This work was supported by the German Research Foundation (DFG, Collaborative Research Centre CRC 1181, subproject Z02; Priority Programme &#956;Bone, projects BA 4027/10-1 and BO 3811), including additional support for the scanning devices (INST&#160;410/77-1&#160;FUGG and INST&#160;410/93-1&#160;FUGG), and by the Emerging Fields Initiative (EFI) &#8220;Big Thera&#8221; of the Friedrich-Alexander-University Erlangen-N&#252;rnberg.

All care and experimental procedures were performed in accordance with national and regional legislation on animal protection, and all animal procedures were approved by the State Government of Franconia, Germany (reference number 55.2 DMS-2532-2-228).
1. Induction of breast cancer bone metastases in the right hind leg of nude rats
NOTE: A detailed description of the induction of breast cancer bone metastases in nude rats has been published elsewhere6,8. The most relevant steps are presented below.
<ol>
	<li>Culture MDA-MB-231 human breast cancer cells in RPMI-1640, supplemented with 10% fetal calf serum (FCS). Keep the cells under standard conditions (37 &#176;C, 5% CO2) and passage the cells 2&#8211;3 times a week.</li>
	<li>Wash near-confluent MDA-MB-231 cells with 2 mM EDTA in phosphate-buffered saline (PBS), and then detach the cells with 0.25% trypsin. Determine the cell concentration with a Neubauer&#8217;s chamber and resuspend them in 200 &#181;L of RPMI-1640 at a concentration of 1.5 x 105 cells/200&#160;&#181;L.</li>
	<li>Use 6&#8211;8 week-old nude rats and keep them under pathogen-free, controlled conditions (21 &#176;C &#177; 2 &#176;C room temperature, 60% humidity, and 12 h light-dark rhythm). Offer autoclaved feed and water ad libitum.</li>
	<li>Before performing the surgery, inject an analgesic drug (e.g., Carprofen 4 mg/kg) subcutaneously. Anesthetize rats with an isoflurane (1&#8211;1.5 vol. %)/oxygen mixture at a flow rate of 2 L/min. Check the anesthetic depth by toe pinching.</li>
	<li>For surgery, use an operating microscope with a 16x magnification.</li>
	<li>Perform a 2&#8211;3 cm cut in the rat&#8217;s right inguinal region. Dissect all arteries in the right inguinal region, including the femoral artery (FA), the superficial epigastric artery (SEA), the descending genicular artery (DGA), the popliteal artery (PA), and the saphenous artery (SA). Place two removable clips on the FA: one proximal to the beginning of the SEA, and another directly proximal to the beginning of the DGA.</li>
	<li>Ligate the distal portion of the SEA. Perform a cut of the SEA&#8217;s wall and insert a 0.3&#160;mm diameter needle into the SEA. Connect a syringe containing the cell suspension from step 1.2 to the needle. Remove the distal clip from the FA and clip the SA instead.</li>
	<li>Slowly inject the MDA-MB-231 cell suspension from step 1.2 (1.5 x 105 cells/200&#160;&#181;L) into the SEA. Remove the needle, ligate the SEA, and remove the artery clips. Close the wound using surgical clips and terminate anesthesia. Monitor the animals daily to assess tumor size and any evidence of pain.</li>
</ol>
2. Magnetic resonance imaging (MRI)
NOTE: For a detailed description of MRI procedures, please see B&#228;uerle et al.11.
<ol>
	<li>Perform MRI 10 days PI using a dedicated experimental scanner (see Table of Materials) or a human MR system with an appropriate animal coil.</li>
	<li>Anesthetize the rat with an isoflurane (1&#8211;1.5 vol. %)/oxygen mixture as described above. Place a catheter in the rat&#8217;s tail vein and tape it to the tail. Connect a syringe containing the contrast agent (0.1 mmol/kg Gd-DTPA in approximately 0.5 mL).</li>
	<li>Place the anesthetized rat in the MR system. Locate the distal femur and proximal tibia of the right hind leg in an anatomic sequence (e.g., T2-weighted turbo spin echo sequence; TR = 8,654 ms; TE = 37 ms; matrix 320 x 272; FOV = 65 mm x 55 mm; slice thickness = 1&#160;mm; scan time 11:24 min).</li>
	<li>Determine the slices covering the distal femur and proximal tibia of the right hind leg and start the DCE-MRI sequence (e.g., fast low angle shot sequence; TR = 3.9 ms; TE = 0.88 ms; matrix = 256 x 216; FOV = 65 x 54&#160;mm2; slice thickness = 1 mm; 8 slices; 100 time points; scan time = 8:25&#160;min). After 30 s, start injecting the contrast agent over a time period of 10&#160;s. 
	NOTE: The total time to perform an MRI examination is approximately 20 min per animal.</li>
</ol>
3. Positron emission tomography/computed tomography (PET/CT)
NOTE: For a detailed description of the PET procedures, please see Cheng at al.12.
<ol>
	<li>Perform PET/CT imaging 10 days PI using a dedicated experimental scanner (see Table of Materials).</li>
	<li>Keep the animals fasted prior to imaging. Anesthetize the rat as described in step 2.2 and insert a catheter in the tail vein as described above.</li>
	<li>Inject 6&#160;MBq of 18F-Fluorodeoxyglucose (18F-FDG) into the tail vein and wait ~30&#160;min to allow the tracer to distribute properly.</li>
	<li>Perform a CT acquisition (tube voltage = 80&#160;kV, tube current = 500&#160;&#181;A, isotropic resolution = 48.9&#160;&#181;m, duration = 10&#160;min).</li>
	<li>Perform a static PET acquisition (lower/upper discriminatory level = 350/650&#160;keV; timing window = 3.438&#160;ns; duration = 15&#160;min).</li>
</ol>
4. Alternative imaging strategies
<ol>
	<li>For an early assessment of MDA-MB-231 cells in the hind leg, inoculate 1.5 x 105 labeled cells /200&#160;&#181;L for bioluminescence (i.e., cells expressing luciferin, MDA-MB-231-LUC13) or fluorescence imaging (i.e., cells expressing green or red fluorescent protein, MDA-MB-231-GFP/RFP13). Use the system for preclinical optical imaging to detect intraosseous MDA-MB-231 cells after tumor cell inoculation14.&#160;</li>
	<li>Perform experimental ultrasound using a dedicated scanner after intravenous injection of microbubbles to derive morphological and functional parameters of vascularization comparable to MRI7.</li>
</ol>
5. MRI analysis
<ol>
	<li>Use a DICOM viewer15 with a DCE Plugin16 and load the DCE sequence in 4D-mode by clicking the &#8220;Import&#8221; button in the top menu, selecting the DICOM folder containing the MR images from step 2.4, and clicking &#8220;4D Viewer&#8221; in the top menu.</li>
	<li>Place a circular 2-dimensional region of interest (ROI), with a target size of 1.5 mm2, in the proximal tibial shaft&#8217;s bone marrow of the right hind leg, preferably using image numbers 4 or 5 from the sequence consisting of 8 images, as these center images provide more stable results.</li>
	<li>Start the DCE plugin from the top menu, select &#8220;Relative Enhancement&#8221; in the &#8220;Plot Type&#8221; field, and define the baseline range from time points 1 to 5 by typing these numbers into the respective fields. Export the analysis as a .txt file with the respective button and choose &#8220;DCEraw.txt&#8221; as the file name.</li>
	<li>Open RStudio17 and load the provided DCE-Script.R file via the &#8220;File&#8221; menu by selecting &#8220;Open File&#8221;. Run the entire script by selecting &#8220;Code&#8221;, then &#8220;Run Region&#8221; and then &#8220;Run All&#8221; from the menu. Copy the output to the provided template file named &#8220;ImagingFeatures.xlsx&#8221; (Figure 2).</li>
	<li>In the DICOM viewer, place a second ROI within the back muscle of the animal and repeat steps 5.2&#8211;5.4 to obtain the muscle DCE measurements for normalization purposes. Within the spreadsheet &#8220;ImagingFeatures.xlsx&#8221;, the respective bone measurements are automatically divided by the respective muscle measurements for normalization purposes.</li>
	<li>Repeat steps 5.1&#8211;5.5 for all animals and complete the spreadsheet.</li>
</ol>
6. PET/CT analysis
<ol>
	<li>Open the PET/CT analysis software and import the data obtained in step 3 by clicking &#8220;File&#8221;, followed by &#8220;Manual import&#8221;. Mark the ct.img.hd and the pet.img.hdr files. Click &#8220;Open&#8221; and select &#8220;Import all&#8221;.</li>
	<li>Open the datasets by selecting &#8220;General analysis&#8221;, followed by &#8220;OK&#8221;.</li>
	<li>Select &#8220;ROI Quantification&#8221;, followed by &#8220;Create&#8221;, and then &#8220;Create a ROI from a template&#8221;. Place a 2-dimensional ROI approximately 4 mm x 6&#160;mm into the proximal tibial shaft&#8217;s bone marrow of the right hind leg.</li>
	<li>Select &#8220;ROIs (Target 1 overlay)&#8221; and write down mean, minimum, and maximum values in Bq/mL.</li>
	<li>Calculate the maximum standardized uptake value (SUVmax): Divide the maximum value (Bq/mL) by the injected activity and multiply the result by the weight of the animal in grams. Enter the result into the spreadsheet (Figure 2).</li>
</ol>
7. Determining the tumor-take rate
<ol>
	<li>To diagnose tumor growth in the right hind leg, repeat MR and PET/CT imaging on day 30&#160;PI, as described above. 
	NOTE: Tumors will be clearly visible on day 30 PI and feature T2w-hyperintense lesions and clear contrast enhancement in MRI, along with a clearly elevated SUVmax in PET/CT. According to previous experiments, 60%&#8211;80% of the animals will develop metastases in their right hind leg.</li>
	<li>Complete the spreadsheet by adding an additional &#8220;Tumor&#8221; column and enter &#8220;1&#8221; for every animal that presents metastases, and &#8220;0&#8221; for every animal without visible tumor burden (Figure 2). Save the spreadsheet as &#8220;ImagingFeatures.xlsx&#8221; within the Downloads folder.</li>
</ol>
8. Feature selection
<ol>
	<li>To determine the most relevant features for prediction of future tumor growth, import the spreadsheet into an open-source data visualization, machine learning, and data mining toolkit18.</li>
	<li>Draw the File-subroutine from the Data menu into the workspace on the right and double-click it. Load the spreadsheet by clicking the &#8220;Folder&#8221; icon and selecting the file &#8220;ImagingFeatures.xlsx&#8221;. Select the &#8220;Export&#8221; worksheet and assign the target-attribute to the variable &#8220;Tumor&#8221;. Assign the &#8220;Skip&#8221; function to the animal number (Figure 3).</li>
	<li>Draw the &#8220;Rank&#8221; subroutine from the Data menu into the workspace and connect the &#8220;File&#8221; and &#8220;Rank&#8221; subroutines by drawing a line between them.</li>
	<li>Open the &#8220;Rank&#8221; subroutine by double-clicking on its icon, and select the &#8220;Information Gain&#8221; algorithm19.</li>
	<li>From the five acquired parameters, use the top three for further analyses (SUVmax, PE, and AUC). 
	Note: These parameters reflect metabolic activity (SUVmax) and tissue vascularization (PE and AUC).</li>
</ol>
9. ML analysis
<ol>
	<li>Open RStudio 3.4.117 and load the provided TrainModel.R-Script via the &#8220;File&#8221; menu.</li>
	<li>Install the required libraries (this only has to be done once) by typing:&#160;install.packages(c(&#34;caret&#34;, &#34;readxl&#34;, &#34;pROC&#34;, &#34;RcmdrPlugin.EZR&#34;, &#34;ggplot2&#34;))</li>
	<li>To load the required libraries and set the Downloads folder as the working directory, select the lines 3&#8211;5 within the TrainModel.R Script.</li>
	<li>Run the selected code by clicking &#8220;Code&#8221; within the menu, and then &#8220;Run Selected Line(s)&#8221;.</li>
</ol>
10. Training an avNNet ML algorithm
<ol>
	<li>To train an avNNet algorithm, select the lines 8&#8211;39 from the TrainModel.R-Script (see step 9.1).</li>
	<li>Run the selected code by clicking &#8220;Code&#8221; within the menu, and then &#8220;Run Selected Line(s)&#8221;.</li>
</ol>
11. Analyzing the ML algorithm&#8217;s results
<ol>
	<li>To assess standard parameters of diagnostic accuracy (sensitivity, specificity, positive and negative predictive values, and likelihood ratios), select the lines 41&#8211;50 from the TrainModel.R-Script.</li>
	<li>Run the selected code by clicking &#8220;Code&#8221; within the menu, and then &#8220;Run Selected Line(s)&#8221;.</li>
</ol>
12. Comparing the final model&#39;s Receiver Operating Characteristic (ROC) curve with the ROC curves of its constituent parameters
<ol>
	<li>To perform DeLong&#8217;s tests to compare the model&#8217;s ROC curve with the ROC curves of its constituent parameters, select the lines 52&#8211;62 from the TrainModel.R-Script (see step 9.1).</li>
	<li>Run the selected code by clicking &#8220;Code&#8221; within the menu, and then &#8220;Run Selected Line(s)&#8221;.</li>
</ol>

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The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

ML algorithms are powerful tools used to integrate several predictive features into a combined model and obtain an accuracy that exceeds that of its separate constituents when used alone. Nonetheless, the actual result depends on several critical steps. First, the ML algorithm used is a crucial factor, because different ML algorithms yield different results. The algorithm used in this protocol is an avNNet, but other promising algorithms include Extreme Gradient Boosting21 or Random Forests. The c...

The purpose of this protocol is to integrate several functional imaging parameters from MRI and PET/CT into a model-averaged neural network (avNNet) ML algorithm. This algorithm predicts the growth of macrometastases in a rat model of breast cancer bone metastases at an early timepoint, when macroscopic changes within the bone are not yet visible.
Prior to the growth of macrometastases, a bone marrow invasion of disseminated tumor cells occurs, commonly referred to as micrometastatic disease1,2. This initial invasion can be considered an early step in metastatic disease, but is typically missed during conventional staging examinations3,4. Although the currently available imaging modalities cannot detect bone marrow microinvasion when used alone, a combination of imaging parameters yielding information on vascularization and metabolic activity has been shown to perform better5. This complementary benefit is achieved by combining different imaging parameters into an avNNet, which is an ML algorithm. Such an avNNet allows for the reliable prediction of bone macrometastases formation before any visible metastases are present. Therefore, integrating imaging biomarkers into an avNNet could serve as a surrogate parameter for bone marrow microinvasion and early metastatic disease.
To develop the protocol, a previously described model of breast cancer bone metastases in nude rats was used6,7,8. The advantage of this model is its site-specificity, meaning that the animals develop bony metastases exclusively in their right hind leg. However, the tumor-take rate of this approach is 60%&#8211;80%, so a considerable number of the animals do not develop any metastases during the study. Using imaging modalities such as MRI and PET/CT, the presence of metastases is detectable from day 30 postinjection (PI). At earlier time points (e.g., 10 PI) imaging does not distinguish between animals that will develop metastatic disease and those will not (Figure 1).
An avNNet trained on functional imaging parameters acquired on day 10 PI, as described in the following protocol, reliably predicts or excludes the growth of macrometastases within the following ~3 weeks. Neural Networks combine artificial nodes within different layers. In the study protocol, the functional imaging parameters for bone marrow blood supply and metabolic activity represent the bottom layer, while the prediction of malignancy represents the top layer. An additional intermediate layer contains hidden nodes that are connected to both the top and the bottom layer. The strength of the connections between the different nodes is updated during the training of the network to perform the respective classification task with high accuracy9. The accuracy of such a neural network can be further increased by averaging the outputs of several models, resulting in an avNNet10.

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	</colgroup>
	<tbody>
		<tr height="21">
			<td height="21" style="height:21px;width:241px;">Binocular Operating Microscope</td>
			<td style="width:239px;">Leica</td>
			<td style="width:154px;">NA</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:241px;">ClinScan MR System</td>
			<td style="width:239px;">Bruker</td>
			<td style="width:154px;">NA</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:241px;">DICOM Viewer</td>
			<td style="width:239px;">Horos</td>
			<td style="width:154px;">NA</td>
			<td style="width:167px;">www.horosproject.org</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:241px;">Excel: Spreadsheet</td>
			<td style="width:239px;">Microsoft</td>
			<td style="width:154px;">NA</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:241px;">FCS</td>
			<td style="width:239px;">Sigma</td>
			<td style="width:154px;">F2442-500ML</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:241px;">Gadovist</td>
			<td style="width:239px;">Bayer-Schering</td>
			<td style="width:154px;">NA</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:241px;">Inveon PET/CT</td>
			<td style="width:239px;">Siemens</td>
			<td style="width:154px;">NA</td>
			<td></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:241px;">Inveon Research Workplace Software</td>
			<td style="width:239px;">Siemens Healthcare GmbH</td>
			<td style="width:154px;">NA</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:241px;">IVIS Spectrum</td>
			<td style="width:239px;">PerkinElmer</td>
			<td style="width:154px;">NA</td>
			<td></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:241px;">MDA-MB-231 human breast cancer cells</td>
			<td style="width:239px;">American Type Culture Collection</td>
			<td style="width:154px;">N/A</td>
			<td></td>
		</tr>
		<tr height="63">
			<td height="63" style="height:63px;width:241px;">Open-source data visualization, machine learning and data mining toolkit.</td>
			<td style="width:239px;">Orange3, University of Ljubljana</td>
			<td style="width:154px;">NA</td>
			<td>https://orange.biolab.si/</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:241px;">RPMI-1640</td>
			<td>Invitrogen/ThermoFisher</td>
			<td align="right" style="width:154px;">11875093</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:241px;">Trypsin</td>
			<td style="width:239px;">Sigma</td>
			<td style="width:154px;">9002-07-7</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:241px;">Vevo 3100</td>
			<td style="width:239px;">VisualSonics</td>
			<td style="width:154px;">NA</td>
			<td></td>
		</tr>
	</tbody>
</table>

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 exceeding its constituents. This protocol describes the development of an ML algorithm to predict the growth of breast cancer bone macrometastases in a rat model before any abnormalities are observable with standard imaging methods. Such an algorithm can facilitate the detection of early metastatic disease (i.e., micrometastasis) that is regularly missed during staging examinations.
The applied metastasis model is site-specific, meaning that the rats develop metastases exclusively in their right hind leg. The model&#8217;s tumor-take rate is 60%&#8211;80%, with macrometastases becoming visible in magnetic resonance imaging (MRI) and positron emission tomography/computed tomography (PET/CT) in a subset of animals 30 days after induction, whereas a second subset of animals exhibit no tumor growth.
Starting from image examinations acquired at an earlier time point, this protocol describes the extraction of features that indicate tissue vascularization detected by MRI, glucose metabolism by PET/CT, and the subsequent determination of the most relevant features for the prediction of macrometastatic disease. These features are then fed into a model-averaged neural network (avNNet) to classify the animals into one of two groups: one that will develop metastases and the other that will not develop any tumors. The protocol also describes the calculation of standard diagnostic parameters, such as overall accuracy, sensitivity, specificity, negative/positive predictive values, likelihood ratios, and the development of a receiver operating characteristic. An advantage of the proposed protocol is its flexibility, as it can be easily adapted to train a plethora of different ML algorithms with adjustable combinations of an unlimited number of features. Moreover, it can be used to analyze different problems in oncology, infection, and inflammation.

The rats recovered quickly from the surgery and injection of the MDA-MB-231 breast cancer cells and were then subjected to MR- and PET/CT imaging on days 10 and 30 PI (Figure 1). A representative DCE analysis of a rat&#8217;s right proximal tibia is presented in Figure 2A. The DCE raw measurements were saved by selecting the &#8220;Export&#8221; button and choosing &#8220;DCEraw.txt&#8221; as the file name.
Subsequent...

Watch this Scientific Journal Video about Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model at JoVE.com

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

This protocol was designed to train a machine learning algorithm to use a combination of imaging parameters derived from magnetic resonance imaging (MRI) and positron emission tomography/computed tomography (PET/CT) in a rat model of breast cancer bone metastases to detect early metastatic disease and predict subsequent progression to macrometastases.

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

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Cancer Research

6.6K Views.  Friedrich-Alexander Universität Erlangen-Nürnberg. This protocol was designed to train a machine learning algorithm to use a combination of imaging parameters derived from magnetic resonance imaging (MRI) and positron emission tomography/computed tomography (PET/CT) in a rat model of breast cancer bone metastases to detect early metastatic disease and predict subsequent progression to macrometastases.

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

Evaluation of the Feasibility, Safety, and Accuracy of an Intraoperative High-intensity Focused Ultrasound Device for Treating Liver Metastases

Today, the only potentially curative option in patients with colorectal liver metastases is surgery. However, liver resection is feasible in less than 20% of patients. Surgery has been widely used in association with radiofrequency, cryotherapy, or microwaves to expand the number of treatments performed with a curative intent. Nevertheless, several limitations have been documented when using these techniques (i.e., a traumatic puncture of the parenchyma, a limited size of lesions, and an inability to monitor the treatment in real-time).High-intensity focused ultrasound (HIFU) technology can achieve precise ablations of biological tissues without incisions or radiation. Current HIFU devices are based on an extracorporeal approach with limited access to the liver. We have developed a HIFU device designed for intraoperative use. The use of a toroidal transducer enables an ablation rate (10 cm3&#183;min-1) higher than any other treatment and is independent of perfusion.
The feasibility, safety, and accuracy of intraoperative HIFU ablation were evaluated during an ablate-and-resect prospective study. This clinical phase I and IIa study was performed in patients undergoing hepatectomy for liver metastases. The HIFU treatment was performed on healthy tissue scheduled for resection.Liver metastases measuring less than 20 mm will be targeted during phase IIb (currently ongoing). This set-up allows the real-time evaluation of HIFU ablation while protecting participating patients from any adverse effects related to this new technique.
Fifteen patients were included in phase I&#8211;IIa and 30 HIFU ablations were safely created within 40 s and with a precision of 1&#8211;2 mm. The average dimensions of the HIFU ablations were 27.5 x 21.0 mm2, corresponding to a volume of approximately 7.5 cm3. The aim of the ongoing phase IIb is to ablate metastases of less than 20 mm in diameter with a 5 mm margin.

Here, we present an ablate-and-resect prospective study to evaluate the feasibility, safety, and accuracy of intraoperative high-intensity focused ultrasound ablation in patients undergoing hepatectomy for liver metastases.

Today, the only potentially curative option in patients with colorectal liver metastases is surgery. However, liver resection is feasible in less than 20% ...

Three-Dimensional Bone Extracellular Matrix Model for Osteosarcoma

Osteosarcoma (OS) is the most common and a highly aggressive primary bone tumor. It is characterized with anatomic and histologic variations along with diagnostic or prognostic difficulties. OS comprises genotypically and phenotypically heterogeneous cancer cells. Bone microenvironment elements are proved to account for tumor heterogeneity and disease progression. Bone extracellular matrix (BEM) retains the microstructural matrices and biochemical components of native extracellular matrix. This tissue-specific niche provides a favorable and long-term scaffold for OS cell seeding and proliferation. This article provides a protocol for the preparation of BEM model and its further experimental application. OS cells can grow and differentiate into multiple phenotypes consistent with the histopathological complexity of OS clinical specimens. The model also allows visualization of diverse morphologies and their association with genetic alterations and underlying regulatory mechanisms. As homologous to human OS, this BEM-OS model can be developed and applied to the pathology and clinical research of OS.

The bone extracellular matrix (BEM) model for osteosarcoma (OS) is well established and shown here. It can be used as a suitable scaffold for mimicking primary tumor growth in vitro and providing an ideal model for studying the histologic and cytogenic heterogeneity of OS.

Osteosarcoma (OS) is the most common and a highly aggressive primary bone tumor. It is characterized with anatomic and histologic variations along with ...

Detection of Lung Tumor Progression in Mice by Ultrasound Imaging

With ~1.6 million victims per year, lung cancer contributes tremendously to the worldwide burden of cancer. Lung cancer is partly driven by genetic alterations in oncogenes such as the KRAS oncogene, which constitutes ~25% of lung cancer cases. The difficulty in therapeutically targeting KRAS-driven lung cancer partly stems from having poor models that can mimic the progression of the disease in the lab. We describe a method that permits the relative quantification of primary KRAS lung tumors in a Cre-inducible LSL-KRAS G12D mouse model via ultrasound imaging. This method relies on brightness (B)-mode acquisition of the lung parenchyma. Tumors that are initially formed in this model are visualized as B-lines and can be quantified by counting the number of B-lines present in the acquired images. These would represent the relative tumor number formed on the surface of the mouse lung. As the formed tumors develop with time, they are perceived as deep clefts within the lung parenchyma. Since the circumference of the formed tumor is well-defined, calculating the relative tumor volume is achieved by measuring the length and width of the tumor and applying them in the formula used for tumor caliper measurements. Ultrasound imaging is a non-invasive, fast and user-friendly technique that is often used for tumor quantifications in mice. Although artifacts may appear when obtaining ultrasound images, it has been shown that this imaging technique is more advantageous for tumor quantifications in mice compared to other imaging techniques such as computed tomography (CT) imaging and bioluminescence imaging (BLI). Researchers can investigate novel therapeutic targets using this technique by comparing lung tumor initiation and progression between different groups of mice.

This protocol describes the steps taken to induce KRAS lung tumors in mice as well as the quantification of formed tumors by ultrasound imaging. Small tumors are visualized in early timepoints as B-lines. At later timepoints, relative tumor volume measurements are achieved by the measurement tool in the ultrasound software.

With ~1.6 million victims per year, lung cancer contributes tremendously to the worldwide burden of cancer. Lung cancer is partly driven by genetic ...

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.

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, ...

An Orthotopic Resectional Mouse Model of Pancreatic Cancer

There is a lack of satisfactory animal models to study adjuvant and/or neoadjuvant therapy in patients being considered for surgery of pancreatic cancer (PC). To address this deficiency, we describe a mouse model involving orthotopic implantation of PC followed by distal pancreatectomy and splenectomy. The model has been demonstrated to be safe and suitably flexible for the study of various therapeutic approaches in adjuvant and neo adjuvant settings.
In this model, a pancreatic tumor is first generated by implanting a mixture of human pancreatic cancer cells (luciferase-tagged AsPC-1) and human cancer associated pancreatic stellate cells into the distal pancreas of Balb/c athymic nude mice. After three weeks, the cancer is resected by re-laparotomy, distal pancreatectomy and splenectomy. In this model, bioluminescence imaging can be used to follow the progress of cancer development and effects of resection/treatments. Following resection, adjuvant therapy can be given. Alternatively, neoadjuvant treatment can be given prior to resection.
Representative data from 45 mice are presented. All mice underwent successful distal pancreatectomy/splenectomy with no issues of hemostasis. A macroscopic proximal pancreatic margin greater than 5 mm was achieved in 43 (96%) mice. The technical success rate of pancreatic resection was 100%, with 0% early mortality and morbidity. None of the animals died during the week after resection.
In summary, we describe a robust and reproducible technique for a surgical resection model of pancreatic cancer in mice which mimics the clinical scenario. The model may be useful for the testing of both adjuvant and neoadjuvant treatments.

In the clinical context, patients with localized pancreatic cancer will undergo pancreatectomy followed by adjuvant treatment. This protocol reported here aims to establish a safe and effective method of modelling this clinical scenario in nude mice, through orthotopic implantation of pancreatic cancer followed by distal pancreatectomy and splenectomy.

There is a lack of satisfactory animal models to study adjuvant and/or neoadjuvant therapy in patients being considered for surgery of pancreatic cancer ...

Experimental Melanoma Immunotherapy Model Using Tumor Vaccination with a Hematopoietic Cytokine

Fms-like tyrosine kinase 3 ligand (Flt3L) is a hematopoietic cytokine that promotes the survival and differentiation of dendritic cells (DCs). It has been used in tumor vaccines to activate innate immunity and enhance antitumor responses. This protocol demonstrates a therapeutic model using cell-based tumor vaccine consisting of Flt3L-expressing B16-F10 melanoma cells along with phenotypic and functional analysis of immune cells in the tumor microenvironment (TME). Procedures for cultured tumor cell preparation, tumor implantation, cell irradiation, tumor size measurement, intratumoral immune cell isolation, and flow cytometry analysis are described. The overall goal of this protocol is to provide a preclinical solid tumor immunotherapy model, and a research platform to study the relationship between tumor cells and infiltrating immune cells. The immunotherapy protocol described here can be combined with other therapeutic modalities, such as immune checkpoint blockade (anti-CTLA-4, anti-PD-1, anti-PD-L1 antibodies) or chemotherapy in order to improve the cancer therapeutic effect of melanoma.

The protocol presents a cancer immunotherapy model using cell-based tumor vaccination with Flt3L-expressing B16-F10 melanoma. This protocol demonstrates the procedures, including preparation of cultured tumor cells, tumor implantation, cell irradiation, measurement of tumor growth, isolation of intratumoral immune cells, and flow cytometry analysis.

Fms-like tyrosine kinase 3 ligand (Flt3L) is a hematopoietic cytokine that promotes the survival and differentiation of dendritic cells (DCs). It has been ...

Mapping the Tumor Immune Landscape: An In-Depth Multiplex IHC Analysis Protocol

Multiplex Immunohistochemical Analysis of the Spatial Immune Cell Landscape of the Tumor Microenvironment

The immune cell landscape of the tumor microenvironment potentially contains information for the discovery of prognostic and predictive biomarkers. Multiplex immunohistochemistry is a valuable tool to visualize and identify different types of immune cells in tumor tissues while retaining its spatial information. Here we provide detailed protocols to analyze lymphocyte, myeloid, and dendritic cell populations in tissue sections. Starting from cutting formalin-fixed paraffin-embedded sections, automatic multiplex staining procedures on an automated platform, scanning of the slides on a multispectral imaging microscope, to the analysis of images using an in-house-developed machine learning algorithm ImmuNet. These protocols can be applied to a variety of tumor specimens by simply switching tumor markers to analyze immune cells in different compartments of the sample (tumor versus invasive margin) and apply nearest-neighbor analysis. This analysis is not limited to tumor samples but can also be applied to other (non-)pathogenic tissues. Improvements to the equipment and workflow over the past few years have significantly shortened throughput times, which facilitates the future application of this procedure in the diagnostic setting.

Author Spotlight: Unlocking Insights into the Immune Cell Landscape of Tumors

This protocol describes in detail how immune cell characterization of the tumor microenvironment using multiplex immunohistochemistry is carried out.

The immune cell landscape of the tumor microenvironment potentially contains information for the discovery of prognostic and predictive biomarkers. ...

Reprogramming PDAC and Pancreatic Cells into Induced Pluripotent Stem Cells

Reprogramming Pancreatic Ductal Adenocarcinoma to Pluripotency

The generation of induced pluripotent stem cells (iPSCs) using transcription factors has been achieved from almost any differentiated cell type and has proved highly valuable for research and clinical applications. Interestingly, iPSC reprogramming of cancer cells, such as pancreatic ductal adenocarcinoma (PDAC), has been shown to revert the invasive PDAC phenotype and override the cancer epigenome. The differentiation of PDAC-derived iPSCs can recapitulate PDAC progression from its early pancreatic intraepithelial neoplasia (PanIN) precursor, revealing the molecular and cellular changes that occur early during PDAC progression. Therefore, PDAC-derived iPSCs can be used to model the earliest stages of PDAC for the discovery of early-detection diagnostic markers. This is particularly important for PDAC patients, who are typically diagnosed at the late metastatic stages due to a lack of reliable biomarkers for the earlier PanIN stages. However, reprogramming cancer cell lines, including PDAC, into pluripotency remains challenging, labor-intensive, and highly variable between different lines. Here, we describe a more consistent protocol for generating iPSCs from various human PDAC cell lines using bicistronic lentiviral vectors. The resulting iPSC lines are stable, showing no dependence on the exogenous expression of reprogramming factors or inducible drugs. Overall, this protocol facilitates the generation of a wide range of PDAC-derived iPSCs, which is essential for discovering early biomarkers that are more specific and representative of PDAC cases.

Author Spotlight: Reprogramming Cancer Cells to iPSCs to Study Disease Progression and Treatment Targets

The present protocol describes the reprogramming of Pancreatic Ductal Adenocarcinoma (PDAC) and normal pancreatic ductal epithelial cells into induced pluripotent stem cells (iPSCs). We provide an optimized and detailed, step-by-step procedure, from preparing lentivirus to establishing stable iPSC lines.

The generation of induced pluripotent stem cells (iPSCs) using transcription factors has been achieved from almost any differentiated cell type and has ...

Real-Time Tracking of Nanoparticles in Brain Tumors Using 2P-IVM in Mice

Two-Photon Intravital Microscopy of Glioblastoma in a Murine Model

The delivery of intravenously administered cancer therapeutics to brain tumors is limited by the blood-brain barrier. A method to directly image the accumulation and distribution of macromolecules in brain tumors in vivo would greatly enhance our ability to understand and optimize drug delivery in preclinical models. This protocol describes a method for real-time in vivo tracking of intravenously administered fluorescent-labeled nanoparticles with two-photon intravital microscopy (2P-IVM) in a mouse model of glioblastoma (GBM).
The protocol contains a multi-step description of the procedure, including anesthesia and analgesia of experimental animals, creating a cranial window, GBM cell implantation, placing a head bar, conducting 2P-IVM studies, and post-surgical care for long-term follow-up studies. We show representative 2P-IVM imaging sessions and image analysis, examine the advantages and disadvantages of this technology, and discuss potential applications.
This method can be easily modified and adapted for different research questions in the field of in vivo preclinical brain imaging.

Author Spotlight: Multimodal Imaging Strategies for Optimizing Drug Delivery and Early Detection in Glioblastoma Treatment

We present a novel approach for two-photon microscopy of the tumor delivery of fluorescent-labeled iron oxide nanoparticles to glioblastoma in a mouse model.

The delivery of intravenously administered cancer therapeutics to brain tumors is limited by the blood-brain barrier. A method to directly image the ...