(March 14, 2024) Webinar: Validation of DEXA for Longitudinal Quantification of Tumor Burden in A Murine Model of Pancreatic Ductal Adenocarcinoma
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Noninvasive imaging is central to preclinical, in vivo models of pancreatic ductal adenocarcinoma (PDAC). While bioluminescent imaging (BLI) is a gold standard, its signal is dependent on the metabolic activity of tumor cells. In contrast, dual energy X-ray absorptiometry (DEXA) is a direct measure of body composition. Thus, this project aimed to assess the potential of using DEXA for longitudinal quantification of tumor burden versus BLI in an orthotopic KCKO murine model of PDAC. In short, DEXA successfully identified a growing tumor burden and accurately predicts ex vivo tumor mass in a time sensitive manner. Learning objectives: Learn to take advantage of DEXA for things other than bone density and bone health (i.e., lean, and fat mass) Understand that DEXA can reproducibly and accurately be used to monitor tumor burden and growth in orthotopic murine models of pancreatic cancer Understand the importance of repurposing techniques and equipment for new analysis Understand that non-invasive in vivo imaging is crucially important in severely compromised models like those for PDAC and other cancers See the value of utilizing multiple techniques throughout an experiment to enhance data collection
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(March 14, 2024) Webinar: Validation of DEXA for Longitudinal Quantification of Tumor Burden in A Murine Model of Pancreatic Ductal Adenocarcinoma
- 1. Validation of Dual Energy X- ray Absorptiometry for Longitudinal Quantification of Tumor Burden in a Murine Model of Pancreatic Ductal Adenocarcinoma Zach Sechrist University of Rochester Medical Center
- 2. WWW.SCINTICA.COM Learning Objectives • Understand that non-invasive in vivo imaging is vital for monitoring the health of the animal to establish humane endpoints in disease models such as PDAC and other cancers • Learn to take advantage of DEXA for things other than bone density and bone health (i.e., fat-free mass, and fat mass) • Understand that longitudinal assessment of tumor burden, in an orthotopic murine model of pancreatic cancer, by DEXA is both valid and reliable • Determine the value of utilizing multiple techniques throughout an experiment to enhance the rigor and reproducibility of outcome measures
- 3. WWW.SCINTICA.COM Pancreatic Ductal Adenocarcinoma (PDAC) • Pancreatic cancer is the eighth most common cancer accounting for 3% of all cancer diagnoses • An estimated 64,050 people in the US will be diagnosed by the end of this year • Presents a devastating all-stage 5-year survival of 12% • Skeletal muscle wasting (SMW) is a common comorbidity associated with PDAC • SMW leads to reduced quality of life and discontinuation of treatment [1] Siegel et al. (2023). ACS Journals [2] Peng et al. (2012). J Gastrointestinal Surgery
- 4. WWW.SCINTICA.COM SMW is Highly Prevalent in PDAC Populations • Present in about 80-85% of pancreatic patients • Directly responsible for 33% of deaths in these patients • Responsible for disruption in homeostasis across many organ systems • Causes unhealthy loss of body mass Created in Biorender (2023) ? ? [1] Poulia et al. (2020). Nutrients
- 5. WWW.SCINTICA.COM In Vivo Orthotopic Model Recapitulates PDAC-Induced SMW
- 6. WWW.SCINTICA.COM PDAC Mice Display Reduced Size and Hunched Appearance Compared to NTC Mice Non-Tumor Control (NTC) PDAC
- 7. WWW.SCINTICA.COM PDAC Muscle Fiber Cross-Sectional Area (CSA) and Area are Significantly Reduced Compared to Healthy Controls Histologic confirmation of skeletal muscle fiber atrophy in terminal mice with PDAC. Following euthanasia, the EDL and SOL muscles were harvested from PDAC baring and NTC mice and stained with DAPI for calculation of CSA (100 fibers per mouse). TA muscles were dissected and weighed. After which, TA muscles were frozen and stained with Laminin and H&E, and CSA was measured. Photomicrographs (10x) show representative DAPI stained (A) EDL and (B) SOL muscle fibers at the time of sacrifice from NT (n = 5) and PDAC baring mice (n = 5), with quantification of (C) EDL and (D) SOL CSA (a significant difference was found between NTC and PDAC mice, using Student’s t-test, mean ± SD ****p<0.0001). Images (10x) of laminin and H&E- stained TA cross-sections of (E,F) NTC and (G,H) PDAC are shown, respectively, along with quantification of TA (I) CSA (μm2) (n = 5) and are presented with the mean ± SD (a significant difference was found between NTC and PDAC mice, using Student’s t-test, mean ± SD **p<0.01). HUGE Problem: This analysis can only be performed at sacrifice Cole et al. (2021). PLOS ONE
- 8. WWW.SCINTICA.COM Dual Energy X-Ray Absorptiometry (DEXA) is a Tool that Accurately Quantifies Changes in Human and Mouse Lean Mass
- 9. WWW.SCINTICA.COM Piximus DEXAAccurately Quantifies Lean Mass in our Murine Model Cole et al. (2021). PLOS ONE Validation of DEXA as an in vivo measure of lean mass in lower limbs of growing mice. Female C57BL/6 were weighed, and DEXA scanned at 5, 8, 12, 18 and 22 weeks of age (n = 4 per group). Postmortem, mice Tibialis Anterior (TA) muscles were dissected and weighed immediately. One-way ANOVA with Tukey’s for multiple comparison test was used to determine the difference in (A) Bodyweight, (B) whole body lean mass (C) lower limb mass, and (D) TA weight for each mouse. All variables increased concomitant with age. Data (mean ±SD) were significantly different (p < 0.05): *versus 5 week, + versus 8 week, # versus12 week, ° versus 18 week, and § versus 22-week-old mice. A Pearson correlation analysis was performed to determine the relationship between DEXA lean mass vs. (E) whole body lean mass, (F) TA weight, and (H) bodyweight. The analysis confirmed a significant (p < 0.0001 for whole body lean mass, TA weight, and bodyweight) direct correlation between the three measurements with r2 = 0.86, 0.94 and 0.89 for whole body lean mass, TA weight, and bodyweight, respectively. Data is expressed as mean ± SD. Lower limb lean mass was determined by DEXA at each time point by two independent observers as described in Fig 1. (I) The intraclass correlation coefficient (ICC = 0.95) shows a strong relationship between reader observations (95% CI: 0.89–0.98).
- 10. WWW.SCINTICA.COM DEXAAccurately Measures PDAC-Induced SMW in Mice Cole et al. (2021). PLOS ONE PDAC mice experience significant differences in muscle weight that is reliably measured via DEXA. (E) TA weights (mg) of the PDAC bearing mice was determined on the day of sacrifice presented as the mean ± SD for each group of mice. Student t-test revealed a significant difference (***p = 0.0005) (****p<0.0001) between TA weights in each cohort. (F) A Pearson correlation analysis was performed on a separate and larger cohort of PDAC tumor bearing mice (n = 15) to determine the relationship between DEXA lean mass and TA weight. The analysis confirmed a significant (p < 0.0001) relationship between the two measures with r2 = 0.76. Data are expressed as mean ± SD.
- 11. WWW.SCINTICA.COM PDAC Mice Display a Reproducible Onset of SMW Starting at Day 38 Longitudinal quantification of lean mass via DEXA demonstrates commencement of pancreatic ductal adenocarcinoma (PDAC)‐related muscle wasting within 38 days of tumor implantation, which cannot be detected by assessment of total body weight. (C) The cross‐sectional TBW for all mice in the study are presented with the mean of the group for Days 0, 14, 21, and 35. For the later time points, TBW is presented for each mouse that is alive at that time point. No differences between groups were observed. (D) The cross‐sectional lower limb mass determined by DEXA of all mice in the study are presented with the mean of the group for Days 0, 14, and 35. For the later time points, lower limb mass is presented for each mouse alive at that time point (**P < 0.01, ***P < 0.001, ****P < 0.0001). [1] Cole et al. (2021). Journal of Cachexia, Sarcopenia, and Muscle
- 12. WWW.SCINTICA.COM In Vivo Orthotopic Model Recapitulates PDAC-Induced SMW
- 13. WWW.SCINTICA.COM Although Accurate, the Piximus is Outdated and Inconvenient • Developed in the late 1900’s (older than me) and is currently discontinued • This makes repairs and service extremely difficult • Currently running on a Windows XP laptop • No alternatives if the computer fails • Fairly long scan time (5-6 minutes per mouse) • Makes 20-30 mice cohorts an all-day process • Open scan area can expose user to low dose radiation
- 14. WWW.SCINTICA.COM iNSiGHT DEXA is a Vastly Better Tool When Compared to the Piximus • Much faster scan time of around 30 seconds • 20-30 mice now takes 1-2 hours • Much higher resolution • 100-micron resolution during DEXA scans • Can achieve up to 30-micron resolution • Completely shielded unit reduces X-Ray exposure • Upgraded analysis tools enhances data collection
- 15. WWW.SCINTICA.COM Upgraded iNSiGHT analysis tools enhances data collection A) B)
- 16. WWW.SCINTICA.COM iNSiGHT DEXA Enhances our Analysis of PDAC-induced SMW -40 -20 0 20 40 %Change in Lean Mass Base to Median Survival ✱✱✱✱ NTC PDAC -40 -20 0 20 40 %Change in Lean Mass Base to Median Survival ✱✱✱✱ NTC PDAC C) A) B) Non-Tumor Control (NTC) PDAC
- 17. WWW.SCINTICA.COM DEXA can Identify Growing Tumor Burdens in PDAC Mice Can iNSiGHT DEXA accurately quantify tumor growth over time?
- 18. Audience Poll
- 19. WWW.SCINTICA.COM In Vivo Orthotopic Model Recapitulates PDAC-Induced SMW
- 20. WWW.SCINTICA.COM Bioluminescent Imaging (BLI) is the Current Standard for Tumor Visualization in Orthotopic Models of PDAC • Identifies a tumor burden using a well characterized firefly luciferase reaction • Requires tumor cells that express luciferase • Limits transgenic and xenograft model of PDAC • Luciferase reactions are ATP dependent and require viable healthy cells • BLI is not an accurate measure of tumor size • BLI is a good measure of tumor presence and location (i.e., metastasis) • Combined weekly BLI and DEXA measures can negatively impact compromised PDAC mice
- 21. WWW.SCINTICA.COM BLI is an Inaccurate and Inconsistent Measure of Tumor Size and Growth
- 22. WWW.SCINTICA.COM Driving Hypothesis • High resolution DEXA has the potential to quantify growing tumor burdens with a higher accuracy than BLI and reduce the burden placed on PDAC mice
- 23. WWW.SCINTICA.COM Experimental Design to Compare DEXA and BLI Tumor Quantification n = 10 NTC mice n = 20 PDAC mice in total (2 independent cohorts) Sechrist et al. (2024). PLOS ONE
- 24. WWW.SCINTICA.COM BLI Displays Imaging Inconsistencies and a Decreasing Tumor Burden Over Time Sechrist et al. (2024). PLOS ONE Limitations of bioluminescent imaging as a longitudinal biomarker of tumor mass and growth over time. Longitudinal images of a representative PDAC mouse #14 from week 1 to week 8 post tumor inoculation using bioluminescent imaging (BLI). A standardized abdominal ROI (red circle) was used to quantify BLI values as total flux in radiance (photons/second). Note the robust BLI signal at week 1, the unexplained absence of BLI signal at week 4, and BLI signal at week 8 that is smaller than the BLI signal at week 1. Representative mouse is selected from all PDAC mice used in the study (n = 20) that received BLI scans.
- 25. WWW.SCINTICA.COM BLI Displays Imaging Inconsistencies and a Decreasing Tumor Burden Over Time Sechrist et al. (2024). PLOS ONE Limitations of bioluminescent imaging as a longitudinal biomarker of tumor mass and growth over time. Weekly BLI signal is presented for each mouse in the second cohort (n = 10) (D) as well as the mean +/- SD for all tumor-bearing mice (n = 20) (E) from weeks 1-10 with the slope. A) B)
- 26. WWW.SCINTICA.COM Establishing an Abdominal Scout ROI to Successfully Compare Changes in Tumor Growth
- 27. WWW.SCINTICA.COM An Abdominal Scout ROI can be Applied to NTC and PDAC Mice to Quantify Tumor Growth Sechrist et al. (2024). PLOS ONE A) B)
- 28. WWW.SCINTICA.COM Abdominal Lean Mass Measured via DEXA Successfully Identifies a Growing PDAC Tumor Burden Sechrist et al. (2024). PLOS ONE A) B) Abdominal lean mass measured via DEXA successfully identifies a growing PDAC tumor burden. Longitudinal DEXA images of a representative non-tumor control (NTC) mouse (A) and PDAC mouse #5 (B) from baseline to week 8 post tumor inoculation are shown. An initial abdominal scout view ROI was used to identify PDAC tumors via manual segmentation from vertebrae T13-L6 and extension to the distal end of the femur (green highlighted region). An exclusion ROI (blue region) was utilized to remove interference from the skull and ear tag. Arrows indicate the presence of the tumor burden visible via DEXA starting at 3-weeks. The weekly percent change in scout ROI abdominal lean mass compared to baseline values from week 1-8 display the significant presence of a tumor burden at week 2 and on (p<0.05) (C). Sample size values represent the living PDAC mice at each weekly DEXA scan. C) Scout ROI Measurements displayed no correlation with tumor weights
- 29. WWW.SCINTICA.COM A More Refined Segmented ROI can be Used to Monitor Direct Tumor Growth Sechrist et al. (2024). PLOS ONE B) A) C) Segmented ROIs display a trend of increasing tumor burden in PDAC mice. DEXA quantification for this study was derived from manual ROI segmentation which reports the focused PDAC ROI in grams (PDAC mouse #13) (A). The asterisk denotes the primary tumor and arrows indicate local spread. Longitudinal DEXA quantification from each mouse is presented starting when tumor burden is visible (B) as well as the mean +/- SD and slope for all PDAC mice (n = 20) from weeks 3-10 (C). n = 20 week 1-5, n = 16 week 6, n = 11 week 7, n = 7 week 8, n = 6 week 9, n = 5 week 10. Note: The discontinued readings for individual mice for panels B and D are due to meeting “failure to thrive criteria” and subsequent euthanasia, and the last DEXA measure is reported.
- 30. WWW.SCINTICA.COM DEXA is a Better Indicator of Tumor Size Compared to BLI and is Reproducible Sechrist et al. (2024). PLOS ONE B) A) C) Superiority of DEXA over BLI as an in vivo biomarker of tumor mass and longitudinal measure of tumor growth. To assess the correlation between PDAC mass and biomarker outcome, linear regression analyses were performed comparing ex vivo tumor weight vs. terminal DEXA (A) and terminal BLI measures (B), and the graphed data are presented with the Pearson coefficient and p-value (n = 15 that were not lost to attrition). Two of the authors completed independent focused PDAC segmentation on end-of-life DEXA scans, and the interobserver reliability of the quantified tumor mass was determined via intraclass correlation coefficient (ICC r2 = 0.9736; p<0.0001) (C).
- 31. WWW.SCINTICA.COM In Summary • DEXA is a crucial tool utilized in our lab to enhance data collection in a murine model of PDAC • DEXA provides valid and reliable longitudinal measures of tumor burden in our orthotopic model of PDAC-induced SMW • Quantifying tumor burden via DEXA enhances our use of humane endpoints for these mice • With quick scan times, the side and prone images for a given mouse takes 3 minutes • DEXA can be explored for monitoring tumor burden in transgenic, subcutaneous and xenograft models of PDAC-induced SMW
- 32. WWW.SCINTICA.COM Our Model of PDAC-Induced SMW Now Utilizes both BLI and DEXA to Optimize Data Collection and Enhance the Well Being of the Animals
- 33. WWW.SCINTICA.COM Acknowledgments • Cole lab: • Calvin Cole PhD • Grace Lee • Scintica: • Tyler Lalonde • Nicholas Betancourt • Thesis Committee: • Edward Schwarz PhD • Darren Carpizo MD, PhD • Robert Dirksen PhD • Gowrishankar Muthukrishnan PhD • CMSR: • Jeff Fox • Vidya Venkatramani • Susan Liebelt • Javier Rangel-Moreno • Pathology PhD Program: • Helene McMurray PhD • Donna Shannon • Funding: • K01 CA240533 • T32 AR 76950-4 S1
- 34. WWW.SCINTICA.COM Q&A WWW.SCINTICA.COM INFO@SCINTICA.COM Please enter your questions in the Q&A section Thank You!
Editor's Notes
- Thank you for that introduction, as mentioned I am . Today I am going to share data from a recent publication that utilizes DEXA for the longitudinal quantification of tumor burden in orthotopic mouse model and this is a model of PDAC and most importantly a model of PDAC induced muscle wasting which I will briefly describe. For the later portions of the talk and for this recent paper, the data comes from this beautiful looking machine down here in the bottom right which is the dexa machine we bought from Scintica at the start of last year.
- Before I get into the data I just want to highlight these learning objectives for todays talk and use it as an outline moving forward. So first any of the work discussed today is driven by our murine model and driven by the fact that this disease is extremely detrimental, and we have had the need to get creative about ways to monitor the animals in a non-invasive way so that and that can be applied to many other models including those of other cancers. With that said it is important to explore preexisting tools like DEXA that was once used as a method to look at bone health and adjust the technology for your particular interest Therefore the main interest in this talk and paper is to explore how DEXA can be used for something like quantifying tumor growth And based on the success of that I will show our new model for monitoring tumor burden in our animals
- Unfortunately, SMW is present in about 85% of pateints with PDAC and is responsible for 33% of deaths in this population. It is defined as a general disruption in homeostasis in the muscle which is further defined by a decrease in protein synthesis and or increase in protein degradation. Unfortunately, despite the prevalence of this pathology, little is known about the mechanistic progression of the disease. However, Due to the vast distance between the tumor and skeletal muscle we posit that there must be a systemic factor that propagated SMW. And with the understanding that this factor can be produced by either the tumor or the host in response to the tumor, we acknowledge that this factor may also regulate tumor growth as well. One of the goals in our lab has been to identify and treat these possible factors that propagate SMW hopefully lowering the burden on these patients and improve outcomes. It would be very difficult for us to do that without a reliable in vivo pre-clinical model
- The model that has been designed and validated in our lab is depicted here you will see this several times today and I will highlight various parts as I walk through the talk. Generally speaking we use c57bl6 mice that receive a baseline DEXA scan and Ill go in depth on what this is used for next. But they are then sorted and on day 1 they receive orthotopic injections (so right into the pancreas) of KCKO cell which are a nonmetastatic PDAC cell line. These cells produce luciferase which can be used with BLI. Starting at week 1 mice undergo BLI or IVIS for tumor growth and DEXA for changes in lean mass as an indicator of muscle wasting. Again, for today I will go more in depth on IVIS and DEXA and how were adapting their use in this model. The median survival for this model is around 50-60 days with some mice living up to 70 days. We can then collect a variety of tissue like muscle tumor and serum As you can see we define day 38 as the onset of SMW in this model and the next several slides will be understanding where this came from. But the first step is validating that this model does in fact experience PDAC-induced SMW.
- One of the earlier papers by DR. Cole does just that. He shows that at sacrifice two fo the three muscles we collected the EDL and the soleus have significantly reduced cross sectional area in the muscle fro mice that had PDAC compared to non-tumor controls or NTC mice as I will refere to it moving forward. This was done via single fiber isolation and staining with DAPI. We can also cut another muscle the TA muscle cross-sectionally and stain with again DAPI and laminin which highlights muscle fibers. And from this we can calculate cross sectional area again but nt just for single fibers, we get an understanding of the whole muscle and can see the altered morphology in PDAC muscle. And so we can show that in fact at sacrifice the muscle in these PDAC mice waste in comparison to NTC mice. But therse a huge problem, this analysis can only be performed at sacrifice and we often lose mice in the cage without an understanding of their cachectic state and so we basically lose a data point. Which as a researcher that is always disappoint but also the NIH is pushing for reduced unnecessary animal use. So there must be a way to measure muscle health in these mice overtime and maintain that data even if we are unabke to collect tissue from a given mouse.
- So the lab turned to the literature and found something called DEXA has been shown to monitor lean mass in both humans and mice. Thankfully as Dr. Cole is a muscle physiologist he has used DEXA in the past and it just so happened that our university had a murine DEXA. It just turned out to be ancient as anyone knows who is familiar with the Piximus. But it was what we had available he attempted to adapte it for monitoring muscle health in this model. As you can see in these images our region of interest is the lower limb region and Ill explain why in a moment but keep this ROI in mind
- First he just showed as done in some other papers that DEXA can quantify muscle mass in healthy NTC mice at various ages. A shows body weight for the animals B and C show DEXA quantified lean mass for both the whole body and the lower limbs and D are the extracted TA weights. E-I shows various correlations first of whole body DEXA and lower limb dexa measures, next Dexa vs the TA weights and DEXA vs the whole body mass of the animals all of which are highly significant. Lastly as seen in I the DEXA measure are very reproducible as measured by two independent observers. And so the reason for making all of these comparisons between the whole body and the lower limb ROIs that I showed is based on the fact that our PDAC mice traditionally have a grow tumor mass that would effect the whole body measures and so the lower limb Is used as a proxy the overall health of the animal. Great, so DEXA works to quantify muscle mas in NTC mice, how about our PDAC mice
- AS you can see here the TA weights for PDAC mice are significantly compared to NTC mice and the DEXA quantification of the lower limb correlates strongly with those measures. So what can we do with that, as these measures are still end of life for the purpose of validating the model and DEXA.
- Well in another publication he goes on to measure PDAC and NTC mice weekly over the whole course of the experiment and shows that as expected because of the growing tumor burden the PDAC mice do not differ from the NTC mice in basic body mass seen in A. But in B looking at DEXa measured LLLm we do have significant differences between the two groups which is validated by the TA weight data we showed on the last slide. And what we see is the onset of this pathology is at day 38
- Which is why you see that reflected in our model. Great so DEXA is a really powerful too in our lab and can accurately monitor mouse health and muscle wasting, Ive hopefully convinced you of that so far. And you may ask are there any downsides to this current method. Well yes but not for the reasons you may think.
- Primarily the reasons are just because the machine we were using is extremely old (apologies to anyone in the audience who is over 25) but seriously the PIXImus was developed in the late 1900’s and is currently discontinued which makes any repairs or service difficult. It was currently running on a windows XP laptop which could’ve expired at any moment with no back up Furthermore, the scan time is very lengthy about 5-6 mins per mouse and so a whole 20-30 mouse cohort could take all day and doing it each week is a lot of time and lastly the open platform does emit low dose radiation which of course isn’t ideal. So about a year and a half ago we started thinking about an upgrade and that’s when we found the Scintica system.
- This system has an accelerated scan time of 30 seconds per mouse and 20-30 mice now take 1-2 hours and that alone was huge. But it also has a much higher resolution of 100 microns and up to 30 at the highest scan level. It also has the nice added benefit of being fully shielded so I can stand next to the machine during a scan and not all the way across the room as before and it also offers upgraded analysis tools that you will see next. The big question, does it work appropriately for tracking muscle loss in our model.
- And of course it does. So the two images on the left are NTC and PDAC mice and you can see the new ROI is more chicken wing shaped than just two rectangles from before. This allows us to get a measure of the whole lower limb muscle mass and it does this very well thanks to an edge effect tool that helps smooth out the lines after a manual trace. As you can see in C looking at the percent change in lean mass from baseline to median survival or 56 days which is often how we report lean mass these days, we see that as expected it detects reductions in our PDAC mice. So the machine does a great job of detecting changes in lean mass and is still a staple in our model. But if you look at this arrow which I am sure was very obvious as soon as I showed the images, there is a large mass which is of course the tumor.
- And not only can we see this on the Scintica DEXA but it was also obvious in the PIXImus as well we just had worse resolution and longer scan times so it mad quantifying it impractical. But with this new machine we are able to ask a long standing question, can DEXA accurately quantify tumor burden in these mice over time.
- Before I get to that I want to explain what we currently use for measure tumor growth and so I bring you back to this slide and highlight this IVIS or BLI statement.
- BLI or BLI is currently the gold standard for reporting tumor burden. It works by utilizing a well characterized firefly luciferase reaction that requires the cells to express luciferase like our cells do and the machine can highlight the tumors as seen here. Unfortunately tis is limited to orthotopic and select xenograft models that allow for luciferase expression. And BLI cannot be used for transgenic and most xenograft models. Additionally, the luciferase reaction is dependent on ATP availability and therefore requires healthy cells and so it is not really a good measure of tumor size but it is what we currently have available and it is really good at identifying tumor location and metastasis based on the light output. But for our model in particular that are getting both BLI and DEXA, that can be a lot of additional strain on the animals which is really a key point and again a potential driver of this project.
- And so even though BLI is considered the gold standard, there are other papers and review articles that acknowledge the flaws that I just mentioned and many of these are cited in our recent publication.
- And based on the resolution of this new DEXA machine and the flaws with using weekly BLI in our model we developed this hypothesis that we can now use DEXA to quantify not only lean mass but also tumor mass to a more accurate degree than BLI.
- WE designed a basic experiment where mice would receive both weekly DEXA and BLI if applicable and we would compare against each other as well as the weights of tumors extracted at sacrifice. For this we had 10 NTC mice and 20 PDAC mice in total from two separate cohorts so you will see either n of 10 or 20 throughout and these mice survived between 8 and 10 weeks. The rest of the talk is showing data that is published in our recent paper on PLOS.
- The first that we see which we have seen previously, is that BLI is rather in consistent where for a given mouse with a growing tumor we have apparently no signal at week 4 but a recovered signal at week 5 which is not possible and if you look at the values, the starting value is higher than the ending value which again for a growing tumor is not really possible. And this is stuff that is echoed in many of those papers that I showed.
- Graphing these values helps prove that point were several mice have sudden dips in signal in A and in B an average of all 20 PDAC mice in the study show a decline in signal over time which again is not possible. So this validated the need for another method and potentially DEXA.
- And so like how they are measured in BLI we wanted to image thse mice in a side view for the best access to the tumor. And as you can see in this particular mouse the tumor is visible both in the lateral image and prone image indicated by the arrows. WE also wanted to develop an analysis method that would be comparable between PDAC mice and NTC mice which obviously don’t have a growin tumor and so just circling this tumor wouldn’t really work. So we decided to use the vertebrae as landmarks help draw a scout abdominal ROI that would give a mass value for this whole region and ideally that would be different in the presence of a tumro or not. We decided that the top of T13 and bottom of L6 would be great for marking that abdominal space and that the distal end of the femur is another good landmark that would be consistent between mice. And so we can draw just that on DEXA and get a lean mass value for this region that I just described and that you see here
- We can do that for both NTC mice in A and DAC mice in B for their whole lifespan. Of note in the PDAC mouse the tumor is visible repeatedly around week 3. We can quantify this a few ways but
- We found that percent changes from baseline to each week is the best to show it and as you see here starting at week 2 there is a detectable increase in abdominal lean mass in the PDAC mice compared to the NTC ones and that continues throughout. This increase is increasing tumor burden in thes mice. And so this was really exciting because we found an easy way to detect tumor growth and we can compare it between groups including those without tumors but also potentially treatment groups. However, there was a slight problem, that these scout ROIs didn’t not correlate well with tumor weight at sacrifice most likely due to the other organs in this region.
- So we decided to take advantage of the ability to visually see the growing tumor in this model. An example of that you see here as was on my first slide. But in this example you can clearly see the primary tumor marked by the asterisk, but it is also sensitive enough to see some of the local spread highlighted by the arrows. And so we can draw this region around the tumor and we can do this for each week and each mouse after detectable tumor which as I mentioned previously is around week 3. This new segmented ROI increases over time as expected in each of these mice with now sudden drops like was seen in BLI and the averaged trend for all 20 mice is a positive slope which really just makes sense in a growing tumor model and so we were really happy with that.
- And what was really exciting is that those segented ROI measures correlated really strongly with extracted tumor weight as seen in A where the Bli measures do not correlate even a little which is our primary point to drive home. Lastly in C these segmented can be drawn easily by multiple observers and the values we get for that correlate again very strongly and so this is a really accurate and reproducible method of tumor detection.
- And lastly I just want to show that our current timeline for this model has changed. We now only do BLI on the first week after inoculation because it is still a powerful took for determining engraftment but after that the mice only receive weekly DEXA for both lower limb muscle mass and now tumor mass measurements.