1
|
Vora N, Shekar P, Hanulia T, Esmail M, Patra A, Georgakoudi I. Deep learning-enabled detection of rare circulating tumor cell clusters in whole blood using label-free, flow cytometry. LAB ON A CHIP 2024; 24:2237-2252. [PMID: 38456773 PMCID: PMC11019838 DOI: 10.1039/d3lc00694h] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Accepted: 01/19/2024] [Indexed: 03/09/2024]
Abstract
Metastatic tumors have poor prognoses for progression-free and overall survival for all cancer patients. Rare circulating tumor cells (CTCs) and rarer circulating tumor cell clusters (CTCCs) are potential biomarkers of metastatic growth, with CTCCs representing an increased risk factor for metastasis. Current detection platforms are optimized for ex vivo detection of CTCs only. Microfluidic chips and size exclusion methods have been proposed for CTCC detection; however, they lack in vivo utility and real-time monitoring capability. Confocal backscatter and fluorescence flow cytometry (BSFC) has been used for label-free detection of CTCCs in whole blood based on machine learning (ML) enabled peak classification. Here, we expand to a deep-learning (DL)-based, peak detection and classification model to detect CTCCs in whole blood data. We demonstrate that DL-based BSFC has a low false alarm rate of 0.78 events per min with a high Pearson correlation coefficient of 0.943 between detected events and expected events. DL-based BSFC of whole blood maintains a detection purity of 72% and a sensitivity of 35.3% for both homotypic and heterotypic CTCCs starting at a minimum size of two cells. We also demonstrate through artificial spiking studies that DL-based BSFC is sensitive to changes in the number of CTCCs present in the samples and does not add variability in detection beyond the expected variability from Poisson statistics. The performance established by DL-based BSFC motivates its use for in vivo detection of CTCCs. Using transfer learning, we additionally validate DL-based BSFC on blood samples from different species and cancer cell types. Further developments of label-free BSFC to enhance throughput could lead to critical applications in the clinical detection of CTCCs and ex vivo isolation of CTCC from whole blood with minimal disruption and processing steps.
Collapse
Affiliation(s)
- Nilay Vora
- Department of Biomedical Engineering, Tufts University, Medford, MA, 02155, USA.
| | - Prashant Shekar
- Department of Mathematics, Embry-Riddle Aeronautical University, Daytona Beach, FL, 32114, USA
| | - Taras Hanulia
- Department of Biomedical Engineering, Tufts University, Medford, MA, 02155, USA.
- Institute of Physics, National Academy of Sciences of Ukraine, Kyiv, Ukraine
| | - Michael Esmail
- Tufts Comparative Medicine Services, Tufts University, Medford, MA, 02155, USA
| | - Abani Patra
- Data Intensive Studies Center, Tufts University, Medford, MA, 02155, USA
| | - Irene Georgakoudi
- Department of Biomedical Engineering, Tufts University, Medford, MA, 02155, USA.
| |
Collapse
|
2
|
Mineva ND, Pianetti S, Das SG, Srinivasan S, Billiald NM, Sonenshein GE. A Novel Class of Human ADAM8 Inhibitory Antibodies for Treatment of Triple-Negative Breast Cancer. Pharmaceutics 2024; 16:536. [PMID: 38675197 PMCID: PMC11054802 DOI: 10.3390/pharmaceutics16040536] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Revised: 04/05/2024] [Accepted: 04/10/2024] [Indexed: 04/28/2024] Open
Abstract
New targeted treatments are urgently needed to improve triple-negative breast cancer (TNBC) patient survival. Previously, we identified the cell surface protein A Disintegrin And Metalloprotease 8 (ADAM8) as a driver of TNBC tumor growth and spread via its metalloproteinase and disintegrin (MP and DI) domains. In proof-of-concept studies, we demonstrated that a monoclonal antibody (mAb) that simultaneously inhibits both domains represents a promising therapeutic approach. Here, we screened a hybridoma library using a multistep selection strategy, including flow cytometry for Ab binding to native conformation protein and in vitro cell-based functional assays to isolate a novel panel of highly specific human ADAM8 dual MP and DI inhibitory mAbs, called ADPs. The screening of four top candidates for in vivo anti-cancer activity in an orthotopic MDA-MB-231 TNBC model of ADAM8-driven primary growth identified two lead mAbs, ADP2 and ADP13. Flow cytometry, hydrogen/deuterium exchange-mass spectrometry (HDX-MS) and alanine (ALA) scanning mutagenesis revealed that dual MP and DI inhibition was mediated via binding to the DI. Further testing in mice showed ADP2 and ADP13 reduce aggressive TNBC characteristics, including locoregional regrowth and metastasis, and improve survival, demonstrating strong therapeutic potential. The continued development of these mAbs into an ADAM8-targeted therapy could revolutionize TNBC treatment.
Collapse
Affiliation(s)
- Nora D. Mineva
- Department of Developmental, Molecular, and Chemical Biology, Tufts University School of Medicine, Boston, MA 02111, USA; (S.P.); (S.G.D.); (S.S.); (N.M.B.)
- Adecto Pharmaceuticals, Inc., Boston, MA 02446, USA
| | - Stefania Pianetti
- Department of Developmental, Molecular, and Chemical Biology, Tufts University School of Medicine, Boston, MA 02111, USA; (S.P.); (S.G.D.); (S.S.); (N.M.B.)
- Adecto Pharmaceuticals, Inc., Boston, MA 02446, USA
| | - Sonia G. Das
- Department of Developmental, Molecular, and Chemical Biology, Tufts University School of Medicine, Boston, MA 02111, USA; (S.P.); (S.G.D.); (S.S.); (N.M.B.)
| | - Srimathi Srinivasan
- Department of Developmental, Molecular, and Chemical Biology, Tufts University School of Medicine, Boston, MA 02111, USA; (S.P.); (S.G.D.); (S.S.); (N.M.B.)
| | - Nicolas M. Billiald
- Department of Developmental, Molecular, and Chemical Biology, Tufts University School of Medicine, Boston, MA 02111, USA; (S.P.); (S.G.D.); (S.S.); (N.M.B.)
| | - Gail E. Sonenshein
- Department of Developmental, Molecular, and Chemical Biology, Tufts University School of Medicine, Boston, MA 02111, USA; (S.P.); (S.G.D.); (S.S.); (N.M.B.)
- Adecto Pharmaceuticals, Inc., Boston, MA 02446, USA
| |
Collapse
|
3
|
Vora N, Shekar P, Esmail M, Patra A, Georgakoudi I. Deep Learning-Enabled, Detection of Rare Circulating Tumor Cell Clusters in Whole Blood Using Label-free, Flow Cytometry. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.01.551485. [PMID: 37577660 PMCID: PMC10418242 DOI: 10.1101/2023.08.01.551485] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/15/2023]
Abstract
Metastatic tumors have poor prognoses for progression-free and overall survival for all cancer patients. Rare circulating tumor cells (CTCs) and rarer circulating tumor cell clusters (CTCCs) are potential biomarkers of metastatic growth, with CTCCs representing an increased risk factor for metastasis. Current detection platforms are optimized for ex vivo detection of CTCs only. Microfluidic chips and size exclusion methods have been proposed for CTCC detection; however, they lack in vivo utility and real-time monitoring capability. Confocal backscatter and fluorescence flow cytometry (BSFC) has been used for label-free detection of CTCCs in whole blood based on machine learning (ML) enabled peak classification. Here, we expand to a deep-learning (DL) -based, peak detection and classification model to detect CTCCs in whole blood data. We demonstrate that DL-based BSFC has a low false alarm rate of 0.78 events/min with a high Pearson correlation coefficient of 0.943 between detected events and expected events. DL-based BSFC of whole blood maintains a detection purity of 72% and a sensitivity of 35.3% for both homotypic and heterotypic CTCCs starting at a minimum size of two cells. We also demonstrate through artificial spiking studies that DL-based BSFC is sensitive to changes in the number of CTCCs present in the samples and does not add variability in detection beyond the expected variability from Poisson statistics. The performance established by DL-based BSFC motivates its use for in vivo detection of CTCCs. Further developments of label-free BSFC to enhance throughput could lead to critical applications in the clinical detection of CTCCs and ex vivo isolation of CTCC from whole blood with minimal disruption and processing steps.
Collapse
Affiliation(s)
- Nilay Vora
- Department of Biomedical Engineering, Tufts University, Medford, MA 02155, USA
| | - Prashant Shekar
- Department of Mathematics, Embry-Riddle Aeronautical University, Daytona Beach, FL, 32114, USA
| | - Michael Esmail
- Tufts Comparative Medicine Services, Tufts University, Medford, MA, 02155, USA
- # Current Affiliation: University of Massachusetts Amherst Animal Care Services, University of Massachusetts Amherst, Amherst, MA 01003, USA
| | - Abani Patra
- Data Intensive Studies Center, Tufts University, Medford, MA 02155, USA
- Department of Mathematics, Tufts University, Medford, MA 02155, USA
| | - Irene Georgakoudi
- Department of Biomedical Engineering, Tufts University, Medford, MA 02155, USA
| |
Collapse
|
4
|
Vora N, Shekhar P, Esmail M, Patra A, Georgakoudi I. Label-free flow cytometry of rare circulating tumor cell clusters in whole blood. Sci Rep 2022; 12:10721. [PMID: 35750889 PMCID: PMC9232518 DOI: 10.1038/s41598-022-14003-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Accepted: 05/31/2022] [Indexed: 11/09/2022] Open
Abstract
Circulating tumor cell clusters (CTCCs) are rare cellular events found in the blood stream of metastatic tumor patients. Despite their scarcity, they represent an increased risk for metastasis. Label-free detection methods of these events remain primarily limited to in vitro microfluidic platforms. Here, we expand on the use of confocal backscatter and fluorescence flow cytometry (BSFC) for label-free detection of CTCCs in whole blood using machine learning for peak detection/classification. BSFC uses a custom-built flow cytometer with three excitation wavelengths (405 nm, 488 nm, and 633 nm) and five detectors to detect CTCCs in whole blood based on corresponding scattering and fluorescence signals. In this study, detection of CTCC-associated GFP fluorescence is used as the ground truth to assess the accuracy of endogenous back-scattered light-based CTCC detection in whole blood. Using a machine learning model for peak detection/classification, we demonstrated that the combined use of backscattered signals at the three wavelengths enable detection of ~ 93% of all CTCCs larger than two cells with a purity of > 82% and an overall accuracy of > 95%. The high level of performance established through BSFC and machine learning demonstrates the potential for label-free detection and monitoring of CTCCs in whole blood. Further developments of label-free BSFC to enhance throughput could lead to important applications in the isolation of CTCCs in whole blood with minimal disruption and ultimately their detection in vivo.
Collapse
Affiliation(s)
- Nilay Vora
- Department of Biomedical Engineering, Tufts University, Medford, MA, 02155, USA
| | - Prashant Shekhar
- Department of Mathematics, Embry-Riddle Aeronautical University, Daytona Beach, FL, 32114, USA
| | - Michael Esmail
- Tufts Comparative Medicine Services, Tufts University, Medford, MA, 02155, USA
| | - Abani Patra
- Department of Computer Science, Tufts University, Medford, MA, 02155, USA
| | - Irene Georgakoudi
- Department of Biomedical Engineering, Tufts University, Medford, MA, 02155, USA.
| |
Collapse
|
5
|
Arifler D, Guillaud M. Assessment of internal refractive index profile of stochastically inhomogeneous nuclear models via analysis of two-dimensional optical scattering patterns. JOURNAL OF BIOMEDICAL OPTICS 2021; 26:JBO-200345RR. [PMID: 33973424 PMCID: PMC8107832 DOI: 10.1117/1.jbo.26.5.055001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/25/2020] [Accepted: 04/22/2021] [Indexed: 06/12/2023]
Abstract
SIGNIFICANCE Optical scattering signals obtained from tissue constituents contain a wealth of structural information. Conventional intensity features, however, are mostly dictated by the overall morphology and mean refractive index of these constituents, making it very difficult to exclusively sense internal refractive index fluctuations. AIM We perform a systematic analysis to elucidate how changes in internal refractive index profile of cell nuclei can best be detected via optical scattering. APPROACH We construct stochastically inhomogeneous nuclear models and numerically simulate their azimuth-resolved scattering patterns. We then process these two-dimensional patterns with the goal of identifying features that directly point to subnuclear structure. RESULTS Azimuth-dependent intensity variations over the side scattering range provide significant insights into subnuclear refractive index profile. A particular feature we refer to as contrast ratio is observed to be highly sensitive to the length scale and extent of refractive index fluctuations; further, this feature is not susceptible to changes in the overall size and mean refractive index of nuclei, thereby allowing for selective tracking of subnuclear structure that can be linked to chromatin distribution. CONCLUSIONS Our analysis will potentially pave the way for scattering-based assessment of chromatin reorganization that is considered to be a key hallmark of precancer progression.
Collapse
Affiliation(s)
- Dizem Arifler
- Middle East Technical University, Northern Cyprus Campus, Physics Group, Kalkanli, Turkey
| | - Martial Guillaud
- British Columbia Cancer Research Center, Department of Integrative Oncology, Imaging Unit, Vancouver BC, Canada
| |
Collapse
|
6
|
Paiva JS, Ribeiro RSR, Cunha JPS, Rosa CC, Jorge PAS. Single Particle Differentiation through 2D Optical Fiber Trapping and Back-Scattered Signal Statistical Analysis: An Exploratory Approach. SENSORS 2018; 18:s18030710. [PMID: 29495502 PMCID: PMC5876792 DOI: 10.3390/s18030710] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/02/2018] [Revised: 02/16/2018] [Accepted: 02/24/2018] [Indexed: 01/01/2023]
Abstract
Recent trends on microbiology point out the urge to develop optical micro-tools with multifunctionalities such as simultaneous manipulation and sensing. Considering that miniaturization has been recognized as one of the most important paradigms of emerging sensing biotechnologies, optical fiber tools, including Optical Fiber Tweezers (OFTs), are suitable candidates for developing multifunctional small sensors for Medicine and Biology. OFTs are flexible and versatile optotools based on fibers with one extremity patterned to form a micro-lens. These are able to focus laser beams and exert forces onto microparticles strong enough (piconewtons) to trap and manipulate them. In this paper, through an exploratory analysis of a 45 features set, including time and frequency-domain parameters of the back-scattered signal of particles trapped by a polymeric lens, we created a novel single feature able to differentiate synthetic particles (PMMA and Polystyrene) from living yeasts cells. This single statistical feature can be useful for the development of label-free hybrid optical fiber sensors with applications in infectious diseases detection or cells sorting. It can also contribute, by revealing the most significant information that can be extracted from the scattered signal, to the development of a simpler method for particles characterization (in terms of composition, heterogeneity degree) than existent technologies.
Collapse
Affiliation(s)
- Joana S Paiva
- INESC TEC-INESC Technology and Science, 4200 Porto, Portugal.
- Physics and Astronomy Department, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal.
| | | | - João P S Cunha
- INESC TEC-INESC Technology and Science, 4200 Porto, Portugal.
- Faculty of Engineering, University of Porto, 4200 Porto, Portugal.
| | - Carla C Rosa
- INESC TEC-INESC Technology and Science, 4200 Porto, Portugal.
- Physics and Astronomy Department, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal.
| | - Pedro A S Jorge
- INESC TEC-INESC Technology and Science, 4200 Porto, Portugal.
- Physics and Astronomy Department, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal.
| |
Collapse
|