4.7 Article

Composition, Spatial Characteristics, and Prognostic Significance of Myeloid Cell Infiltration in Pancreatic Cancer

Journal

CLINICAL CANCER RESEARCH
Volume 27, Issue 4, Pages 1069-1081

Publisher

AMER ASSOC CANCER RESEARCH
DOI: 10.1158/1078-0432.CCR-20-3141

Keywords

-

Categories

Funding

  1. Finnish Cultural Foundation
  2. Orion Research Foundation
  3. NIH [R35 CA197735, U01 CA210171, P50 CA127003]
  4. Hale Family Center for Pancreatic Cancer Research
  5. Lustgarten Foundation Dedicated Laboratory program
  6. Stand Up to Cancer
  7. Pancreatic Cancer Action Network
  8. Noble Effort Fund
  9. Wexler Family Fund
  10. Promises for Purple
  11. McCarthy Strong

Ask authors/readers for more resources

The study revealed a diverse set of myeloid cells within the PDAC tumor microenvironment, which are distributed heterogeneously across patient tumors. Beyond cell density, the spatial locations of immune cells are also associated with patient outcomes, underscoring the potential role of spatially resolved myeloid cell subtypes as quantitative biomarkers for PDAC prognosis and therapy.
Purpose: Although abundant myeloid cell populations in the pancreatic ductal adenocarcinoma (PDAC) microenvironment have been postulated to suppress antitumor immunity, the composition of these populations, their spatial locations, and how they relate to patient outcomes are poorly understood. Experimental Design: To generate spatially resolved tumor and immune cell data at single-cell resolution, we developed two quantitative multiplex immunofluorescence assays to interrogate myeloid cells (CD15, CD14, ARG1, CD33, HLA-DR) and macrophages [CD68, CD163, CD86, IFN regulatory factor 5, MRC1 (CD206)] in the PDAC tumor microenvironment. Spatial point pattern analyses were conducted to assess the degree of colocalization between tumor cells and immune cells. Multivariable-adjusted Cox proportional hazards regression was used to assess associations with patient outcomes. Results: In a multi-institutional cohort of 305 primary PDAC resection specimens, myeloid cells were abundant, enriched within stmmal regions, highly heterogeneous across tumors, and differed by somatic genotype. High densities of CD15(+) ARG1(+) immunosuppressive granulocytic cells and M2-polarized macrophages were associated with worse patient survival. Moreover, beyond cell density, doser proximity of M2-polarized macrophages to tumor cells was strongly associated with disease-free survival, revealing the clinical significance and biologic importance of immune cell localization within tumor areas. Conclusions: A diverse set of myeloid cells are present within the PDAC tumor microenvironment and are distributed heterogeneously across patient tumors. Not only the densities but also the spatial locations of myeloid immune cells are associated with patient outcomes, highlighting the potential role of spatially resolved myeloid cell subtypes as quantitative biomarkers for PDAC prognosis and therapy.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.7
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

Article Oncology

Spatially Resolved Single-Cell Assessment of Pancreatic Cancer Expression Subtypes Reveals Co-expressor Phenotypes and Extensive Intratumoral Heterogeneity

Hannah L. Williams, Andressa Dias Costa, Jinming Zhang, Srivatsan Raghavan, Peter S. Winter, Kevin S. Kapner, Scott P. Ginebaugh, Sara A. Vayrynen, Juha P. Vayrynen, Chen Yuan, Andrew W. Navia, Junning Wang, Annan Yang, Timothy L. Bosse, Radha L. Kalekar, Kristen E. Lowder, Mai Chan Lau, Dalia Elganainy, Vicente Morales-Oyarvide, Douglas A. Rubinson, Harshabad Singh, Kimberly Perez, James M. Cleary, Thomas E. Clancy, Jiping Wang, Joseph D. Mancias, Lauren K. Brais, Emma R. Hill, Margaret M. Kozak, David C. Linehan, Richard F. Dunne, Daniel T. Chang, Albert C. Koong, Aram F. Hezel, William C. Hahn, Alex K. Shalek, Andrew J. Aguirre, Jonathan A. Nowak, Brian M. Wolpin

Summary: A multiplex immunofluorescence (mIF) pipeline was developed to investigate the protein expression and intratumoral heterogeneity of pancreatic ductal adenocarcinoma (PDAC). The results showed that PDAC tumors exhibit extensive intratumoral heterogeneity, which is associated with patient outcomes and can be reduced in patient-derived organoid cultures. Co-expressor cells expressing both classical and basal markers were found in the majority of tumors, suggesting an intermediate state between subtype poles. This clinically applicable pipeline has implications for prognosis and treatment selection in PDAC patients.

CANCER RESEARCH (2023)

Article Oncology

Association of Machine Learning-Based Assessment of Tumor-Infiltrating Lymphocytes on Standard Histologic Images With Outcomes of Immunotherapy in Patients With NSCLC

Mehrdad Rakaee, Elio Adib, Biagio Ricciuti, Lynette M. Sholl, Weiwei Shi, Joao Alessi, Alessio Cortellini, Claudia A. M. Fulgenzi, Patrizia Viola, David J. Pinato, Sayed Hashemi, Idris Bahce, Ilias Houda, Ezgi B. Ulas, Teodora Radonic, Juha P. Vayrynen, Elin Richardsen, Simin Jamaly, Sigve Andersen, Tom Donnem, Mark M. Awad, David J. Kwiatkowski

Summary: This study developed a machine-learning-based approach to evaluate the association between tumor-infiltrating lymphocytes (TILs) and immune checkpoint inhibitor (ICI) therapy in lung cancer patients. The results showed that high levels of TILs were associated with ICI treatment response and could be used for patient selection and precision therapy guidance.

JAMA ONCOLOGY (2023)

Article Oncology

Immunological and prognostic significance of tumour necrosis in colorectal cancer

Meeri Kastinen, Paeivi Sirnio, Hanna Elomaa, Maarit Ahtiainen, Sara A. Vayrynen, Karl-Heinz Herzig, Sanna Merilainen, Raila Aro, Reetta Haivala, Tero Rautio, Juha Saarnio, Erkki-Ville Wirta, Olli Helminen, Toni T. Seppala, Teijo Kuopio, Jan Bohm, Anne Tuomisto, Jukka-Pekka Mecklin, Markus J. Makinen, Juha P. Vayrynen

Summary: This study analyzed data from 1413 patients with colorectal cancer and found associations between tumor necrosis percentage and tumor characteristics, immune cell infiltrates, serum cytokine concentrations, as well as prognosis. The results showed that high tumor necrosis percentage is associated with shorter colorectal cancer-specific survival, independent of other factors. Tumor necrosis is therefore considered an important prognostic factor in colorectal cancer.

BRITISH JOURNAL OF CANCER (2023)

Article Oncology

Spatially resolved multimarker evaluation of CD274 (PD-L1)/PDCD1 (PD-1) immune checkpoint expression and macrophage polarisation in colorectal cancer

Hanna Elomaa, Maarit Ahtiainen, Sara A. Vaeyrynen, Shuji Ogino, Jonathan A. Nowak, Mai Chan Lau, Olli Helminen, Erkki-Ville Wirta, Toni T. Seppaelae, Jan Boehm, Jukka-Pekka Mecklin, Teijo Kuopio, Juha P. Vaeyrynen

Summary: The expression patterns and prognostic significance of PD-L1 and PD-1 in the colorectal cancer microenvironment are inadequately characterised. This study found that PD-L1(+) macrophages and PD-1(+) T cells were associated with better clinical outcomes in colorectal cancer patients. These findings enhance the understanding of immune checkpoints in the tumor microenvironment and could contribute to the development of improved immunotherapies.

BRITISH JOURNAL OF CANCER (2023)

Letter Hematology

R-bendamustine in the treatment of nodular lymphocyte predominant Hodgkin lymphoma-An extended follow-up

Tero Vuolio, Outi Kuittinen, Juha P. Vayrynen, Hanna-Riikka Teppo, Roosa E. I. Prusil, Maria Ramet, Hanne Kuitunen, Timo Paloneva, Milla E. L. Kuusisto

BRITISH JOURNAL OF HAEMATOLOGY (2023)

Article Biochemistry & Molecular Biology

A deep learning algorithm to predict risk of pancreatic cancer from disease trajectories

Davide Placido, Bo Yuan, Jessica X. Hjaltelin, Chunlei Zheng, Amalie D. Haue, Piotr J. Chmura, Chen Yuan, Jihye Kim, Renato Umeton, Gregory Antell, Alexander Chowdhury, Alexandra Franz, Lauren Brais, Elizabeth Andrews, Debora S. Marks, Aviv Regev, Siamack Ayandeh, Mary T. Brophy, Nhan V. Do, Peter Kraft, Brian M. Wolpin, Michael H. Rosenthal, Nathanael R. Fillmore, Soren Brunak, Chris Sander

Summary: This study used artificial intelligence methods to analyze clinical data from Denmark and the United States and developed a model to predict the occurrence of pancreatic cancer within 36 months. The model showed promising performance in retrospective validation and can potentially aid in early detection.

NATURE MEDICINE (2023)

Article Multidisciplinary Sciences

Domain-specific transfer learning in the automated scoring of tumor-stroma ratio from histopathological images of colorectal cancer

Liisa Petainen, Juha P. Vayrynen, Pekka Ruusuvuori, Ilkka Polonen, Sami Ayramo, Teijo Kuopio

Summary: This study proposes an automated method for estimating tumor-stroma ratio (TSR) from histopathological images of colorectal cancer using convolutional neural networks. The models achieved high accuracy in classifying stroma, tumor, and other tissues, with the best model reaching 99.3% accuracy for tumor classification. The predicted TSR values showed moderate correlation with pathologist estimations (0.57).

PLOS ONE (2023)

Article Pathology

Tertiary lymphoid structures in pulmonary metastases of microsatellite stable colorectal cancer

Topias Karjula, Anne Niskakangas, Olli Mustonen, Iiris Puro, Hanna Elomaa, Maarit Ahtiainen, Teijo Kuopio, Jukka-Pekka Mecklin, Toni T. Seppala, Erkki-Ville Wirta, Eero Sihvo, Fredrik Yannopoulos, Olli Helminen, Juha P. Vayrynen

Summary: In this study, the prognostic effect of tertiary lymphoid structures (TLSs) on colorectal cancer (CRC) pulmonary metastases and primary tumors was analyzed. The density and diameter of TLSs showed no prognostic value in pulmonary metastases, but higher density and diameter were associated with lower mortality in primary tumors.

VIRCHOWS ARCHIV (2023)

Article Multidisciplinary Sciences

Pancreatic cancer is associated with medication changes prior to clinical diagnosis

Yin Zhang, Qiao-Li Wang, Chen Yuan, Alice A. Lee, Ana Babic, Kimmie Ng, Kimberly Perez, Jonathan A. Nowak, Jesper Lagergren, Meir J. Stampfer, Edward L. Giovannucci, Chris Sander, Michael H. Rosenthal, Peter Kraft, Brian M. Wolpin

Summary: Recent medication changes, such as initiating antidiabetic or anticoagulant medications, as well as discontinuing antihypertensive medications, are associated with an increased risk of pancreatic cancer diagnosis within the next two years. The risk of pancreatic cancer increases as the number of relevant medication changes increases. These findings suggest that recent medication changes should be considered as potential factors in multi-factor risk models for pancreatic cancer.

NATURE COMMUNICATIONS (2023)

Article Multidisciplinary Sciences

Histopathology images predict multi-omics aberrations and prognoses in colorectal cancer patients

Pei-Chen Tsai, Tsung-Hua Lee, Kun-Chi Kuo, Fang-Yi Su, Tsung-Lu Michael Lee, Eliana Marostica, Tomotaka Ugai, Melissa Zhao, Mai Chan Lau, Juha P. Vayrynen, Marios Giannakis, Yasutoshi Takashima, Seyed Mousavi Kahaki, Kana Wu, Mingyang Song, Jeffrey A. Meyerhardt, Andrew T. Chan, Jung-Hsien Chiang, Jonathan Nowak, Shuji Ogino, Kun-Hsing Yu

Summary: The authors develop a machine learning-based platform, MOMA, to systematically identify and interpret the relationship between patients' histologic patterns, multi-omics, and clinical profiles. MOMA successfully predicts the overall survival, disease-free survival, and copy number alterations of colorectal cancer patients. Additionally, MOMA identifies interpretable pathology patterns predictive of gene expression profiles, microsatellite instability status, and clinically actionable genetic alterations.

NATURE COMMUNICATIONS (2023)

Article Biochemistry & Molecular Biology

A pan-cancer analysis shows immunoevasive characteristics in NRF2 hyperactive squamous malignancies

Jouni Harkonen, Petri Polonen, Ashik Jawahar Deen, Ilakya Selvarajan, Hanna-Riikka Teppo, Elitsa Y. Dimova, Thomas Kietzmann, Maarit Ahtiainen, Juha P. Vayrynen, Sara A. Vayrynen, Hanna Elomaa, Niko Tynkkynen, Tiia Eklund, Teijo Kuopio, Eva-Maria Talvitie, Pekka Taimen, Markku Kallajoki, Minna U. Kaikkonen, Merja Heinaniemi, Anna-Liisa Levonen

Summary: The NRF2 pathway is frequently activated in various cancer types, but a comprehensive analysis of its effects across different malignancies is currently lacking. We developed a NRF2 activity metric and conducted a pan-cancer analysis of oncogenic NRF2 signaling using it. Our findings revealed an immunoevasive phenotype in squamous malignancies of the lung, head and neck area, cervix, and esophagus, where high NRF2 activity is associated with low interferon-gamma (IFNγ), HLA-I expression, and T cell and macrophage infiltration. These tumors have a molecular phenotype with amplification of SOX2/TP63, TP53 mutation, and CDKN2A loss, and are associated with upregulation of immunomodulatory genes NAMPT, WNT5A, SPP1, SLC7A11, SLC2A1, and PD-L1. Our functional genomics analyses suggest that these genes are candidate NRF2 targets, indicating a direct modulation of the tumor immune milieu. Single-cell mRNA data shows that cancer cells of this subtype exhibit decreased expression of IFNγ responsive ligands and increased expression of immunosuppressive ligands NAMPT, SPP1, and WNT5A involved in intercellular crosstalk. Moreover, the negative relationship between NRF2 and immune cells is explained by stromal populations of lung squamous cell carcinoma, indicating a potential effect across multiple squamous malignancies based on our molecular subtyping and deconvolution data.

REDOX BIOLOGY (2023)

Article Oncology

Bayesian risk prediction model for colorectal cancer mortality through integration of clinicopathologic and genomic data

Melissa Zhao, Mai Chan Lau, Koichiro Haruki, Juha P. Vayrynen, Carino Gurjao, Sara A. Vayrynen, Andressa Dias Costa, Jennifer Borowsky, Kenji Fujiyoshi, Kota Arima, Tsuyoshi Hamada, Jochen K. Lennerz, Charles S. Fuchs, Reiko Nishihara, Andrew T. Chan, Kimmie Ng, Xuehong Zhang, Jeffrey A. Meyerhardt, Mingyang Song, Molin Wang, Marios Giannakis, Jonathan A. Nowak, Kun-Hsing Yu, Tomotaka Ugai, Shuji Ogino

Summary: By using Bayesian additive regression trees (BART), a statistical learning technique, the study aimed to improve the prediction of prognosis for colorectal cancer by comprehensively analyzing patient-specific tumor characteristics. The BART risk model identified seven stable survival predictors and the risk stratifications based on model-predicted survival were statistically significant. BART demonstrated flexibility, interpretability, and comparable or superior performance to other machine-learning models, making it a valuable tool for prognostic stratifications in colorectal cancer patients.

NPJ PRECISION ONCOLOGY (2023)

Article Respiratory System

Results of intention-to-treat pulmonary metastasectomies in northern Finland revealing significant number of new lung primary carcinomas: time to move on from wedge resections?

Topias Karjula, Anne Niskakangas, Olli Mustonen, Iiris Puro, Juha P. Vayrynen, Olli Helminen, Fredrik Yannopoulos

Summary: A considerable proportion of intended pulmonary metastasectomies turn out as new incidental primary lung cancers. The study highlights the diagnostic importance of pulmonary metastasectomy and suggests segmentectomy as a primary procedure for patients with a long disease-free interval and a heavy smoking history.

JOURNAL OF THORACIC DISEASE (2023)

No Data Available