Name: quantifying the brain metastatic tumor micro-environment using an organ-on-a chip 3d model, machine learning, and confocal tomography
Uploaded: 2020-08-16T15:00:00
Description: Watch this Scientific Journal Video about Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography at JoVE.com

We thank the Steeg Lab, at the National Cancer Institute for the generous donation of MDA-MB-231-BR-GFP cells. Confocal microscopy was performed at the University of Michigan Biointerfaces Institute (BI). Flow cytometry was performed at the University of Michigan Flow Cytometry Core. Viral vectors were created by the University of Michigan Vector Core. We also thank Kelley Kidwell for guidance in statistical analysis of these data.
FUNDING:
C.R.O. was partially supported by an NIH T-32 Training Fellowship (T32CA009676) and 1R21CA245597-01. T.M.W. was partially supported by 1R21CA245597-01 and the National Center for Advancing Translational Sciences of the National Institutes of Health under Award Number UL1TR002240. Funding for materials and characterization was provided by National Cancer Institute of the National Institutes of Health under award number 1R21CA245597-01, P30CA046592, 5T32CA009676-23, CA196018, AI116482, METAvivor Foundation, and the Breast Cancer Research Foundation. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health

1. Prepare the blood brain barrier niche mold
NOTE: The culturing device used in this platform is a PDMS based scaffold that we build a cellular blood brain barrier niche upon. It is made of two parts separated by a porous membrane. To prepare the blood brain barrier niche two SU-8 molds made using photolithography are necessary26,27. The protocol will be described for the 100 &#181;m thick mold first and then notes will be given for the...

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We have developed and presented a new method that adapts tools often utilized in clinical imaging analyses for measurement of extravasation and migration of cancer cells through an endothelial barrier into brain tissue. We pose this approach can be useful for both in vivo and in vitro measurements; we have demonstrated its use on a 3D microfluidic system recapitulating brain vasculature. Cancer cell measurements including distance extravasated, percent extravasated by volume, sphericity, and volume are quantified using t...

Brain metastases are the most lethal cancer lesions; 10-30% of all cancers metastasize to the brain, with a median survival of only ~5-20 months, depending on the cancer type1,2. A principal question that arises when studying cancer metastasis is how sub clones migrate from the humoral environment of the bloodstream into an organ such as the brain3,4. This question has led to many variations of migration, invasion, and extravasation assays. All these methods share the critical step of counting or measuring properties of cells that move from one location to another in response to a stimulus. Most migration assays readily available are used to study two-dimensional (2D) migration of cancer cells. These have elucidated a wealth of knowledge; however, they do not recapitulate the three-dimensional nature of the in vivo system that other methods can provide5. Therefore, it is necessary to study the tumor micro-environment (TME) in three-dimensional (3D) systems, but the analysis approaches available for 3D structures are limited and often inconsistent.
One of the most popular 3D tools is a Boyden chamber that consists of a membrane suspended at the bottom of a well, separating two distinct regions. Boyden introduced the assay to study leukocyte chemotaxis4. The bottom regions may be varied by chemistry or other means6,7 to induce cells in the upper region to migrate to the lower region. The most common approach to quantifying the number of cells that have migrated is to release the cells from the bottom of the membrane using a buffer solution, lyse them, and then count them based on the quantity of DNA content in the solution7. This indirect approach is prone to operator error due to technique variability and the procedure destroys information about the cancer phenotype and the micro-environment. Variations of the Boyden chamber assay involve fixation of migratory cells that remain on the membrane, but only provides a count of cells that are no longer viable for continued study6,8,9.
Due to limitations of the Boyden chamber and the growth of innovations in the microfluidic community, migration assay chips have been developed which observe the motion of cells in response to a stimulus in one direction rather than three10,11,12. These migration assays facilitate control over factors such as flow or single cell separation13,14 that enable better interpretation of the results; however, their 2D format inevitably loses some dynamic information. Recent studies have focused on extravasation (i.e., the movement of cells from circulation into a tissue, such as the blood brain barrier) in a 3D environment14,15. The extravasation distance into tissue and probing behavior that occurs at the cellular barrier/membrane is more refined than measurements gleaned using either the Boyden chamber or a 2D microfluidic migration device16. Thus, devices that enable appropriate imaging and analysis of 3D extravasation are critical to capture these sophisticated measurements but are lacking in the literature.
Independent of migration assays, robust imaging techniques have been developed for magnetic resonance imaging (MRI) and tomography that are able to identify and accurately reconstruct tissue in 3D space17,18. These techniques acquire images in z-stacks and segment portions of the image based on the properties of the tissue and then convert the segmented images into three-dimensional meshes19,20,21. This allows physicians to visualize in 3D individual organs, bones, and vessels to aid in surgical planning or aid in diagnosis of cancer or heart disease22,23. Here, we will show that these approaches can be adapted for use on microscopic specimens and 3D extravasation devices.
To this end, we developed the innovative confocal tomography technique, presented herein, which affords flexibility to study the extravasation of tumor cells across a membrane by adapting existing tomography tools. This approach enables the study of the full gamut of cancer cell behaviors as they interact with a cellular barrier, such as an endothelial cell layer. Cancer cells exhibit probing behaviors; some may invade but remain close to the membrane, while others traverse the barrier readily. This technique is capable of yielding information about the phenotype of the cell in all dimensions24. Using this approach to study the TME is both relatively inexpensive, easy to interpret, and reproducible, when compared to more complex in vivo murine models. The presented methodology should provide a strong basis for the study of many types of tumors and micro-environments by adapting the stromal region.
We describe and demonstrate the use of a 3D microfluidic blood brain niche (&#181;mBBN) platform (Figure 1) where critical elements of the barrier and niche (brain microvascular endothelial cells and astrocytes) can be cultured for an extended period (approximately up to 9 days), fluorescently imaged by confocal microscopy, and the images reconstructed using our confocal tomography technique (Figure 2); all aimed to understand the development of micro-metastasis and changes to the tumor micro-environment in a repeatable and quantitative manner. The blood brain barrier interface with the brain niche is composed of brain microvascular endothelial cells that are strengthened by basement membrane, astrocyte feet, and pericytes25. We selectively focused on the astrocyte and endothelial components given their importance in the formation and regulation of the blood brain barrier. We demonstrate how to fabricate, seed, image, and analyze the cancer cells and tumor micro-environment cellular and humoral components, using this platform. Finally, we show how machine learning can be used to identify the intrinsic phenotypic differences of cancer cells that are capable of transit through a model &#181;mBBN and to assign them an objective index of brain metastatic potential24. The data sets generated by this method can be used to answer basic and translational questions about metastasis, therapeutic strategies, and the role of the TME in both.

<table>
	<tbody>
		<tr>
			<td>0.25% Trypsin-EDTA with phenol red</td>
			<td>Thermo Fisher Scientific</td>
			<td>25200056</td>
			<td></td>
		</tr>
		<tr>
			<td>1.5 mm biopsy punch with plunger</td>
			<td>Integra LifeSciences Corporation</td>
			<td>33-31A-P/25</td>
			<td></td>
		</tr>
		<tr>
			<td>10x MEM</td>
			<td>Thermo Fisher Scientific</td>
			<td>11430030</td>
			<td></td>
		</tr>
		<tr>
			<td>150 mm petri dishes</td>
			<td>Fisher Scientific</td>
			<td>FB0875714</td>
			<td></td>
		</tr>
		<tr>
			<td>1x DPBS, without Ca and Mg</td>
			<td>Thermo Fisher Scientific</td>
			<td>14190144</td>
			<td></td>
		</tr>
		<tr>
			<td>200uL pipette tip</td>
			<td>Fisher Scientific</td>
			<td>02-707-411</td>
			<td></td>
		</tr>
		<tr>
			<td>4 inch silicon wafer</td>
			<td>University Wafer</td>
			<td>452</td>
			<td></td>
		</tr>
		<tr>
			<td>48 mm wide packing tape</td>
			<td>Fisher Scientific</td>
			<td>19-072-097</td>
			<td></td>
		</tr>
		<tr>
			<td>50 x 75 mm glass slide</td>
			<td>Fisher Scientific</td>
			<td>12-550C</td>
			<td></td>
		</tr>
		<tr>
			<td>A1 confocal microscope</td>
			<td>Nikon</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>acetone</td>
			<td>Fisher Scientific</td>
			<td>A9-20</td>
			<td></td>
		</tr>
		<tr>
			<td>antibiotic/antimycotic (penicillin/streptomycin/amphotericin)</td>
			<td>Gibco</td>
			<td>15240062</td>
			<td></td>
		</tr>
		<tr>
			<td>box cutter blade</td>
			<td>Fisher Scientific</td>
			<td>NC1721575</td>
			<td></td>
		</tr>
		<tr>
			<td>dissection scissors</td>
			<td>Fisher Scientific</td>
			<td>08-951-5</td>
			<td></td>
		</tr>
		<tr>
			<td>DMEM with 4.5 g/L glucose</td>
			<td>Thermo Fisher Scientific</td>
			<td>11960-044</td>
			<td></td>
		</tr>
		<tr>
			<td>double sided tape</td>
			<td>Fisher Scientific</td>
			<td>NC0879005</td>
			<td></td>
		</tr>
		<tr>
			<td>EGM-2</td>
			<td>Lonza</td>
			<td>CC-3162</td>
			<td></td>
		</tr>
		<tr>
			<td>Fetal Bovine Serum, Heat inactivated</td>
			<td>Corning</td>
			<td>MT35011CV</td>
			<td></td>
		</tr>
		<tr>
			<td>Fiji software</td>
			<td>ImageJ</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>glass vial</td>
			<td>Fisher Scientific</td>
			<td>03-341-25D</td>
			<td></td>
		</tr>
		<tr>
			<td>glutamax</td>
			<td>Thermo Fisher Scientific</td>
			<td>35050061</td>
			<td></td>
		</tr>
		<tr>
			<td>hCMEC/D3</td>
			<td>EMD Millipore</td>
			<td>SCC066</td>
			<td></td>
		</tr>
		<tr>
			<td>Jupyter notebook</td>
			<td>Anaconda</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>L-glutamine</td>
			<td>Thermo Fisher Scientific</td>
			<td>25030081</td>
			<td></td>
		</tr>
		<tr>
			<td>Matrigel - growth factor reduced with phenol red</td>
			<td>Corning</td>
			<td>CB-40230A</td>
			<td></td>
		</tr>
		<tr>
			<td>MDA-MB-231</td>
			<td>ATCC</td>
			<td>HTB-26</td>
			<td></td>
		</tr>
		<tr>
			<td>MDA-MB-231-BR-GFP</td>
			<td>Dr. Patricia Steeg, NIH</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>N-2 growth supplement</td>
			<td>Thermo Fisher Scientific</td>
			<td>17502048</td>
			<td></td>
		</tr>
		<tr>
			<td>normal human astrocytes (NHA)</td>
			<td>Lonza</td>
			<td>CC-2565</td>
			<td></td>
		</tr>
		<tr>
			<td>Orange software</td>
			<td>University of Ljubljana</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Pasteur pipette</td>
			<td>Fisher Scientific</td>
			<td>13-711-9AM</td>
			<td></td>
		</tr>
		<tr>
			<td>Photolithography masks</td>
			<td>Photosciences Incorporated</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>pLL3.7-dsRed</td>
			<td>University of Michigan Vector Core</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>pLL-EV-GFP</td>
			<td>University of Michigan Vector Core</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>pLOX-TERT-iresTK</td>
			<td>Addgene</td>
			<td>12245</td>
			<td></td>
		</tr>
		<tr>
			<td>pMD2.G</td>
			<td>Addgene</td>
			<td>12259</td>
			<td></td>
		</tr>
		<tr>
			<td>polycarbonate membrane, 5um pore size</td>
			<td>Millipore</td>
			<td>TMTP04700</td>
			<td></td>
		</tr>
		<tr>
			<td>psPAX2</td>
			<td>Addgene</td>
			<td>12260</td>
			<td></td>
		</tr>
		<tr>
			<td>PureCol, 3 mg/mL</td>
			<td>Advanced Biomatrix</td>
			<td>5005</td>
			<td>Type I bovine collagen</td>
		</tr>
		<tr>
			<td>sodium bicarbonate</td>
			<td>Thermo Fisher Scientific</td>
			<td>25080094</td>
			<td></td>
		</tr>
		<tr>
			<td>sodium pyruvate</td>
			<td>Thermo Fisher Scientific</td>
			<td>11360070</td>
			<td></td>
		</tr>
		<tr>
			<td>Solo cup</td>
			<td>Fisher Scientific</td>
			<td>NC1416545</td>
			<td></td>
		</tr>
		<tr>
			<td>SU-8 2075</td>
			<td>MicroChem Corporation</td>
			<td>Y111074 0500L1GL</td>
			<td></td>
		</tr>
		<tr>
			<td>SU8 developer</td>
			<td>MicroChem Corporation</td>
			<td>Y020100 4000L1PE</td>
			<td></td>
		</tr>
		<tr>
			<td>Sylgard 184</td>
			<td>Ellsworth Adhesive Company</td>
			<td>NC0162601</td>
			<td></td>
		</tr>
		<tr>
			<td>Toluene</td>
			<td>Sigma-Aldrich</td>
			<td>179965-1L</td>
			<td></td>
		</tr>
		<tr>
			<td>Tricholoro perfluoro octyl silane</td>
			<td>Sigma-Aldrich</td>
			<td>448931-10G</td>
			<td></td>
		</tr>
	</tbody>
</table>

quantifying the brain metastatic tumor micro-environment using an organ-on-a chip 3d model, machine learning, and confocal tomography

Brain metastases are the most lethal cancer lesions; 10-30% of all cancers metastasize to the brain, with a median survival of only ~5-20 months, depending on the cancer type. To reduce the brain metastatic tumor burden, gaps in basic and translational knowledge need to be addressed. Major challenges include a paucity of reproducible preclinical models and associated tools. Three-dimensional models of brain metastasis can yield the relevant molecular and phenotypic data used to address these needs when combined with dedicated analysis tools. Moreover, compared to murine models, organ-on-a-chip models of patient tumor cells traversing the blood brain barrier into the brain microenvironment generate results rapidly and are more interpretable with quantitative methods, thus amenable to high throughput testing. Here we describe and demonstrate the use of a novel 3D microfluidic blood brain niche (&#181;mBBN) platform where multiple elements of the niche can be cultured for an extended period (several days), fluorescently imaged by confocal microscopy, and the images reconstructed using an innovative confocal tomography technique; all aimed to understand the development of micro-metastasis and changes to the tumor micro-environment (TME) in a repeatable and quantitative manner. We demonstrate how to fabricate, seed, image, and analyze the cancer cells and TME cellular and humoral components, using this platform. Moreover, we show how artificial intelligence (AI) is used to identify the intrinsic phenotypic differences of cancer cells that are capable of transit through a model &#181;mBBN and to assign them an objective index of brain metastatic potential. The data sets generated by this method can be used to answer basic and translational questions about metastasis, the efficacy of therapeutic strategies, and the role of the TME in both.

Using this technique, we analyzed cell types labeled with different fluorescent proteins or dyes. We demonstrate the use of this approach with a &#181;mBBN chip formulated with hCMEC/D3-DsRed and non-fluorescent astrocytes. The brain microvascular endothelial cells were seeded onto a porous membrane (5 &#181;m track etched pores) and placed in an incubator34 at 37 &#176;C under 5% CO2. After three days the confluency of the endothelial layer was confirmed via microscopy and then cancer ...

Watch this Scientific Journal Video about Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography at JoVE.com

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

Here, we present a protocol for preparing and culturing a blood brain barrier metastatic tumor micro-environment and then quantifying its state using confocal imaging and artificial intelligence (machine learning).

Brain metastases are the most lethal cancer lesions; 10-30% of all cancers metastasize to the brain, with a median survival of only ~5-20 months, ...

Confocal tomography was developed to obtain the full gamut of cancer cell behaviors, as they interact with the cellular barrier and niche. This method provides a basis for studying metastases as they occur. This approach is useful for quantifying live cell behavior.Confocal tomography, when combined with machine learning, is useful for a variety of organ-on-a-chip platforms. Analyzing primary or recurrent tumor or circulating tumor cells can constitute an important diagnostic tool to predict brain metastases and to discern brain metastatic from non brain metastatic cells. To assemble the microfluidic BBN device, transfer a device from the vacuum desiccator onto an appropriate surface with the inlets facing down and place the entire setup into a plasma chamber.Place the entire setup into a plasma chamber and pull a vacuum before treating the device with plasma for 30 seconds at 80 Watts. At the end of the treatment, use a guide to quickly place the device, inlet side up onto the glass slide on the lab bench to create a permanent bond between the PDMs of the device and the slide. Next, insert 200 microliter pipette tips, cut two millimeters from the tip into all of the inlets and outlets and place the device into the plasma chamber for an eight minute, 200 watt plasma treatment.After the device has cooled, place the device into a sterile secondary container and within 15 minutes of plasmid treatment, transfer 120 microliters of a collagen astrocyte solution into the device through the pipette tip for the bottom chamber. Allow the solution to wick across the chamber into the opposite pipette tip and fill the next channel of the device. When all four channels of the device have been filled, place the chip in the cell culture incubator for one hour.When the collagen has set, bill all of the pipette tips feeding the bottom chamber with the appropriate complete cell culture medium and coat the upper chamber with 2%growth factor reduced matrigel, in complete endothelial medium. When both chambers have been coded, place the device into the incubator for one hour before rinsing the upper chamber with the appropriate medium. Alternating tips, see 30 microliters of one times 10 to the sixth endothelial cells per milliliter of appropriate medium, through the tips into the upper chamber every 15 minutes, for a total of four aliquots of cells per tip.Incubate the device in between seedings. When all of the endothelial cells have been seeded, fill all of the tips with the medium and return the device to the cell culture incubator for 48 hours. When the endothelial layer has matured, replace the medium in the chip to replenish the cell culture medium and seed each top chamber channel with 30 microliters of one times 10 to the six cancer cells per milliliter of appropriate medium.After each 30 microliter aliquot of cells has been seeded, return the device to the incubator for 15 minutes then fill all tips with the appropriate medium return the device to the cell culture incubator for 24 to 48 hours. After the device has been cultured and imaged, to measure the phenotype of the cancer cells, open the provided software and read the confocal image into memory. The software will save each color channel from the 3D confocal image into a separate TIFF file.Select Change opacity values for each microscope channel'and adjust the channel alpha value for the color channels present in the image, such that the background is removed and only the fluorescents within the cells remains. To visualize the effect, select View a 3D rendering'to verify the threshold are set correctly and if the image looks correct, save the opacity values in the experiment tracker spreadsheet. Then, use marching cubes to convert the volume image into individual 3D triangular meshed objects saved in the VTK data format.To fit a plane to the endothelial barrier, first locate the cell centroids. Use the polydata connectivity filter to iterate through the list of meshes in the VTK file to extract the regions that are not connected. Calculate the centroid of each mesh and add the measurement to a list of centroids filtering for meshes that are too large or too small.To fit a plane to the list of centroids for the endothelial cells, use a minimization of error method, run Visualize RFP centroids and plane fit'to generate and plot the plane fit and inspect the fit of the plane, adjusting the fit manually as necessary. When the plane has been properly fitted, save the normal of the plane to the experiment tracker file. To measure each cancer cells phenotype after characterizing the endothelial layer with a plane, load the cell analysis function and run Read in the experiment tracker information and analyze the channels that exist"to iterate over and analyze each region in the VTK file.For each region, the function will clip each cell so that the mesh below the membrane is calculated. To measure the cellular phenotype, calculate the shape, volume and position of each cancer cell. Then measure the volume and position of each clipped cancer cell to calculate the percentage of cell that extravasated through the endothelial barrier.After performing these steps for several experiments run Export the data as a single xlsx file'to save the data in a spreadsheet. A microfluidic BBN chip with a confluent endothelial barrier is acceptable for experimentation, while a microfluidic BBN chip with especially poor endothelial coverage, is not. In this representative analysis the brain seeking cancer cell line, exhibits a subpopulation of cells that extravasates across the endothelial barrier and migrates deep into the brain niche space of the microfluidic BBN chip At two and nine days after exposure, the parental cancer cells maintained a substantial proportion of cells on top of the barrier away from the brain niche space, while the brain metastatic cancer cell population maintained a proportion of cells that were greater than 100%extravasated.Morphological quantification of the cancer cell shape indicated that parental cells demonstrated fewer spherically shaped cells day one post-seeding, while after two and nine days of interaction both cancer cell lines trended toward decreasing their sphericity. In addition, the cancer cell subpopulations that extravasated into the astricydic niche were smaller in size, compared to the cancer cells that remained in interaction with the endothelial barrier without extravasating into the brain. Brain seeking cancer cell lines and patient derived xenografts, exhibit a phenotypic pattern in the microfluidic BBN chip that could be exploited to differentiate between the brain metastatic and non brain metastatic cancer cells by machine learning.Self-selection is important. Endothelial cells with a higher, low passage number exhibited variable barrier behaviors. In addition, cancer cells and their fluorescent expression may change over time.After this procedure, researchers may employ molecular profiling of the secret's home or cells in the device in order to study molecular mechanisms of metastases. This technique enables the exploration of complex interactions between cancer cells and their microenvironments, by combining an engineered microenvironment with a quantitative imaging of niche components over time.

Research

JoVE Journal

Cancer Research

The Drosophila Imaginal Disc Tumor Model: Visualization and Quantification of Gene Expression and Tumor Invasiveness Using Genetic Mosaics

The Drosophila Imaginal Disc Tumor Model: Visualization and Quantification of Gene Expression and Tumor Invasiveness Using Genetic Mosaics

Drosophila melanogaster has emerged as a powerful experimental system for functional and mechanistic studies of tumor development and progression in the ...

An Integrated Platform for Genome-wide Mapping of Chromatin States Using High-throughput ChIP-sequencing in Tumor Tissues

Histone modifications constitute a major component of the epigenome and play important regulatory roles in determining the transcriptional status of ...

Native Chromatin Immunoprecipitation Using Murine Brain Tumor Neurospheres

Epigenetic modifications may be involved in the development and progression of glioma. Changes in methylation and acetylation of promoters and regulatory ...

Utilization of Ultrasound Guided Tissue-directed Cellular Implantation for the Establishment of Biologically Relevant Metastatic Tumor Xenografts

Preclinical testing of anticancer therapies relies on relevant xenograft models that mimic the innate tendencies of cancer. Advantages of standard ...

Live-Cell Imaging Assays to Study Glioblastoma Brain Tumor Stem Cell Migration and Invasion

Glioblastoma (GBM) is an aggressive brain tumor that is poorly controlled with the currently available treatment options. Key features of GBMs include ...

Digital Polymerase Chain Reaction Assay for the Genetic Variation in a Sporadic Familial Adenomatous Polyposis Patient Using the Chip-in-a-tube Format

The quantitative analysis of human genetic variation is crucial for understanding the molecular characteristics of serious medical conditions, such as ...

The Clinical Application of Tumor Treating Fields Therapy in Glioblastoma

Glioblastoma is the most common and lethal form of brain cancer, with a median survival of 15 months after diagnosis and a 5 year survival rate of only 5% ...

Characterization of the Effects of Migrastatic Inhibitors on 3D Tumor Spheroid Invasion by High-resolution Confocal Microscopy

Drug discovery and development in cancer research is increasingly being based on drug screens in a 3D format. Novel inhibitors targeting the migratory and ...

A Novel Stromal Fibroblast-Modulated 3D Tumor Spheroid Model for Studying Tumor-Stroma Interaction and Drug Discovery

Tumor-stroma interactions play an important role in cancer progression. Three-dimensional (3D) tumor spheroid models are the most widely used in vitro ...

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine learning (ML) algorithms permit the integration of different features into a model to perform classification or regression tasks with an accuracy ...