that can be examined in a single sample. To obtain high-plex multi-omic data from rare cells, we utilized a novel workflow, integrating fluorescence-activated cell sorting (FACS) with the NanoString nCounter® Vantage 3D™ RNA:Protein Immune Cell Profiling Assay to deeply characterize multiple flow-sorted immune cell populations. This enabled us to simultaneously interrogate 30 cell surface proteins and 770 immune-related mRNA starting from a heterogenous cell suspension. We employed antibody-conjugated panels from BioLegend that were designed to minimize interference between fluorescently tagged and NanoString oligo-tagged antibodies. To streamline this workflow, cells were co-stained with both BioLegend and NanoString antibodies in a single 30+ antibody stain. Multiple T-cell populations were sorted and profiled from PBMC, including memory and naïve Tregs and CD8 T cells with and without stimulation, allowing for analysis of differentially regulated genes and proteins across samples. Co-stain with both fluorescently and oligo-tagged antibodies did not alter the phenotypic profiles of immune cells, validating the design of the compatible panels used to sort Tregs and CD8 T cells. Demonstrating the value of this workflow in analyzing rare populations, cells were titrated to determine the sensitivity of the workflow, producing concordant protein data from only 500 cells. High-plex RNA data were obtained from 5,000 cells without the requirement for additional molecular biology methods, such as amplification or sequencing library construction, reducing potential for technical biases. Data analysis and visualization was accomplished with nSolver™ and Cytobank software packages to rapidly analyze this high-plex data. Our analysis highlighted the population diversity and activation of key pathways between these different T-cell subtypes. Focused analysis revealed discordance between mRNA and protein levels in key immuno-oncology target genes, including PD-L1. These experiments illustrate the "3D Flow™ Analysis" method as ideally suited for incorporation into cell-sorting workflows, simultaneously producing high-plex, multi-omic data from multiple rare immune cell populations.

Citation Format: Kit Fuhrman, Brian Birditt, SuFey Ong, Alyssa Rosenbloom, Douglas Hinderfeld, Gary Geiss, Miguel Tam, Christina Bailey. Simple and rapid high-plex analysis of RNA and protein from low-frequency sorted T cells [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 1609.

Abstract 3377: Simultaneous analysis of the mutational landscape and RNA and protein expression profile of HER2-positive breast cancer using 3D BiologyTM

Background: Understanding heterogeneity within individual breast tumors is key to the ability to predict therapeutic outcome. Molecular heterogeneity is commonly evaluated based on genomic features, including mRNA abundance, gene copy number events, and somatic mutations. The expression profile and activation state of key proteins is widely recognized as another key element in defining tumor heterogeneity. We have taken advantage of NanoString 3D Biology™ technology (for research use only) and curated nCounter Vantage 3DTM Solid Tumor Assay to interrogate a survey panel of HER2-positive breast tumors with the ultimate goal of determining key relationships between multiple genomic and proteomic profiles in individual tumors.

Methods: We analyzed samples from 24 HER2+ breast cancer patients using NanoString technology to quantify the expression profile for over 25 total and phospho signaling proteins, including PI3K/MAPK/EGFR/HER2, 770 mRNA corresponding to 13 canonical cancer pathways, and 104 somatic mutations and small INDELS that are commonly associated with cancer, including 8 known PIK3CA mutations. These analyses were carried out in a matched fresh frozen and FFPE samples on the nCounter paltform. Data were analyzed by nSolver to identify genotype specific expression profiles across the 24 samples.

Results: In our proof-of-concept data set, we successfully demonstrate that NanoString’s 3D biology Technology shows concordance across both FFPE and fresh frozen sample types for DNA, RNA, and protein. NanoString analysis also showed high concordance to gold-standard techniques used to assess genotype and RNA expression profiles. The combination of digital DNA, RNA, and protein data from our HER2+ breast cancer samples yielded potentially actionable data based on mapping of mutational status as the driver of key differences in protein expression and mRNA abundance of the signaling targets profiled. This work sheds new light on HER2+ breast cancer biology and the interplay between genomic and proteomic profiles while setting the stage for future studies that further probe the differences observed in this sample set.

Conclusions: Simultaneous analysis of mutational status (SNV) and expression at the level of both mRNA and protein promises to enable a more detailed view of the relationship between genotype and the biological and clinical behavior of key tumor types. The NanoString Vantage 3DTM Solid Tumor platform provides a rapid, reliable, and economic means of assessing these analytes simultaneously. The application of these analytes to models that make clinically actionable predictions will require additional analyses of large sample cohorts, but such analysis is quite feasible using a variety of sample types.

Acknowledgements: Supported in part by grants from the Breast Cancer Research Foundation and the 26.2 with Donna Foundation.

Citation Format: Sarayna Chumsri, Daniel J. Serie, Brian M. Necela, Jennifer M. Kachergus, Bianca C. Axenfeld, Gokhan Demirkan, Gavin Meredith, P. Martin Ross, Anisha Kharkia, Erin Piazza, Afshin Mashadi-Hossein, Sarah Warren, Sarah A. McLaughlin, Joseph Beechem, Gary Geiss, E. Aubrey Thompson. Simultaneous analysis of the mutational landscape and RNA and protein expression profile of HER2-positive breast cancer using 3D BiologyTM [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 3377. doi:10.1158/1538-7445.AM2017-3377

Abstract 5563: 3D Biology™ view of cancer: Simultaneous detection of somatic DNA mutations and expression profiling of genes and signaling proteins from melanoma tumor FFPE samples

Prognosis is favorable in patients with primary localized melanoma but poor in patients with metastatic disease. With more than 76,000 cases expected to be diagnosed in 2016, more precise prognostic technologies and new therapies are needed. Although targeted treatment regimens have been approved in recent years, resistance has emerged in large part due to adaptive response mechanisms and intratumoral heterogeneity. Analysis of tumor samples across multiple molecular platforms will help elucidate the complexities within and across tumors which may underlie response to therapy as well as assist in identifying predictive biomarkers; however, these approaches require significant amounts of sample, time, and resources.

In order to integrate the strengths of analyzing different molecular analytes, we have modularized Nanostring Technologies' molecular barcoding technology to permit simultaneous digital measurement of cancer-associated DNA mutation variants, mRNA expression, and protein expression in one assay from the same sample (3D Biology). Novel nucleotide variant probes enable sensitive and specific identification of DNA mutant allele sequences down to a level of detection of ≤ 5% from 5 ng of FFPE-extracted genomic DNA. Gene expression is measured via unique digital barcoding technology to measure mRNA transcripts, and protein expression and activity (via phosphorylation) is measured by DNA-labeled antibodies. The multi-omic workflow requires only two 5-10 micron sections of FFPE tissue, whereby DNA and RNA are extracted from one section and multiplex digital protein profiling is conducted on the second.

As proof of concept demonstrating the utility of this 3D Biology platform, we have simultaneously analyzed DNA variants, RNA expression, and protein expression using NanoString’s nCounter Vantage 3D™ Solid Tumor Panel on 12 FFPE melanoma tumor samples and one normal tissue from six patients. This sample set included two metastatic tumors from each patient, and in one instance multiple regions from each metastatic site (7 and 2 regions) in order to assess intratumoral heterogeneity. Importantly, this sample set has associated mRNA expression measured using the nCounter® PanCancer Pathways for Human panel as well as whole exome sequencing (WES) data. Somatic variants seen in this latter dataset were compared with the results from the DNA SNV Solid Tumor Panel. Samples with variants that were detected in the nCounter assay but not WES were subjected to deep sequencing for validation. Overall, we show that this multiplex and multi-omic platform has the potential for rapid and sensitive assessment of patient samples that will impact clinical care.

Citation Format: Jinho Lee, Christopher P. Vellano, Gavin Meredith, Jill Mckay-Fleisch, P. Martin Ross, Michael Tetzlaff, Alexandre Reuben, Courtney Hudgens, Jennifer Wargo, Jessica Garber, Andrew White, Joseph Pan, Mike Krouse, Mekala Pansalawatta, Lucas Dennis, Anisha Kharkia, Erin Piazza, Afshin Mashadi-Hossein, Rich Boykin, Nathan Elliott, Brian Filanoski, Gokhan Demirkan, Sarah Warren, Gary Geiss, Dae Kim, Joseph Beechem, Gordon B. Mills. 3D Biology™ view of cancer: Simultaneous detection of somatic DNA mutations and expression profiling of genes and signaling proteins from melanoma tumor FFPE samples [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 5563. doi:10.1158/1538-7445.AM2017-5563

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 &gt;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

Abstract 5350: A comprehensive and integrated approach to genomic and proteomic analysis of FFPE NSCLC tumor specimens

This poster describes a novel fully integrated method for the multiplexed DNA, RNA and Protein analysis of formalin fixed paraffin embedded tumor specimens. Cancer progression is typically a result of aberrations in the molecular pathways that regulate cell growth. Identifying and understanding these changes is necessary for the detection of drug targets as well as the identification of novel biomarkers that can predict response to such drugs. Deep molecular profiling of clinical tumor samples is challenged by limited material and the compromised quality resulting from variable handling and the fixation processes that are commonly employed in the clinical laboratories. While multiplexed gene expression and mutational analysis of such samples has become well established, we describe a new method that enables multiplexed DNA, RNA and protein analysis from a single formalin fixed paraffin embedded (FFPE) tumor specimen on the NanoString nCounter system. We have adapted the sample preparation methods to enable the multi-analyte analysis of FFPE samples and have demonstrated concordance with the clinical gold standard immunohistochemistry (IHC) methodology. As a proof-of-concept, five FFPE non-small cell lung cancer (NSCLC) specimens were simultaneously stained with a panel of antibodies specific to a variety of cancer-relevant proteins, five of which were validated on serial sections by IHC using antibodies from Cell Signaling Technology. In addition to the protein analysis, from the same specimen, expression of 770 cancer-related genes and &gt;100 single nucleotide variants (SNVs) were also analyzed on the nCounter platform. By integrating the analysis of DNA SNV’s, as well as RNA and protein expression from single FFPE specimens, it is now possible to reveal the molecular mechanisms of tumor progression, uncover novel drug targets and identify biomarkers from precious specimens.

Citation Format: Douglas Hinerfield, Jennifer Mellen, Gary Geiss, Philippa Webster, Chris Merritt, Kristi Barker, Gokhan Demirkan, Brian Filanoski, Roberto Polakiewicz, Katherine Crosby. A comprehensive and integrated approach to genomic and proteomic analysis of FFPE NSCLC tumor specimens [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 5350. doi:10.1158/1538-7445.AM2017-5350

A simple and integrated workflow for deep proteomic and transcriptomic analysis of sorted cell populations

In addition to the long standing efforts to understand and manipulate the immune system in the treatment of autoimmune diseases, the immune system is increasingly becoming a direct target for cancer therapeutics. Recent advances in flow and mass cytometry have greatly expanded the number of immune cell parameters that can be interrogated resulting in an improved understanding of the immune system heterogeneity. These technologies, however, remain limited in the number and types of analytes that can be examined in a single clinical sample. The NanoString nCounter® platform enables the highly multiplexed digital analysis of both RNA and protein from a single biological specimen for multiple research applications. We have recently developed the nCounter® Vantage 3D™ RNA:Protein Immune Cell Profiling Assay for research applications, which interrogates 30 cell surface proteins and 770 immune-related RNAs starting with cells in suspension. Expanding on this, we demonstrate the development of a streamlined workflow that integrates standard immune cell sorting with downstream nCounter® analysis. By co-staining PBMCs with both fluorescently-labeled and DNA barcoded antibodies, CD8+ and CD4+ T cells and CD19+ B cells were isolated followed by analysis of dozens of additional proteins and 770 RNA from each sorted population. Demonstrating the value of this workflow in analyzing potentially rare cell populations, the number of target cells were titrated to determine the sensitivity of the workflow. Without the requirement for additional molecular biology methods, such as RNA purification or sequencing library construction, this method is ideally suited for incorporation into any cell sorting workflow.

Abstract B095: Multiplexed detection of RNA and proteins on the nCounter® platform with low sample input protocol

As our understanding of the immune responses to cancer continues to grow, the need to extract greater amounts of information from ever smaller sample sizes increases. One of the biggest challenges facing the field is to develop a comprehensive understanding of how the immune system responds to a tumor, and multi-omic profiling (DNA, RNA, and protein) is crucial to building a holistic model of tumor immunity. The NanoString nCounter platform has become an important tool in quantifying transcriptional responses from a wide variety of sample types by enabling direct digital counting of up to 800 targets from a single sample using nucleic acid probes that directly hybridize to the RNA sequence of interest and then are quantified with optical barcoding technology. The platform has now been extended to permit quantification of proteins at the same time using primary antibodies conjugated to DNA oligos that hybridize with the barcodes. We have developed new technology that enables characterization of up to 30 proteins and 770 RNA transcripts in key immuno-oncology pathways from a very low amount of starting material - as few as 50,000 cells. NanoString has previously developed the nCounter Vantage RNA:Protein Immune Profiling Panel which allows digital counting of extracellular proteins which facilitates quantitation of multiple immune cell populations and provides information about their activation status. We have now applied this technology to detect intracellular and secreted proteins as well in the new nCounter Vantage RNA:Protein Immune Signaling panel. This panel is able to detect key transcription factors, signaling molecules, and secreted proteins from peripheral blood mononuclear cells (PBMC), dissociated cells, or cell culture. Additionally, we have recently developed a universal cell capture technology that utilizes anti-β2M antibody coupled to magnetic beads to pull down nucleated cells from a large starting volume. As proof of concept, PBMC from a healthy donor were treated with phorbol 12-myristate 13-acetate (PMA) and ionomycin, TNFα, or IFNγ and RNA and protein were characterized with both the Immune Profiling and Immune Signaling panels. nCounter protein detection compared favorably when validated with flow cytometry, and the additional information imparted by simultaneous profiling of the RNA transcriptome enabled mapping of signaling pathways activated by treatment. Furthermore, RNA and protein counts from the low input protocol were representative of counts obtained from higher cell inputs, indicating that no loss or skewing of data resulted from reducing the starting material. This advance in multi-analyte, multiplexed digital molecular profiling will accelerate immuno-oncology research by reducing sample size requirements and may enable the discovery and development of novel immunotherapies and their associated companion diagnostics.

Citation Format: Sarah Warren, Gary Geiss, Brian Burditt, Qian Mei, Alan Huang, Maribeth Eagen, Eduardo Ignacio, Dwayne Dunaway, Lucas Dennis, Joseph Beechem. Multiplexed detection of RNA and proteins on the nCounter® platform with low sample input protocol [abstract]. In: Proceedings of the Second CRI-CIMT-EATI-AACR International Cancer Immunotherapy Conference: Translating Science into Survival; 2016 Sept 25-28; New York, NY. Philadelphia (PA): AACR; Cancer Immunol Res 2016;4(11 Suppl):Abstract nr B095.

Development and verification of the PAM50-based Prosigna breast cancer gene signature assay

BACKGROUND

The four intrinsic subtypes of breast cancer, defined by differential expression of 50 genes (PAM50), have been shown to be predictive of risk of recurrence and benefit of hormonal therapy and chemotherapy. Here we describe the development of Prosigna™, a PAM50-based subtype classifier and risk model on the NanoString nCounter Dx Analysis System intended for decentralized testing in clinical laboratories.

METHODS

514 formalin-fixed, paraffin-embedded (FFPE) breast cancer patient samples were used to train prototypical centroids for each of the intrinsic subtypes of breast cancer on the NanoString platform. Hierarchical cluster analysis of gene expression data was used to identify the prototypical centroids defined in previous PAM50 algorithm training exercises. 304 FFPE patient samples from a well annotated clinical cohort in the absence of adjuvant systemic therapy were then used to train a subtype-based risk model (i.e. Prosigna ROR score). 232 samples from a tamoxifen-treated patient cohort were used to verify the prognostic accuracy of the algorithm prior to initiating clinical validation studies.

RESULTS

The gene expression profiles of each of the four Prosigna subtype centroids were consistent with those previously published using the PCR-based PAM50 method. Similar to previously published classifiers, tumor samples classified as Luminal A by Prosigna had the best prognosis compared to samples classified as one of the three higher-risk tumor subtypes. The Prosigna Risk of Recurrence (ROR) score model was verified to be significantly associated with prognosis as a continuous variable and to add significant information over both commonly available IHC markers and Adjuvant! Online.

CONCLUSIONS

The results from the training and verification data sets show that the FDA-cleared and CE marked Prosigna test provides an accurate estimate of the risk of distant recurrence in hormone receptor positive breast cancer and is also capable of identifying a tumor's intrinsic subtype that is consistent with the previously published PCR-based PAM50 assay. Subsequent analytical and clinical validation studies confirm the clinical accuracy and technical precision of the Prosigna PAM50 assay in a decentralized setting.

Abstract LB-398: Detection of copy number alterations in 124 head and neck squamous cell carcinomas using the Nanostring nCounter assay

Background: Head and Neck Squamous Cell Carcinoma (HNSCC) is the 6th most common cancer worldwide. Little is known about changes in copy number (CN) in many cancer associated genes in this tumor type, which may contribute to carcinogenesis and could become useful treatment targets in the future. Many analyses of CN are complicated by the need to use formalin-fixed paraffin-embedded (FFPE) tissues posing technical challenges. We determined copy number alterations (CNA) using a novel, medium-throughput technology (NanoString nCounter) in order to determine common cancer associated CNAs and assess its performance in FFPE tissues. Results were compared with more established technologies such as SNP array, aCGH, and qPCR. Methods: We investigated CNA in 124 tumor specimens and 22 cell lines for 100 literature curated cancer genes using the NanoString nCounter. Most samples were OCT frozen tissues, with a small subset having both OCT frozen, and FFPE tissues. Slides were assessed for tumor content by a HNC pathologist and samples with at least 60% tumor content selected. DNA was extracted using standard column-based methods (Qiagen). We performed CN analysis in 124 frozen (+4 matching FFPE) HNSCC specimens and cell lines (Nanostring nCounter assay) focusing on a selection of cancer associated genes. Furthermore we used aCGH and SNP-CHIP to analyse 20 and 4 cell lines respectively two of which were covered by all three methods. FGFR1 was assessed by qPCR. For FFPE samples a special Nanostring probeset was used with 3-5 probes per gene to provide redundancy with degraded DNA samples. HPV status of samples was assessed by a nested PCR for E6. Results Copy number changes detected by Nanostring and aCGH correlated well. The Nanostring nCounter assay appeared more accurate in calling deletions, which were detected in MST1R, PBRM1, PTPRD for instance. We found amplifications in multiple samples and genes, e.g. CCND1, EGFR, MDM4, MYC, VEGFA, PAX9, ITGB4, SSND1, CTTN, FADD, FGF19, ORAOV1, PPFIA1, some of which were frequently (n&gt;50 samples) or highly amplified (&gt;30 copies). Some of these amplifications such as ORAOV1 and PPFIA1 seemed higher/more frequent in HPV(-) compared to HPV(+) samples. Samples with FGFR1 amplification were validated using qPCR and correlated very closely. FFPE sample processing was uncomplicated using the FFPE probeset. While some probes failed, using degraded FFPE derived DNA, the redundancy of probes allowed accurate calling of CNA that closely correlated with frozen sample results. Conclusions Copy number alterations are frequent in HNSCC and involve many cancer associated genes, including potentially targetable genes such as EGFR, MDM4, and PIK3CA. Most of the CN changes are recurrent. Amplifications and deletions to some extent differed depending on HPV status. The role and implications of these CN aberrations in a clinical setting need to be further elucidated and validated.

Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr LB-398. doi:1538-7445.AM2012-LB-398

Discovery of novel methylation biomarkers in cervical carcinoma by global demethylation and microarray analysis

A genome-wide screening study for identification of hypermethylated genes in invasive cervical cancer (ICC) was carried out to augment our previously discovered panel of three genes found to be useful for detection of ICC and its precursor neoplasia. Putatively hypermethylated and silenced genes were reactivated in four ICC cell lines by treatment with 5-aza-2'-deoxycytidine and trichostatin A and identified on expression microarrays. Thirty-nine of the 235 genes up-regulated in multiple ICC cell lines were further examined to determine the methylation status of associated CpG islands. The diagnostic use of 23 genes that were aberrantly methylated in multiple ICC cell lines were then analyzed in DNA from exfoliated cells obtained from patients with or without ICC. We show, for the first time, that aberrant methylation of six genes (SPARC, TFPI2, RRAD, SFRP1, MT1G, and NMES1) is present in a high proportion of ICC clinical samples but not in normal samples. Of these genes, SPARC and TFPI2 showed the highest frequency of aberrant methylation in ICC specimens (86.4% for either) and together were hypermethylated in all but one ICC cases examined. We conclude that expression profiling of epigenetically reactivated genes followed by methylation analysis in clinical samples is a powerful tool for comprehensive identification of methylation markers. Several novel genes identified in our study may be clinically useful for detection or stratification of ICC and/or of its precursor lesions and provide a basis for better understanding of mechanisms involved in development of ICC.

A portable surface plasmon resonance sensor system for real-time monitoring of small to large analytes

Many environmental applications exist for biosensors capable of providing real-time analyses. One pressing current need is monitoring for agents of chemical- and bio-terrorism. These applications require systems that can rapidly detect small organics including nerve agents, toxic proteins, viruses, spores and whole microbes. A second area of application is monitoring for environmental pollutants. Processing of grab samples through chemical laboratories requires significant time delays in the analyses, preventing the rapid mapping and cleanup of chemical spills. The current state of development of miniaturized, integrated surface plasmon resonance (SPR) sensor elements has allowed for the development of inexpensive, portable biosensor systems capable of the simultaneous analysis of multiple analytes. Most of the detection protocols make use of antibodies immobilized on the sensor surface. The Spreeta 2000 SPR biosensor elements manufactured by Texas Instruments provide three channels for each sensor element in the system. A temperature-controlled two-element system that monitors for six analytes is currently in use, and development of an eight element sensor system capable of monitoring up to 24 different analytes will be completed in the near future. Protein toxins can be directly detected and quantified in the low picomolar range. Elimination of false positives and increased sensitivity is provided by secondary antibodies with specificity for different target epitopes, and by sensor element redundancy. Inclusion of more than a single amplification step can push the sensitivity of toxic protein detection to femtomolar levels. The same types of direct detection and amplification protocols are used to monitor for viruses and whole bacteria or spores. Special protocols are required for the detection of small molecules. Either a competition type assay where the presence of analyte inhibits the binding of antibodies to surface-immobilized analyte, or a displacement assay, where antibodies bound to analyte on the sensor surface are displaced by free analyte, can be used. The small molecule detection assays vary in sensitivity from the low micromolar range to the high picomolar.

A comprehensive view of regulation of gene expression by double-stranded RNA-mediated cell signaling

Double-stranded (ds) RNA, a common component of virus-infected cells, is a potent inducer of the type I interferon and other cellular genes. For identifying the full repertoire of human dsRNA-regulated genes, a cDNA microarray hybridization screening was conducted using mRNA from dsRNA-treated GRE cells. Because these cells lack all type I interferon genes, the possibility of gene induction by autocrine actions of interferon was eliminated. Our screen identified 175 dsRNA-stimulated genes (DSG) and 95 dsRNA-repressed genes. A subset of the DSGs was also induced by different inflammatory cytokines and viruses demonstrating interconnections among disparate signaling pathways. Functionally, the DSGs encode proteins involved in signaling, apoptosis, RNA synthesis, protein synthesis and processing, cell metabolism, transport, and structure. Induction of such a diverse family of genes by dsRNA has major implications in host-virus interactions and in the use of RNA(i) technology for functional ablation of specific genes.