36 results on '"Kiemen, Ashley L."'
Search Results
2. PanIN and CAF transitions in pancreatic carcinogenesis revealed with spatial data integration
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Bell, Alexander T.F., Mitchell, Jacob T., Kiemen, Ashley L., Lyman, Melissa, Fujikura, Kohei, Lee, Jae W., Coyne, Erin, Shin, Sarah M., Nagaraj, Sushma, Deshpande, Atul, Wu, Pei-Hsun, Sidiropoulos, Dimitrios N., Erbe, Rossin, Stern, Jacob, Chan, Rena, Williams, Stephen, Chell, James M., Ciotti, Lauren, Zimmerman, Jacquelyn W., Wirtz, Denis, Ho, Won Jin, Zaidi, Neeha, Thompson, Elizabeth, Jaffee, Elizabeth M., Wood, Laura D., Fertig, Elana J., and Kagohara, Luciane T.
- Published
- 2024
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3. PanIN or IPMN? Redefining Lesion Size in 3 Dimensions
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Kiemen, Ashley L., Dequiedt, Lucie, Shen, Yu, Zhu, Yutong, Matos-Romero, Valentina, Forjaz, André, Campbell, Kurtis, Dhana, Will, Cornish, Toby, Braxton, Alicia M., Wu, Pei-Hsun, Fishman, Elliot K., Wood, Laura D., Wirtz, Denis, and Hruban, Ralph H.
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- 2024
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4. CODA: quantitative 3D reconstruction of large tissues at cellular resolution
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Kiemen, Ashley L., Braxton, Alicia M., Grahn, Mia P., Han, Kyu Sang, Babu, Jaanvi Mahesh, Reichel, Rebecca, Jiang, Ann C., Kim, Bridgette, Hsu, Jocelyn, Amoa, Falone, Reddy, Sashank, Hong, Seung-Mo, Cornish, Toby C., Thompson, Elizabeth D., Huang, Peng, Wood, Laura D., Hruban, Ralph H., Wirtz, Denis, and Wu, Pei-Hsun
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- 2022
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5. Tissue clearing and 3D reconstruction of digitized, serially sectioned slides provide novel insights into pancreatic cancer
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Kiemen, Ashley L., Damanakis, Alexander Ioannis, Braxton, Alicia M., He, Jin, Laheru, Daniel, Fishman, Elliot K., Chames, Patrick, Pérez, Cristina Almagro, Wu, Pei-Hsun, Wirtz, Denis, Wood, Laura D., and Hruban, Ralph H.
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- 2023
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6. AI and immunology
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Germain, Ronald N., primary, Van Allen, Eliezer M., additional, Trynka, Gosia, additional, Tsang, John S., additional, Grün, Dominic, additional, Kiemen, Ashley L., additional, Shi, Yigong, additional, Pugh Bernard, Aimee, additional, and Nolan, Garry P., additional
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- 2024
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7. A disrupted compartment boundary underlies abnormal cardiac patterning and congenital heart defects
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Kathiriya, Irfan S., primary, Dominguez, Martin H., additional, Rao, Kavitha S., additional, Muncie-Vasic, Jonathon M., additional, Devine, W. Patrick, additional, Hu, Kevin M., additional, Hota, Swetansu K., additional, Garay, Bayardo I., additional, Quintero, Diego, additional, Goyal, Piyush, additional, Matthews, Megan N., additional, Thomas, Reuben, additional, Sukonnik, Tatyana, additional, Perez, Dario Miguel-, additional, Winchester, Sarah, additional, Brower, Emily F., additional, Forjaz, André, additional, Wu, Pei-Hsun, additional, Wirtz, Denis, additional, Kiemen, Ashley L., additional, and Bruneau, Benoit G., additional
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- 2024
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8. Power-law growth models explain incidences and sizes of pancreatic cancer precursor lesions and confirm spatial genomic findings.
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Kiemen, Ashley L., Pei-Hsun Wu, Braxton, Alicia M., Cornish, Toby C., Hruban, Ralph H., Wood, Laura D., Wirtz, Denis, and Zwicker, David
- Abstract
Pancreatic ductal adenocarcinoma is a rare but lethal cancer. Recent evidence suggests that pancreatic intraepithelial neoplasia (PanIN), a microscopic precursor lesion that gives rise to pancreatic cancer, is larger and more prevalent than previously believed. Better understanding of the growth-law dynamics of PanINs may improve our ability to understand how a miniscule fraction makes the transition to invasive cancer. Here, using three-dimensional tissue mapping, we analyzed >1000 PanINs and found that lesion size is distributed according to a power law. Our data suggest that in bulk, PanIN size can be predicted by general growth behavior without consideration for the heterogeneity of the pancreatic microenvironment or an individual's age, history, or lifestyle. Our models suggest that intraductal spread and fusing of lesions drive our observed size distribution. This analysis lays the groundwork for future mathematical modeling efforts integrating PanIN incidence, morphology, and molecular features to understand tumorigenesis and demonstrates the utility of combining experimental measurement with dynamic modeling in understanding tumorigenesis. [ABSTRACT FROM AUTHOR]
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- 2024
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9. High‐Resolution 3D Printing of Pancreatic Ductal Microanatomy Enabled by Serial Histology
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Kiemen, Ashley L., primary, Forjaz, André, additional, Sousa, Ricardo, additional, Han, Kyu Sang, additional, Hruban, Ralph H., additional, Wood, Laura D., additional, Wu, PeiHsun, additional, and Wirtz, Denis, additional
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- 2024
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10. Abstract A061: A novel genetically engineered mouse model of Myc-driven pancreatic cancer recapitulates phenotypic heterogeneity, metastasis, and therapy resistance seen in clinical populations
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English, Isabel A., primary, Worth, Patrick J., additional, MacPherson, Kevin A., additional, Heskett, Michael B., additional, Shah, Vidhi, additional, Phipps, Jackie L., additional, Betts, Courtney, additional, Sivagnanam, Shamilene, additional, Pelz, Carl, additional, Farrell, Amy S., additional, Allen-Petersen, Brittany L., additional, Joly, Meghan M., additional, Tsuda, Motoyuki, additional, Daniel, Colin J., additional, Eng, Jenny, additional, Wang, Xiaoyan, additional, Kiemen, Ashley L., additional, Wood, Laura D., additional, Morgan, Terry K., additional, Thoma, Mary C., additional, Chu, Jennifer M., additional, Spellman, Paul T., additional, Coussens, Lisa M., additional, Langer, Ellen M., additional, and Sears, Rosalie C., additional
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- 2024
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11. Abstract B107: Spatial transcriptomics analysis of PanIN reveals loss of pro-inflammatory signaling and the presence of cancer-associated fibroblasts
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Bell, Alexander T.F., primary, Mitchell, Jacob T., additional, Kiemen, Ashley L., additional, Lyman, Melissa, additional, Fujikura, Kohei, additional, Lee, Jae W., additional, Coyne, Erin, additional, Shin, Sarah M., additional, Wu, Pei-Hsun, additional, Zimmerman, Jacquelyn W., additional, Wirtz, Denis, additional, Ho, Won J., additional, Zaidi, Neeha, additional, Thompson, Elizabeth, additional, Jaffee, Elizabeth M., additional, Wood, Laura D., additional, Fertig, Elana J., additional, and Kagohara, Luciane T., additional
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- 2024
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12. Magnetic Resonance Imaging–Based Assessment of Pancreatic Fat Strongly Correlates With Histology-Based Assessment of Pancreas Composition
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Kiemen, Ashley L., primary, Dbouk, Mohamad, additional, Diwan, Elizabeth Abou, additional, Forjaz, André, additional, Dequiedt, Lucie, additional, Baghdadi, Azarakhsh, additional, Madani, Seyedeh Panid, additional, Grahn, Mia P., additional, Jones, Craig, additional, Vedula, Swaroop, additional, Wu, PeiHsun, additional, Wirtz, Denis, additional, Kern, Scott, additional, Goggins, Michael, additional, Hruban, Ralph H., additional, Kamel, Ihab R., additional, and Canto, Marcia Irene, additional
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- 2024
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13. Engineering self-propelled tumor-infiltrating CAR T cells using synthetic velocity receptors
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Johnston, Adrian C., primary, Alicea, Gretchen M., additional, Lee, Cameron C., additional, Patel, Payal V., additional, Hanna, Eban A., additional, Vaz, Eduarda, additional, Forjaz, Andre, additional, Wan, Zeqi, additional, Nair, Praful R., additional, Lim, Yeongseo, additional, Chen, Tina, additional, Du, Wenxuan, additional, Kim, Dongjoo, additional, Nichakawade, Tushar D., additional, Rebecca, Vito W., additional, Bonifant, Challice L., additional, Fan, Rong, additional, Kiemen, Ashley L., additional, Wu, Pei-Hsun, additional, and Wirtz, Denis, additional
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- 2023
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14. Three-dimensional reconstruction of fetal rhesus macaque kidneys at single-cell resolution reveals complex inter-relation of structures
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Dequiedt, Lucie, primary, Forjaz, Andre, additional, Lo, Jamie O., additional, McCarty, Owen J, additional, Wu, Pei-Hsun, additional, Rosenberg, Avi, additional, Wirtz, Denis, additional, and Kiemen, Ashley L., additional
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- 2023
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15. Three-dimensional assessments are necessary to determine the true, spatially-resolved composition of tissues
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Forjaz, André, primary, Vaz, Eduarda, additional, Romero, Valentina Matos, additional, Joshi, Saurabh, additional, Braxton, Alicia M., additional, Jiang, Ann C., additional, Fujikura, Kohei, additional, Cornish, Toby, additional, Hong, Seung-Mo, additional, Hruban, Ralph H., additional, Wu, Pei-Hsun, additional, Wood, Laura D., additional, Kiemen, Ashley L., additional, and Wirtz, Denis, additional
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- 2023
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16. Power-law growth models explain incidences and sizes of pancreatic cancer precursor lesions and confirm spatial genomic findings
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Kiemen, Ashley L., primary, Wu, Pei-Hsun, additional, Braxton, Alicia M., additional, Cornish, Toby C., additional, Hruban, Ralph H., additional, Wood, Laura, additional, Wirtz, Denis, additional, and Zwicker, David, additional
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- 2023
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17. Digitize your Biology! Modeling multicellular systems through interpretable cell behavior
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Johnson, Jeanette A.I., primary, Stein-O'Brien, Genevieve L, additional, Booth, Max, additional, Heiland, Randy, additional, Kurtoglu, Furkan, additional, Bergman, Daniel, additional, Bucher, Elmar, additional, Deshpande, Atul, additional, Forjaz, Andre, additional, Getz, Michael, additional, Godet, Ines, additional, Lyman, Melissa, additional, Metzcar, John, additional, Mitchell, Jacob, additional, Raddatz, Andrew, additional, Rocha, Heber L., additional, Solorzano, Jacobo, additional, Sundus, Aneequa, additional, Wang, Yafei, additional, Gilkes, Daniele M., additional, Kagohara, Luciane T., additional, Kiemen, Ashley L., additional, Thompson, Elizabeth D., additional, Wirtz, Denis, additional, Wu, Pei-Hsun, additional, Zaidi, Neeha, additional, Zheng, Lei, additional, Zimmerman, Jacquelyn W., additional, Jaffee, Elizabeth M., additional, Chang, Young Hwan, additional, Coussens, Lisa, additional, Gray, Joe, additional, Heiser, Laura M., additional, Fertig, Elana J., additional, and Macklin, Paul, additional
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- 2023
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18. Mechano-induced homotypic patterned domain formation by monocytes
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Du, Wenxuan, primary, Zhu, Jingyi, additional, Wu, Yufei, additional, Kiemen, Ashley L., additional, Sun, Sean X., additional, and Wirtz, Denis, additional
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- 2023
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19. Three-dimensional genomic mapping of human pancreatic tissue reveals striking multifocality and genetic heterogeneity in precancerous lesions
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Braxton, Alicia M, primary, Kiemen, Ashley L, additional, Grahn, Mia P, additional, Forjaz, André, additional, Babu, Jaanvi Mahesh, additional, Zheng, Lily, additional, Jiang, Liping, additional, Cheng, Haixia, additional, Song, Qianqian, additional, Reichel, Rebecca, additional, Graham, Sarah, additional, Damanakis, Alexander I, additional, Fischer, Catherine G, additional, Mou, Stephanie, additional, Metz, Cameron, additional, Granger, Julie, additional, Liu, Xiao-Ding, additional, Bachmann, Niklas, additional, Almagro-Pérez, Cristina, additional, Jiang, Ann Chenyu, additional, Yoo, Jeonghyun, additional, Kim, Bridgette, additional, Du, Scott, additional, Foster, Eli, additional, Hsu, Jocelyn Y, additional, Rivera, Paula Andreu, additional, Chu, Linda C, additional, Liu, Fengze, additional, Niknafs, Noushin, additional, Fishman, Elliot K, additional, Yuille, Alan, additional, Roberts, Nicholas J, additional, Thompson, Elizabeth D, additional, Scharpf, Robert B, additional, Cornish, Toby C, additional, Jiao, Yuchen, additional, Karchin, Rachel, additional, Hruban, Ralph H, additional, Wu, Pei-Hsun, additional, Wirtz, Denis, additional, and Wood, Laura D, additional
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- 2023
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20. Intraparenchymal metastases as a cause for local recurrence of pancreatic cancer
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Kiemen, Ashley L, primary, Choi, YoungGeun, additional, Braxton, Alicia M, additional, Almagro Pérez, Cristina, additional, Graham, Sarah, additional, Grahn, Mia P, additional, Nanda, Neha, additional, Roberts, Nicholas, additional, Wood, Laura, additional, Wu, PeiHsun, additional, Hruban, Ralph H, additional, and Wirtz, Denis, additional
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- 2022
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21. PanIN and CAF Transitions in Pancreatic Carcinogenesis Revealed with Spatial Data Integration
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Bell, Alexander T.F., primary, Mitchell, Jacob T., additional, Kiemen, Ashley L., additional, Fujikura, Kohei, additional, Fedor, Helen, additional, Gambichler, Bonnie, additional, Deshpande, Atul, additional, Wu, Pei-Hsun, additional, Sidiropoulos, Dimitri N., additional, Erbe, Rossin, additional, Stern, Jacob, additional, Chan, Rena, additional, Williams, Stephen, additional, Chell, James M., additional, Zimmerman, Jacquelyn W., additional, Wirtz, Denis, additional, Jaffee, Elizabeth M., additional, Wood, Laura D., additional, Fertig, Elana J., additional, and Kagohara, Luciane T., additional
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- 2022
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22. Characterization of tumor‐associated macrophages in prostate cancer transgenic mouse models
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Groot, Amber E., primary, Myers, Kayla V., additional, Krueger, Timothy E. G., additional, Kiemen, Ashley L., additional, Nagy, Natalia H., additional, Brame, Alexandria, additional, Torres, Vicente E., additional, Zhang, Zhongyuan, additional, Trabzonlu, Levent, additional, Brennen, W. Nathaniel, additional, Wirtz, Denis, additional, De Marzo, Angelo M., additional, Amend, Sarah R., additional, and Pienta, Kenneth J., additional
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- 2021
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23. Intraparenchymal metastases as a cause for local recurrence of pancreatic cancer.
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Kiemen, Ashley L, Choi, YoungGeun, Braxton, Alicia M, Almagro Pérez, Cristina, Graham, Sarah, Grahn, Mia P, Nanda, Neha, Roberts, Nicholas, Wood, Laura, Wu, PeiHsun, Hruban, Ralph H, and Wirtz, Denis
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PANCREATIC cancer , *CANCER relapse , *PANCREAS , *MACHINE learning , *METASTASIS , *SOLAR plexus - Abstract
We determined that the Tp53 stain is wild-type and the Smad4 stain is intact in both regions. gl Our identification of a microscopic, intraparenchymal pancreatic cancer metastasis highlights one mechanism for tumour recurrence in the remnant pancreas after apparently curative surgery. Using CODA, a technique for three-dimensional reconstruction of large tissues, Kiemen et al. report observation of a microscopic focus of pancreatic cancer found in the vasculature of grossly normal human pancreas tissue resected adjacent to a large tumour. Gain of tumour protein I Tp53 i is found in approximately 85% of pancreatic cancers, while loss of I Smad4 i is found in approximately 55% of pancreatic cancers.[5] We found that both foci had wild-type labelling for I Tp53 i and intact I Smad4 i expression (Figure 2). [Extracted from the article]
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- 2023
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24. Combined assembloid modeling and 3D whole-organ mapping captures the microanatomy and function of the human fallopian tube.
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Crawford, Ashleigh J., Forjaz, André, Bons, Joanna, Bhorkar, Isha, Roy, Triya, Schell, David, Queiroga, Vasco, Ren, Kehan, Kramer, Donald, Huang, Wilson, Russo, Gabriella C., Meng-Horng Lee, Pei-Hsun Wu, Ie-Ming Shih, Tian-Li Wang, Atkinson, Mark A., Schilling, Birgit, Kiemen, Ashley L., and Wirtz, Denis
- Abstract
The fallopian tubes play key roles in processes from pregnancy to ovarian cancer where three-dimensional (3D) cellular and extracellular interactions are important to their pathophysiology. Here, we develop a 3D multicompartment assembloid model of the fallopian tube that molecularly, functionally, and architecturally resembles the organ. Global label-free proteomics, innovative assays capturing physiological functions of the fallopian tube (i.e., oocyte transport), and whole-organ single-cell resolution mapping are used to validate these assembloids through a multifaceted platform with direct comparisons to fallopian tube tissue. These techniques converge at a unique combination of assembloid parameters with the highest similarity to the reference fallopian tube. This work establishes (i) an optimized model of the human fallopian tubes for in vitro studies of their pathophysiology and (ii) an iterative platform for customized 3D in vitro models of human organs that are molecularly, functionally, and microanatomically accurate by combining tunable assembloid and tissue mapping methods. [ABSTRACT FROM AUTHOR]
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- 2024
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25. 3D histology reveals that immune response to pancreatic precancers is heterogeneous and depends on global pancreas structure.
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Kiemen AL, Almagro-Pérez C, Matos V, Forjaz A, Braxton AM, Dequiedt L, Parksong J, Cannon CD, Yuan X, Shin SM, Babu JM, Thompson ED, Cornish TC, Ho WJ, Wood LD, Wu PH, Barrutia AM, Hruban RH, and Wirtz D
- Abstract
Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal cancer for which few effective therapies exist. Immunotherapies specifically are ineffective in pancreatic cancer, in part due to its unique stromal and immune microenvironment. Pancreatic intraepithelial neoplasia, or PanIN, is the main precursor lesion to PDAC. Recently it was discovered that PanINs are remarkably abundant in the grossly normal pancreas, suggesting that the vast majority will never progress to cancer. Here, through construction of 48 samples of cm
3 -sized human pancreas tissue, we profiled the immune microenvironment of 1,476 PanINs in 3D and at single-cell resolution to better understand the early evolution of the pancreatic tumor microenvironment and to determine how inflammation may play a role in cancer progression. We found that bulk pancreatic inflammation strongly correlates to PanIN cell fraction. We found that the immune response around PanINs is highly heterogeneous, with distinct immune hotspots and cold spots that appear and disappear in a span of tens of microns. Immune hotspots generally mark locations of higher grade of dysplasia or locations near acinar atrophy. The immune composition at these hotspots is dominated by naïve, cytotoxic, and regulatory T cells, cancer associated fibroblasts, and tumor associated macrophages, with little similarity to the immune composition around less-inflamed PanINs. By mapping FOXP3+ cells in 3D, we found that regulatory T cells are present at higher density in larger PanIN lesions compared to smaller PanINs, suggesting that the early initiation of PanINs may not exhibit an immunosuppressive response. This analysis demonstrates that while PanINs are common in the pancreases of most individuals, inflammation may play a pivotal role, both at the bulk and the microscopic scale, in demarcating regions of significance in cancer progression.- Published
- 2024
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26. High-motility pro-tumorigenic monocytes drive macrophage enrichment in the tumor microenvironment.
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Du W, Zhou B, Forjaz A, Shin SM, Wu F, Crawford AJ, Nair PR, Johnston AC, West-Foyle H, Tang A, Kim D, Fan R, Kiemen AL, Wu PH, Phillip JM, Ho WJ, Sanin DE, and Wirtz D
- Abstract
Enrichment of tumor-associated macrophages (TAMΦs) in the tumor microenvironment correlates with worse clinical outcomes in triple-negative breast cancer (TNBC) patients, prompting the development of therapies to inhibit TAMΦ infiltration. However, the lackluster efficacy of CCL2-based chemotaxis blockade in clinical trials suggests that a new understanding of monocyte/macrophage infiltration may be necessary. Here we demonstrate that random migration, and not only chemotaxis, drives macrophage tumor infiltration. We identified tumor- associated monocytes (TAMos) that display a dramatically enhanced migration capability, induced rapidly by the tumor microenvironment, that drives effective tumor infiltration, in contrast to low-motility differentiated macrophages. TAMo, not TAMΦ, promotes cancer cell proliferation through activation of the MAPK pathway. IL-6 secreted both by cancer cells and TAMo themselves enhances TAMo migration by increasing dendritic protrusion dynamics and myosin- based contractility via the JAK2/STAT3 signaling pathway. Independent from CCL2 mediated chemotaxis, IL-6 driven enhanced migration and pro-proliferative effect of TAMo were validated in a syngeneic TNBC mouse model. Depletion of IL-6 in cancer cells significantly attenuated monocyte infiltration and reversed TAMo-induced cancer cell proliferation. This work reveals the critical role random migration plays in monocyte driven TAMΦ enrichment in a tumor and pinpoints IL-6 as a potential therapeutic target in combination with CCL2 to ameliorate current strategies against TAMΦ infiltration.
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- 2024
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27. qMAP enabled microanatomical mapping of human skin aging.
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Han KS, Sander IB, Kumer J, Resnick E, Booth C, Cheng G, Im Y, Starich B, Kiemen AL, Phillip JM, Reddy S, Joshu CE, Sunshine JC, Walston JD, Wirtz D, and Wu PH
- Abstract
Aging is a major driver of diseases in humans. Identifying features associated with aging is essential for designing robust intervention strategies and discovering novel biomarkers of aging. Extensive studies at both the molecular and organ/whole-body physiological scales have helped determined features associated with aging. However, the lack of meso-scale studies, particularly at the tissue level, limits the ability to translate findings made at molecular scale to impaired tissue functions associated with aging. In this work, we established a tissue image analysis workflow - quantitative micro-anatomical phenotyping (qMAP) - that leverages deep learning and machine vision to fully label tissue and cellular compartments in tissue sections. The fully mapped tissue images address the challenges of finding an interpretable feature set to quantitatively profile age-related microanatomic changes. We optimized qMAP for skin tissues and applied it to a cohort of 99 donors aged 14 to 92. We extracted 914 microanatomic features and found that a broad spectrum of these features, represented by 10 cores processes, are strongly associated with aging. Our analysis shows that microanatomical features of the skin can predict aging with a mean absolute error (MAE) of 7.7 years, comparable to state-of-the-art epigenetic clocks. Our study demonstrates that tissue-level architectural changes are strongly associated with aging and represent a novel category of aging biomarkers that complement molecular markers. Our results highlight the complex and underexplored multi-scale relationship between molecular and tissue microanatomic scales., Competing Interests: Conflict of Interest The authors declare no financial/commercial conflicts of interest.
- Published
- 2024
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28. Generative interpolation and restoration of images using deep learning for improved 3D tissue mapping.
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Joshi S, Forjaz A, Han KS, Shen Y, Queiroga V, Xenes D, Matelsk J, Wester B, Barrutia AM, Kiemen AL, Wu PH, and Wirtz D
- Abstract
The development of novel imaging platforms has improved our ability to collect and analyze large three-dimensional (3D) biological imaging datasets. Advances in computing have led to an ability to extract complex spatial information from these data, such as the composition, morphology, and interactions of multi-cellular structures, rare events, and integration of multi-modal features combining anatomical, molecular, and transcriptomic (among other) information. Yet, the accuracy of these quantitative results is intrinsically limited by the quality of the input images, which can contain missing or damaged regions, or can be of poor resolution due to mechanical, temporal, or financial constraints. In applications ranging from intact imaging (e.g. light-sheet microscopy and magnetic resonance imaging) to sectioning based platforms (e.g. serial histology and serial section transmission electron microscopy), the quality and resolution of imaging data has become paramount. Here, we address these challenges by leveraging frame interpolation for large image motion (FILM), a generative AI model originally developed for temporal interpolation, for spatial interpolation of a range of 3D image types. Comparative analysis demonstrates the superiority of FILM over traditional linear interpolation to produce functional synthetic images, due to its ability to better preserve biological information including microanatomical features and cell counts, as well as image quality, such as contrast, variance, and luminance. FILM repairs tissue damages in images and reduces stitching artifacts. We show that FILM can decrease imaging time by synthesizing skipped images. We demonstrate the versatility of our method with a wide range of imaging modalities (histology, tissue-clearing/light-sheet microscopy, magnetic resonance imaging, serial section transmission electron microscopy), species (human, mouse), healthy and diseased tissues (pancreas, lung, brain), staining techniques (IHC, H&E), and pixel resolutions (8 nm, 2 μm, 1mm). Overall, we demonstrate the potential of generative AI in improving the resolution, throughput, and quality of biological image datasets, enabling improved 3D imaging., Competing Interests: Conflict of interest statement The authors declare no conflicts of interest.
- Published
- 2024
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29. Three-dimensional assessments are necessary to determine the true, spatially-resolved composition of tissues.
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Forjaz A, Vaz E, Romero VM, Joshi S, Braxton AM, Jiang AC, Fujikura K, Cornish T, Hong SM, Hruban RH, Wu PH, Wood LD, Kiemen AL, and Wirtz D
- Abstract
Methods for spatially resolved cellular profiling using thinly cut sections have enabled in-depth quantitative tissue mapping to study inter-sample and intra-sample differences in normal human anatomy and disease onset and progression. These methods often profile extremely limited regions, which may impact the evaluation of heterogeneity due to tissue sub-sampling. Here, we applied CODA, a deep learning-based tissue mapping platform, to reconstruct the three-dimensional (3D) microanatomy of grossly normal and cancer-containing human pancreas biospecimens obtained from individuals who underwent pancreatic resection. To compare inter- and intra-sample heterogeneity, we assessed bulk and spatially resolved tissue composition in a cohort of two-dimensional (2D) whole slide images (WSIs) and a cohort of thick slabs of pancreas tissue that were digitally reconstructed in 3D from serial sections. To demonstrate the marked under sampling of 2D assessments, we simulated the number of WSIs and tissue microarrays (TMAs) necessary to represent the compositional heterogeneity of 3D data within 10% error to reveal that tens of WSIs and hundreds of TMA cores are sometimes needed. We show that spatial correlation of different pancreatic structures decay significantly within a span of microns, demonstrating that 2D histological sections may not be representative of their neighboring tissues. In sum, we demonstrate that 3D assessments are necessary to accurately assess tissue composition in normal and abnormal specimens and in order to accurately determine neoplastic content. These results emphasize the importance of intra-sample heterogeneity in tissue mapping efforts., Competing Interests: Conflict of interest statement The authors declare no conflicts of interest.
- Published
- 2024
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30. Engineering self-propelled tumor-infiltrating CAR T cells using synthetic velocity receptors.
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Johnston AC, Alicea GM, Lee CC, Patel PV, Hanna EA, Vaz E, Forjaz A, Wan Z, Nair PR, Lim Y, Chen T, Du W, Kim D, Nichakawade TD, Rebecca VW, Bonifant CL, Fan R, Kiemen AL, Wu PH, and Wirtz D
- Abstract
Chimeric antigen receptor (CAR) T cells express antigen-specific synthetic receptors, which upon binding to cancer cells, elicit T cell anti-tumor responses. CAR T cell therapy has enjoyed success in the clinic for hematological cancer indications, giving rise to decade-long remissions in some cases. However, CAR T therapy for patients with solid tumors has not seen similar success. Solid tumors constitute 90% of adult human cancers, representing an enormous unmet clinical need. Current approaches do not solve the central problem of limited ability of therapeutic cells to migrate through the stromal matrix. We discover that T cells at low and high density display low- and high-migration phenotypes, respectively. The highly migratory phenotype is mediated by a paracrine pathway from a group of self-produced cytokines that include IL5, TNFα, IFNγ, and IL8. We exploit this finding to "lock-in" a highly migratory phenotype by developing and expressing receptors, which we call velocity receptors (VRs). VRs target these cytokines and signal through these cytokines' cognate receptors to increase T cell motility and infiltrate lung, ovarian, and pancreatic tumors in large numbers and at doses for which control CAR T cells remain confined to the tumor periphery. In contrast to CAR therapy alone, VR-CAR T cells significantly attenuate tumor growth and extend overall survival. This work suggests that approaches to the design of immune cell receptors that focus on migration signaling will help current and future CAR cellular therapies to infiltrate deep into solid tumors., Competing Interests: Conflict of interest statement ACJ and DW are inventors on a patent application based on the work presented here.
- Published
- 2024
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31. A disrupted compartment boundary underlies abnormal cardiac patterning and congenital heart defects.
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Kathiriya IS, Dominguez MH, Rao KS, Muncie-Vasic JM, Devine WP, Hu KM, Hota SK, Garay BI, Quintero D, Goyal P, Matthews MN, Thomas R, Sukonnik T, Miguel-Perez D, Winchester S, Brower EF, Forjaz A, Wu PH, Wirtz D, Kiemen AL, and Bruneau BG
- Abstract
Failure of septation of the interventricular septum (IVS) is the most common congenital heart defect (CHD), but mechanisms for patterning the IVS are largely unknown. We show that a Tbx5
+ /Mef2cAHF+ progenitor lineage forms a compartment boundary bisecting the IVS. This coordinated population originates at a first- and second heart field interface, subsequently forming a morphogenetic nexus. Ablation of Tbx5+ /Mef2cAHF+ progenitors cause IVS disorganization, right ventricular hypoplasia and mixing of IVS lineages. Reduced dosage of the CHD transcription factor TBX5 disrupts boundary position and integrity, resulting in ventricular septation defects (VSDs) and patterning defects, including Slit2 and Ntn1 misexpression. Reducing NTN1 dosage partly rescues cardiac defects in Tbx5 mutant embryos. Loss of Slit2 or Ntn1 causes VSDs and perturbed septal lineage distributions. Thus, we identify essential cues that direct progenitors to pattern a compartment boundary for proper cardiac septation, revealing new mechanisms for cardiac birth defects., Competing Interests: Declaration of Interests: B.G.B. is a co-founder and shareholder of Tenaya Therapeutics. B.G.B. is an advisor for Silver Creek Pharmaceuticals. None of the work presented here is related to these commercial interests.- Published
- 2024
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32. Power-law growth models explain incidences and sizes of pancreatic cancer precursor lesions and confirm spatial genomic findings.
- Author
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Kiemen AL, Wu PH, Braxton AM, Cornish TC, Hruban RH, Wood L, Wirtz D, and Zwicker D
- Abstract
Pancreatic ductal adenocarcinoma is a rare but lethal cancer. Recent evidence reveals that pancreatic intraepithelial neoplasms (PanINs), the microscopic precursor lesions in the pancreatic ducts that can give rise to invasive pancreatic cancer, are significantly larger and more prevalent than previously believed. Better understanding of the growth law dynamics of PanINs may improve our ability to understand how a miniscule fraction of these lesions makes the transition to invasive cancer. Here, using artificial intelligence (AI)-based three-dimensional (3D) tissue mapping method, we measured the volumes of >1,000 PanIN and found that lesion size is distributed according to a power law with a fitted exponent of -1.7 over > 3 orders of magnitude. Our data also suggest that PanIN growth is not very sensitive to the pancreatic microenvironment or an individual's age, family history, and lifestyle, and is rather shaped by general growth behavior. We analyze several models of PanIN growth and fit the predicted size distributions to the observed data. The best fitting models suggest that both intraductal spread of PanIN lesions and fusing of multiple lesions into large, highly branched structures drive PanIN growth patterns. This work lays the groundwork for future mathematical modeling efforts integrating PanIN incidence, morphology, genomic, and transcriptomic features to understand pancreas tumorigenesis, and demonstrates the utility of combining experimental measurement of human tissues with dynamic modeling for understanding cancer tumorigenesis., Competing Interests: Conflict of interest statement The authors declare no conflicts of interest
- Published
- 2023
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33. Digitize your Biology! Modeling multicellular systems through interpretable cell behavior.
- Author
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Johnson JAI, Stein-O'Brien GL, Booth M, Heiland R, Kurtoglu F, Bergman DR, Bucher E, Deshpande A, Forjaz A, Getz M, Godet I, Lyman M, Metzcar J, Mitchell J, Raddatz A, Rocha H, Solorzano J, Sundus A, Wang Y, Gilkes D, Kagohara LT, Kiemen AL, Thompson ED, Wirtz D, Wu PH, Zaidi N, Zheng L, Zimmerman JW, Jaffee EM, Hwan Chang Y, Coussens LM, Gray JW, Heiser LM, Fertig EJ, and Macklin P
- Abstract
Cells are fundamental units of life, constantly interacting and evolving as dynamical systems. While recent spatial multi-omics can quantitate individual cells' characteristics and regulatory programs, forecasting their evolution ultimately requires mathematical modeling. We develop a conceptual framework-a cell behavior hypothesis grammar-that uses natural language statements (cell rules) to create mathematical models. This allows us to systematically integrate biological knowledge and multi-omics data to make them computable. We can then perform virtual "thought experiments" that challenge and extend our understanding of multicellular systems, and ultimately generate new testable hypotheses. In this paper, we motivate and describe the grammar, provide a reference implementation, and demonstrate its potential through a series of examples in tumor biology and immunotherapy. Altogether, this approach provides a bridge between biological, clinical, and systems biology researchers for mathematical modeling of biological systems at scale, allowing the community to extrapolate from single-cell characterization to emergent multicellular behavior., Competing Interests: Declaration of interests JZ receives other support from Roche/Genentech. LZ receives grant support from Bristol-Myers Squibb, Merck, Astrazeneca, iTeos, Amgen, NovaRock, Inxmed, and Halozyme. LZ is a paid consultant/Advisory Board Member at Biosion, Alphamab, NovaRock, Ambrx, Akrevia/Xilio, QED, Tempus, Pfizer, Novagenesis, Snow Lake Capitals, Amberstone, Tavotek Lab, ClinicalTrial Options, LLC, and Mingruizhiyao. LZ holds shares at Amberstone, Alphamab, Cellaration, and Mingruizhiyao. EJ reports other support from Abmeta, Adventris, personal fees from Achilles, DragonFly, Neuvogen, Parker Institute, CPRIT, Surge, Mestag, Medical Home Group, and HDTbio, grants from Lustgarten, and other grant support from Genentech, BMS, NeoTX, and Break Through Cancer outside the submitted work. Dr. Jaffee is the Dana and Albert “Cubby” Broccoli Professor of Oncology. EJF is on the Scientific Advisory of Resistance Bio/Viosera Therapeutics, a paid consultant for Merck and Mestag, and receives research funds from Abbvie Inc and Roche/Genetech.
- Published
- 2023
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34. Mechano-induced homotypic patterned domain formation by monocytes.
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Du W, Zhu J, Wu Y, Kiemen AL, Sun SX, and Wirtz D
- Abstract
Matrix stiffness and corresponding mechano-signaling play indispensable roles in cellular phenotypes and functions. How tissue stiffness influences the behavior of monocytes, a major circulating leukocyte of the innate system, and how it may promote the emergence of collective cell behavior is less understood. Here, using tunable collagen-coated hydrogels of physiological stiffness, we show that human primary monocytes undergo a dynamic local phase separation to form highly patterned multicellular multi-layered domains on soft matrix. Local activation of the β2 integrin initiates inter-cellular adhesion, while global soluble inhibitory factors maintain the steady-state domain pattern over days. Patterned domain formation generated by monocytes is unique among other key immune cells, including macrophages, B cells, T cells, and NK cells. While inhibiting their phagocytic capability, domain formation promotes monocytes' survival. We develop a computational model based on the Cahn-Hilliard equation, which includes combined local activation and global inhibition mechanisms of intercellular adhesion suggested by our experiments, and provides experimentally validated predictions of the role of seeding density and both chemotactic and random cell migration on pattern formation.
- Published
- 2023
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35. Three-dimensional genomic mapping of human pancreatic tissue reveals striking multifocality and genetic heterogeneity in precancerous lesions.
- Author
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Braxton AM, Kiemen AL, Grahn MP, Forjaz A, Babu JM, Zheng L, Jiang L, Cheng H, Song Q, Reichel R, Graham S, Damanakis AI, Fischer CG, Mou S, Metz C, Granger J, Liu XD, Bachmann N, Almagro-Pérez C, Jiang AC, Yoo J, Kim B, Du S, Foster E, Hsu JY, Rivera PA, Chu LC, Liu F, Niknafs N, Fishman EK, Yuille A, Roberts NJ, Thompson ED, Scharpf RB, Cornish TC, Jiao Y, Karchin R, Hruban RH, Wu PH, Wirtz D, and Wood LD
- Abstract
Pancreatic intraepithelial neoplasia (PanIN) is a precursor to pancreatic cancer and represents a critical opportunity for cancer interception. However, the number, size, shape, and connectivity of PanINs in human pancreatic tissue samples are largely unknown. In this study, we quantitatively assessed human PanINs using CODA, a novel machine-learning pipeline for 3D image analysis that generates quantifiable models of large pieces of human pancreas with single-cell resolution. Using a cohort of 38 large slabs of grossly normal human pancreas from surgical resection specimens, we identified striking multifocality of PanINs, with a mean burden of 13 spatially separate PanINs per cm
3 of sampled tissue. Extrapolating this burden to the entire pancreas suggested a median of approximately 1000 PanINs in an entire pancreas. In order to better understand the clonal relationships within and between PanINs, we developed a pipeline for CODA-guided multi-region genomic analysis of PanINs, including targeted and whole exome sequencing. Multi-region assessment of 37 PanINs from eight additional human pancreatic tissue slabs revealed that almost all PanINs contained hotspot mutations in the oncogene KRAS , but no gene other than KRAS was altered in more than 20% of the analyzed PanINs. PanINs contained a mean of 13 somatic mutations per region when analyzed by whole exome sequencing. The majority of analyzed PanINs originated from independent clonal events, with distinct somatic mutation profiles between PanINs in the same tissue slab. A subset of the analyzed PanINs contained multiple KRAS mutations, suggesting a polyclonal origin even in PanINs that are contiguous by rigorous 3D assessment. This study leverages a novel 3D genomic mapping approach to describe, for the first time, the spatial and genetic multifocality of human PanINs, providing important insights into the initiation and progression of pancreatic neoplasia., Competing Interests: Competing Interests Statement: A pending patent application “COMPUTATIONAL TECHNIQUES FOR THREE-DIMENSIONAL RECONSTRUCTION AND MULTI-LABELING OF SERIALLY SECTIONED TISSUE” was filed on 6/24/2022 by authors AK, RHH, PHW, DW, and LDW. The other authors report no competing interests.- Published
- 2023
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36. Characterization of tumor-associated macrophages in prostate cancer transgenic mouse models.
- Author
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de Groot AE, Myers KV, Krueger TEG, Kiemen AL, Nagy NH, Brame A, Torres VE, Zhang Z, Trabzonlu L, Brennen WN, Wirtz D, De Marzo AM, Amend SR, and Pienta KJ
- Subjects
- Animals, Male, Mice, Mice, Transgenic, Prostatic Neoplasms pathology, Tumor-Associated Macrophages pathology, Gene Expression Regulation, Neoplastic physiology, Prostatic Neoplasms genetics, Prostatic Neoplasms metabolism, Tumor Microenvironment physiology, Tumor-Associated Macrophages metabolism
- Abstract
Background: Tumor-associated macrophages (TAMs) are critical components of the tumor microenvironment (TME) in prostate cancer. Commonly used orthotopic models do not accurately reflect the complete TME of a human patient or the natural initiation and progression of a tumor. Therefore, genetically engineered mouse models are essential for studying the TME as well as advancing TAM-targeted therapies. Two common transgenic (TG) models of prostate cancer are Hi-Myc and transgenic adenocarcinoma of the mouse prostate (TRAMP), but the TME and TAM characteristics of these models have not been well characterized., Methods: To advance the Hi-Myc and TRAMP models as tools for TAM studies, macrophage infiltration and characteristics were assessed using histopathologic, flow cytometric, and expression analyses in these models at various timepoints during tumor development and progression., Results: In both Hi-Myc and TRAMP models, macrophages adopt a more pro-tumor phenotype in higher histological grade tumors and in older prostate tissue. However, the Hi-Myc and TRAMP prostates differ in their macrophage density, with Hi-Myc tumors exhibiting increased macrophage density and TRAMP tumors exhibiting decreased macrophage density compared to age-matched wild type mice., Conclusions: The macrophage density and the adenocarcinoma cancer subtype of Hi-Myc appear to better mirror patient tumors, suggesting that the Hi-Myc model is the more appropriate in vivo TG model for studying TAMs and TME-targeted therapies., (© 2021 The Authors. The Prostate published by Wiley Periodicals LLC.)
- Published
- 2021
- Full Text
- View/download PDF
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