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10. The ACROBAT 2022 Challenge: Automatic Registration Of Breast Cancer Tissue

Author

Weitz, Philippe, Valkonen, Masi, Solorzano, Leslie, Carr, Circe, Kartasalo, Kimmo, Boissin, Constance, Koivukoski, Sonja, Kuusela, Aino, Rasic, Dusan, Feng, Yanbo, Pouplier, Sandra Sinius, Sharma, Abhinav, Eriksson, Kajsa Ledesma, Robertson, Stephanie, Marzahl, Christian, Gatenbee, Chandler D., Anderson, Alexander R. A., Wodzinski, Marek, Jurgas, Artur, Marini, Niccolò, Atzori, Manfredo, Müller, Henning, Budelmann, Daniel, Weiss, Nick, Heldmann, Stefan, Lotz, Johannes, Wolterink, Jelmer M., De Santi, Bruno, Patil, Abhijeet, Sethi, Amit, Kondo, Satoshi, Kasai, Satoshi, Hirasawa, Kousuke, Farrokh, Mahtab, Kumar, Neeraj, Greiner, Russell, Latonen, Leena, Laenkholm, Anne-Vibeke, Hartman, Johan, Ruusuvuori, Pekka, and Rantalainen, Mattias

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

The alignment of tissue between histopathological whole-slide-images (WSI) is crucial for research and clinical applications. Advances in computing, deep learning, and availability of large WSI datasets have revolutionised WSI analysis. Therefore, the current state-of-the-art in WSI registration is unclear. To address this, we conducted the ACROBAT challenge, based on the largest WSI registration dataset to date, including 4,212 WSIs from 1,152 breast cancer patients. The challenge objective was to align WSIs of tissue that was stained with routine diagnostic immunohistochemistry to its H&E-stained counterpart. We compare the performance of eight WSI registration algorithms, including an investigation of the impact of different WSI properties and clinical covariates. We find that conceptually distinct WSI registration methods can lead to highly accurate registration performances and identify covariates that impact performances across methods. These results establish the current state-of-the-art in WSI registration and guide researchers in selecting and developing methods.

Published
2023
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11. Increasing the usefulness of already existing annotations through WSI registration

Author

Weitz, Philippe, Sartor, Viktoria, Acs, Balazs, Robertson, Stephanie, Budelmann, Daniel, Hartman, Johan, and Rantalainen, Mattias

Subjects
Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Computational pathology methods have the potential to improve access to precision medicine, as well as the reproducibility and accuracy of pathological diagnoses. Particularly the analysis of whole-slide-images (WSIs) of immunohistochemically (IHC) stained tissue sections could benefit from computational pathology methods. However, scoring biomarkers such as KI67 in IHC WSIs often necessitates the detection of areas of invasive cancer. Training cancer detection models often requires annotations, which is time-consuming and therefore costly. Currently, cancer regions are typically annotated in WSIs of haematoxylin and eosin (H&E) stained tissue sections. In this study, we investigate the possibility to register annotations that were made in H&E WSIs to their IHC counterparts. Two pathologists annotated regions of invasive cancer in WSIs of 272 breast cancer cases. For each case, a matched H&E and KI67 WSI are available, resulting in 544 WSIs with invasive cancer annotations. We find that cancer detection CNNs that were trained with annotations registered from the H&E to the KI67 WSIs only differ slightly in calibration but not in performance compared to cancer detection models trained on annotations made directly in the KI67 WSIs in a test set consisting of 54 cases. The mean slide-level AUROC is 0.974 [0.964, 0.982] for models trained with the KI67 annotations and 0.974 [0.965, 0.982] for models trained using registered annotations. This indicates that WSI registration has the potential to reduce the need for IHC-specific annotations. This could significantly increase the usefulness of already existing annotations.

Published
2023

12. ACROBAT -- a multi-stain breast cancer histological whole-slide-image data set from routine diagnostics for computational pathology

Author

Weitz, Philippe, Valkonen, Masi, Solorzano, Leslie, Carr, Circe, Kartasalo, Kimmo, Boissin, Constance, Koivukoski, Sonja, Kuusela, Aino, Rasic, Dusan, Feng, Yanbo, Pouplier, Sandra Kristiane Sinius, Sharma, Abhinav, Eriksson, Kajsa Ledesma, Latonen, Leena, Laenkholm, Anne-Vibeke, Hartman, Johan, Ruusuvuori, Pekka, and Rantalainen, Mattias

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Quantitative Biology - Quantitative Methods
Abstract

The analysis of FFPE tissue sections stained with haematoxylin and eosin (H&E) or immunohistochemistry (IHC) is an essential part of the pathologic assessment of surgically resected breast cancer specimens. IHC staining has been broadly adopted into diagnostic guidelines and routine workflows to manually assess status and scoring of several established biomarkers, including ER, PGR, HER2 and KI67. However, this is a task that can also be facilitated by computational pathology image analysis methods. The research in computational pathology has recently made numerous substantial advances, often based on publicly available whole slide image (WSI) data sets. However, the field is still considerably limited by the sparsity of public data sets. In particular, there are no large, high quality publicly available data sets with WSIs of matching IHC and H&E-stained tissue sections. Here, we publish the currently largest publicly available data set of WSIs of tissue sections from surgical resection specimens from female primary breast cancer patients with matched WSIs of corresponding H&E and IHC-stained tissue, consisting of 4,212 WSIs from 1,153 patients. The primary purpose of the data set was to facilitate the ACROBAT WSI registration challenge, aiming at accurately aligning H&E and IHC images. For research in the area of image registration, automatic quantitative feedback on registration algorithm performance remains available through the ACROBAT challenge website, based on more than 37,000 manually annotated landmark pairs from 13 annotators. Beyond registration, this data set has the potential to enable many different avenues of computational pathology research, including stain-guided learning, virtual staining, unsupervised pre-training, artefact detection and stain-independent models.

Published
2022

13. Twenty-Year Benefit From Adjuvant Goserelin and Tamoxifen in Premenopausal Patients With Breast Cancer in a Controlled Randomized Clinical Trial.

Author

Tobin, Nicholas, Perez-Tenorio, Gizeh, Nordenskjöld, Anna, Johansson, Ulla, Hartman, Johan, Skoog, Lambert, Yau, Christina, Benz, Christopher, Stål, Olle, Nordenskjöld, Bo, Fornander, Tommy, Lindström, Linda, Johansson, Annelie, Dar, Huma, Esserman, Laura, and van t Veer, Laura

Subjects
Female, Humans, Middle Aged, Breast Neoplasms, Genomics, Goserelin, Receptors, Estrogen, Tamoxifen, Premenopause
Abstract

PURPOSE: To assess the long-term (20-year) endocrine therapy benefit in premenopausal patients with breast cancer. METHODS: Secondary analysis of the Stockholm trial (STO-5, 1990-1997) randomly assigning 924 premenopausal patients to 2 years of goserelin (3.6 mg subcutaneously once every 28 days), tamoxifen (40 mg orally once daily), combined goserelin and tamoxifen, or no adjuvant endocrine therapy (control) is performed. Random assignment was stratified by lymph node status; lymph node-positive patients (n = 459) were allocated to standard chemotherapy (cyclophosphamide, methotrexate, and fluorouracil). Primary tumor immunohistochemistry (n = 731) and gene expression profiling (n = 586) were conducted in 2020. The 70-gene signature identified genomic low-risk and high-risk patients. Kaplan-Meier analysis, multivariable Cox proportional hazard regression, and multivariable time-varying flexible parametric modeling assessed the long-term distant recurrence-free interval (DRFI). Swedish high-quality registries allowed a complete follow-up of 20 years. RESULTS: In estrogen receptor-positive patients (n = 584, median age 47 years), goserelin, tamoxifen, and the combination significantly improved long-term distant recurrence-free interval compared with control (multivariable hazard ratio [HR], 0.49; 95% CI, 0.32 to 0.75, HR, 0.57; 95% CI, 0.38 to 0.87, and HR, 0.63; 95% CI, 0.42 to 0.94, respectively). Significant goserelin-tamoxifen interaction was observed (P = .016). Genomic low-risk patients (n = 305) significantly benefitted from tamoxifen (HR, 0.24; 95% CI, 0.10 to 0.60), and genomic high-risk patients (n = 158) from goserelin (HR, 0.24; 95% CI, 0.10 to 0.54). Increased risk from the addition of tamoxifen to goserelin was seen in genomic high-risk patients (HR, 3.36; 95% CI, 1.39 to 8.07). Moreover, long-lasting 20-year tamoxifen benefit was seen in genomic low-risk patients, whereas genomic high-risk patients had early goserelin benefit. CONCLUSION: This study shows 20-year benefit from 2 years of adjuvant endocrine therapy in estrogen receptor-positive premenopausal patients and suggests differential treatment benefit on the basis of tumor genomic characteristics. Combined goserelin and tamoxifen therapy showed no benefit over single treatment. Long-term follow-up to assess treatment benefit is critical.

Published
2022

14. Systematically higher Ki67 scores on core biopsy samples compared to corresponding resection specimen in breast cancer: a multi-operator and multi-institutional study

Author

Acs, Balazs, Leung, Samuel CY, Kidwell, Kelley M, Arun, Indu, Augulis, Renaldas, Badve, Sunil S, Bai, Yalai, Bane, Anita L, Bartlett, John MS, Bayani, Jane, Bigras, Gilbert, Blank, Annika, Buikema, Henk, Chang, Martin C, Dietz, Robin L, Dodson, Andrew, Fineberg, Susan, Focke, Cornelia M, Gao, Dongxia, Gown, Allen M, Gutierrez, Carolina, Hartman, Johan, Kos, Zuzana, Lænkholm, Anne-Vibeke, Laurinavicius, Arvydas, Levenson, Richard M, Mahboubi-Ardakani, Rustin, Mastropasqua, Mauro G, Nofech-Mozes, Sharon, Osborne, C Kent, Penault-Llorca, Frédérique M, Piper, Tammy, Quintayo, Mary Anne, Rau, Tilman T, Reinhard, Stefan, Robertson, Stephanie, Salgado, Roberto, Sugie, Tomoharu, van der Vegt, Bert, Viale, Giuseppe, Zabaglo, Lila A, Hayes, Daniel F, Dowsett, Mitch, Nielsen, Torsten O, and Rimm, David L

Subjects
Cancer, Clinical Research, Breast Cancer, Biomarkers, Tumor, Biopsy, Breast Neoplasms, Female, Humans, Image Processing, Computer-Assisted, Immunohistochemistry, Ki-67 Antigen, Receptors, Estrogen, International Ki67 in Breast Cancer Working Group of the Breast International Group and North American Breast Cancer Group, Medical and Health Sciences, Pathology
Abstract

Ki67 has potential clinical importance in breast cancer but has yet to see broad acceptance due to inter-laboratory variability. Here we tested an open source and calibrated automated digital image analysis (DIA) platform to: (i) investigate the comparability of Ki67 measurement across corresponding core biopsy and resection specimen cases, and (ii) assess section to section differences in Ki67 scoring. Two sets of 60 previously stained slides containing 30 core-cut biopsy and 30 corresponding resection specimens from 30 estrogen receptor-positive breast cancer patients were sent to 17 participating labs for automated assessment of average Ki67 expression. The blocks were centrally cut and immunohistochemically (IHC) stained for Ki67 (MIB-1 antibody). The QuPath platform was used to evaluate tumoral Ki67 expression. Calibration of the DIA method was performed as in published studies. A guideline for building an automated Ki67 scoring algorithm was sent to participating labs. Very high correlation and no systematic error (p = 0.08) was found between consecutive Ki67 IHC sections. Ki67 scores were higher for core biopsy slides compared to paired whole sections from resections (p ≤ 0.001; median difference: 5.31%). The systematic discrepancy between core biopsy and corresponding whole sections was likely due to pre-analytical factors (tissue handling, fixation). Therefore, Ki67 IHC should be tested on core biopsy samples to best reflect the biological status of the tumor.

Published
2022

15. The ACROBAT 2022 challenge: Automatic registration of breast cancer tissue

Author

Weitz, Philippe, Valkonen, Masi, Solorzano, Leslie, Carr, Circe, Kartasalo, Kimmo, Boissin, Constance, Koivukoski, Sonja, Kuusela, Aino, Rasic, Dusan, Feng, Yanbo, Pouplier, Sandra Sinius, Sharma, Abhinav, Eriksson, Kajsa Ledesma, Robertson, Stephanie, Marzahl, Christian, Gatenbee, Chandler D., Anderson, Alexander R.A., Wodzinski, Marek, Jurgas, Artur, Marini, Niccolò, Atzori, Manfredo, Müller, Henning, Budelmann, Daniel, Weiss, Nick, Heldmann, Stefan, Lotz, Johannes, Wolterink, Jelmer M., De Santi, Bruno, Patil, Abhijeet, Sethi, Amit, Kondo, Satoshi, Kasai, Satoshi, Hirasawa, Kousuke, Farrokh, Mahtab, Kumar, Neeraj, Greiner, Russell, Latonen, Leena, Laenkholm, Anne-Vibeke, Hartman, Johan, Ruusuvuori, Pekka, and Rantalainen, Mattias

Published
2024
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20. Using deep learning to detect patients at risk for prostate cancer despite benign biopsies

Author

Liu, Bojing, Wang, Yinxi, Weitz, Philippe, Lindberg, Johan, Hartman, Johan, Egevad, Lars, Grönberg, Henrik, Eklund, Martin, and Rantalainen, Mattias

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Quantitative Biology - Quantitative Methods
Abstract

Background: Transrectal ultrasound guided systematic biopsies of the prostate is a routine procedure to establish a prostate cancer diagnosis. However, the 10-12 prostate core biopsies only sample a relatively small volume of the prostate, and tumour lesions in regions between biopsy cores can be missed, leading to a well-known low sensitivity to detect clinically relevant cancer. As a proof-of-principle, we developed and validated a deep convolutional neural network model to distinguish between morphological patterns in benign prostate biopsy whole slide images from men with and without established cancer. Methods: This study included 14,354 hematoxylin and eosin stained whole slide images from benign prostate biopsies from 1,508 men in two groups: men without an established prostate cancer (PCa) diagnosis and men with at least one core biopsy diagnosed with PCa. 80% of the participants were assigned as training data and used for model optimization (1,211 men), and the remaining 20% (297 men) as a held-out test set used to evaluate model performance. An ensemble of 10 deep convolutional neural network models was optimized for classification of biopsies from men with and without established cancer. Hyperparameter optimization and model selection was performed by cross-validation in the training data . Results: Area under the receiver operating characteristic curve (ROC-AUC) was estimated as 0.727 (bootstrap 95% CI: 0.708-0.745) on biopsy level and 0.738 (bootstrap 95% CI: 0.682 - 0.796) on man level. At a specificity of 0.9 the model had an estimated sensitivity of 0.348. Conclusion: The developed model has the ability to detect men with risk of missed PCa due to under-sampling of the prostate. The proposed model has the potential to reduce the number of false negative cases in routine systematic prostate biopsies and to indicate men who could benefit from MRI-guided re-biopsy., Comment: 13 pages, 3 figures

Published
2021

23. Predicting molecular phenotypes from histopathology images: a transcriptome-wide expression-morphology analysis in breast cancer

Author

Wang, Yinxi, Kartasalo, Kimmo, Valkonen, Masi, Larsson, Christer, Ruusuvuori, Pekka, Hartman, Johan, and Rantalainen, Mattias

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Quantitative Biology - Quantitative Methods
Abstract

Molecular phenotyping is central in cancer precision medicine, but remains costly and standard methods only provide a tumour average profile. Microscopic morphological patterns observable in histopathology sections from tumours are determined by the underlying molecular phenotype and associated with clinical factors. The relationship between morphology and molecular phenotype has a potential to be exploited for prediction of the molecular phenotype from the morphology visible in histopathology images. We report the first transcriptome-wide Expression-MOrphology (EMO) analysis in breast cancer, where gene-specific models were optimised and validated for prediction of mRNA expression both as a tumour average and in spatially resolved manner. Individual deep convolutional neural networks (CNNs) were optimised to predict the expression of 17,695 genes from hematoxylin and eosin (HE) stained whole slide images (WSIs). Predictions for 9,334 (52.75%) genes were significantly associated with RNA-sequencing estimates (FDR adjusted p-value < 0.05). 1,011 of the genes were brought forward for validation, with 876 (87%) and 908 (90%) successfully replicated in internal and external test data, respectively. Predicted spatial intra-tumour variabilities in expression were validated in 76 genes, out of which 59 (77.6%) had a significant association (FDR adjusted p-value < 0.05) with spatial transcriptomics estimates. These results suggest that the proposed methodology can be applied to predict both tumour average gene expression and intra-tumour spatial expression directly from morphology, thus providing a scalable approach to characterise intra-tumour heterogeneity., Comment: 42 pages, 6 figures

Published
2020

26. Prelude to malignancy: A gene expression signature in normal mammary gland from breast cancer patients suggests pre‐tumorous alterations and is associated with adverse outcomes

Author

Andreou, Maria, primary, Jąkalski, Marcin, additional, Duzowska, Katarzyna, additional, Filipowicz, Natalia, additional, Kostecka, Anna, additional, Davies, Hanna, additional, Horbacz, Monika, additional, Ławrynowicz, Urszula, additional, Chojnowska, Katarzyna, additional, Bruhn‐Olszewska, Bożena, additional, Jankau, Jerzy, additional, Śrutek, Ewa, additional, Las‐Jankowska, Manuela, additional, Bała, Dariusz, additional, Hoffman, Jacek, additional, Hartman, Johan, additional, Pęksa, Rafał, additional, Skokowski, Jarosław, additional, Jankowski, Michał, additional, Szylberg, Łukasz, additional, Maniewski, Mateusz, additional, Zegarski, Wojciech, additional, Nowikiewicz, Magdalena, additional, Nowikiewicz, Tomasz, additional, Dumanski, Jan P., additional, Mieczkowski, Jakub, additional, and Piotrowski, Arkadiusz, additional

Published
2024
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28. High prevalence of somatic PIK3CA and TP53 pathogenic variants in the normal mammary gland tissue of sporadic breast cancer patients revealed by duplex sequencing

Author

Kostecka, Anna, Nowikiewicz, Tomasz, Olszewski, Paweł, Koczkowska, Magdalena, Horbacz, Monika, Heinzl, Monika, Andreou, Maria, Salazar, Renato, Mair, Theresa, Madanecki, Piotr, Gucwa, Magdalena, Davies, Hanna, Skokowski, Jarosław, Buckley, Patrick G., Pęksa, Rafał, Śrutek, Ewa, Szylberg, Łukasz, Hartman, Johan, Jankowski, Michał, Zegarski, Wojciech, Tiemann-Boege, Irene, Dumanski, Jan P., and Piotrowski, Arkadiusz

Published
2022
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30. Evaluation of alternative prognostic thresholds for SP142 and 22C3 immunohistochemical PD-L1 expression in triple-negative breast cancer: results from a population-based cohort

Author

Sigurjonsdottir, Gudbjörg, primary, De Marchi, Tommaso, additional, Ehinger, Anna, additional, Hartman, Johan, additional, Ullén, Susann, additional, Leandersson, Karin, additional, Bosch, Ana, additional, Staaf, Johan, additional, Killander, Fredrika, additional, and Niméus, Emma, additional

Published
2024
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31. Tumor-associated NK cells drive MDSC-mediated tumor immune tolerance through the IL-6/STAT3 axis

Author

Neo, Shi Yong, primary, Tong, Le, additional, Chong, Joni, additional, Liu, Yaxuan, additional, Jing, Xu, additional, Oliveira, Mariana M. S., additional, Chen, Yi, additional, Chen, Ziqing, additional, Lee, Keene, additional, Burduli, Nutsa, additional, Chen, Xinsong, additional, Gao, Juan, additional, Ma, Ran, additional, Lim, Jia Pei, additional, Huo, Jianxin, additional, Xu, Shengli, additional, Alici, Evren, additional, Wickström, Stina L., additional, Haglund, Felix, additional, Hartman, Johan, additional, Wagner, Arnika K., additional, Cao, Yihai, additional, Kiessling, Rolf, additional, Lam, Kong Peng, additional, Westerberg, Lisa S., additional, and Lundqvist, Andreas, additional

Published
2024
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32. Human Epidermal Growth Factor Receptor 2 (HER2) PET Imaging of HER2-Low Breast Cancer with [68Ga]Ga-ABY-025: Results from a Pilot Study

Author

Altena, Renske, primary, Burén, Siri af, additional, Blomgren, August, additional, Karlsson, Emelie, additional, Tzortzakakis, Antonios, additional, Brun, Nikolai, additional, Moein, Mohammad M., additional, Jussing, Emma, additional, Frejd, Fredrik Y., additional, Bergh, Jonas, additional, Tran, Thuy A., additional, Hartman, Johan, additional, and Axelsson, Rimma, additional

Published
2024
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33. Adherence to and optimization of guidelines for Risk of Recurrence/Prosigna testing using a machine learning model: a Swedish multicenter study

Author

Kjällquist, Una, primary, Tsiknakis, Nikolaos, additional, Acs, Balazs, additional, Margolin, Sara, additional, Kessler, Luisa Edman, additional, Levy, Scarlett, additional, Ekholm, Maria, additional, Lundgren, Christine, additional, Olsson, Erik, additional, Lindman, Henrik, additional, Valachis, Antonios, additional, Hartman, Johan, additional, Foukakis, Theodoros, additional, and Matikas, Alexios, additional

Published
2024
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34. Unveiling the Power of Model-Agnostic Multiscale Analysis for Enhancing Artificial Intelligence Models in Breast Cancer Histopathology Images

Author

Tsiknakis, Nikos, Manikis, Georgios, Tzoras, Evangelos, Salgkamis, Dimitrios, Vidal, Joan Martinez, Wang, Kang, Zaridis, Dimitris, Sifakis, Emmanouil, Zerdes, Ioannis, Bergh, Jonas, Hartman, Johan, Acs, Balazs, Marias, Kostas, and Foukakis, Theodoros

Abstract

Developing AI models for digital pathology has traditionally relied on single-scale analysis of histopathology slides. However, a whole slide image is a rich digital representation of the tissue, captured at various magnification levels. Limiting our analysis to a single scale overlooks critical information, spanning from intricate high-resolution cellular details to broad low-resolution tissue structures. In this study, we propose a model-agnostic multiresolution feature aggregation framework tailored for the analysis of histopathology slides in the context of breast cancer, on a multicohort dataset of 2038 patient samples. We have adapted 9 state-of-the-art multiple instance learning models on our multi-scale methodology and evaluated their performance on grade prediction, TP53 mutation status prediction and survival prediction. The results prove the dominance of the multiresolution methodology, and specifically, concatenating or linearly transforming via a learnable layer the feature vectors of image patches from a high (20x) and low (10x) magnification factors achieve improved performance for all prediction tasks across domain-specific and imagenet-based features. On the contrary, the performance of uniresolution baseline models was not consistent across domain-specific and imagenet-based features. Moreover, we shed light on the inherent inconsistencies observed in models trained on whole-tissue-sections when validated against biopsy-based datasets. Despite these challenges, our findings underscore the superiority of multiresolution analysis over uniresolution methods. Finally, cross-scale analysis also benefits the explainability aspects of attention-based architectures, since one can extract attention maps at the tissue- and cell-levels, improving the interpretation of the model's decision.

Published
2024
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35. Immune cell infiltrate in ductal carcinoma in situ and the risk of dying from breast cancer : case-control study

Author

Rask, Gunilla, Wadsten, Charlotta, Acs, Balazs, Hartman, Johan, Fredriksson, Irma, Garmo, Hans, Wärnberg, Fredrik, Sund, Malin, Rask, Gunilla, Wadsten, Charlotta, Acs, Balazs, Hartman, Johan, Fredriksson, Irma, Garmo, Hans, Wärnberg, Fredrik, and Sund, Malin

Abstract

Background: Studies identifying risk factors for death from breast cancer after ductal carcinoma in situ (DCIS) are rare. In this retrospective nested case-control study, clinicopathological factors in women treated for DCIS and who died from breast cancer were compared with those of patients with DCIS who were free from metastatic disease. Methods: The study included patients registered with DCIS without invasive carcinoma in Sweden between 1992 and 2012. This cohort was linked to the National Cause of Death Registry. Of 6964 women with DCIS, 96 were registered with breast cancer as cause of death (cases). For each case, up to four controls (318; women with DCIS, alive and without metastatic breast cancer at the time of death of the corresponding case) were selected randomly by incidence density sampling. Whole slides of tumour tissue were evaluated for DCIS grade, comedo necrosis, and intensity of periductal lymphocytic infiltrate. Composition of the immune cell infiltrate, expression of oestrogen receptor, progesterone receptor, human epidermal growth factor receptor 2, and proliferation marker Ki-67 were scored on tissue microarrays. Clinical information was obtained from medical records. Information on date, site, and histological characteristics of local and distant recurrences was obtained from medical records for both cases and controls. Results: Tumour tissue was analysed from 65 cases and 195 controls. Intense periductal lymphocytic infiltrate around DCIS was associated with an increased risk of later dying from breast cancer (OR 2.21. 95% c.i. 1.01 to 4.84). Tumours with more intense lymphocytic infiltrate had a lower T cell/B cell ratio. None of the other biomarkers correlated with increased risk of breast cancer death. Conclusion: The immune response to DCIS may influence the risk of dying from breast cancer.

Published
2024
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36. CopyVAE: a variational autoencoder-based approach for copy number variation inference using single-cell transcriptomics

Author

Kurt, Semih, Chen, Mandi, Toosi, Hosein, Chen, Xinsong, Engblom, Camilla, Mold, Jeff, Hartman, Johan, Lagergren, Jens, Kurt, Semih, Chen, Mandi, Toosi, Hosein, Chen, Xinsong, Engblom, Camilla, Mold, Jeff, Hartman, Johan, and Lagergren, Jens

Abstract

Motivation: Copy number variations (CNVs) are common genetic alterations in tumour cells. The delineation of CNVs holds promise for enhancing our comprehension of cancer progression. Moreover, accurate inference of CNVs from single-cell sequencing data is essential for unravelling intratumoral heterogeneity. However, existing inference methods face limitations in resolution and sensitivity. Results: To address these challenges, we present CopyVAE, a deep learning framework based on a variational autoencoder architecture. Through experiments, we demonstrated that CopyVAE can accurately and reliably detect CNVs from data obtained using single-cell RNA sequencing. CopyVAE surpasses existing methods in terms of sensitivity and specificity. We also discussed CopyVAE’s potential to advance our understanding of genetic alterations and their impact on disease advancement. Availability and implementation: CopyVAE is implemented and freely available under MIT license at https://github.com/kurtsemih/copyVAE., QC 20240524

Published
2024
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37. Human Epidermal Growth Factor Receptor 2 (HER2) PET Imaging of HER2-Low Breast Cancer with [ 68 Ga]Ga-ABY-025 : Results from a Pilot Study

Author

Altena, Renske, af Buren, Siri, Blomgren, August, Karlsson, Emelie, Tzortzakakis, Antonios, Brun, Nikolai, Moein, Mohammad M., Jussing, Emma, Frejd, Fredrik, Bergh, Jonas, Tran, Thuy A., Hartman, Johan, Axelsson, Rimma, Altena, Renske, af Buren, Siri, Blomgren, August, Karlsson, Emelie, Tzortzakakis, Antonios, Brun, Nikolai, Moein, Mohammad M., Jussing, Emma, Frejd, Fredrik, Bergh, Jonas, Tran, Thuy A., Hartman, Johan, and Axelsson, Rimma

Abstract

Patients with HER2-low metastatic breast cancer (mBC), defined as an immunohistochemistry (IHC) score of 1+ or 2+ without HER2 gene amplification, may benefit from HER2 antibody-drug conjugates. Identifying suitable candidates is a clinical challenge because of spatial and temporal heterogeneity in HER2 expression and discrepancies in pathologic reporting. We aimed to investigate the feasibility and safety of HER2-specific PET imaging with [ 68 Ga]Ga-ABY-025 for visualization of HER2-low mBC. Methods: A prospective pilot study was done with 10 patients who had HER2-low mBC, as part of a phase 2 basket imaging study with [ 68 Ga]Ga-ABY-025 in HER2-expressing solid tumors. Patients were recruited at the Breast Clinic at the Karolinska University Hospital, Stockholm, Sweden. PET/CT images were acquired 3 h after injection of 200 MBq of [ 68 Ga]Ga-ABY-025. The SUV max was used to quantify tracer uptake. Ultrasound-guided tumor biopsies were guided by results from the HER2 PET. The main outcome-the safety and feasibility of HER2 PET in patients with HER2low mBC, measured the occurrence of possible procedure -related adverse events. Results: Ten patients with HER2-low mBC underwent [ 68 Ga]Ga-ABY-025 PET/CT with paired tumor biopsies. No adverse events occurred. In all patients, [ 68 Ga]Ga-ABY-025-avid lesions with substantial intra- and interindividual heterogeneity in tracer uptake were noted. In 8 of 10 patients with ABY-025-avid lesions, the HER2low status of the corresponding lesions was confirmed by IHC or in situ hybridization. Two patients had an IHC score of 0 in the tumor biopsies:1 in a cutaneous lesion with a low SUV max and 1 in a liver metastasis with a high SUV max but a "cold" core. Conclusion: The visualization of HER2-low mBC with [ 68 Ga]Ga-ABY-025 PET/CT was feasible and safe. Areas of tracer uptake showed varying levels of HER2 expression on IHC. The observed intra- and interindividual heterogeneity in [ 68 Ga]Ga-ABY-025 uptake suggested that HER2 PET

Published
2024
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38. Image-based multiplex immune profiling of cancer tissues: translational implications. A report of the International Immuno-oncology Biomarker Working Group on Breast Cancer

Author

Pathologie, Cancer, Jahangir, Chowdhury Arif, Page, David B., Broeckx, Glenn, Gonzalez, Claudia A., Burke, Caoimbhe, Murphy, Clodagh, Reis-Filho, Jorge S., Ly, Amy, Harms, Paul W., Gupta, Rajarsi R., Vieth, Michael, Hida, Akira I., Kahila, Mohamed, Kos, Zuzana, van Diest, Paul J., Verbandt, Sara, Thagaard, Jeppe, Khiroya, Reena, Abduljabbar, Khalid, Acosta Haab, Gabriela, Acs, Balazs, Adams, Sylvia, Almeida, Jonas S., Alvarado-Cabrero, Isabel, Azmoudeh-Ardalan, Farid, Badve, Sunil, Baharun, Nurkhairul Bariyah, Bellolio, Enrique R., Bheemaraju, Vydehi, Blenman, Kim R.M., Botinelly Mendonça Fujimoto, Luciana, Burgues, Octavio, Chardas, Alexandros, Cheang, Maggie Chon U., Ciompi, Francesco, Cooper, Lee A.D., Coosemans, An, Corredor, Germán, Dantas Portela, Flavio Luis, Deman, Frederik, Demaria, Sandra, Dudgeon, Sarah N., Elghazawy, Mahmoud, Fernandez-Martín, Claudio, Fineberg, Susan, Fox, Stephen B., Giltnane, Jennifer M., Gnjatic, Sacha, Gonzalez-Ericsson, Paula I., Grigoriadis, Anita, Halama, Niels, Hanna, Matthew G., Harbhajanka, Aparna, Hart, Steven N., Hartman, Johan, Hewitt, Stephen, Horlings, Hugo M., Husain, Zaheed, Irshad, Sheeba, Janssen, Emiel A.M., Kataoka, Tatsuki R., Kawaguchi, Kosuke, Khramtsov, Andrey I., Kiraz, Umay, Kirtani, Pawan, Kodach, Liudmila L., Korski, Konstanty, Akturk, Guray, Scott, Ely, Kovács, Anikó, Lænkholm, Anne Vibeke, Lang-Schwarz, Corinna, Larsimont, Denis, Lennerz, Jochen K., Lerousseau, Marvin, Li, Xiaoxian, Madabhushi, Anant, Maley, Sai K., Manur Narasimhamurthy, Vidya, Marks, Douglas K., McDonald, Elizabeth S., Mehrotra, Ravi, Michiels, Stefan, Kharidehal, Durga, Minhas, Fayyaz ul Amir Afsar, Mittal, Shachi, Moore, David A., Mushtaq, Shamim, Nighat, Hussain, Papathomas, Thomas, Penault-Llorca, Frederique, Perera, Rashindrie D., Pinard, Christopher J., Pinto-Cardenas, Juan Carlos, Pruneri, Giancarlo, Pusztai, Lajos, Rajpoot, Nasir Mahmood, Rapoport, Bernardo Leon, Rau, Tilman T., Ribeiro, Joana M., Rimm, David, Vincent-Salomon, Anne, Saltz, Joel, Sayed, Shahin, Hytopoulos, Evangelos, Mahon, Sarah, Siziopikou, Kalliopi P., Sotiriou, Christos, Stenzinger, Albrecht, Sughayer, Maher A., Sur, Daniel, Symmans, Fraser, Tanaka, Sunao, Taxter, Timothy, Tejpar, Sabine, Teuwen, Jonas, Thompson, E. Aubrey, Tramm, Trine, Tran, William T., van der Laak, Jeroen, Verghese, Gregory E., Viale, Giuseppe, Wahab, Noorul, Walter, Thomas, Waumans, Yannick, Wen, Hannah Y., Yang, Wentao, Yuan, Yinyin, Bartlett, John, Loibl, Sibylle, Denkert, Carsten, Savas, Peter, Loi, Sherene, Specht Stovgaard, Elisabeth, Salgado, Roberto, Gallagher, William M., Rahman, Arman, Pathologie, Cancer, Jahangir, Chowdhury Arif, Page, David B., Broeckx, Glenn, Gonzalez, Claudia A., Burke, Caoimbhe, Murphy, Clodagh, Reis-Filho, Jorge S., Ly, Amy, Harms, Paul W., Gupta, Rajarsi R., Vieth, Michael, Hida, Akira I., Kahila, Mohamed, Kos, Zuzana, van Diest, Paul J., Verbandt, Sara, Thagaard, Jeppe, Khiroya, Reena, Abduljabbar, Khalid, Acosta Haab, Gabriela, Acs, Balazs, Adams, Sylvia, Almeida, Jonas S., Alvarado-Cabrero, Isabel, Azmoudeh-Ardalan, Farid, Badve, Sunil, Baharun, Nurkhairul Bariyah, Bellolio, Enrique R., Bheemaraju, Vydehi, Blenman, Kim R.M., Botinelly Mendonça Fujimoto, Luciana, Burgues, Octavio, Chardas, Alexandros, Cheang, Maggie Chon U., Ciompi, Francesco, Cooper, Lee A.D., Coosemans, An, Corredor, Germán, Dantas Portela, Flavio Luis, Deman, Frederik, Demaria, Sandra, Dudgeon, Sarah N., Elghazawy, Mahmoud, Fernandez-Martín, Claudio, Fineberg, Susan, Fox, Stephen B., Giltnane, Jennifer M., Gnjatic, Sacha, Gonzalez-Ericsson, Paula I., Grigoriadis, Anita, Halama, Niels, Hanna, Matthew G., Harbhajanka, Aparna, Hart, Steven N., Hartman, Johan, Hewitt, Stephen, Horlings, Hugo M., Husain, Zaheed, Irshad, Sheeba, Janssen, Emiel A.M., Kataoka, Tatsuki R., Kawaguchi, Kosuke, Khramtsov, Andrey I., Kiraz, Umay, Kirtani, Pawan, Kodach, Liudmila L., Korski, Konstanty, Akturk, Guray, Scott, Ely, Kovács, Anikó, Lænkholm, Anne Vibeke, Lang-Schwarz, Corinna, Larsimont, Denis, Lennerz, Jochen K., Lerousseau, Marvin, Li, Xiaoxian, Madabhushi, Anant, Maley, Sai K., Manur Narasimhamurthy, Vidya, Marks, Douglas K., McDonald, Elizabeth S., Mehrotra, Ravi, Michiels, Stefan, Kharidehal, Durga, Minhas, Fayyaz ul Amir Afsar, Mittal, Shachi, Moore, David A., Mushtaq, Shamim, Nighat, Hussain, Papathomas, Thomas, Penault-Llorca, Frederique, Perera, Rashindrie D., Pinard, Christopher J., Pinto-Cardenas, Juan Carlos, Pruneri, Giancarlo, Pusztai, Lajos, Rajpoot, Nasir Mahmood, Rapoport, Bernardo Leon, Rau, Tilman T., Ribeiro, Joana M., Rimm, David, Vincent-Salomon, Anne, Saltz, Joel, Sayed, Shahin, Hytopoulos, Evangelos, Mahon, Sarah, Siziopikou, Kalliopi P., Sotiriou, Christos, Stenzinger, Albrecht, Sughayer, Maher A., Sur, Daniel, Symmans, Fraser, Tanaka, Sunao, Taxter, Timothy, Tejpar, Sabine, Teuwen, Jonas, Thompson, E. Aubrey, Tramm, Trine, Tran, William T., van der Laak, Jeroen, Verghese, Gregory E., Viale, Giuseppe, Wahab, Noorul, Walter, Thomas, Waumans, Yannick, Wen, Hannah Y., Yang, Wentao, Yuan, Yinyin, Bartlett, John, Loibl, Sibylle, Denkert, Carsten, Savas, Peter, Loi, Sherene, Specht Stovgaard, Elisabeth, Salgado, Roberto, Gallagher, William M., and Rahman, Arman

Published
2024

39. Image-based multiplex immune profiling of cancer tissues:translational implications. A report of the International Immuno-oncology Biomarker Working Group on Breast Cancer

Author

Jahangir, Chowdhury Arif, Page, David B., Broeckx, Glenn, Gonzalez, Claudia A., Burke, Caoimbhe, Murphy, Clodagh, Reis-Filho, Jorge S., Ly, Amy, Harms, Paul W., Gupta, Rajarsi R., Vieth, Michael, Hida, Akira I., Kahila, Mohamed, Kos, Zuzana, van Diest, Paul J., Verbandt, Sara, Thagaard, Jeppe, Khiroya, Reena, Abduljabbar, Khalid, Acosta Haab, Gabriela, Acs, Balazs, Adams, Sylvia, Almeida, Jonas S., Alvarado-Cabrero, Isabel, Azmoudeh-Ardalan, Farid, Badve, Sunil, Baharun, Nurkhairul Bariyah, Bellolio, Enrique R., Bheemaraju, Vydehi, Blenman, Kim R.M., Botinelly Mendonça Fujimoto, Luciana, Burgues, Octavio, Chardas, Alexandros, Cheang, Maggie Chon U., Ciompi, Francesco, Cooper, Lee A.D., Coosemans, An, Corredor, Germán, Dantas Portela, Flavio Luis, Deman, Frederik, Demaria, Sandra, Dudgeon, Sarah N., Elghazawy, Mahmoud, Fernandez-Martín, Claudio, Fineberg, Susan, Fox, Stephen B., Giltnane, Jennifer M., Gnjatic, Sacha, Gonzalez-Ericsson, Paula I., Grigoriadis, Anita, Halama, Niels, Hanna, Matthew G., Harbhajanka, Aparna, Hart, Steven N., Hartman, Johan, Hewitt, Stephen, Horlings, Hugo M., Husain, Zaheed, Irshad, Sheeba, Janssen, Emiel A.M., Kataoka, Tatsuki R., Kawaguchi, Kosuke, Khramtsov, Andrey I., Kiraz, Umay, Kirtani, Pawan, Kodach, Liudmila L., Korski, Konstanty, Akturk, Guray, Scott, Ely, Kovács, Anikó, Lænkholm, Anne Vibeke, Lang-Schwarz, Corinna, Larsimont, Denis, Lennerz, Jochen K., Lerousseau, Marvin, Li, Xiaoxian, Madabhushi, Anant, Maley, Sai K., Manur Narasimhamurthy, Vidya, Marks, Douglas K., McDonald, Elizabeth S., Mehrotra, Ravi, Michiels, Stefan, Kharidehal, Durga, Minhas, Fayyaz ul Amir Afsar, Mittal, Shachi, Moore, David A., Mushtaq, Shamim, Nighat, Hussain, Papathomas, Thomas, Penault-Llorca, Frederique, Perera, Rashindrie D., Pinard, Christopher J., Pinto-Cardenas, Juan Carlos, Pruneri, Giancarlo, Pusztai, Lajos, Rajpoot, Nasir Mahmood, Rapoport, Bernardo Leon, Rau, Tilman T., Ribeiro, Joana M., Rimm, David, Vincent-Salomon, Anne, Saltz, Joel, Sayed, Shahin, Hytopoulos, Evangelos, Mahon, Sarah, Siziopikou, Kalliopi P., Sotiriou, Christos, Stenzinger, Albrecht, Sughayer, Maher A., Sur, Daniel, Symmans, Fraser, Tanaka, Sunao, Taxter, Timothy, Tejpar, Sabine, Teuwen, Jonas, Thompson, E. Aubrey, Tramm, Trine, Tran, William T., van der Laak, Jeroen, Verghese, Gregory E., Viale, Giuseppe, Wahab, Noorul, Walter, Thomas, Waumans, Yannick, Wen, Hannah Y., Yang, Wentao, Yuan, Yinyin, Bartlett, John, Loibl, Sibylle, Denkert, Carsten, Savas, Peter, Loi, Sherene, Specht Stovgaard, Elisabeth, Salgado, Roberto, Gallagher, William M., Rahman, Arman, Jahangir, Chowdhury Arif, Page, David B., Broeckx, Glenn, Gonzalez, Claudia A., Burke, Caoimbhe, Murphy, Clodagh, Reis-Filho, Jorge S., Ly, Amy, Harms, Paul W., Gupta, Rajarsi R., Vieth, Michael, Hida, Akira I., Kahila, Mohamed, Kos, Zuzana, van Diest, Paul J., Verbandt, Sara, Thagaard, Jeppe, Khiroya, Reena, Abduljabbar, Khalid, Acosta Haab, Gabriela, Acs, Balazs, Adams, Sylvia, Almeida, Jonas S., Alvarado-Cabrero, Isabel, Azmoudeh-Ardalan, Farid, Badve, Sunil, Baharun, Nurkhairul Bariyah, Bellolio, Enrique R., Bheemaraju, Vydehi, Blenman, Kim R.M., Botinelly Mendonça Fujimoto, Luciana, Burgues, Octavio, Chardas, Alexandros, Cheang, Maggie Chon U., Ciompi, Francesco, Cooper, Lee A.D., Coosemans, An, Corredor, Germán, Dantas Portela, Flavio Luis, Deman, Frederik, Demaria, Sandra, Dudgeon, Sarah N., Elghazawy, Mahmoud, Fernandez-Martín, Claudio, Fineberg, Susan, Fox, Stephen B., Giltnane, Jennifer M., Gnjatic, Sacha, Gonzalez-Ericsson, Paula I., Grigoriadis, Anita, Halama, Niels, Hanna, Matthew G., Harbhajanka, Aparna, Hart, Steven N., Hartman, Johan, Hewitt, Stephen, Horlings, Hugo M., Husain, Zaheed, Irshad, Sheeba, Janssen, Emiel A.M., Kataoka, Tatsuki R., Kawaguchi, Kosuke, Khramtsov, Andrey I., Kiraz, Umay, Kirtani, Pawan, Kodach, Liudmila L., Korski, Konstanty, Akturk, Guray, Scott, Ely, Kovács, Anikó, Lænkholm, Anne Vibeke, Lang-Schwarz, Corinna, Larsimont, Denis, Lennerz, Jochen K., Lerousseau, Marvin, Li, Xiaoxian, Madabhushi, Anant, Maley, Sai K., Manur Narasimhamurthy, Vidya, Marks, Douglas K., McDonald, Elizabeth S., Mehrotra, Ravi, Michiels, Stefan, Kharidehal, Durga, Minhas, Fayyaz ul Amir Afsar, Mittal, Shachi, Moore, David A., Mushtaq, Shamim, Nighat, Hussain, Papathomas, Thomas, Penault-Llorca, Frederique, Perera, Rashindrie D., Pinard, Christopher J., Pinto-Cardenas, Juan Carlos, Pruneri, Giancarlo, Pusztai, Lajos, Rajpoot, Nasir Mahmood, Rapoport, Bernardo Leon, Rau, Tilman T., Ribeiro, Joana M., Rimm, David, Vincent-Salomon, Anne, Saltz, Joel, Sayed, Shahin, Hytopoulos, Evangelos, Mahon, Sarah, Siziopikou, Kalliopi P., Sotiriou, Christos, Stenzinger, Albrecht, Sughayer, Maher A., Sur, Daniel, Symmans, Fraser, Tanaka, Sunao, Taxter, Timothy, Tejpar, Sabine, Teuwen, Jonas, Thompson, E. Aubrey, Tramm, Trine, Tran, William T., van der Laak, Jeroen, Verghese, Gregory E., Viale, Giuseppe, Wahab, Noorul, Walter, Thomas, Waumans, Yannick, Wen, Hannah Y., Yang, Wentao, Yuan, Yinyin, Bartlett, John, Loibl, Sibylle, Denkert, Carsten, Savas, Peter, Loi, Sherene, Specht Stovgaard, Elisabeth, Salgado, Roberto, Gallagher, William M., and Rahman, Arman

Abstract

Recent advances in the field of immuno-oncology have brought transformative changes in the management of cancer patients. The immune profile of tumours has been found to have key value in predicting disease prognosis and treatment response in various cancers. Multiplex immunohistochemistry and immunofluorescence have emerged as potent tools for the simultaneous detection of multiple protein biomarkers in a single tissue section, thereby expanding opportunities for molecular and immune profiling while preserving tissue samples. By establishing the phenotype of individual tumour cells when distributed within a mixed cell population, the identification of clinically relevant biomarkers with high-throughput multiplex immunophenotyping of tumour samples has great potential to guide appropriate treatment choices. Moreover, the emergence of novel multi-marker imaging approaches can now provide unprecedented insights into the tumour microenvironment, including the potential interplay between various cell types. However, there are significant challenges to widespread integration of these technologies in daily research and clinical practice. This review addresses the challenges and potential solutions within a structured framework of action from a regulatory and clinical trial perspective. New developments within the field of immunophenotyping using multiplexed tissue imaging platforms and associated digital pathology are also described, with a specific focus on translational implications across different subtypes of cancer.

Published
2024

41. Image‐based multiplex immune profiling of cancer tissues: translational implications. A report of the International Immuno‐oncology Biomarker Working Group on Breast Cancer

Author

Jahangir, Chowdhury Arif, primary, Page, David B, additional, Broeckx, Glenn, additional, Gonzalez, Claudia A, additional, Burke, Caoimbhe, additional, Murphy, Clodagh, additional, Reis‐Filho, Jorge S, additional, Ly, Amy, additional, Harms, Paul W, additional, Gupta, Rajarsi R, additional, Vieth, Michael, additional, Hida, Akira I, additional, Kahila, Mohamed, additional, Kos, Zuzana, additional, van Diest, Paul J, additional, Verbandt, Sara, additional, Thagaard, Jeppe, additional, Khiroya, Reena, additional, Abduljabbar, Khalid, additional, Acosta Haab, Gabriela, additional, Acs, Balazs, additional, Adams, Sylvia, additional, Almeida, Jonas S, additional, Alvarado‐Cabrero, Isabel, additional, Azmoudeh‐Ardalan, Farid, additional, Badve, Sunil, additional, Baharun, Nurkhairul Bariyah, additional, Bellolio, Enrique R, additional, Bheemaraju, Vydehi, additional, Blenman, Kim RM, additional, Botinelly Mendonça Fujimoto, Luciana, additional, Burgues, Octavio, additional, Chardas, Alexandros, additional, Cheang, Maggie Chon U, additional, Ciompi, Francesco, additional, Cooper, Lee AD, additional, Coosemans, An, additional, Corredor, Germán, additional, Dantas Portela, Flavio Luis, additional, Deman, Frederik, additional, Demaria, Sandra, additional, Dudgeon, Sarah N, additional, Elghazawy, Mahmoud, additional, Fernandez‐Martín, Claudio, additional, Fineberg, Susan, additional, Fox, Stephen B, additional, Giltnane, Jennifer M, additional, Gnjatic, Sacha, additional, Gonzalez‐Ericsson, Paula I, additional, Grigoriadis, Anita, additional, Halama, Niels, additional, Hanna, Matthew G, additional, Harbhajanka, Aparna, additional, Hart, Steven N, additional, Hartman, Johan, additional, Hewitt, Stephen, additional, Horlings, Hugo M, additional, Husain, Zaheed, additional, Irshad, Sheeba, additional, Janssen, Emiel AM, additional, Kataoka, Tatsuki R, additional, Kawaguchi, Kosuke, additional, Khramtsov, Andrey I, additional, Kiraz, Umay, additional, Kirtani, Pawan, additional, Kodach, Liudmila L, additional, Korski, Konstanty, additional, Akturk, Guray, additional, Scott, Ely, additional, Kovács, Anikó, additional, Lænkholm, Anne‐Vibeke, additional, Lang‐Schwarz, Corinna, additional, Larsimont, Denis, additional, Lennerz, Jochen K, additional, Lerousseau, Marvin, additional, Li, Xiaoxian, additional, Madabhushi, Anant, additional, Maley, Sai K, additional, Manur Narasimhamurthy, Vidya, additional, Marks, Douglas K, additional, McDonald, Elizabeth S, additional, Mehrotra, Ravi, additional, Michiels, Stefan, additional, Kharidehal, Durga, additional, Minhas, Fayyaz ul Amir Afsar, additional, Mittal, Shachi, additional, Moore, David A, additional, Mushtaq, Shamim, additional, Nighat, Hussain, additional, Papathomas, Thomas, additional, Penault‐Llorca, Frederique, additional, Perera, Rashindrie D, additional, Pinard, Christopher J, additional, Pinto‐Cardenas, Juan Carlos, additional, Pruneri, Giancarlo, additional, Pusztai, Lajos, additional, Rajpoot, Nasir Mahmood, additional, Rapoport, Bernardo Leon, additional, Rau, Tilman T, additional, Ribeiro, Joana M, additional, Rimm, David, additional, Vincent‐Salomon, Anne, additional, Saltz, Joel, additional, Sayed, Shahin, additional, Hytopoulos, Evangelos, additional, Mahon, Sarah, additional, Siziopikou, Kalliopi P, additional, Sotiriou, Christos, additional, Stenzinger, Albrecht, additional, Sughayer, Maher A, additional, Sur, Daniel, additional, Symmans, Fraser, additional, Tanaka, Sunao, additional, Taxter, Timothy, additional, Tejpar, Sabine, additional, Teuwen, Jonas, additional, Thompson, E Aubrey, additional, Tramm, Trine, additional, Tran, William T, additional, van der Laak, Jeroen, additional, Verghese, Gregory E, additional, Viale, Giuseppe, additional, Wahab, Noorul, additional, Walter, Thomas, additional, Waumans, Yannick, additional, Wen, Hannah Y, additional, Yang, Wentao, additional, Yuan, Yinyin, additional, Bartlett, John, additional, Loibl, Sibylle, additional, Denkert, Carsten, additional, Savas, Peter, additional, Loi, Sherene, additional, Specht Stovgaard, Elisabeth, additional, Salgado, Roberto, additional, Gallagher, William M, additional, and Rahman, Arman, additional

Published
2024
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42. Spatial transcriptomics of B cell and T cell receptors reveals lymphocyte clonal dynamics

Author

Engblom, Camilla, primary, Thrane, Kim, additional, Lin, Qirong, additional, Andersson, Alma, additional, Toosi, Hosein, additional, Chen, Xinsong, additional, Steiner, Embla, additional, Lu, Chang, additional, Mantovani, Giulia, additional, Hagemann-Jensen, Michael, additional, Saarenpää, Sami, additional, Jangard, Mattias, additional, Saez-Rodriguez, Julio, additional, Michaëlsson, Jakob, additional, Hartman, Johan, additional, Lagergren, Jens, additional, Mold, Jeff E., additional, Lundeberg, Joakim, additional, and Frisén, Jonas, additional

Published
2023
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43. Interplay between copy number alterations and immune profiles in the early breast cancer Scandinavian Breast Group 2004-1 randomized phase II trial: results from a feasibility study

Author

Zerdes, Ioannis, Simonetti, Michele, Matikas, Alexios, Harbers, Luuk, Acs, Balazs, Boyaci, Ceren, Zhang, Ning, Salgkamis, Dimitrios, Agartz, Susanne, Moreno-Ruiz, Pablo, Bai, Yalai, Rimm, David L., Hartman, Johan, Mezheyeuski, Artur, Bergh, Jonas, Crosetto, Nicola, and Foukakis, Theodoros

Published
2021
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46. CD73 immune checkpoint defines regulatory NK cells within the tumor microenvironment

Author

Neo, Shi Yong, Yang, Ying, Record, Julien, Ma, Ran, Chen, Xinsong, Chen, Ziqing, Tobin, Nicholas P., Blake, Emily, Seitz, Christina, Thomas, Ron, Wagner, Arnika Kathleen, Andersson, John, de Boniface, Jana, Bergh, Jonas, Murray, Shannon, Alici, Evren, Childs, Richard, Johansson, Martin, Westerberg, Lisa S., Haglund, Felix, Hartman, Johan, and Lundqvist, Andreas

Subjects
MedImmune L.L.C., Killer cells, Biotechnology industries, Tumors -- Development and progression, Immunotherapy, Polymerization, T cells, Muscle proteins, Breast cancer, Actin, Nucleotidases, Health care industry
Abstract

High levels of ecto-5'-nucleotidase (CD73) have been implicated in immune suppression and tumor progression, and have also been observed in cancer patients who progress on anti-PD-1 immunotherapy. Although regulatory T cells can express CD73 and inhibit T cell responses via the production of adenosine, less is known about CD73 expression in other immune cell populations. We found that tumor-infiltrating NK cells upregulate CD73 expression and the frequency of these CD73-positive NK cells correlated with larger tumor size in breast cancer patients. In addition, the expression of multiple alternative immune checkpoint receptors including LAG-3, VISTA, PD-1, and PD-L1 was significantly higher in [CD73.sup.-]-positive NK cells than in [CD73.sup.-]-negative NK cells. Mechanistically, NK cells transport CD73 in intracellular vesicles to the cell surface and the extracellular space via actin polymerization-dependent exocytosis upon engagement of 4-1BBL on tumor cells. These CD73positive NK cells undergo transcriptional reprogramming and upregulate IL-10 production via STAT3 transcriptional activity, suppressing CD4-positive T cell proliferation and IFN-[gamma] production. Taken together, our results support the notion that tumors can hijack NK cells as a means to escape immunity and that CD73 expression defines an inducible population of NK cells with immunoregulatory properties within the tumor microenvironment., Introduction The CD73 metabolic immune checkpoint orchestrates a crucial homeostatic balance of extracellular adenosine levels as part of a negative feedback mechanism to control inflammatory responses within a stressed or [...]

Published
2020
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47. Three-dimensional single-cell imaging for the analysis of RNA and protein expression in intact tumour biopsies

Author

Tanaka, Nobuyuki, Kanatani, Shigeaki, Kaczynska, Dagmara, Fukumoto, Keishiro, Louhivuori, Lauri, Mizutani, Tomohiro, Kopper, Oded, Kronqvist, Pauliina, Robertson, Stephanie, Lindh, Claes, Kis, Lorand, Pronk, Robin, Niwa, Naoya, Matsumoto, Kazuhiro, Oya, Mototsugu, Miyakawa, Ayako, Falk, Anna, Hartman, Johan, Sahlgren, Cecilia, Clevers, Hans, and Uhlén, Per

Published
2020
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