Waiting
Login processing...

Trial ends in Request Full Access Tell Your Colleague About Jove

Cancer Research

Pathological Analysis of Lung Metastasis Following Lateral Tail-Vein Injection of Tumor Cells

Published: May 20, 2020 doi: 10.3791/61270

ERRATUM NOTICE

Summary

Intravenous injection of cancer cells is often used in metastasis research, but the metastatic tumor burden can be difficult to analyze. Herein, we demonstrate a tail-vein injection model of metastasis and include a novel approach to analyze the resulting metastatic lung tumor burden.

Abstract

Metastasis, the primary cause of morbidity and mortality for most cancer patients, can be challenging to model preclinically in mice. Few spontaneous metastasis models are available. Thus, the experimental metastasis model involving tail-vein injection of suitable cell lines is a mainstay of metastasis research. When cancer cells are injected into the lateral tail-vein, the lung is their preferred site of colonization. A potential limitation of this technique is the accurate quantification of the metastatic lung tumor burden. While some investigators count macrometastases of a pre-defined size and/or include micrometastases following sectioning of tissue, others determine the area of metastatic lesions relative to normal tissue area. Both of these quantification methods can be exceedingly difficult when the metastatic burden is high. Herein, we demonstrate an intravenous injection model of lung metastasis followed by an advanced method for quantifying metastatic tumor burden using image analysis software. This process allows for investigation of multiple end-point parameters, including average metastasis size, total number of metastases, and total metastasis area, to provide a comprehensive analysis. Furthermore, this method has been reviewed by a veterinary pathologist board-certified by the American College of Veterinary Pathologists (SEK) to ensure accuracy.

Introduction

Despite being a highly complex and inefficient process1, metastasis is a significant contributor to the morbidity and mortality of cancer patients2. In fact, most cancer-related deaths are attributed to metastatic spread of disease3,4. In order for tumor cells to successfully metastasize, they must detach from the primary site, invade through adjoining stroma, intravasate into blood circulation or lymphatics, travel to the capillary bed of a secondary site, extravasate into the secondary tissue, and proliferate or grow to form metastatic lesions5. The use of mouse models has been critical to furthering the understanding of the molecular mechanisms responsible for metastatic seeding and growth6,7. Herein, we focus on breast cancer metastasis, for which both genetically modified mouse models as well as methods of transplantation are often used – each with their own set of advantages and limitations.

Genetically engineered mammary tumor models make use of mammary gland specific promoters, including MMTV-LTR (mouse mammary tumor virus long terminal repeat) and WAP (Whey Acidic Protein), to drive expression of transgenes in the mammary epithelium8. Oncogenes including polyoma middle T antigen (PyMT), ErbB2/Neu, c-Myc, Wnt-1, and simian virus 40 (SV40) have been expressed in this manner9,10,11,12,13, and while these genetic models are useful for studying primary tumor initiation and progression, few readily metastasize to distant organs. Furthermore, these genetic mouse models are often more time and cost prohibitive than spontaneous or experimental metastasis models. Given the limitation of most genetically engineered mammary tumor models to study metastasis, transplantation techniques have become attractive methods to study this complex process. This includes orthotopic, tail-vein, intracardiac, and intracranial injection of suitable cell lines.

Although several breast cancer cell lines readily metastasize following orthotopic injection into the mammary fat pad14,15, the consistency and reproducibility of metastatic tumor burden can be a challenge, and the duration of such studies can be on the order of several months. For evaluating lung metastasis, in particular, intravenous injection into the tail-vein is often a more reproducible and time-effective method with metastatic spread typically occurring within the span of a few weeks. However, since the intravenous injection model bypasses initial steps of the metastatic cascade, care must be taken in interpreting the results of these studies. In this demonstration, we show tail-vein injection of mammary tumor cells along with an accurate and comprehensive method of analysis.

Even though the research community has made significant progress in understanding the complex process of breast cancer metastasis, it is estimated that over 150,000 women currently have metastatic breast cancer16. Of those with stage IV breast cancer, >36% of patients have lung metastasis17; however, the site-specific pattern and incidence of metastases can vary based on molecular subtype18,19,20,21. Patients with breast cancer-associated lung metastases have a median survival of only 21 months highlighting the need to identify effective treatments and novel biomarkers for this disease17. The use of experimental metastasis models, including the intravenous injection of tumor cells, will continue to advance our knowledge of this important clinical challenge. When combined with digital imaging pathology and the method of metastatic lung tumor burden analysis described within this protocol, tail-vein injections are a valuable tool for breast cancer metastasis research.

Subscription Required. Please recommend JoVE to your librarian.

Protocol

Animal use followed University Laboratory Animal Resources (ULAR) regulations under the OSU Institutional Animal Care and Use Committee (IACUC)–approved protocol 2007A0120-R4 (PI: Dr. Gina Sizemore).

1. Tail-vein injection of breast cancer cells

  1. Preparation of cells and syringe for injection
    1. Plate an appropriate number of cells based on the number of mice and cell concentration to be used.
      NOTE: The number of cells injected and time to the development of metastases will depend on the cell line used and will need to be optimized. In this demonstration, 1 x 106 MDA-MB-231 cells are injected intravenously into NOD.Cg-Prkdcscid Il2rgtm1Wjl/SzJ (NSG) mice, and macroscopic lung lesions are observed no later than 24 days post-injection. For the MVT1 murine mammary tumor cell line17, 3 x 106 cells are injected into immune-competent FVB/N mice with numerous lung metastases observed by 14 days22,23.
    2. Aspirate media and rinse cell plates with 1x PBS. Trypsinize cells in minimal volume, add appropriate volume of media, and count cells using a hematocytometer or another preferred method. Trypan blue (0.4%) or other live/dead cell dyes can be used to determine viable cell counts.
    3. Pellet cells by spinning at 180 x g for 5 min.
    4. Resuspend appropriate number of cells in sterile 1x PBS such that a volume of 100 µL is injected per mouse. Keep cell suspension on ice to maintain viability.
    5. Prior to injection, thoroughly resuspend cells with a 200 µL or 1 mL pipette to avoid clumping. Draw up 100 µL in a 28 G insulin syringe (see Table of Materials).
    6. Eliminate any air bubbles by keeping the syringe vertical, tapping on the syringe, and slowly adjusting the plunger. Injection of air bubbles into the vein is likely to cause an air/gas embolism that can be fatal.
  2. Lateral tail-vein injection
    NOTE: For experimental breast cancer metastasis assays, injections are performed on > 6 weeks old female mice.
    1. Handle the mouse by the tail and slide animal into a slotted tube/restraint device of an appropriate size (see the Table of Materials for restraint device used).
    2. Insert the plug portion of the restraint device and position the mouse on its side such that its lateral tail vein is easy to view. The mouse has a ventral artery in line with the genitalia, a dorsal vein, and two lateral caudal veins.
    3. Clean the surface of the mouse’s tail with an aseptic wipe. Grasp the tail between index finger and thumb with non-dominant hand and apply slight tension.
    4. Beginning at the distal portion of the tail, insert the needle parallel to the vein with the bevel end up.
    5. If allowed, carefully recap the needle and bend to a 20-30° angle. A single-handed approach or needle recapping device is highly recommended.
      NOTE: It is not necessary to aspirate as this may cause the vein to collapse. However, a small flash of blood may be seen when first placed. The needle will advance smoothly into the vein with proper placement.
    6. Slowly dispense the complete volume into the vein. There should not be resistance when the plunger is pushed.
    7. If any resistance is felt, promptly remove the syringe needle. If needed, re-insert the needle (ideally no more than 3 attempts) moving toward the proximal end of the tail or opposing lateral vein.
    8. A small volume of blood will likely be displaced after injection. Apply gentle pressure with sterile gauze and wipe with aseptic wipe.
    9. Promptly dispose of syringe in appropriate sharps container.
    10. Return the mouse to a clean, ventilated cage and monitor for signs of distress.
    11. Monitor mice 2-3x/weekly for signs of metastasis formation (labored breathing, hunched posture, weight loss) and general distress. The time to development of metastasis will depend on cell line and mouse strain.
    12. If using an in vivo live animal imaging device, image mice immediately after tail-vein injection to confirm successful injection of cells and obtain time “zero” data (specific details on in vivo bioluminescence imaging are not included herein, but are described by Yang et al.24).

2. Lung tissue fixation and analysis of metastatic lung tumor burden

  1. Lung tissue inflation to maintain the structural format of the lungs for histopathology
    1. After approved euthanasia procedures (e.g. carbon dioxide at 30 - 70% displacement of the chamber volume/min) are followed, secure the mouse carcass to a dissecting board with pins. Either spray or apply 70% ethanol to keep the mouse’s fur out of the way during dissection. 
      NOTE: Carbon dioxide asphyxiation can cause pulmonary hemorhhage as an expected background lesion, especially at slower flow rates.
    2. Open the thorax with a midline incision, extend the incision cranially/caudally through the peritoneum, and cut away the diaphragm by grasping the xyphoid process.
    3. Using a separate set of scissors to not dull the blades, cut the ribs along each side of the sternum and carefully remove rib cage leaving room for the lungs to expand.
    4. Isolate the trachea by removing submandibular salivary glands and infrahyoid musculature. Placing pins on either side of the trachea can prevent unwanted movement during needle insertion.
    5. Fill a 26 G syringe with 2-3 mL of 10% neutral buffered formalin and insert into the trachea.
    6. Slowly inject formalin and watch for the lungs to expand (usually requires ~1.5 mL of formalin).
    7. Once formalin begins leaking out of the lungs (avoid over inflation), pinch off the trachea with a pair of forceps, remove syringe needle and detach the entire respiratory apparatus. Place lungs, heart, etc. directly into formalin as additional trimming of tissue can be done post-fixation.
    8. Complete processing, embedding, sectioning of tissue, and hematoxylin and eosin (H&E) staining using standard methods.
  2. Analysis of metastatic lung tumor burden
    1. Scan H&E-stained lung sections on a high-resolution, slide scanner at 40x magnification (Figure 3).
    2. Import images into image analysis software (e.g., Visiopharm Image Analysis) for quantification of lung metastases.
      NOTE: We recommend that new users either obtain onsite or online training to use the image analysis software. Numerous webinars are also available through the commercial webpage.
    3. Select the Visiopharm 10118 H&E Lung Metastasis App from the software’s app library.
      NOTE: The purpose of this app is to label and quantify lung metastasis on H&E-stained slides. As part of the 10118 H&E Lung Metastasis App, the first image processing step segments the lung tissue with the Tissue Detection App. The second image processing step uses the Metastasis Detect App which identifies the metastases inside the lung tissue. Metastases are identified via shape together with regions that are either too misshaped, too red or too sparse for being identified as metastases.
    4. Adjust the parameters defining shape and sparseness to best fit representative images. Segmented areas of tumor metastases and normal lung tissue can be displayed using different color labels for each tissue type.
      NOTE: In the event that the app cannot accurately separate metastases from normal lung tissue, a custom app using the Visiopharm Decision Forest program may need to be written as was done for the experiments (see Figure 2 and Figure 3). Details for writing a custom algorithm follow below. Otherwise, proceed to step 2.2.9.
    5. Open the Decision Forest program, which works by training multiple Classes [i.e., lung tissue (non-neoplastic), metastases, red blood cells, epithelium, and/or white space] on a desired image. In Figure 2, tumor metastases are blue, normal tissue is green, and bronchiolar epithelium is yellow. Also, red blood cells are in red and air spaces in pink.
    6. Follow the prompted series of yes or no questions to appropriately train each Class for an image. The accuracy of the algorithm will determine the number of yes/no questions. For the analysis, the custom algorithm/app was written with accuracy set to 50 (range 0-100).
    7. Adjust Features for each class by applying filters to sharpen, blur, sort by shape, etc. to enhance the accuracy of the algorithm/App. Visiopharm views each Class through one or multiple lenses known as Features. Features change how the Class sees the image to bring out certain colors or intensities.
      NOTE: For the custom algorithm, metastases measuring 8500 µm2 and above are labeled and measured as metastases. This accounts for size variance and metastases too small to detect. Small misshaped areas and small metastatic areas under 8500 µm2 were included in the normal tissue quantification.
    8. Save the modified settings from either the app or custom algorithm and then, apply the algorithm/app to an entire set or series of H&E-stained tissues.
    9. Finally, export all output variables, which includes those listed in Table 1. Area in microns squared (µm2) can be quantified for each tissue type and percentages are derived from specimen total net tissue area (i.e., total tissue minus air space).
    10. When creating a custom algorithm, review tissue markups in consultation with a veterinary pathologist board-certified by the American College of Veterinary Pathologists to ensure accurate measurements and differentiate between tissue types.

Subscription Required. Please recommend JoVE to your librarian.

Representative Results

If using unlabeled cells for tail-vein injection, it may be difficult to confirm lung colonization until (1) the time of necropsy if macrometastases can be observed or (2) following histological analysis if microscopic metastases exist. With extensive metastatic lung tumor burden, mice will have labored breathing. As with any tumor study, mice should be carefully monitored throughout the study duration. The use of labeled cells is an easy way to confirm successful tail-vein injection; hence the use of luciferase-tagged MDA-MB-231 cells in the demonstration. However, in vivo imaging is not always possible or necessary depending on the experimental design and other factors. Figure 1A shows bioluminescence signal in the thoracic space less than 2 hours after tail-vein injection of luciferase-tagged MDA-MB-231 cells as confirmation of accurate injection. For this experiment, photon counts in the thoracic region increase over time and a strong bioluminescence signal is present at day 24 post-injection (Figure 1B and C; note the change in scale bar). At the time of necropsy, many macroscopic lung lesions were observed in these mice (Figure 1D).

After proper tissue processing and staining, H&E-stained lung sections can be scanned or imaged. Metastatic lung tumor burden quantification can be effectively achieved using image analysis software and a custom algorithm. Using the customized algorithm, whole lung tissue is segmented by different tissue features (Figure 2A and B). By segmenting the lung tissue in this manner, the software can quantify the various parameters listed in Table 1. This analysis has been performed on lung tissue from mice injected with MDA-MB-231 cells followed by treatment with a drug designed to block metastatic colonization or a vehicle control (DMSO). The raw data from this analysis are shown in Table 2. Furthermore, Figure 3A shows representative H&E images of MDA-MB-231 lung metastases from either DMSO or drug-treated mice. While a difference in metastatic tumor burden between these treatment groups may have easily been overlooked as the total number of lung nodules is no different, a comprehensive analysis of all parameters indicates a significant difference in the percent net lung metastasis area (Figure 3B,C). This underscores the need for a comprehensive and thorough approach to analyze metastatic lung tumor burden such as the method described herein.

For the data presented in Figure 3, all statistical analyses were conducted using GraphPad Prism 7. Data were considered normally distributed upon passing any of the following standard normality tests: D’Agostino-Pearson omnibus, Shapiro-Wilk, and Kolmogorov-Smirnov. Comparison between the vehicle and drug-treated groups (Figure 3) was done by unpaired two-tailed Student’s t-test. Statistical significance was established at P ≤ 0.05.

Figure 1
Figure 1: In vivo bioluminescence confirmation of successful tail-vein injection.
(A) Representative bioluminescence signal in mice 1 hour after tail-vein injection of luciferase-tagged MDA-MB-231 cells. (B) Representative bioluminescence signal in the same set of mice as (A) 24 days after tail-vein injection of luciferase-tagged MDA-MB-231 cells. [Note the change in scale bar between (A) and (B)]. (C) Quantification of photon counts over time in MDA-MB-231 tail-vein injected mice. Error bars represent mean ± SEM. (n = 8 mice) (D) Representative non-tumor bearing lung tissue (right) and MDA-MB-231 macrometastases in the lungs (left) at time of necropsy. Scale bars = 50 mm. Please click here to view a larger version of this figure.

Figure 2
Figure 2: Tissue segmentation using Visiopharm software.
(A) Representative snips of unsegmented and segmented tissue mark-ups using the customized software algorithm. (B) Legend for all tissue categories segmented using software. Please click here to view a larger version of this figure.

Figure 3
Figure 3: Representative metastatic lung tumor burden analysis of H&E-stained tissues.
(A) Representative H&E staining of lung tissue from uninjected mice and control (DMSO) and drug-treated mice following tail-vein injection of MDA-MB-231 cells. Representative tumor metastases are indicated with arrows. Scale bars = 500 µm for 4x magnification and 200 µm for 10x magnification. (B) Graph of percent net lung metastasis area of control and drug-treated mice. Error bars represent mean ± SD. (*) P = 0.022 by Student’s t-test. (C) Table summarizing the metastatic lung tumor burden analysis (n = 9 DMSO; n = 9 drug-treated). After checking for normal distribution of data, all P-values in the table were determined by unpaired, two-tailed Student’s t-test. Please click here to view a larger version of this figure.

Parameter Description
Total Tissue Area (µm2) Area in square microns inclusive of all tumor metastases, normal lung and areas of red blood cells.
Metastasis Count Total number of metastases within the lung tissue.
Metastasis Area Percentage (µm2) Total metastasis area divided by net tissue area x 100.
Total Tissue + White Space Area (µm2) Area in square microns inclusive of all tissue and white space.
Net Tissue Area (µm2) Tissue area in square microns (mets and normal lung) without white space and red blood cells.
Total Metastasis Area (µm2) Total metastasis area in square microns as segmented by the Decision Forest algorithim.
Mean metastasis Area (µm2) Mean (average) area in square microns of metastases within each image.
Median Metastasis Area (µm2) Median metastasis area in square microns. An equal number of metastases falls below this value and an equal number of metastases are greater than the median value.

Table 1: Parameters measured with software. List of parameters along with a description of each measurement that is computed using the custom algorithm.

Slide Metastasis Count Metastasis Area Percentage (µm2) Total Tissue + White Space Area (µm2) Total White Space (µm2) Net Tissue Area (µm2) Total Metastasis Area (µm2) Red Blood Cells Area (µm2) Mean Metastasis Area (µm2) Median Metastasis Area (µm2)
171 Lung Slide 1 435 8.90 185698000 83201800 92031400 8189250 10464800 18825.86 14748.73
171 Lung Slide 2 323 8.37 185698000 83201800 92054740 7708990 10441460 23866.84 14748.73
172 Lung Slide 1 151 2.73 181546000 89509904 81571296 2225220 10464800 14736.56 12486.37
172 Lung Slide 2 142 2.60 170708000 81735504 80558196 2093040 8414300 14739.72 12119.62
173 Lung Slide 1 634 11.60 234104992 102153000 115606692 13406800 16345300 21146.37 15472.22
173 Lung Slide 2 667 12.70 223180992 86778600 122374592 15542700 14027800 23302.40 16531.00
174 Lung Slide 1 40 0.55 192452992 80340896 87591096 485121 24521000 12128.03 10484.05
174 Lung Slide 2 34 0.51 183918000 71287904 91242796 464830 21387300 13671.47 11181.81
175 Lung Slide 1 780 23.93 179544992 44799200 126995782 30388600 7750010 38959.74 19307.76
175 Lung Slide 2 1001 12.58 169191536 43425608 120610754 15169100 5155174 15153.95 19703.08
188 Lung Slide 1 569 13.20 162290000 54210000 98486310 12997300 9593690 22842.36 14463.91
188 Lung Slide 2 271 5.15 157146000 54250800 91996500 4738100 10898700 17483.76 12657.83
189 Lung Slide 1 74 1.70 185292992 95700800 77779392 1318820 11812800 17821.89 14551.08
189 Lung Slide 2 74 1.76 182272992 95700800 74759392 1318820 11812800 17821.89 14551.08
816 Lung Slide 1 246 5.65 185876000 87568896 81916204 4631050 16390900 18825.41 14371.99
816 Lung Slide 2 565 6.05 183220000 76954304 90305396 5462670 15960300 9668.44 14244.82
876 Lung Slide 1 468 10.36 208308000 99300096 100947064 10454500 8060840 22338.68 16011.37
876 Lung Slide 2 528 11.74 199750896 81642568 110450391 12963400 7657937 24551.89 16699.13
877 Lung Slide 1 732 17.98 219340992 99918600 107869992 19397100 11552400 26498.77 18137.52
877 Lung Slide 2 605 14.64 207925504 88539712 108168329 15839700 11217463 26181.32 18014.64
878 Lung Slide 1 377 10.05 178534000 85610896 81931104 8232340 10992000 21836.45 16671.03
878 Lung Slide 2 376 9.88 170544000 75337904 86108406 8511710 9097690 22637.53 16754.38
879 Lung Slide 1 205 5.22 167556000 89999000 68123630 3553860 9433370 17335.90 13845.69
879 Lung Slide 2 213 4.64 167931008 80789400 78489588 3638720 8652020 17083.19 14058.12
881 Lung Slide 1 1122 38.81 218880000 79713504 130893816 50802300 8272680 45278.34 22044.99
881 Lung Slide 2 628 21.67 184200992 74502600 99122692 21475200 10575700 34196.18 19857.40
882 Lung Slide 1 678 24.05 194476992 83941904 98484788 23684500 12050300 34932.89 20748.06
882 Lung Slide 2 645 21.93 185537040 75790040 101412430 22241700 8334570 34483.26 20325.11
883 Lung Slide 1 429 10.79 179400992 84955696 84699866 9138800 9745430 21302.56 15080.23
883 Lung Slide 2 342 85.30 175220992 76210896 90472386 77170200 8537710 225643.86 17078.26
884 Lung Slide 1 359 6.42 206751008 87752600 103825008 6669710 15173400 18578.58 14333.41
884 Lung Slide 2 480 9.12 200990000 77052496 111060804 10125700 12876700 21095.21 15679.88
885 Lung Slide 1 332 7.79 191398000 92896304 84752596 6605490 13749100 19896.05 14500.11
885 Lung Slide 2 537 81.02 187475008 85938000 89378408 72411104 12158600 134843.77 15360.29
886 Lung Slide 1 305 7.93 158435008 80433296 76541662 6068720 1460050 19897.44 14500.11
886 Lung Slide 2 898 8.84 155460000 70808600 83457470 7380490 1193930 8218.81 14744.92

Table 2: Representative table of results. Table of results for each parameter of the algorithm from a cohort of mice tail-vein injected with MDA-MB-231 cells.

Subscription Required. Please recommend JoVE to your librarian.

Discussion

As researchers continue to use intravenous injection of tumor cells as an experimental model for metastasis, standard practices to analyze the resulting metastatic tumor burden are lacking. In some cases, significant differences in metastatic tumor burden upon manipulation of particular cell lines and/or use of chemical compounds can be observed macroscopically. However, in other instances, subtle differences in metastatic seeding and growth may be overlooked or misinterpreted without thorough pathological analysis. This study advances previously published tail-vein injection protocols by including a comprehensive method of metastatic lung tumor burden analysis. Importantly, this method of digital pathology analysis can also be applied to the quantification of lung metastatic tumor burden following orthotopic injection of tumor cells which are capable of spontaneous metastasis as well as other experimental metastasis models (i.e. intracardiac, etc.) and patient-derived xenograft (PDX) models. The use of digital imaging and software algorithm development by veterinary pathologists ensures the reproducibility, accuracy, and thoroughness of this approach to analyze metastatic lung tumor burden25.

Thoughtful decision of cell lines, cell concentration, and endpoints based on either previously published studies or careful experimental optimization is absolutely necessary. Given that metastatic seeding and colonization are highly dependent on interactions with various immune cell populations26,27, the use of immune-competent mice is ideal, albeit not always feasible. For the same reason, the interpretation of experimental metastasis studies using athymic or NSG mice, which lack key immune cell components, should be taken with caution. There are several mouse mammary tumor cell lines, including the MVT1 cells used in this study, that have been derived from the FVB/N mouse strain22,28,29. Other syngeneic models exist as well. In regard to cell concentration, injection of a large number of cells may greatly accelerate and enhance metastatic lung tumor burden. However, if the lungs are overwhelmed, it may be difficult to distinguish individual metastatic foci and emboli are more likely to occur. Also, the tail-vein injection procedure requires ample practice and training before safely and/or routinely performing injections. Many institutions will offer technical training and may provide mice for practice purposes. Proper placement of the needle and a smooth injection should indicate success; however, for training/practice purposes, Evans Blue dye can be used to help determine successful injection (1% in sterile PBS). The extremities of the mouse will turn blue shortly after injection, but the animal should be euthanized afterwards.

Additionally, the importance of standard necropsy and tissue sampling techniques to control and prevent slide artifacts that may impair slide scanning and analysis by the image analysis software cannot be stressed enough. Inflation of the lungs at time of necropsy is a critical step in maintaining tissue integrity and improves subsequent H&E staining as well as final analysis. For consistency with resolution and reproducibility, it is recommended that all slides in a study set are scanned with the same objective. In this study, all slides were scanned at 40x to ensure accuracy of algorithm settings and proper identification of tumor metastases when applied to analyzed fields. For each slide, the same lung lobes were consistently scanned and analyzed for each mouse. It is also strongly recommended that a pathologist review tissue markups for accuracy of the applied algorithm and that the same algorithm is applied to every slide in a study.

The presented protocol can be modified according to experimental design, user preferences, and desired outcome measurements. One such modification includes the use of an anesthesia induction chamber rather than conventional restrainer device for a conscious animal. In terms of animal health and wellness, neither approach is superior to the other and each has its own set of advantages as well as limitations30. Also, for black or brown mice, a light source or heating device may be needed in order to visualize the tail veins. Infrared lamps or a warm water bath can be used to dilate the veins. However, temperature should be carefully monitored. Furthermore, there are illuminated restraint devices available as well as other commercial versions of rodent restraint devices. Some investigators prefer Luer-Lok syringes for injections. We find it more difficult to eliminate air bubbles with Luer-Lok syringes, but it is a matter of preference. The viability of cells is an important consideration for the tail-vein injection procedure, and therefore, accurate cell counts as well as maintaining cells on ice prior to injection are necessary steps. If comparing lung seeding and colonization of manipulated cell lines, it is critical to determine any differences in cell size and viability prior to injection as these may complicate the interpretation of results. Cell death and/or damage may occur when using a narrow gauge needle; however, it is not recommended that a needle larger than 25 G be used as it may cause pain and discomfort to the animal.

As a way to validate that lung lesions are formed by injected tumor cells, immunostaining can be done on tissue sections. If using human cell lines, human-specific antibodies can be used to discern metastatic lesions. Alternatively, if using tagged cell lines (e.g., GFP), corresponding antibodies can be used. Also, many breast cancer cell lines are positive for epithelial markers (i.e., cytokeratins, E-cadherin, and EpCAM), but prior knowledge of expression is essential. However, the lung epithelium lining the airways will also be positive for these markers and thus, structure must also be considered. There may be cases in which primary lung tumor development must be ruled out. To do so, immunohistochemical staining for Thyroid Transcription Factor-1 (TTF1) can be used as a marker of primary lung adenocarcinoma. TTF1 staining should be evaluated by a certified pathologist.

Herein, a custom algorithm was written using a Decision Forest classification algorithm because the established lung metastasis algorithm could not be fine-tuned for accurate detection of metastases that varied in size. This customized algorithm enables complex measurements, allows for accurate segmentation of metastases by size, and supports a size cutoff so that small misshaped areas and normal structures are not misinterpreted and can therefore be included in the final data set. We anticipate that this algorithm will be applicable to most in vivo lung metastasis studies, but users may need to adjust settings within the software to fit their individual study needs. However, this algorithm serves as a platform for investigators wishing to analyze lung metastatic burden in a similar manner. There are many different options for image analysis platforms whereby access or availability, cost and training, as well as experience level may dictate the best platform to utilize36. The range of options include free platforms such as QuPath and more expensive, but sophisticated platforms, such as Visiopharm. It is advised that one consults with an image analysis pathology core and pathologist when deciding which platform may be available and best utilized for a particular research project.

Spontaneous mouse mammary tumor models (e.g., MMTV-PyMT) or orthotopic mammary fat pad injection methods represent the most physiologically relevant model for studying lung metastasis. A serious drawback to the tail-vein injection model is that it does not recapitulate the full metastatic cascade and is therefore limited to the study of tumor cell extravasation and secondary organ colonization. However, this experimental metastasis model is relevant for breast cancer research as lung metastases formed following tail-vein injection have genomic profiles similar to metastatic lesions that develop after orthotopic implantation of the same cells31. In order to establish a lung metastasis model, a large number of cells are often injected intravenously which may not accurately represent the process of metastasis as it pertains to seeding, immune reaction, and dormancy. Also, based on the circulatory pathway, pulmonary metastases are most common with tail-vein injection32. With most breast cancer cell lines, published reports indicate a relatively low incidence of bone, liver, or brain metastases following tail-vein injection7. Alternative experimental metastasis methods such as intracardiac, intratibial, portal vein and intracarotid injections are more appropriate for examining metastases to other sites33,34,35,36,37. Again, spontaneous mammary tumor models or orthotopic fat pad injection methods that recapitulate all steps of the metastatic cascade are preferred. Issues with consistent metastatic tumor burden, duration of study, and numbers of animals required for such studies are a downside. However, the method of digital pathology analysis presented here in can be applied to lung metastases formed through any spontaneous or experimental metastasis model.

The method of analysis also yields certain limitations such as subjectivity in algorithm creation. Even though whole slide imaging allows for digital analysis on an entire tissue section and on all lung lobes of a single mouse, it is limited to a two-dimensional analysis of a 3D tissue. Stereology is becoming a common practice that obtains 3D information for image analysis and can account for factors such as tissue shrinkage that occurs during tissue processing38. Stereology, however, has its own limitations such as tissue, resource, and time constraints.

Given the number of cancer patients affected by metastatic spread of their disease, the tail-vein injection method to study metastasis will continue to be a useful tool in terms of understanding the complicated biology of metastatic spread and in determining the pre-clinical efficacy of novel therapeutics. In vivo mouse models of metastasis, particularly those using immune-competent animals, are becoming even more important for cancer research given the widespread interest in immunotherapy29. Also, experimental metastasis models are critical in terms of investigating metastasis suppressor genes (i.e., those that suppress the metastatic potential of cancer cells without affecting primary tumor growth), and therefore, continue to be a valuable research tool.

Digital imaging and slide analysis have rapidly become a mainstay in diagnostic and experimental mouse modeling39. Using the type of approach described herein to analyze lung metastatic tumor burden will allow for high throughput analyses in a more comprehensive and accurate manner. Furthermore, digital imaging pathology provides an avenue for more collaborative research projects involving pathologists that specialize in areas such as mouse models of breast cancer metastasis. As multiplex tissue imaging methods and 3D imaging technologies (as mentioned above) continue to be developed, digital imaging pathology, sophisticated software programs for image analysis, and the expertise of pathologists will certainly be necessary for advancing metastasis research.

Subscription Required. Please recommend JoVE to your librarian.

Disclosures

The authors have nothing to disclose.

Acknowledgments

Representative data was funded through the National Cancer Institute (K22CA218549 to S.T.S). In addition to their assistance in developing the comprehensive analysis method reported herein, we thank The Ohio State University Comprehensive Cancer Center Comparative Pathology and Mouse Phenotyping Shared Resource (Director – Krista La Perle, DVM, PhD) for histology and immunohistochemistry services and the Pathology Imaging Core for algorithm development and analysis.

Materials

Name Company Catalog Number Comments
alcohol prep pads Fisher Scientific 22-363-750 for cleaning tail prior to injection
dissection scissors Fisher Scientific 08-951-5 for mouse dissection and lung tissue inflation
DMEM with L-Glutamine, 4.5g/L Glucose and Sodium Pyruvate Fisher Scientific MT10013CV cell culture media base for MDA-MB-231 and MVT1 cell lines
Dulbecco's Phosphate-Buffered Salt Solution 1x Fisher Scientific MT21030CV used for resuspending tumor cells for injection
ethanol (70 % solution) OSU used to minimize mouse's fur during dissection; use caution - flammable
Evan's blue dye Millipore Sigma E2129 used at 1 % in sterile PBS for practice with tail-vein injection method; use caution - dangerous reagent
Fetal Bovine Serum Millipore Sigma F4135 cell culture media additive; used at 10% in DMEM
forceps Fisher Scientific 10-270 for dissection and lung tissue inflation
FVB/NJ mice The Jackson Laboratory 001800 syngeneic mouse strain for MVT1 cells
hemacytometer (Bright-Line) Millipore Sigma Z359629 for use in cell culture to obtain cell counts
insulin syringe (28 G) Fisher Scientific 14-829-1B for tail-vein injections (BD 329424)
MDA-MB-231 cells ATCC human breast cancer cell line
MVT1 cells mouse mammary tumor cells
needles (26 G) Fisher Scientific 14-826-15 used to inflate the mouse's lungs
neutral buffered formalin (10%) Fisher Scientific 245685 used as a tissue fixative and to inflate lung tissue; use caution - dangerous reagent
NOD.Cg-Prkdcscid Il2rgtm1Wjl/SzJ (NSG) mice The Jackson Laboratory 005557 maintained by OSUCCC Target Validation Shared Resource
Penicillin Streptomycin 100x ThermoFisher 15140163 cell culture media additive
sterile gauze Fisher Scientific NC9379092 for applying pressue to mouse's tail if bleeding occurs
syringe (5 mL) Fisher Scientific 14-955-458 used to inflate mouse lung tissue
tail-vein restrainer Braintree Scientific, Inc. TV-150 STD used to restrain mouse for tail-vein injections
Trypan blue (0.4 %) ThermoFisher 15250061 used in cell culture to assess viability
Trypsin-EDTA 0.25 % ThermoFisher 25200-114 used in cell culture to detach tumor cells from plate

DOWNLOAD MATERIALS LIST

References

  1. Chambers, A. F., Groom, A. C., MacDonald, I. C. Dissemination and growth of cancer cells in metastatic sites. Nature Reviews: Cancer. 2 (8), 563-572 (2002).
  2. Steeg, P. S. Targeting metastasis. Nature Reviews: Cancer. 16 (4), 201-218 (2016).
  3. Gupta, G. P., Massague, J. Cancer metastasis: building a framework. Cell. 127 (4), 679-695 (2006).
  4. Steeg, P. S. Tumor metastasis: mechanistic insights and clinical challenges. Nature Medicine. 12 (8), 895-904 (2006).
  5. Chaffer, C. L., Weinberg, R. A. A perspective on cancer cell metastasis. Science. 331 (6024), 1559-1564 (2011).
  6. Eckhardt, B. L., Francis, P. A., Parker, B. S., Anderson, R. L. Strategies for the discovery and development of therapies for metastatic breast cancer. Nature Reviews Drug Discovery. 11 (6), 479-497 (2012).
  7. Gomez-Cuadrado, L., Tracey, N., Ma, R., Qian, B., Brunton, V. G. Mouse models of metastasis: progress and prospects. Disease Models & Mechanisms. 10 (9), 1061-1074 (2017).
  8. Fantozzi, A., Christofori, G. Mouse models of breast cancer metastasis. Breast Cancer Research. 8 (4), 212 (2006).
  9. Schoenenberger, C. A., et al. Targeted c-myc gene expression in mammary glands of transgenic mice induces mammary tumours with constitutive milk protein gene transcription. EMBO Journal. 7 (1), 169-175 (1988).
  10. Nusse, R., Varmus, H. E. Many tumors induced by the mouse mammary tumor virus contain a provirus integrated in the same region of the host genome. Cell. 31 (1), 99-109 (1982).
  11. Muller, W. J., Sinn, E., Pattengale, P. K., Wallace, R., Leder, P. Single-step induction of mammary adenocarcinoma in transgenic mice bearing the activated c-neu oncogene. Cell. 54 (1), 105-115 (1988).
  12. Lin, E. Y., et al. Progression to malignancy in the polyoma middle T oncoprotein mouse breast cancer model provides a reliable model for human diseases. American Journal of Pathology. 163 (5), 2113-2126 (2003).
  13. Green, J. E., et al. The C3(1)/SV40 T-antigen transgenic mouse model of mammary cancer: ductal epithelial cell targeting with multistage progression to carcinoma. Oncogene. 19 (1), 1020-1027 (2000).
  14. Iorns, E., et al. A new mouse model for the study of human breast cancer metastasis. PloS One. 7 (10), 47995 (2012).
  15. Kim, I. S., Baek, S. H. Mouse models for breast cancer metastasis. Biochemical and Biophysical Research Communications. 394 (3), 443-447 (2010).
  16. Mariotto, A. B., Etzioni, R., Hurlbert, M., Penberthy, L., Mayer, M. Estimation of the Number of Women Living with Metastatic Breast Cancer in the United States. Cancer Epidemiology, Biomarkers and Prevention. 26 (6), 809-815 (2017).
  17. Xiao, W., et al. Risk factors and survival outcomes in patients with breast cancer and lung metastasis: a population-based study. Cancer Medicine. 7 (3), 922-930 (2018).
  18. Smid, M., et al. Subtypes of breast cancer show preferential site of relapse. Cancer Research. 68 (9), 3108-3114 (2008).
  19. Kennecke, H., et al. Metastatic behavior of breast cancer subtypes. Journal of Clinical Oncology. 28 (20), 3271-3277 (2010).
  20. Soni, A., et al. Breast cancer subtypes predispose the site of distant metastases. American Journal of Clinical Pathology. 143 (4), 471-478 (2015).
  21. Leone, B. A., et al. Prognostic impact of metastatic pattern in stage IV breast cancer at initial diagnosis. Breast Cancer Research and Treatment. 161 (3), 537-548 (2017).
  22. Pei, X. F., et al. Explant-cell culture of primary mammary tumors from MMTV-c-Myc transgenic mice. In Vitro Cellular and Developmental Biology: Animal. 40 (1-2), 14-21 (2004).
  23. Mathsyaraja, H., et al. CSF1-ETS2-induced microRNA in myeloid cells promote metastatic tumor growth. Oncogene. 34 (28), 3651-3661 (2015).
  24. Yang, S., Zhang, J. J., Huang, X. Y. Mouse models for tumor metastasis. Methods in Molecular Biology. 928, 221-228 (2012).
  25. La Perle, K. M. D. Comparative Pathologists: Ultimate Control Freaks Seeking Validation. Veterinary Pathology. 56 (1), 19-23 (2019).
  26. Blomberg, O. S., Spagnuolo, L., de Visser, K. E. Immune regulation of metastasis: mechanistic insights and therapeutic opportunities. Disease Models & Mechanisms. 11 (10), (2018).
  27. Gonzalez, H., Hagerling, C., Werb, Z. Roles of the immune system in cancer: from tumor initiation to metastatic progression. Genes and Development. 32 (19-20), 1267-1284 (2018).
  28. Borowsky, A. D., et al. Syngeneic mouse mammary carcinoma cell lines: two closely related cell lines with divergent metastatic behavior. Clinical and Experimental Metastasis. 22 (1), 47-59 (2005).
  29. Yang, Y., et al. Immunocompetent mouse allograft models for development of therapies to target breast cancer metastasis. Oncotarget. 8 (19), 30621-30643 (2017).
  30. Resch, M., Neels, T., Tichy, A., Palme, R., Rulicke, T. Impact assessment of tail-vein injection in mice using a modified anaesthesia induction chamber versus a common restrainer without anaesthesia. Laboratory Animals. 53 (2), 190-201 (2019).
  31. Rashid, O. M., et al. Is tail vein injection a relevant breast cancer lung metastasis model. Journal of Thoracic Disease. 5 (4), 385-392 (2013).
  32. Goodale, D., Phay, C., Postenka, C. O., Keeney, M., Allan, A. L. Characterization of tumor cell dissemination patterns in preclinical models of cancer metastasis using flow cytometry and laser scanning cytometry. Cytometry Part A. 75 (4), 344-355 (2009).
  33. Goddard, E. T., Fischer, J., Schedin, P. A Portal Vein Injection Model to Study Liver Metastasis of Breast Cancer. Journal of Visualized Experiments. (118), (2016).
  34. Wright, L. E., et al. Murine models of breast cancer bone metastasis. BoneKEy Reports. 5, 804 (2016).
  35. Simmons, J. K., et al. Animal Models of Bone Metastasis. Veterinary Pathology. 52 (5), 827-841 (2015).
  36. Liu, Z., et al. Improving orthotopic mouse models of patient-derived breast cancer brain metastases by a modified intracarotid injection method. Scientific Reports. 9 (1), 622 (2019).
  37. Kodack, D. P., Askoxylakis, V., Ferraro, G. B., Fukumura, D., Jain, R. K. Emerging strategies for treating brain metastases from breast cancer. Cancer Cell. 27 (2), 163-175 (2015).
  38. Brown, D. L. Practical Stereology Applications for the Pathologist. Veterinary Pathology. 54 (3), 358-368 (2017).
  39. Aeffner, F., et al. Digital Microscopy, Image Analysis, and Virtual Slide Repository. Institute for Laboratory Animal Research Journal. 59 (1), 66-79 (2018).

Tags

Pathological Analysis Lung Metastasis Lateral Tail-vein Injection Tumor Cells Digital Image Analysis Quantify Lung Metastases Time And Cost Effective Comprehensive Analysis Human Breast Cancer Cells Cell Culture Medium Cell Concentration Trypsin Centrifugation PBS Injection Procedure Insulin Syringe Needle Bending Mouse Restraint

Erratum

Formal Correction: Erratum: Pathological Analysis of Lung Metastasis Following Lateral Tail-Vein Injection of Tumor Cells
Posted by JoVE Editors on 12/02/2020. Citeable Link.

An erratum was issued for: Pathological Analysis of Lung Metastasis Following Lateral Tail-Vein Injection of Tumor Cells. The author list was updated.

The author list was updated from:

Katie A. Thies1, Sue E. Knoblaugh2, and Steven T. Sizemore1
1Department of Radiation Oncology, Arthur G. James Comprehensive Cancer Center and Richard L. Solove Research Institute, The Ohio State University Medical Center
2Department of Veterinary Biosciences, Comparative Pathology and Digital Imaging Shared Resource, The Ohio State University

to:

Katie A. Thies1, Sarah Steck1, Sue E. Knoblaugh2, and Steven T. Sizemore1
1Department of Radiation Oncology, Arthur G. James Comprehensive Cancer Center and Richard L. Solove Research Institute, The Ohio State University Medical Center
2Department of Veterinary Biosciences, Comparative Pathology and Digital Imaging Shared Resource, The Ohio State University

Pathological Analysis of Lung Metastasis Following Lateral Tail-Vein Injection of Tumor Cells
Play Video
PDF DOI DOWNLOAD MATERIALS LIST

Cite this Article

Thies, K. A., Steck, S., Knoblaugh,More

Thies, K. A., Steck, S., Knoblaugh, S. E., Sizemore, S. T. Pathological Analysis of Lung Metastasis Following Lateral Tail-Vein Injection of Tumor Cells. J. Vis. Exp. (159), e61270, doi:10.3791/61270 (2020).

Less
Copy Citation Download Citation Reprints and Permissions
View Video

Get cutting-edge science videos from JoVE sent straight to your inbox every month.

Waiting X
Simple Hit Counter