Publications by authors named "Joe W Gray"

229 Publications

VISTA: VIsual Semantic Tissue Analysis for pancreatic disease quantification in murine cohorts.

Sci Rep 2020 12 1;10(1):20904. Epub 2020 Dec 1.

Department of Biomedical Engineering and OHSU Center for Spatial Systems Biomedicine (OCSSB), Portland, OR, USA.

Mechanistic disease progression studies using animal models require objective and quantifiable assessment of tissue pathology. Currently quantification relies heavily on staining methods which can be expensive, labor/time-intensive, inconsistent across laboratories and batch, and produce uneven staining that is prone to misinterpretation and investigator bias. We developed an automated semantic segmentation tool utilizing deep learning for rapid and objective quantification of histologic features relying solely on hematoxylin and eosin stained pancreatic tissue sections. The tool segments normal acinar structures, the ductal phenotype of acinar-to-ductal metaplasia (ADM), and dysplasia with Dice coefficients of 0.79, 0.70, and 0.79, respectively. To deal with inaccurate pixelwise manual annotations, prediction accuracy was also evaluated against biological truth using immunostaining mean structural similarity indexes (SSIM) of 0.925 and 0.920 for amylase and pan-keratin respectively. Our tool's disease area quantifications were correlated to the quantifications of immunostaining markers (DAPI, amylase, and cytokeratins; Spearman correlation score = 0.86, 0.97, and 0.92) in unseen dataset (n = 25). Moreover, our tool distinguishes ADM from dysplasia, which are not reliably distinguished with immunostaining, and demonstrates generalizability across murine cohorts with pancreatic disease. We quantified the changes in histologic feature abundance for murine cohorts with oncogenic Kras-driven disease, and the predictions fit biological expectations, showing stromal expansion, a reduction of normal acinar tissue, and an increase in both ADM and dysplasia as disease progresses. Our tool promises to accelerate and improve the quantification of pancreatic disease in animal studies and become a unifying quantification tool across laboratories.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s41598-020-78061-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7708430PMC
December 2020

Large-Scale Characterization of Drug Responses of Clinically Relevant Proteins in Cancer Cell Lines.

Cancer Cell 2020 Dec 5;38(6):829-843.e4. Epub 2020 Nov 5.

Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; Graduate Program in Quantitative and Computational Biosciences, Baylor College of Medicine, Houston, TX 77030, USA. Electronic address:

Perturbation biology is a powerful approach to modeling quantitative cellular behaviors and understanding detailed disease mechanisms. However, large-scale protein response resources of cancer cell lines to perturbations are not available, resulting in a critical knowledge gap. Here we generated and compiled perturbed expression profiles of ∼210 clinically relevant proteins in >12,000 cancer cell line samples in response to ∼170 drug compounds using reverse-phase protein arrays. We show that integrating perturbed protein response signals provides mechanistic insights into drug resistance, increases the predictive power for drug sensitivity, and helps identify effective drug combinations. We build a systematic map of "protein-drug" connectivity and develop a user-friendly data portal for community use. Our study provides a rich resource to investigate the behaviors of cancer cells and the dependencies of treatment responses, thereby enabling a broad range of biomedical applications.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.ccell.2020.10.008DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7738392PMC
December 2020

SHIFT: speedy histological-to-immunofluorescent translation of a tumor signature enabled by deep learning.

Sci Rep 2020 10 15;10(1):17507. Epub 2020 Oct 15.

Computational Biology Program, Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR, USA.

Spatially-resolved molecular profiling by immunostaining tissue sections is a key feature in cancer diagnosis, subtyping, and treatment, where it complements routine histopathological evaluation by clarifying tumor phenotypes. In this work, we present a deep learning-based method called speedy histological-to-immunofluorescent translation (SHIFT) which takes histologic images of hematoxylin and eosin (H&E)-stained tissue as input, then in near-real time returns inferred virtual immunofluorescence (IF) images that estimate the underlying distribution of the tumor cell marker pan-cytokeratin (panCK). To build a dataset suitable for learning this task, we developed a serial staining protocol which allows IF and H&E images from the same tissue to be spatially registered. We show that deep learning-extracted morphological feature representations of histological images can guide representative sample selection, which improved SHIFT generalizability in a small but heterogenous set of human pancreatic cancer samples. With validation in larger cohorts, SHIFT could serve as an efficient preliminary, auxiliary, or substitute for panCK IF by delivering virtual panCK IF images for a fraction of the cost and in a fraction of the time required by traditional IF.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s41598-020-74500-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7566625PMC
October 2020

Transcriptional signatures in histologic structures within glioblastoma tumors may predict personalized drug sensitivity and survival.

Neurooncol Adv 2020 Jan-Dec;2(1):vdaa093. Epub 2020 Aug 3.

Department of Neurology, Blood-Brain Barrier Program, Oregon Health and Science University, Portland, Oregon, USA.

Background: Glioblastoma is a rapidly fatal brain cancer that exhibits extensive intra- and intertumoral heterogeneity. Improving survival will require the development of personalized treatment strategies that can stratify tumors into subtypes that differ in therapeutic vulnerability and outcomes. Glioblastoma stratification has been hampered by intratumoral heterogeneity, limiting our ability to compare tumors in a consistent manner. Here, we develop methods that mitigate the impact of intratumoral heterogeneity on transcriptomic-based patient stratification.

Methods: We accessed open-source transcriptional profiles of histological structures from 34 human glioblastomas from the Ivy Glioblastoma Atlas Project. Principal component and correlation network analyses were performed to assess sample inter-relationships. Gene set enrichment analysis was used to identify enriched biological processes and classify glioblastoma subtype. For survival models, Cox proportional hazards regression was utilized. Transcriptional profiles from 156 human glioblastomas were accessed from The Cancer Genome Atlas to externally validate the survival model.

Results: We showed that intratumoral histologic architecture influences tumor classification when assessing established subtyping and prognostic gene signatures, and that indiscriminate sampling can produce misleading results. We identified the cellular tumor as a glioblastoma structure that can be targeted for transcriptional analysis to more accurately stratify patients by subtype and prognosis. Based on expression from cellular tumor, we created an improved risk stratification gene signature.

Conclusions: Our results highlight that biomarker performance for diagnostics, prognostics, and prediction of therapeutic response can be improved by analyzing transcriptional profiles in pure cellular tumor, which is a critical step toward developing personalized treatment for glioblastoma.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1093/noajnl/vdaa093DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7462280PMC
August 2020

Development and implementation of the SUM breast cancer cell line functional genomics knowledge base.

NPJ Breast Cancer 2020 21;6:30. Epub 2020 Jul 21.

Department of Pathology and Laboratory Medicine, Medical University of South Carolina, Charleston, SC USA.

Several years ago, the SUM panel of human breast cancer cell lines was developed, and these cell lines have been distributed to hundreds of labs worldwide. Our lab and others have developed extensive omics data sets from these cells. More recently, we performed genome-scale shRNA essentiality screens on the entire SUM line panel, as well as on MCF10A cells, MCF-7 cells, and MCF-7LTED cells. These gene essentiality data sets allowed us to perform orthogonal analyses that functionalize the otherwise descriptive genomic data obtained from traditional genomics platforms. To make these omics data sets available to users of the SUM lines, and to allow users to mine these data sets, we developed the SUM Breast Cancer Cell Line Knowledge Base. This knowledge base provides information on the derivation of each cell line, provides protocols for the proper maintenance of the cells, and provides a series of data mining tools that allow rapid identification of the oncogene signatures for each line, the enrichment of KEGG pathways with screen hit and gene expression data, an analysis of protein and phospho-protein expression for the cell lines, as well as a gene search tool and a functional-druggable signature tool. Recently, we expanded our database to include genomic data for an additional 27 commonly used breast cancer cell lines. Thus, the SLKBase provides users with deep insights into the biology of human breast cancer cell lines that can be used to develop strategies for the reverse engineering of individual breast cancer cell lines.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s41523-020-0173-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7374090PMC
July 2020

Oligonucleotide conjugated antibodies permit highly multiplexed immunofluorescence for future use in clinical histopathology.

J Biomed Opt 2020 05;25(5):1-18

Oregon Health and Science University, Biomedical Engineering Department, Portland, Oregon, United States.

Significance: Advanced genetic characterization has informed cancer heterogeneity and the challenge it poses to effective therapy; however, current methods lack spatial context, which is vital to successful cancer therapy. Conventional immunolabeling, commonplace in the clinic, can provide spatial context to protein expression. However, these techniques are spectrally limited, resulting in inadequate capacity to resolve the heterogenous cell subpopulations within a tumor.

Aim: We developed and optimized oligonucleotide conjugated antibodies (Ab-oligo) to facilitate cyclic immunofluorescence (cyCIF), resulting in high-dimensional immunostaining.

Approach: We employed a site-specific conjugation strategy to label antibodies with unique oligonucleotide sequences, which were hybridized in situ with their complementary oligonucleotide sequence tagged with a conventional fluorophore. Antibody concentration, imaging strand concentration, and configuration as well as signal removal strategies were optimized to generate maximal staining intensity using our Ab-oligo cyCIF strategy.

Results: We successfully generated 14 Ab-oligo conjugates and validated their antigen specificity, which was maintained in single color staining studies. With the validated antibodies, we generated up to 14-color imaging data sets of human breast cancer tissues.

Conclusions: Herein, we demonstrated the utility of Ab-oligo cyCIF as a platform for highly multiplexed imaging, its utility to measure tumor heterogeneity, and its potential for future use in clinical histopathology.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1117/1.JBO.25.5.056004DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7245007PMC
May 2020

A workflow for visualizing human cancer biopsies using large-format electron microscopy.

Methods Cell Biol 2020 12;158:163-181. Epub 2020 Mar 12.

OHSU Center for Spatial Systems Biomedicine, Oregon Health and Sciences University, Portland, OR, United States. Electronic address:

Recent developments in large format electron microscopy have enabled generation of images that provide detailed ultrastructural information on normal and diseased cells and tissues. Analyses of these images increase our understanding of cellular organization and interactions and disease-related changes therein. In this manuscript, we describe a workflow for two-dimensional (2D) and three-dimensional (3D) imaging, including both optical and scanning electron microscopy (SEM) methods, that allow pathologists and cancer biology researchers to identify areas of interest from human cancer biopsies. The protocols and mounting strategies described in this workflow are compatible with 2D large format EM mapping, 3D focused ion beam-SEM and serial block face-SEM. The flexibility to use diverse imaging technologies available at most academic institutions makes this workflow useful and applicable for most life science samples. Volumetric analysis of the biopsies studied here revealed morphological, organizational and ultrastructural aspects of the tumor cells and surrounding environment that cannot be revealed by conventional 2D EM imaging. Our results indicate that although 2D EM is still an important tool in many areas of diagnostic pathology, 3D images of ultrastructural relationships between both normal and cancerous cells, in combination with their extracellular matrix, enables cancer researchers and pathologists to better understand the progression of the disease and identify potential therapeutic targets.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/bs.mcb.2020.01.005DOI Listing
March 2020

The Human Tumor Atlas Network: Charting Tumor Transitions across Space and Time at Single-Cell Resolution.

Cell 2020 04;181(2):236-249

Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Present address: Foundation Medicine, Cambridge, MA 02141, USA.

Crucial transitions in cancer-including tumor initiation, local expansion, metastasis, and therapeutic resistance-involve complex interactions between cells within the dynamic tumor ecosystem. Transformative single-cell genomics technologies and spatial multiplex in situ methods now provide an opportunity to interrogate this complexity at unprecedented resolution. The Human Tumor Atlas Network (HTAN), part of the National Cancer Institute (NCI) Cancer Moonshot Initiative, will establish a clinical, experimental, computational, and organizational framework to generate informative and accessible three-dimensional atlases of cancer transitions for a diverse set of tumor types. This effort complements both ongoing efforts to map healthy organs and previous large-scale cancer genomics approaches focused on bulk sequencing at a single point in time. Generating single-cell, multiparametric, longitudinal atlases and integrating them with clinical outcomes should help identify novel predictive biomarkers and features as well as therapeutically relevant cell types, cell states, and cellular interactions across transitions. The resulting tumor atlases should have a profound impact on our understanding of cancer biology and have the potential to improve cancer detection, prevention, and therapeutic discovery for better precision-medicine treatments of cancer patients and those at risk for cancer.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.cell.2020.03.053DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7376497PMC
April 2020

Fluorescent Imaging for Measurement of Drug Target Engagement and Cell Signaling Pathways.

Proc SPIE Int Soc Opt Eng 2020 Feb 19;11219. Epub 2020 Feb 19.

Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR 97201.

Successful cancer treatment continues to elude modern medicine and its arsenal of therapeutic strategies. Therapy resistance is driven by significant tumor heterogeneity, complex interactions between malignant, microenvironmental and immune cells and cross talk between signaling pathways. Advances in molecular characterization technologies such as next generation sequencing have helped unravel this network of interactions and identify druggable therapeutic targets. Tyrosine kinase inhibitors (TKI) are a class of drugs seeking to inhibit signaling pathways critical to sustaining proliferative signaling, resisting cell death, and the other hallmarks of cancer. While tumors may initially respond to TKI therapy, disease progression is near universal due to mechanisms of acquired resistance largely involving cellular signaling pathway reprogramming. With the ultimate goal of improved TKI therapeutic efficacy our group has developed intracellular paired agent imaging (iPAI) to quantify drug target interactions and oligonucleotide conjugated antibody (Ab-oligo) cyclic immunofluorescence (cycIF) imaging to characterize perturbed signaling pathways in response to therapy. iPAI uses spectrally distinct, fluorescently labeled targeted and untargeted drug derivatives, correcting for non-specific drug distribution and facilitating quantitative assessment of the drug binding before and after therapy. Ab-oligo cycIF exploits hybridization of complementary oligonucleotides for biomarker labeling while oligonucleotide modifications facilitate signal removal for sequential rounds of fluorescent tagging and imaging. Ab-oligo CycIF is capable of generating extreme multi-parametric images for quantifying total and phosphorylated protein expression to quantify protein activation, expression, and spatial distribution. Together iPAI and Ab-oligo cycIF can be applied to interrogate drug uptake and target binding as well as changes to heterogenous cell populations within tumors that drive variable therapeutic responses in patients.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7158854PMC
February 2020

How Machine Learning Will Transform Biomedicine.

Cell 2020 04;181(1):92-101

Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA.

This Perspective explores the application of machine learning toward improved diagnosis and treatment. We outline a vision for how machine learning can transform three broad areas of biomedicine: clinical diagnostics, precision treatments, and health monitoring, where the goal is to maintain health through a range of diseases and the normal aging process. For each area, early instances of successful machine learning applications are discussed, as well as opportunities and challenges for machine learning. When these challenges are met, machine learning promises a future of rigorous, outcomes-based medicine with detection, diagnosis, and treatment strategies that are continuously adapted to individual and environmental differences.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.cell.2020.03.022DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7141410PMC
April 2020

RESTORE: Robust intEnSiTy nORmalization mEthod for multiplexed imaging.

Commun Biol 2020 03 9;3(1):111. Epub 2020 Mar 9.

Department of Biomedical Engineering, OHSU Center for Spatial Systems Biomedicine, Oregon Health & Science University, Portland, OR, 97201, USA.

Recent advances in multiplexed imaging technologies promise to improve the understanding of the functional states of individual cells and the interactions between the cells in tissues. This often requires compilation of results from multiple samples. However, quantitative integration of information between samples is complicated by variations in staining intensity and background fluorescence that obscure biological variations. Failure to remove these unwanted artifacts will complicate downstream analysis and diminish the value of multiplexed imaging for clinical applications. Here, to compensate for unwanted variations, we automatically identify negative control cells for each marker within the same tissue and use their expression levels to infer background signal level. The intensity profile is normalized by the inferred level of the negative control cells to remove between-sample variation. Using a tissue microarray data and a pair of longitudinal biopsy samples, we demonstrated that the proposed approach can remove unwanted variations effectively and shows robust performance.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s42003-020-0828-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7062831PMC
March 2020

DIRAS3 (ARHI) Blocks RAS/MAPK Signaling by Binding Directly to RAS and Disrupting RAS Clusters.

Cell Rep 2019 12;29(11):3448-3459.e6

Department of Experimental Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA. Electronic address:

Oncogenic RAS mutations drive cancers at many sites. Recent reports suggest that RAS dimerization, multimerization, and clustering correlate strongly with activation of RAS signaling. We have found that re-expression of DIRAS3, a RAS-related small GTPase tumor suppressor that is downregulated in multiple cancers, inhibits RAS/mitogen-activated protein kinase (MAPK) signaling by interacting directly with RAS-forming heteromers, disrupting RAS clustering, inhibiting Raf kinase activation, and inhibiting transformation and growth of cancer cells and xenografts. Disruption of K-RAS cluster formation requires the N terminus of DIRAS3 and interaction of both DIRAS3 and K-RAS with the plasma membrane. Interaction of DIRAS3 with both K-RAS and H-RAS suggests a strategy for inhibiting oncogenic RAS function.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.celrep.2019.11.045DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6948147PMC
December 2019

Precision Medicine in Pancreatic Disease-Knowledge Gaps and Research Opportunities: Summary of a National Institute of Diabetes and Digestive and Kidney Diseases Workshop.

Pancreas 2019 Nov/Dec;48(10):1250-1258

Division of Gastroenterology and Hepatology, Stanford University, Stanford, CA.

A workshop on research gaps and opportunities for Precision Medicine in Pancreatic Disease was sponsored by the National Institute of Diabetes and Digestive Kidney Diseases on July 24, 2019, in Pittsburgh. The workshop included an overview lecture on precision medicine in cancer and 4 sessions: (1) general considerations for the application of bioinformatics and artificial intelligence; (2) omics, the combination of risk factors and biomarkers; (3) precision imaging; and (4) gaps, barriers, and needs to move from precision to personalized medicine for pancreatic disease. Current precision medicine approaches and tools were reviewed, and participants identified knowledge gaps and research needs that hinder bringing precision medicine to pancreatic diseases. Most critical were (a) multicenter efforts to collect large-scale patient data sets from multiple data streams in the context of environmental and social factors; (b) new information systems that can collect, annotate, and quantify data to inform disease mechanisms; (c) novel prospective clinical trial designs to test and improve therapies; and (d) a framework for measuring and assessing the value of proposed approaches to the health care system. With these advances, precision medicine can identify patients early in the course of their pancreatic disease and prevent progression to chronic or fatal illness.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1097/MPA.0000000000001412DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7282491PMC
September 2020

High-throughput, single-particle tracking reveals nested membrane domains that dictate KRas diffusion and trafficking.

Elife 2019 11 1;8. Epub 2019 Nov 1.

Department of Biomedical Engineering, Oregon Health and Science University, Portland, United States.

Membrane nanodomains have been implicated in Ras signaling, but what these domains are and how they interact with Ras remain obscure. Here, using single particle tracking with photoactivated localization microscopy (spt-PALM) and detailed trajectory analysis, we show that distinct membrane domains dictate KRas (an active KRas mutant) diffusion and trafficking in U2OS cells. KRas exhibits an immobile state in ~70 nm domains, each embedded in a larger domain (~200 nm) that confers intermediate mobility, while the rest of the membrane supports fast diffusion. Moreover, KRas is continuously removed from the membrane via the immobile state and replenished to the fast state, reminiscent of Ras internalization and recycling. Importantly, both the diffusion and trafficking properties of KRas remain invariant over a broad range of protein expression levels. Our results reveal how membrane organization dictates membrane diffusion and trafficking of Ras and offer new insight into the spatial regulation of Ras signaling.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.7554/eLife.46393DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7060040PMC
November 2019

PLK1 and EGFR targeted nanoparticle as a radiation sensitizer for non-small cell lung cancer.

Cancer Lett 2019 12 26;467:9-18. Epub 2019 Sep 26.

Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, 97239, USA; PDX Pharmaceuticals, LLC, Portland, OR, 97239, USA. Electronic address:

Radiation sensitizers that can selectively act on cancer cells hold great promise to patients who receive radiation therapy. We developed a novel targeted therapy and radiation sensitizer for non-small cell lung cancer (NSCLC) based on cetuximab conjugated nanoparticle that targets epidermal growth factor receptor (EGFR) and delivers small interfering RNA (siRNA) against polo-like kinase 1 (PLK1). EGFR is overexpressed in 50% of lung cancer patients and a mediator of DNA repair, while PLK1 is a key mitotic regulator whose inhibition enhances radiation sensitivity. The nanoparticle construct (C-siPLK1-NP) effectively targets EGFR + NSCLC cells and reduces PLK1 expression, leading to G2/M arrest and cell death. Furthermore, we show a synergistic combination between C-siPLK1-NP and radiation, which was confirmed in vivo in A549 flank tumors. We also demonstrate the translational potential of C-siPLK1-NP as a systemic therapeutic in an orthotopic lung tumor model, where administration of C-siPLK1-NP reduced tumor growth and led to prolonged survival. Our findings demonstrate that C-siPLK1-NP is effective as a targeted therapy and as a potent radiation sensitizer for NSCLC. Potential application to other EGFR + cancer types such as colorectal and breast cancer is also demonstrated.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.canlet.2019.09.014DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6927399PMC
December 2019

Cyclic Multiplexed-Immunofluorescence (cmIF), a Highly Multiplexed Method for Single-Cell Analysis.

Methods Mol Biol 2020 ;2055:521-562

Department of Biomedical Engineering and OHSU Center for Spatial Systems Biomedicine, Oregon Health and Science University, Portland, OR, USA.

Immunotherapy harnesses the power of the adaptive immune system and has revolutionized the field of oncotherapy, as novel therapeutic strategies have been introduced into clinical use. The development of immune checkpoint inhibitors has led to durable control of disease in a subset of advanced cancer patients, such as those with melanoma and non-small cell lung cancer. However, predicting patient responses to therapy remains a major challenge, due to the remarkable genomic, epigenetic, and microenvironmental heterogeneity present in each tumor. Breast cancer (BC) is the most common cancer in women, where hormone receptor-positive (HR+; estrogen receptor and/or progesterone receptor) BC comprises the majority (>50%) and has better prognosis, while a minority (<20%) are triple negative BC (TNBC), which has an aggressive phenotype. There is a clinical need to identify predictors of late recurrence in HR+ BC and predictors of immunotherapy outcomes in advanced TNBC. Tumor-infiltrating lymphocytes (TILs) have recently been shown to predict late recurrence in HR+, counter to the findings that TILs confer good prognosis in TNBC and human epidermal growth factor receptor 2 positive (HER2+) subtypes. Furthermore, the spatial arrangement of TILs also appears to have prognostic value, with dense clusters of immune cells predicting poor prognosis in HR+ and good prognosis in TNBC. Whether TIL clusters in different breast cancer subtypes represent the same or different landscapes of TILs is unknown and may have treatment implications for a significant portion of breast cancer patients. Current histopathological staining technology is not sufficient for characterizing the ensembles of TILs and their spatial patterns, in addition to tumor and microenvironmental heterogeneity. However, recent advances in cyclic immunofluorescence enable differentiation of the subsets based on TILs, tumor heterogeneity, and microenvironment composition between good and poor responders. A computational framework for understanding the importance of the spatial relationships between TILs and tumor cells in cancer tissues, which will allow for quantitative interpretation of cyclic immunostaining, is also under development. This chapter will explore the workflow for a newly developed cyclic multiplexed-immunofluorescence (cmIF) assay, which has been optimized for formalin-fixed. paraffin-embedded tissues and developed to process digital images for quantitative single-cell based spatial analysis of tumor heterogeneity and microenvironment, including immune cell composition.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1007/978-1-4939-9773-2_24DOI Listing
November 2020

Targeting the Mevalonate Pathway to Overcome Acquired Anti-HER2 Treatment Resistance in Breast Cancer.

Mol Cancer Res 2019 11 16;17(11):2318-2330. Epub 2019 Aug 16.

Lester & Sue Smith Breast Center, Baylor College of Medicine, Houston, Texas.

Despite effective strategies, resistance in HER2 breast cancer remains a challenge. While the mevalonate pathway (MVA) is suggested to promote cell growth and survival, including in HER2 models, its potential role in resistance to HER2-targeted therapy is unknown. Parental HER2 breast cancer cells and their lapatinib-resistant and lapatinib + trastuzumab-resistant derivatives were used for this study. MVA activity was found to be increased in lapatinib-resistant and lapatinib + trastuzumab-resistant cells. Specific blockade of this pathway with lipophilic but not hydrophilic statins and with the N-bisphosphonate zoledronic acid led to apoptosis and substantial growth inhibition of R cells. Inhibition was rescued by mevalonate or the intermediate metabolites farnesyl pyrophosphate or geranylgeranyl pyrophosphate, but not cholesterol. Activated Yes-associated protein (YAP)/transcriptional coactivator with PDZ-binding motif (TAZ) and mTORC1 signaling, and their downstream target gene product Survivin, were inhibited by MVA blockade, especially in the lapatinib-resistant/lapatinib + trastuzumab-resistant models. Overexpression of constitutively active YAP rescued Survivin and phosphorylated-S6 levels, despite blockade of the MVA. These results suggest that the MVA provides alternative signaling leading to cell survival and resistance by activating YAP/TAZ-mTORC1-Survivin signaling when HER2 is blocked, suggesting novel therapeutic targets. MVA inhibitors including lipophilic statins and N-bisphosphonates may circumvent resistance to anti-HER2 therapy warranting further clinical investigation. IMPLICATIONS: The MVA was found to constitute an escape mechanism of survival and growth in HER2 breast cancer models resistant to anti-HER2 therapies. MVA inhibitors such as simvastatin and zoledronic acid are potential therapeutic agents to resensitize the tumors that depend on the MVA to progress on anti-HER2 therapies.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1158/1541-7786.MCR-19-0756DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6825570PMC
November 2019

Variational Autoencoding Tissue Response to Microenvironment Perturbation.

Proc SPIE Int Soc Opt Eng 2019 Feb 15;10949. Epub 2019 Mar 15.

Dept. of Biomedical Engineering, Center for Spatial Systems Biomedicine, Oregon Health & Science University, Portland, OR, USA.

This work applies deep variational autoencoder learning architecture to study multi-cellular growth characteristics of human mammary epithelial cells in response to diverse microenvironment perturbations. Our approach introduces a novel method of visualizing learned feature spaces of trained variational autoencoding models that enables visualization of principal features in two dimensions. We find that unsupervised learned features more closely associate with expert annotation of cell colony organization than biologically-inspired hand-crafted features, demonstrating the utility of deep learning systems to meaningfully characterize features of multi-cellular growth characteristics in a fully unsupervised and data-driven manner.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1117/12.2512660DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6677277PMC
February 2019

A Multi-center Study on the Reproducibility of Drug-Response Assays in Mammalian Cell Lines.

Cell Syst 2019 07 10;9(1):35-48.e5. Epub 2019 Jul 10.

Laboratory of Systems Pharmacology, HMS LINCS Center, Harvard Medical School, Boston, MA 02115, USA. Electronic address:

Evidence that some high-impact biomedical results cannot be repeated has stimulated interest in practices that generate findable, accessible, interoperable, and reusable (FAIR) data. Multiple papers have identified specific examples of irreproducibility, but practical ways to make data more reproducible have not been widely studied. Here, five research centers in the NIH LINCS Program Consortium investigate the reproducibility of a prototypical perturbational assay: quantifying the responsiveness of cultured cells to anti-cancer drugs. Such assays are important for drug development, studying cellular networks, and patient stratification. While many experimental and computational factors impact intra- and inter-center reproducibility, the factors most difficult to identify and control are those with a strong dependency on biological context. These factors often vary in magnitude with the drug being analyzed and with growth conditions. We provide ways to identify such context-sensitive factors, thereby improving both the theory and practice of reproducible cell-based assays.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.cels.2019.06.005DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6700527PMC
July 2019

Using Microarrays to Interrogate Microenvironmental Impact on Cellular Phenotypes in Cancer.

J Vis Exp 2019 05 21(147). Epub 2019 May 21.

Department of Biomedical Engineering, Knight Cancer Institute, Oregon Health and Science University;

Understanding the impact of the microenvironment on the phenotype of cells is a difficult problem due to the complex mixture of both soluble growth factors and matrix-associated proteins in the microenvironment in vivo. Furthermore, readily available reagents for the modeling of microenvironments in vitro typically utilize complex mixtures of proteins that are incompletely defined and suffer from batch to batch variability. The microenvironment microarray (MEMA) platform allows for the assessment of thousands of simple combinations of microenvironment proteins for their impact on cellular phenotypes in a single assay. The MEMAs are prepared in well plates, which allows the addition of individual ligands to separate wells containing arrayed extracellular matrix (ECM) proteins. The combination of the soluble ligand with each printed ECM forms a unique combination. A typical MEMA assay contains greater than 2,500 unique combinatorial microenvironments that cells are exposed to in a single assay. As a test case, the breast cancer cell line MCF7 was plated on the MEMA platform. Analysis of this assay identified factors that both enhance and inhibit the growth and proliferation of these cells. The MEMA platform is highly flexible and can be extended for use with other biological questions beyond cancer research.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.3791/58957DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6736605PMC
May 2019

Therapeutic Clues from an Integrated Omic Assessment of East Asian Triple Negative Breast Cancers.

Cancer Cell 2019 03;35(3):341-343

Center for Spatial Systems Biomedicine, Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR 97201, USA. Electronic address:

In this issue of Cancer Cell, Jiang et al. report a genomic and transcriptional analysis of triple negative breast cancers (TNBCs) from an East Asian population. Their study shows minor differences with published studies of European and North American populations and suggests a therapeutic decision tree for treatment of TNBC.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.ccell.2019.02.012DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6499473PMC
March 2019

Robust Cell Detection and Segmentation for Image Cytometry Reveal Th17 Cell Heterogeneity.

Cytometry A 2019 04 4;95(4):389-398. Epub 2019 Feb 4.

Department of Biomedical Engineering, Oregon Health & Science University, Portland, Oregon, USA.

Image cytometry enables quantitative cell characterization with preserved tissue architecture; thus, it has been highlighted in the advancement of multiplex immunohistochemistry (IHC) and digital image analysis in the context of immune-based biomarker monitoring associated with cancer immunotherapy. However, one of the challenges in the current image cytometry methodology is a technical limitation in the segmentation of nuclei and cellular components particularly in heterogeneously stained cancer tissue images. To improve the detection and specificity of single-cell segmentation in hematoxylin-stained images (which can be utilized for recently reported 12-biomarker chromogenic sequential multiplex IHC), we adapted a segmentation algorithm previously developed for hematoxlin and eosin-stained images, where morphological features are extracted based on Gabor-filtering, followed by stacking of image pixels into n-dimensional feature space and unsupervised clustering of individual pixels. Our proposed method showed improved sensitivity and specificity in comparison with standard segmentation methods. Replacing previously proposed methods with our method in multiplex IHC/image cytometry analysis, we observed higher detection of cell lineages including relatively rare T 17 cells, further enabling sub-population analysis into T 1-like and T 2-like phenotypes based on T-bet and GATA3 expression. Interestingly, predominance of T 2-like T 17 cells was associated with human papilloma virus (HPV)-negative status of oropharyngeal squamous cell carcinoma of head and neck, known as a poor-prognostic subtype in comparison with HPV-positive status. Furthermore, T 2-like T 17 cells in HPV-negative head and neck cancer tissues were spatiotemporally correlated with CD66b granulocytes, presumably associated with an immunosuppressive microenvironment. Our cell segmentation method for multiplex IHC/image cytometry potentially contributes to in-depth immune profiling and spatial association, leading to further tissue-based biomarker exploration. © 2019 International Society for Advancement of Cytometry.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1002/cyto.a.23726DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6461524PMC
April 2019

Modeling Tumor Phenotypes In Vitro with Three-Dimensional Bioprinting.

Cell Rep 2019 01;26(3):608-623.e6

Department of Medical and Molecular Genetics, Oregon Health & Science University, Portland, OR 97201, USA; Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97201, USA. Electronic address:

The tumor microenvironment plays a critical role in tumor growth, progression, and therapeutic resistance, but interrogating the role of specific tumor-stromal interactions on tumorigenic phenotypes is challenging within in vivo tissues. Here, we tested whether three-dimensional (3D) bioprinting could improve in vitro models by incorporating multiple cell types into scaffold-free tumor tissues with defined architecture. We generated tumor tissues from distinct subtypes of breast or pancreatic cancer in relevant microenvironments and demonstrate that this technique can model patient-specific tumors by using primary patient tissue. We assess intrinsic, extrinsic, and spatial tumorigenic phenotypes in bioprinted tissues and find that cellular proliferation, extracellular matrix deposition, and cellular migration are altered in response to extrinsic signals or therapies. Together, this work demonstrates that multi-cell-type bioprinted tissues can recapitulate aspects of in vivo neoplastic tissues and provide a manipulable system for the interrogation of multiple tumorigenic endpoints in the context of distinct tumor microenvironments.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.celrep.2018.12.090DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6366459PMC
January 2019

Implementing a comprehensive translational oncology platform: from molecular testing to actionability.

J Transl Med 2018 12 14;16(1):358. Epub 2018 Dec 14.

Oregon Health and Science University, 3181 SW Sam Jackson Hall, Portland, OR, 97239-3098, USA.

Background: In order to establish the workflows required to implement a real-time process involving multi-omic analysis of patient samples to support precision-guided therapeutic intervention, a tissue acquisition and analysis trial was implemented. This report describes our findings to date, including the frequency with which mutational testing led to precision-guided therapy and outcome for those patients.

Methods: Eligible patients presenting to Oregon Health and Science University Knight Cancer Institute were enrolled on the study. Patients with biopsy proven metastatic or locally advanced unresectable prostate cancer, breast cancer, pancreatic adenocarcinoma, or refractory acute myelogenous leukemia receiving standard of care therapy were eligible. Metastatic site biopsies were collected and analyzed using the Knight Diagnostic Lab GeneTrails comprehensive solid tumor panel (124 genes). CLIA certified genomic information was made available to the treating physician.

Results: Between 1/26/2017 and 5/30/2018, 38 patients were enrolled, with 28 successfully undergoing biopsy. Of these, 25 samples yielded sufficient tumor for analysis. The median biopsy cellularity and number of cores collected were 70% (15-90%) and 5 (2-20), respectively. No procedure-related complications occurred. GeneTrails analysis revealed that 22 of 25 (88%) tumor samples harbored at least one potentially actionable mutation, and 18 (72%) samples harbored 2 or more potentially actionable mutations. The most common genetic alterations identified involved: DNA damage repair genes, cell cycle regulating genes, PIK3CA/Akt/mTOR pathway, and FGF gene family. To date, CLIA certified genomic results were used by treating physicians for precision-guided therapy in 5 (23%) patients.

Conclusion: We report the feasibility of real-time tissue acquisition and analysis to support a successful translational oncology platform. The workflow will provide the foundation to improve access and accrual to biomarker driven precision oncology trials.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12967-018-1733-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6295039PMC
December 2018

Transcriptional Programming of Normal and Inflamed Human Epidermis at Single-Cell Resolution.

Cell Rep 2018 10;25(4):871-883

Department of Dermatology, University of California, San Francisco, San Francisco, CA, USA. Electronic address:

Perturbations in the transcriptional programs specifying epidermal differentiation cause diverse skin pathologies ranging from impaired barrier function to inflammatory skin disease. However, the global scope and organization of this complex cellular program remain undefined. Here we report single-cell RNA sequencing profiles of 92,889 human epidermal cells from 9 normal and 3 inflamed skin samples. Transcriptomics-derived keratinocyte subpopulations reflect classic epidermal strata but also sharply compartmentalize epithelial functions such as cell-cell communication, inflammation, and WNT pathway modulation. In keratinocytes, ∼12% of assessed transcript expression varies in coordinate patterns, revealing undescribed gene expression programs governing epidermal homeostasis. We also identify molecular fingerprints of inflammatory skin states, including S100 activation in the interfollicular epidermis of normal scalp, enrichment of a CD1CCD301A myeloid dendritic cell population in psoriatic epidermis, and IL1βCCL3CD14 monocyte-derived macrophages enriched in foreskin. This compendium of RNA profiles provides a critical step toward elucidating epidermal diseases of development, differentiation, and inflammation.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.celrep.2018.09.006DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6367716PMC
October 2018

SHIFT: speedy histopathological-to-immunofluorescent translation of whole slide images using conditional generative adversarial networks.

Proc SPIE Int Soc Opt Eng 2018 Feb 6;10581. Epub 2018 Mar 6.

Oregon Health and Science University, Portland, OR, USA.

Multiplexed imaging such as multicolor immunofluorescence staining, multiplexed immunohistochemistry (mIHC) or cyclic immunofluorescence (cycIF) enables deep assessment of cellular complexity and, in conjunction with standard histology stains like hematoxylin and eosin (H&E), can help to unravel the complex molecular relationships and spatial interdependencies that undergird disease states. However, these multiplexed imaging methods are costly and can degrade both tissue quality and antigenicity with each successive cycle of staining. In addition, computationally intensive image processing such as image registration across multiple channels is required. We have developed a novel method, speedy histopathological-to-immunofluorescent translation (SHIFT) of whole slide images (WSIs) using conditional generative adversarial networks (cGANs). This approach is rooted in the assumption that specific patterns captured in IF images by stains like DAPI, pan-cytokeratin (panCK), or -smooth muscle actin ( -SMA) are encoded in H&E images, such that a SHIFT model can learn useful feature representations or architectural patterns in the H&E stain that help generate relevant IF stain patterns. We demonstrate that the proposed method is capable of generating realistic tumor marker IF WSIs conditioned on corresponding H&E-stained WSIs with up to 94.5% accuracy in a matter of seconds. Thus, this method has the potential to not only improve our understanding of the mapping of histological and morphological profiles into protein expression profiles, but also greatly increase the e ciency of diagnostic and prognostic decision-making.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1117/12.2293249DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6166432PMC
February 2018

Differentiation-state plasticity is a targetable resistance mechanism in basal-like breast cancer.

Nat Commun 2018 09 19;9(1):3815. Epub 2018 Sep 19.

Department of Molecular and Medical Genetics, Oregon Health & Science University, 3181 SW Sam Jackson Park Road L103, Portland, OR, 97239, USA.

Intratumoral heterogeneity in cancers arises from genomic instability and epigenomic plasticity and is associated with resistance to cytotoxic and targeted therapies. We show here that cell-state heterogeneity, defined by differentiation-state marker expression, is high in triple-negative and basal-like breast cancer subtypes, and that drug tolerant persister (DTP) cell populations with altered marker expression emerge during treatment with a wide range of pathway-targeted therapeutic compounds. We show that MEK and PI3K/mTOR inhibitor-driven DTP states arise through distinct cell-state transitions rather than by Darwinian selection of preexisting subpopulations, and that these transitions involve dynamic remodeling of open chromatin architecture. Increased activity of many chromatin modifier enzymes, including BRD4, is observed in DTP cells. Co-treatment with the PI3K/mTOR inhibitor BEZ235 and the BET inhibitor JQ1 prevents changes to the open chromatin architecture, inhibits the acquisition of a DTP state, and results in robust cell death in vitro and xenograft regression in vivo.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s41467-018-05729-wDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6145927PMC
September 2018

Lack of acquired resistance in HER2-positive breast cancer cells after long-term HER2 siRNA nanoparticle treatment.

PLoS One 2018 7;13(6):e0198141. Epub 2018 Jun 7.

Department of Biomedical Engineering, Oregon Health & Science University, Portland, Oregon, United States of America.

Intrinsic and acquired resistance to current HER2 targeted therapies remains a challenge in clinics. We have developed a therapeutic HER2 siRNA delivered using mesoporous silica nanoparticles modified with polymers and conjugated with HER2 targeting antibodies. Our previous studies have shown that our HER2 siRNA nanoparticles could overcome intrinsic and acquired resistance to trastuzumab and lapatinib in HER2-positive breast cancers. In this study, we investigated the effect of long-term (7 months) treatment using our therapeutic HER2 siRNA. Even after the removal of HER2 siRNA, the long-term treated cells grew much slower (67% increase in doubling time) than cells that have not received any treatment. The treated cells did not undergo epithelial-mesenchymal transition or showed enrichment of tumor initiating cells. Unlike trastuzumab and lapatinib, which induced resistance in BT474 cells after 6 months of treatment, HER2 siRNA did not induce resistance to HER2 siRNA, trastuzumab, or lapatinib. HER2 ablation with HER2 siRNA prevented reactivation of HER2 signaling that was observed in cells resistant to lapatinib. Altogether, our results indicate that a HER2 siRNA based therapeutic provides a more durable inhibition of HER2 signaling in vitro and can potentially be more effective than the existing therapeutic monoclonal antibodies and small molecule inhibitors.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0198141PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5991725PMC
January 2019

Molecular Cytogenetics Guides Massively Parallel Sequencing of a Radiation-Induced Chromosome Translocation in Human Cells.

Radiat Res 2018 07 11;190(1):88-97. Epub 2018 May 11.

c   KromaTiD Inc., Fort Collins, Colorado 80523.

Chromosome rearrangements are large-scale structural variants that are recognized drivers of oncogenic events in cancers of all types. Cytogenetics allows for their rapid, genome-wide detection, but does not provide gene-level resolution. Massively parallel sequencing (MPS) promises DNA sequence-level characterization of the specific breakpoints involved, but is strongly influenced by bioinformatics filters that affect detection efficiency. We sought to characterize the breakpoint junctions of chromosomal translocations and inversions in the clonal derivatives of human cells exposed to ionizing radiation. Here, we describe the first successful use of DNA paired-end analysis to locate and sequence across the breakpoint junctions of a radiation-induced reciprocal translocation. The analyses employed, with varying degrees of success, several well-known bioinformatics algorithms, a task made difficult by the involvement of repetitive DNA sequences. As for underlying mechanisms, the results of Sanger sequencing suggested that the translocation in question was likely formed via microhomology-mediated non-homologous end joining (mmNHEJ). To our knowledge, this represents the first use of MPS to characterize the breakpoint junctions of a radiation-induced chromosomal translocation in human cells. Curiously, these same approaches were unsuccessful when applied to the analysis of inversions previously identified by directional genomic hybridization (dGH). We conclude that molecular cytogenetics continues to provide critical guidance for structural variant discovery, validation and in "tuning" analysis filters to enable robust breakpoint identification at the base pair level.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1667/RR15053.1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6055522PMC
July 2018

Microenvironment-Mediated Mechanisms of Resistance to HER2 Inhibitors Differ between HER2+ Breast Cancer Subtypes.

Cell Syst 2018 Mar 14;6(3):329-342.e6. Epub 2018 Mar 14.

Department of Biomedical Engineering, Knight Cancer Institute, OHSU Center for Spatial Systems Biomedicine, Oregon Health and Science University, 3181 SW Sam Jackson Park Road, Portland, OR 97239, USA. Electronic address:

Extrinsic signals are implicated in breast cancer resistance to HER2-targeted tyrosine kinase inhibitors (TKIs). To examine how microenvironmental signals influence resistance, we monitored TKI-treated breast cancer cell lines grown on microenvironment microarrays composed of printed extracellular matrix proteins supplemented with soluble proteins. We tested ∼2,500 combinations of 56 soluble and 46 matrix microenvironmental proteins on basal-like HER2+ (HER2E) or luminal-like HER2+ (L-HER2+) cells treated with the TKIs lapatinib or neratinib. In HER2E cells, hepatocyte growth factor, a ligand for MET, induced resistance that could be reversed with crizotinib, an inhibitor of MET. In L-HER2+ cells, neuregulin1-β1 (NRG1β), a ligand for HER3, induced resistance that could be reversed with pertuzumab, an inhibitor of HER2-HER3 heterodimerization. The subtype-specific responses were also observed in 3D cultures and murine xenografts. These results, along with bioinformatic pathway analysis and siRNA knockdown experiments, suggest different mechanisms of resistance specific to each HER2+ subtype: MET signaling for HER2E and HER2-HER3 heterodimerization for L-HER2+ cells.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.cels.2018.02.001DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5927625PMC
March 2018