Cell DIVE Selected Publications
Multiparametric MRI is an important tool for the diagnosis of prostate cancer. However, this technology fails to identify about 15% of clinically significant prostate cancers. Here, the authors used Cell DIVE, alongside gene expression profiling and AI-based analytic algorithms to explore the ecosystem of these tumors. Using these technologies, the authors uncovered deep molecular, cellular, and structural characteristics of prostate tumors, and found that the MRI-invisible examples tended to display a less complex tumor environment, contributing to their difficulty in detection.
Single-cell Spatial Proteomic Revelations on the Multiparametric MRI Heterogeneity of Clinically Significant Prostate Cancer
Pachynski RK, et al.
Clin Cancer Res. 2021 Jun 15;27(12):3478-3490. DOI: 10.1158/1078-0432.CCR-20-4217.
Tumor cells that enter circulation are often the precursors of metastatic cancer. Previously, the authors of this work found that specific genomic regions in these cells are frequently subject to copy-number gains and used this information to identify tumors that share this genomic signature—bridging the gap between genomics and spatial biology. This study showed that these so-called circulating tumor cell (CTC)-associated tumors formed more frequently in breast cancer patients after multiple rounds of treatment, and often showed an increased level of spatial heterogeneity. Cell DIVE analysis demonstrated that these, more heterogeneous tumors, have a high degree of B lymphocyte infiltration in triple-negative breast cancer. These data show how genetic subclones arise, how they are selected and how they ultimately shape the microenvironments around them.
Heterogeneity of Circulating Tumor Cell-Associated Genomic Gains in Breast Cancer and Its Association with the Host Immune Response
Kanwar N, et al.
Cancer Res. 2021 Dec 15;81(24):6196-6206. DOI: 10.1158/0008-5472.CAN-21-1079.
Colorectal cancer is a high incidence and high mortality cancer. Currently, postoperative chemotherapy benefits only a minority of patients, and thus, new tools are necessary to screen patients and identify those at increased risk. Tissue samples from hundreds of patients were analyzed using Cell DIVE to reveal the fine cellular determinants of survival following cancer treatment. By using multimarker analysis enabled by Cell DIVE and a unique analysis algorithm, the authors show that the recruitment of a particular T cell subtype, the PD-1 negative Treg, was most highly associated with disease-free survival. Thus, multiplex imaging and analysis of tumor samples may provide a future means to target clinical resources towards those patients with higher risk profiles.
Stratification of chemotherapy-treated stage III colorectal cancer patients using multiplexed imaging and single-cell analysis of T-cell populations
Stachtea X, et al.
Mod Pathol. 2021 Nov 3. DOI: 10.1038/s41379-021-00953-0.
Cancer immunotherapies can yield powerful, lasting effects and tumor regression. However, identifying patients and tumors in which these therapies can be most effective remains challenging. In this study, the authors used Cell DIVE to explore the phenotypes of tumor cells in patients who had received and showed a strong response to immunotherapy, as well as in those patients who did not respond. In the case of “extreme responders” who featured complete eradication of tumor cells after IL2 injection immunotherapy, proliferating CD8+ T cells with a particular phenotype (PD-1+LAG-3+TIM-3+) and IFNγ and IL2 response gene expression characterized the tumors, while loss of membrane bound MHC class I typified lesions that resisted the therapy. This study indicates that antigen presentation from tumor cells is critical for an efficacious response to IL2 therapy and demonstrates the power of multi-marker phenotypic analysis enabled by Cell DIVE.
Tumor MHC Class I Expression Associates with Intralesional IL2 Response in Melanoma.
Pourmaleki M, et al.
Cancer Immunol Res. 2022 Mar 1. DOI: 10.1158/2326-6066.CIR-21-1083.
Cell DIVE technology development publications
- Highly multiplexed single-cell analysis of formalin-fixed, paraffin-embedded cancer tissue
Gerdes MJ, et al.
Proc Natl Acad Sci U S A. 2013 Jul 16;110(29):11982-7. DOI: 10.1073/pnas.1300136110.
- A novel, automated technology for multiplex biomarker imaging and application to breast cancer
Clarke, GM, et al.
Histopathology 64(2), pp.242–255 (2014). DOI: 10.1111/his.12240
- Microfluidic Tissue Mesodissection in Molecular Cancer Diagnostics
Surrette C, et al.
SLAS Technol. 2017 Aug;22(4):425–430. DOI: 10.1177/2211068216680208
- Multi-modal imaging of histological tissue sections
Can, A, et al. (2008)
IEEE International Symposium on Biomedical Imaging: From Nano to Macro pp. 288–291 (2008). DOI: 10.1109/ISBI.2008.4540989
Multiplexed image processing and single cell analysis
- Autofluorescence removal using a customized filter set
Pang Z, et al.
Microsc Res Tech. 2013 Oct;76(10):1007-15. DOI: 10.1002/jemt.22261.
- Dark pixel intensity determination and its applications in normalizing different exposure time and autofluorescence removal
2. Pang Z, et al.
J Microsc. 2012 Apr;246(1):1-10. DOI: 10.1111/j.1365-2818.2011.03581.x.
- Autofluorescence removal by non-negative matrix factorization
Woolfe, F., et al.
IEEE T Image Process 20(4), 1085–1093 (2011). DOI: 10.1109/TIP.2010.2079810
- Characterization of biological processes through automated image analysis
Annu Rev Biomed Eng. 2010 Aug 15;12:315-44. DOI: 10.1146/annurev-bioeng-070909-105235.
- Techniques for Cellular and Tissue-Based Image Quantitation of Protein Biomarkers
Can, A. et al.
Microscopic Image Analysis for Lifescience Applications, 1–8 (2008).
- Quantitative single cell analysis of cell population dynamics during submandibular salivary gland development and differentiation
Nelson DA, et al.
Biol Open. 2013 Apr 18;2(5):439-47. DOI: 10.1242/bio.20134309.
- Characterizing the heterogeneity of tumor tissues from spatially resolved molecular measures
Graf JF, Zavodszky MI
PLoS One. 2017 Nov 30;12(11):e0188878. DOI: 10.1371/journal.pone.0188878
- Platform for Quantitative Evaluation of Spatial Intratumoral Heterogeneity in Multiplexed Fluorescence Images
Spagnolo DM, et al.
Cancer Res. 2017 Nov 1;77(21):e71-e74. DOI: 10.1158/0008-5472.CAN-17-0676
- Optimized multiplex immunofluorescence single-cell analysis reveals tuft cell heterogeneity
McKinley ET, et al.
JCI Insight. 2017 Jun 2;2(11):e93487. DOI: 10.1172/jci.insight.93487
- Pointwise mutual information quantifies intratumor heterogeneity in tissue sections labeled with multiple fluorescent biomarkers
Spagnolo DM, et al.
J Pathol Inform. 2016 Nov 29;7:47. DOI: 10.4103/2153-3539.194839
- Emerging understanding of multiscale tumor heterogeneity
Gerdes MJ, et al.
Front Oncol. 2014 Dec 18; 2014;4:366. DOI: 10.3389/fonc.2014.00366
- Multiscale, multimodal analysis of tumor heterogeneity in IDH1 mutant vs wild-type diffuse gliomas
Berens ME, et al.
PLoS One. 2019 Dec 27;14(12):e0219724. DOI: 10.1371/journal.pone.0219724
Tumor microenvironment and immuno-oncology
- Robust single cell quantification of immune cell subtypes in histological samples
Santamaria-Pang, A. et al. (2017), February
IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), (p. 121–124). IEEE. DOI: 10.1109/BHI.2017.7897220
- Stromal-Based Signatures for the Classification of Gastric Cancer
Uhlik MT, et al.
Cancer Res. 2016 May 1;76(9):2573-86. DOI: 10.1158/0008-5472.CAN-16-0022
- Multi-channel algorithm for segmentation of tumor blood vessels using multiplexed image data
Al-Kofahi, Y. et al. 2016, April
Biomedical Imaging (ISBI), 2016 IEEE 13th International Symposium on (pp. 213–216). IEEE. DOI: 10.1109/ISBI.2016.7493247
- Oncolytic Virotherapy Promotes Intratumoral T Cell Infiltration and Improves Anti-PD-1 Immunotherapy
Ribas A, et al.
Cell. 2017 Sep 7;170(6):1109-1119.e10. DOI: 10.1016/j.cell.2017.08.027. Erratum in: Cell. 2018 Aug 9;174(4):1031-1032.
- Immune Profiling and Quantitative Analysis Decipher the Clinical Role of Immune-Checkpoint Expression in the Tumor Immune Microenvironment of DLBCL
Xu-Monette ZY, et al.
Cancer Immunol Res. 2019 Apr;7(4):644-657. DOI: 10.1158/2326-6066.CIR-18-0439
- Efficacy and tolerability of anti-programmed death-ligand 1 (PD-L1) antibody (Avelumab) treatment in advanced thymoma
Rajan A, et al.
J Immunother Cancer. 2019 Oct 21;7(1):269. DOI: 10.1186/s40425-019-0723-9
- Understanding heterogeneous tumor microenvironment in metastatic melanoma
Yan Y, et al.
PLoS One. 2019 Jun 5;14(6):e0216485. DOI: 10.1371/journal.pone.0216485
- Characterization of the liver immune microenvironment in liver biopsies from patients with chronic HBV infection
van Buuren, N, et al.
JHEP Reports. 2021 Oct 23. DOI: 10.1016/j.jhepr.2021.100388
- Antitumor immune effects of preoperative sitravatinib and nivolumab in oral cavity cancer: SNOW window-of-opportunity study
Oliva M, et al.
J Immunother Cancer. 2021 Oct;9(10):e003476. DOI: 10.1136/jitc-2021-003476
Cancer characterization and prognosis
- Excess PLAC8 promotes an unconventional ERK2-dependent EMT in colon cancer
Li C, et al.
J Clin Invest. 2014 May;124(5):2172-87. DOI: 10.1172/JCI71103
- The relative distribution of membranous and cytoplasmic met is a prognostic indicator in stage I and II colon cancer
Ginty F, et al.
Clin Cancer Res. 2008 Jun 15;14(12):3814-22. DOI: 10.1158/1078-0432.CCR-08-0180
- Cytometry-based single-cell analysis of intact epithelial signaling reveals MAPK activation divergent from TNF-α-induced apoptosis in vivo
Simmons AJ, et al.
Mol Syst Biol. 2015 Oct 30;11(10):835. DOI: 10.15252/msb.20156282. Erratum in: Mol Syst Biol. 2016 Aug 29;12(8):881.
- Stratification of chemotherapy-treated stage III colorectal cancer patients using multiplexed imaging and single-cell analysis of T-cell populations
Stachtea X, et al.
Mod Pathol. 2021 Nov 3. DOI: 10.1038/s41379-021-00953-0
- Single-cell Spatial Proteomic Revelations on the Multiparametric MRI Heterogeneity of Clinically Significant Prostate Cancer
Pachynski RK, et al.
Clin Cancer Res. 2021 Jun 15;27(12):3478-3490. DOI: 10.1158/1078-0432.CCR-20-4217
- A single slide multiplex assay for the evaluation of classical Hodgkin lymphoma
Hollman-Hewgley D, et al.
Am J Surg Pathol. 2014 Sep;38(9):1193-202. DOI: 10.1097/PAS.0000000000000242
- Single-cell heterogeneity in ductal carcinoma in situ of breast
Gerdes MJ, et al.
Mod Pathol. 2018 Mar;31(3):406-417. DOI: 10.1038/modpathol.2017.143
- Multiplexed immunofluorescence delineates proteomic cancer cell states associated with metabolism
Sood A, et al.
JCI Insight. 2016 May 5;1(6):e87030. DOI: 10.1172/jci.insight.87030
- Taxonomy of breast cancer based on normal cell phenotype predicts outcome
Santagata, S. et al.
J Clin Invest. 2014 Feb;124(2):859-70. DOI: 10.1172/JCI70941.
- Multi-protein spatial signatures in ductal carcinoma in situ (DCIS) of breast
Badve SS, et al.
Br J Cancer. 2021 Mar;124(6):1150-1159. DOI: 10.1038/s41416-020-01216-6
- Heterogeneity of Circulating Tumor Cell-Associated Genomic Gains in Breast Cancer and Its Association with the Host Immune Response
Kanwar N, et al.
Cancer Res. 2021 Dec 15;81(24):6196-6206. DOI: 10.1158/0008-5472.CAN-21-1079
- Regulatable interleukin-12 gene therapy in patients with recurrent high-grade glioma: Results of a phase 1 trial
Chiocca EA, et al.
Sci Transl Med. 2019 Aug 14;11(505):eaaw5680. DOI: 10.1126/scitranslmed.aaw5680
Amyotrophic lateral sclerosis
- Quantitative patterns of motor cortex proteinopathy across ALS genotypes
Nolan M, et al.
Acta Neuropathol Commun. 2020 Jul 2;8(1):98. DOI: 10.1186/s40478-020-00961-2
- Unsupervised Trajectory Analysis of Single-Cell RNA-Seq and Imaging Data Reveals Alternative Tuft Cell Origins in the Gut
Herring CA, et al.
Cell Syst. 2018 Jan 24;6(1):37-51.e9. DOI: 10.1016/j.cels.2017.10.012