Article
Pathology
Anette H. Skjervold, Henrik Sahlin Pettersen, Marit Valla, Signe Opdahl, Anna M. Bofin
Summary: This study compared digital and visual assessment of Ki-67 protein expression levels in breast cancer. The findings suggest that digital assessment identifies a higher proportion of cases with high Ki-67 levels compared to visual assessment, and call for appropriate calibration of diagnostic cut-off levels when introducing new methods.
DIAGNOSTIC PATHOLOGY
(2022)
Review
Cell Biology
Emad A. Rakha, Konstantinos Vougas, Puay Hoon Tan
Summary: Digital technology in breast pathology includes automated IHC staining, image analysis systems for interpreting IHC staining, and AI tools for predicting marker expression from digitalized images stained with haematoxylin and eosin.
Article
Engineering, Biomedical
Salman Ahmed, Maria Tariq, Hammad Naveed
Summary: The mortality rate of breast cancer is increasing, emphasizing the importance of early detection. This study introduces a method for breast cancer detection in whole slide images, outperforming current benchmarks. Additionally, a method for generating annotations for medical data is proposed.
COMPUTERIZED MEDICAL IMAGING AND GRAPHICS
(2021)
Review
Cell Biology
Tracy Z. Tien, Justina N. L. W. Lee, Jeffrey C. T. Lim, Xiao-Yang Chen, Aye Aye Thike, Puay Hoon Tan, Joe P. S. Yeong
Summary: Breast cancer is the most common malignancy and leading cause of cancer death among females worldwide, with triple-negative cases posing particular challenges in treatment. Advancements in technology have allowed for detailed profiling of immune infiltrates in breast tumors, opening up opportunities for personalized drug targets and potentially improving clinical outcomes.
Article
Oncology
Abeer M. Mahmoud, Eileen Brister, Odile David, Klara Valyi-Nagy, Maria Sverdlov, Peter H. Gann, Sage J. Kim
Summary: We trained and validated a machine learning digital scoring method for PRMT6 protein expression in lung cancer tissues, which showed excellent concordance with manual scoring by pathologists. This optimized digital scoring can serve as a more efficient and accurate method for evaluating PRMT6 expression.
Review
Oncology
Peter Bankhead
Summary: The potential of quantitative image analysis and artificial intelligence in digital pathology is highlighted. However, the lack of available software and the complexity of existing methods hinder widespread adoption. This review emphasizes the need for collaboration and multidisciplinary approaches in developing new algorithms and calls for greater attention to openness, implementation, and usability. The interaction between digital pathology and the bioimage analysis community is seen as beneficial in terms of data sharing and idea exchange.
JOURNAL OF PATHOLOGY
(2022)
Article
Engineering, Biomedical
Kaan Aykut Kabakci, Asli Cakir, Ilknur Turkmen, Behcet Ugur Toreyin, Abdulkerim Capar
Summary: This study proposes a cell-based image analysis method to automatically determine CerbB2/HER2 scores in breast tissue images in accordance with ASCO/CAP recommendations, providing an explainable artificial intelligence solution. The results suggest that the proposed method is highly effective in HER2 tissue scoring.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2021)
Article
Cell Biology
T. Jagomast, C. Idel, L. Klapper, P. Kuppler, L. Proppe, S. Beume, M. Falougy, D. Steller, S. G. Hakim, A. Offermann, M. C. Roesch, K. L. Bruchhage, S. Perner, J. Ribbat-Idel
Summary: Quantifying protein expression in immunohistochemically stained histological slides is crucial for oncologic research. This study compared the results obtained from manual and automated digital image analysis systems and found high correlation and agreement between the two methods in measuring chromogenic intensity and positive index. Both methods were shown to be reliable for patient evaluation and aDIA was preferred due to its time-saving and reproducibility advantages.
HISTOLOGY AND HISTOPATHOLOGY
(2022)
Review
Oncology
Yanjun Hou, Hiroaki Nitta, Zaibo Li
Summary: HER2 intratumoral heterogeneity (ITH) is a common phenomenon in breast cancer, characterized by the coexistence of tumor cell subpopulations with different HER2 gene or protein expression. It has been associated with poor prognosis in patients receiving anti-HER2 targeted therapies and proposed as a potential mechanism for anti-HER2 resistance. HER2 ITH can be categorized into non-genetic and genetic ITH based on different HER2 genetic amplification, with genetic ITH exhibiting clustered, mosaic, and scattered distribution patterns. Digital image analysis has emerged as a promising method to accurately and objectively assess HER2 ITH.
Article
Oncology
Henrik Failmezger, Harald Hessel, Ansh Kapil, Guenter Schmidt, Nathalie Harder
Summary: Identifying new tumor biomarkers is crucial in cancer research. Our novel approaches for quantitatively scoring spatial marker expression heterogeneity outperform expression averages, showing the importance of considering spatial variability in tumor biology research.
FRONTIERS IN ONCOLOGY
(2022)
Article
Cell Biology
Anastasia Alataki, Lila Zabaglo, Holly Tovey, Andrew Dodson, Mitch Dowsett
Summary: The study evaluated the use of the Cognition Master Professional Suite (CogM) image analysis software for Ki67 scoring in primary breast cancer samples. The results showed a high correlation between manual and digital scores, as well as no significant bias, demonstrating the reliability and accuracy of CogM in automated Ki67 scoring.
Article
Computer Science, Artificial Intelligence
Adrien Foucart, Olivier Debeir, Christine Decaestecker
Summary: Biomedical image analysis competitions often use a single metric to rank participants, which makes it difficult to assess the strengths and weaknesses of algorithms. The MoNuSAC 2020 challenge provides an interesting opportunity to study the information lost by using entangled metrics by involving multiple capabilities and releasing prediction masks from different teams. The results analysis using Panoptic Quality (PQ) and disentangled metrics shows that PQ hides interesting aspects of the results and is sensitive to small changes in prediction masks, making it hard to interpret and draw insights from the results. Access to raw predictions from participating teams is necessary for better analysis and usefulness to the research community.
PATTERN RECOGNITION
(2023)
Review
Oncology
Andrea Duggento, Allegra Conti, Alessandro Mauriello, Maria Guerrisi, Nicola Toschi
Summary: Deep Learning algorithms utilize large and complex datasets for cross-domain prediction and classification, particularly excelling in computer vision tasks. In medical imaging, DL strategies can outperform human experts significantly in the analysis of histopathology images. The shift towards semi-supervised learning methods provides more flexibility and applicability in the development of specialized DL algorithms for pathology.
SEMINARS IN CANCER BIOLOGY
(2021)
Article
Oncology
Y. Wang, B. Acs, S. Robertson, B. Liu, L. Solorzano, C. Wahlby, J. Hartman, M. Rantalainen
Summary: The study developed a novel histological grade model (DeepGrade) based on digital whole-slide histopathology images and deep learning to improve risk stratification of NHG 2 breast cancer patients. DeepGrade provides independent prognostic information for stratification of NHG 2 cases and adds clinically relevant information over routine histological grading, serving as a cost-effective alternative to extract relevant information for clinical decisions.
ANNALS OF ONCOLOGY
(2022)
Article
Biochemical Research Methods
Kaimei Huang, Binghu Lin, Jinyang Liu, Yankun Liu, Jingwu Li, Geng Tian, Jialiang Yang
Summary: In this study, a multi-modal deep learning model based on histopathological images and clinical data is proposed to predict the TMB status of colorectal cancer patients. The model outperforms other methods in terms of prediction accuracy and also reveals significant associations between TMB values and tumor stage and N, M stages.
Article
Oncology
Xiaoyu Wang, Puya Gharahkhani, David M. Levine, Rebecca C. Fitzgerald, Ines Gockel, Douglas A. Corley, Harvey A. Risch, Leslie Bernstein, Wong-Ho Chow, Lynn Onstad, Nicholas J. Shaheen, Jesper Lagergren, Laura J. Hardie, Anna H. Wu, Paul D. P. Pharoah, Geoffrey Liu, Lesley A. Anderson, Prasad G. Iyer, Marilie D. Gammon, Carlos Caldas, Weimin Ye, Hugh Barr, Paul Moayyedi, Rebecca Harrison, R. G. Peter Watson, Stephen Attwood, Laura Chegwidden, Sharon B. Love, David MacDonald, John DeCaestecker, Hans Prenen, Katja Ott, Susanne Moebus, Marino Venerito, Hauke Lang, Rupert Mayershofer, Michael Knapp, Lothar Veits, Christian Gerges, Josef Weismueller, Matthias Reeh, Markus M. Noethen, Jakob R. Izbicki, Hendrik Manner, Horst Neuhaus, Thomas Roesch, Anne C. Boehmer, Arnulf H. Hoelscher, Mario Anders, Oliver Pech, Brigitte Schumacher, Claudia Schmidt, Thomas Schmidt, Tania Noder, Dietmar Lorenz, Michael Vieth, Andrea May, Timo Hess, Nicole Kreuser, Jessica Becker, Christian Ell, Ian Tomlinson, Claire Palles, Janusz A. Jankowski, David C. Whiteman, Stuart MacGregor, Johannes Schumacher, Thomas L. Vaughan, Matthew F. Buas, James Y. Dai
Summary: This study identified novel genetic susceptibility loci for esophageal adenocarcinoma and Barrett esophagus using an eQTL set-based genetic association approach, expanding the pool of genetic susceptibility loci.
CANCER EPIDEMIOLOGY BIOMARKERS & PREVENTION
(2022)
Article
Oncology
Ashley Weir, Eun-Young Kang, Nicola S. Meagher, Gregg S. Nelson, Prafull Ghatage, Cheng-Han Lee, Marjorie J. Riggan, Aleksandra Gentry-Maharaj, Andy Ryan, Naveena Singh, Martin Widschwendter, Jennifer Alsop, Michael S. Anglesio, Matthias W. Beckmann, Jessica Berger, Christiani Bisinotto, Jessica Boros, Alison H. Brand, James D. Brenton, Angela Brooks-Wilson, Michael E. Carney, Julie M. Cunningham, Kara L. Cushing-Haugen, Cezary Cybulski, Esther Elishaev, Ramona Erber, Sian Fereday, Anna Fischer, Luis Paz-Ares, Javier Gayarre, Blake C. Gilks, Marcel Grube, Paul R. Harnett, Holly R. Harris, Arndt Hartmann, Alexander Hein, Joy Hendley, Brenda Y. Hernandez, Sabine Heublein, Yajue Huang, Tomasz Huzarski, Anna Jakubowska, Mercedes Jimenez-Linan, Catherine J. Kennedy, Felix K. F. Kommoss, Jennifer M. Koziak, Bernhard Kraemer, Nhu D. Le, Jaime Lesnock, Jenny Lester, Jan Lubinski, Janusz Menkiszak, Britta Ney, Alexander Olawaiye, Sandra Orsulic, Ana Osorio, Luis Robles-Diaz, Matthias Ruebner, Mitul Shah, Raghwa Sharma, Yurii B. Shvetsov, Helen Steed, Aline Talhouk, Sarah E. Taylor, Nadia Traficante, Robert A. Vierkant, Chen Wang, Lynne R. Wilkens, Stacey J. Winham, Javier Benitez, Andrew Berchuck, David D. Bowtell, Francisco J. Candido dos Reis, Linda S. Cook, Anna DeFazio, Jennifer A. Doherty, Peter A. Fasching, Maria J. Garcia, Ellen L. Goode, Marc T. Goodman, Jacek Gronwald, David G. Huntsman, Beth Y. Karlan, Stefan Kommoss, Francesmary Modugno, Joellen M. Schildkraut, Hans-Peter Sinn, Annette Staebler, Linda E. Kelemen, Caroline E. Ford, Usha Menon, Paul D. P. Pharoah, Martin Koebel, Susan J. Ramus, D. Bowtell, A. DeFazio, N. Traficante, S. Fereday, A. Brand, P. Harnett, R. Sharma
Summary: Recently, a study found a positive correlation between protein expression of FOXJ1 and the 5-year survival rate of patients with tubo-ovarian high-grade serous carcinoma (HGSC). However, protein expression of GMNN was not significantly associated with patient survival. This study provides preliminary evidence for the prognostic value of FOXJ1 in HGSC and validates the prior mRNA-based prognostic association through immunohistochemistry.
BRITISH JOURNAL OF CANCER
(2023)
Article
Oncology
Eun-Young Kang, Ashley Weir, Nicola S. Meagher, Kyo Farrington, Gregg S. Nelson, Prafull Ghatage, Cheng-Han Lee, Marjorie J. Riggan, Adelyn Bolithon, Gordana Popovic, Betty Leung, Katrina Tang, Neil Lambie, Joshua Millstein, Jennifer Alsop, Michael S. Anglesio, Beyhan Ataseven, Ellen Barlow, Matthias W. Beckmann, Jessica Berger, Christiani Bisinotto, Hans Boesmueller, Jessica Boros, Alison H. Brand, Angela Brooks-Wilson, Sara Y. Brucker, Michael E. Carney, Yovanni Casablanca, Alicia Cazorla-Jimenez, Paul A. Cohen, Thomas P. Conrads, Linda S. Cook, Penny Coulson, Madeleine Courtney-Brooks, Daniel W. Cramer, Philip Crowe, Julie M. Cunningham, Cezary Cybulski, Kathleen M. Darcy, Mona A. El-Bahrawy, Esther Elishaev, Ramona Erber, Rhonda Farrell, Sian Fereday, Anna Fischer, Maria J. Garcia, Simon A. Gayther, Aleksandra Gentry-Maharaj, C. Blake Gilks, Marcel Grube, Paul R. Harnett, Shariska Petersen Harrington, Philipp Harter, Arndt Hartmann, Jonathan L. Hecht, Sebastian Heikaus, Alexander Hein, Florian Heitz, Joy Hendley, Brenda Y. Hernandez, Susanna Hernando Polo, Sabine Heublein, Akira Hirasawa, Estrid Hogdall, Claus K. Hogdall, Hugo M. Horlings, David G. Huntsman, Tomasz Huzarski, Andrea Jewell, Mercedes Jimenez-Linan, Michael E. Jones, Scott H. Kaufmann, Catherine J. Kennedy, Dineo Khabele, Felix K. F. Kommoss, Roy F. P. M. Kruitwagen, Diether Lambrechts, Nhu D. Le, Marcin Lener, Jenny Lester, Yee Leung, Anna Linder, Liselore Loverix, Jan Lubinski, Rashna Madan, G. Larry Maxwell, Francesmary Modugno, Susan L. Neuhausen, Alexander Olawaiye, Siel Olbrecht, Sandra Orsulic, Jose Palacios, Celeste Leigh Pearce, Malcolm C. Pike, Carmel M. Quinn, Ganendra Raj Mohan, Cristina Rodriguez-Antona, Matthias Ruebner, Andy Ryan, Stuart G. Salfinger, Naoko Sasamoto, Joellen M. Schildkraut, Minouk J. Schoemaker, Mitul Shah, Raghwa Sharma, Yurii B. Shvetsov, Naveena Singh, Gabe S. Sonke, Linda Steele, Colin J. R. Stewart, Karin Sundfeldt, Anthony J. Swerdlow, Aline Talhouk, Adeline Tan, Sarah E. Taylor, Kathryn L. Terry, Aleksandra Toloczko, Nadia Traficante, Koen K. Van de Vijver, Maaike A. van der Aa, Toon Van Gorp, Els Van Nieuwenhuysen, Lilian Van-Wagensveld, Ignace Vergote, Robert A. Vierkant, Chen Wang, Lynne R. Wilkens, Stacey J. Winham, Anna H. Wu, Javier Benitez, Andrew Berchuck, Francisco J. Candido Dos Reis, Anna DeFazio, Peter A. Fasching, Ellen L. Goode, Marc T. Goodman, Jacek Gronwald, Beth Y. Karlan, Stefan Kommoss, Usha Menon, Hans-Peter Sinn, Annette Staebler, James D. Brenton, David D. Bowtell, Paul D. P. Pharoah, Susan J. Ramus, Martin Kobel
Summary: This study validates that high-level amplification of CCNE1 is associated with shorter survival in tubo-ovarian high-grade serous carcinoma (HGSC), supporting its use as a prognostic biomarker in this disease.
Article
Oncology
Minh Tung Phung, Penelope M. Webb, Anna DeFazio, Sian Fereday, Alice W. Lee, David D. L. Bowtell, Peter A. Fasching, Ellen L. Goode, Marc T. Goodman, Beth Y. Karlan, Jenny Lester, Keitaro Matsuo, Francesmary Modugno, James D. Brenton, Toon Van Gorp, Paul D. P. Pharoah, Joellen M. Schildkraut, Karen McLean, Rafael Meza, Bhramar Mukherjee, Jean Richardson, Bronwyn Grout, Anne Chase, Cindy McKinnon Deurloo, Kathryn L. Terry, Gillian E. Hanley, Malcolm C. Pike, Andrew Berchuck, Susan J. Ramus, Celeste Leigh Pearce, Ovarian Canc Assoc Consortium
Summary: This study analyzed the association between 12 lifestyle and personal exposures and having residual disease after surgery in patients with high-grade serous ovarian cancer (HGSC). The use of menopausal estrogen-only therapy (ET) was associated with a lower likelihood of having macroscopic residual disease, while parous women who did not breastfeed also had a lower likelihood of residual disease. These factors could potentially be included in risk stratification models for HGSC patients.
GYNECOLOGIC ONCOLOGY
(2023)
Article
Oncology
Nicola S. Meagher, Phineas Hamilton, Katy Milne, Shelby Thornton, Bronwyn Harris, Ashley Weir, Jennifer Alsop, Christiani Bisinoto, James D. Brenton, Angela Brooks-Wilsoni, Derek S. Chiu, Kara L. Cushing-Haugen, Sian Fereday, Dale W. Garsed, Simon A. Gayther, Aleksandra Gentry-Maharaj, Blake Gilks, Mercedes Jimenez-Linan, Catherine J. Kennedy, Nhu D. Le, Anna M. Piskorz, Marjorie J. Riggan, Mitul Shah, Naveena Singh, Aline Talhoukj, Martin Widschwendter, David D. L. Bowtell, Francisco J. Candido dos Reis, Linda S. Cook, Renee T. Fortner, Maria J. Garcia, Holly R. Harris, David G. Huntsman, Anthony N. Karnezis, Martin Kobel, Usha Menon, Paul D. P. Pharoah, Jennifer A. Doherty, Michael S. Anglesioj, Malcolm C. Pike, Celeste Leigh Pearce, Michael L. Friedlander, Anna DeFazio, Brad H. Nelson, Susan J. Ramus
Summary: This study conducted immunohistochemistry and immunofluorescence on 126 patients with mucinous ovarian carcinoma (MOC) and found that MOCs are mainly characterized by high densities of immune suppressor cells in the tumor epithelium, suggesting limited response to current immunotherapies.
GYNECOLOGIC ONCOLOGY
(2023)
Review
Immunology
Funingana Ionut-Gabriel, Jacob S. Bedia, Ying-Wen Huang, Antonio Delgado Gonzalez, Kenyi Donoso, Veronica D. Gonzalez, James D. Brenton, Alan Ashworth, Wendy J. Fantl
Summary: High-grade serous ovarian cancer (HGSOC) is a challenging gynecological malignancy due to its advanced stage diagnosis, diverse carboplatin resistance mechanisms, and lack of response to immunotherapy. Multiplex single-cell proteomic technologies could provide insights into the functional interplay between cell autonomous responses to carboplatin and the HGSOC immune tumor microenvironment. This review suggests that the incorporation of multiplex single-cell proteomic technologies into biomarker development, along with genomics and radiomics, could improve clinical care for HGSOC patients.
SEMINARS IN IMMUNOPATHOLOGY
(2023)
Article
Oncology
Denise G. O'Mahony, Susan J. Ramus, Melissa C. Southey, Nicola S. Meagher, Andreas Hadjisavvas, Esther M. John, Ute Hamann, Evgeny N. Imyanitov, Irene L. Andrulis, Priyanka Sharma, Mary B. Daly, Christopher R. Hake, Jeffrey N. Weitzel, Anna Jakubowska, Andrew K. Godwin, Adalgeir Arason, Anita Bane, Jacques Simard, Penny Soucy, Maria A. Caligo, Phuong L. Mai, Kathleen B. M. Claes, Manuel R. Teixeira, Wendy K. Chung, Conxi Lazaro, Peter J. Hulick, Amanda E. Toland, Inge Sokilde Pedersen, Susan L. Neuhausen, Ana Vega, Miguel de la Hoya, Heli Nevanlinna, Mallika Dhawan, Valentina Zampiga, Rita Danesi, Liliana Varesco, Viviana Gismondi, Valerio Gaetano Vellone, Paul A. James, Ramunas Janavicius, Liene Nikitina-Zake, Finn Cilius Nielsen, Thomas van Overeem Hansen, Tanja Pejovic, Ake Borg, Johanna Rantala, Kenneth Offit, Marco Montagna, Katherine L. Nathanson, Susan M. Domchek, Ana Osorio, Maria J. Garcia, Beth Y. Karlan, Anna De Fazio, David Bowtell, Lesley McGuffog, Goska Leslie, Michael T. Parsons, Thilo Doerk, Lisa-Marie Speith, Elizabeth Santana dos Santos, Alexandre Andre B. A. da Costa, Paolo Radice, Paolo Peterlongo, Laura Papi, Christoph Engel, Eric Hahnen, Rita K. Schmutzler, Barbara Wappenschmidt, Douglas F. Easton, Marc Tischkowitz, Christian F. Singer, Yen Yen Tan, Alice S. Whittemore, Weiva Sieh, James D. Brenton, Drakoulis Yannoukakos, Florentia Fostira, Irene Konstantopoulou, Jana Soukupova, Michal Vocka, Georgia Chenevix-Trench, Paul D. P. Pharoah, Antonis C. Antoniou, David E. Goldgar, Amanda B. Spurdle, Kyriaki Michailidou, Marian J. E. Mourits, Fabienne Lesueur
Summary: This study assessed the utility of ovarian tumour characteristics as predictors of BRCA1 and BRCA2 variant pathogenicity and provided evidence for improved classification and clinical management of carriers.
BRITISH JOURNAL OF CANCER
(2023)
Article
Oncology
Robert D. Morgan, Andrew R. Clamp, Bethany M. Barnes, Kirsten Timms, Helene Schlecht, Laura Yarram-Smith, Yvonne Wallis, Mikel Valganon-Petrizan, Suzanne MacMahon, Rhian White, Sian Morgan, Sarah McKenna, Emma Hudson, Laura Tookman, Angela George, Ranjit Manchanda, Sudha S. Sundar, Shibani Nicum, James D. Brenton, Rebecca S. Kristeleit, Susana Banerjee, Iain A. McNeish, Jonathan A. Ledermann, Stephen S. Taylor, D. Gareth R. Evans, Gordon C. Jayson
Summary: This study reports data from the first year of routine homologous recombination deficiency testing in the NHS in England, Wales, and Northern Ireland, demonstrating the significant survival benefits of olaparib plus bevacizumab maintenance therapy in women with newly diagnosed, advanced, high-grade ovarian cancer.
INTERNATIONAL JOURNAL OF GYNECOLOGICAL CANCER
(2023)
Article
Biochemistry & Molecular Biology
David B. Morse, Aleksandra M. Michalowski, Michele Ceribelli, Joachim De Jonghe, Maria Vias, Deanna Riley, Theresa Davies-Hill, Ty Voss, Stefania Pittaluga, Christoph Muus, Jiamin Liu, Samantha Boyle, David A. Weitz, James D. Brenton, Jason D. Buenrostro, Tuomas P. J. Knowles, Craig J. Thomas
Summary: Single-cell RNA sequencing (scRNA-seq) is used to describe cell states, but the spatial arrangement of these states in tissues is challenging. Segmentation by exogenous perfusion (SEEP) is a method that links surface proximity and environment accessibility to transcriptional identity within 3D disease models. Using SEEP, analysis of ovarian cancer models reveals the relationship between cell state and position, and shows how microenvironments influence individual cell identities.
Article
Oncology
Maria Delgado-Ortet, Marika A. V. Reinius, Cathal McCague, Vlad Bura, Ramona Woitek, Leonardo Rundo, Andrew B. Gill, Marcel Gehrung, Stephan Ursprung, Helen Bolton, Krishnayan Haldar, Pubudu Pathiraja, James D. Brenton, Mireia Crispin-Ortuzar, Mercedes Jimenez-Linan, Lorena Escudero Sanchez, Evis Sala
Summary: In this study, a research pathway and automated computational pipeline were developed to create lesion-specific 3D-printed moulds based on preoperative imaging, allowing for detailed spatial correlation between imaging and tissue-derived data. This method can guide comprehensive multi-sampling of various pelvic tumors.
FRONTIERS IN ONCOLOGY
(2023)
Article
Biology
Thomas Buddenkotte, Lorena Escudero Sanchez, Mireia Crispin-Ortuzar, Ramona Woitek, Cathal McCague, James D. Brenton, Ozan Oktem, Evis Sala, Leonardo Rundo
Summary: Uncertainty quantification is highly desired in automated image analysis. Current approaches fail to scale well in high-dimensional real-world problems. We propose a scalable framework that approximates classification probability and suggest the usage of k-fold cross-validation to overcome the need for calibration data. Our method can be adopted in active learning and human-machine collaboration.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Article
Multidisciplinary Sciences
Philip Smith, Thomas Bradley, Lena Morrill Gavarro, Teodora Goranova, Darren P. Ennis, Hasan B. Mirza, Dilrini De Silva, Anna M. Piskorz, Carolyn Sauer, Sarwah Al-Khalidi, Ionat-Gabriel Funingana, Marika A. V. Reinius, Gaia Giannone, Liz-Anne Lewsley, Jamie Stobo, John McQueen, Gareth Bryson, Matthew Eldridge, R. M. Glasspool, C. Gourley, R. Kennedy, G. Hall, R. Edmondson, A. Clamp, S. Sundar, A. Walter, M. Hall, H. Gabra, C. Fotopoulou, E. Brockbank, A. Montes, M. Lockley, Geoff Macintyre, Florian Markowetz, James D. Brenton, Iain A. McNeish
Summary: Treatment resistance is common in ovarian high grade serous carcinoma, often leading to relapse. Here, the authors leverage shallow whole genome and panel sequencing of 276 patients with available diagnostic and relapse samples and show high concordance of copy number and mutation status.
NATURE COMMUNICATIONS
(2023)
Article
Oncology
Elin Bernson, Oisin Huhn, Veronika Karlsson, Delia Hawkes, Maria Lycke, Valentina Cazzetta, Joanna Mikulak, James Hall, Anna M. Piskorz, Rosalba Portuesi, Domenico Vitobello, Barbara Fiamengo, Gabriele Siesto, Amir Horowitz, Hormas Ghadially, Domenico Mavilio, James D. Brenton, Karin Sundfeldt, Francesco Colucci
Summary: Ovarian cancer, the deadliest among gynecological cancers, requires new treatment options. Immunotherapy shows great potential but has not yet been successful for most ovarian cancer patients. By studying lymphocytes in high-grade serous ovarian cancer patients, we found the presence of natural killer cells and T cells in primary tumors and ascites. These cells express tissue-resident markers and the inhibitory receptor, NKG2A, and can kill ovarian cancer cells. In summary, we report a functional subset of lymphocytes that may be targeted in future immunotherapeutic approaches.
Article
Multidisciplinary Sciences
Ha-Linh Nguyen, Tatjana Geukens, Marion Maetens, Samuel Aparicio, Ayse Bassez, Ake Borg, Jane Brock, Annegien Broeks, Carlos Caldas, Fatima Cardoso, Maxim De Schepper, Mauro Delorenzi, Caroline A. Drukker, Annuska M. Glas, Andrew R. Green, Edoardo Isnaldi, Jorunn Eyfjoro, Hazem Khout, Stian Knappskog, Savitri Krishnamurthy, Sunil R. Lakhani, Anita Langerod, John W. M. Martens, Amy E. McCart Reed, Leigh Murphy, Stefan Naulaerts, Serena Nik-Zainal, Ines Nevelsteen, Patrick Neven, Martine Piccart, Coralie Poncet, Kevin Punie, Colin Purdie, Emad A. Rakha, Andrea Richardson, Emiel Rutgers, Anne Vincent-Salomon, Peter T. Simpson, Marjanka K. Schmidt, Christos Sotiriou, Paul N. Span, Kiat Tee Benita Tan, Alastair Thompson, Stefania Tommasi, Karen Van Baelen, Marc Van de Vijver, Steven Van Laere, Laura van't Veer, Giuseppe Viale, Alain Viari, Hanne Vos, Anke T. Witteveen, Hans Wildiers, Giuseppe Floris, Abhishek D. Garg, Ann Smeets, Diether Lambrechts, Elia Biganzoli, Francois Richard, Christine Desmedt
Summary: Obesity is associated with an increased risk of developing breast cancer and worse prognosis in breast cancer patients. This study investigates the biological differences in untreated primary breast cancer according to patients' body mass index (BMI). The study finds several genomic alterations that are differentially prevalent in overweight or obese patients compared to lean patients. It also reveals an elevated and unresolved inflammation of the breast cancer tumor microenvironment associated with obesity. The findings suggest that patient adiposity may play a significant role in the heterogeneity of breast cancer and should be considered for tailored treatment.
NATURE COMMUNICATIONS
(2023)
Review
Oncology
Emily A. Kolyvas, Carlos Caldas, Kathleen Kelly, Saif S. Ahmad
Summary: Breast cancer is a common and deadly cancer, and the role of AR in this type of cancer is still not fully understood. However, targeting AR in breast cancer may have therapeutic potential and further research is needed for effective treatment.
BREAST CANCER RESEARCH
(2022)