Browsing by Subject "Tumour biomarkers"

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    Report on computational assessment of Tumor Infiltrating Lymphocytes from the International Immuno-Oncology Biomarker Working Group
    (Nature Research, 2020-05-12) Amgad, Mohamed; Stovgaard, Elisabeth Specht; Balslev, Eva; Thagaard, Jeppe; Chen, Weijie; Dudgeon, Sarah; Sharma, Ashish; Kerner, Jennifer K.; Denkert, Carsten; Yuan, Yinyin; AbdulJabbar, Khalid; Wienert, Stephan; Savas, Peter; Voorwerk, Leonie; Beck, Andrew H.; Madabhushi, Anant; Hartman, Johan; Sebastian, Manu M.; Horlings, Hugo M.; Hudeček, Jan; Ciompi, Francesco; Moore, David A.; Singh, Rajendra; Roblin, Elvire; Balancin, Marcelo Luiz; Mathieu, Marie-Christine; Lennerz, Jochen K.; Kirtani, Pawan; Chen, I-Chun; Braybrooke, Jeremy P.; Pruneri, Giancarlo; Demaria, Sandra; Adams, Sylvia; Schnitt, Stuart J.; Lakhani, Sunil R.; Rojo, Federico; Comerma, Laura; Badve, Sunil S.; Khojasteh, Mehrnoush; Symmans, W. Fraser; Sotiriou, Christos; Gonzalez-Ericsson, Paula; Pogue-Geile, Katherine L.; Kim, Rim S.; Rimm, David L.; Viale, Giuseppe; Hewitt, Stephen M.; Bartlett, John M. S.; Penault-Llorca, Frédérique; Goel, Shom; Lien, Huang-Chun; Loibl, Sibylle; Kos, Zuzana; Loi, Sherene; Hanna, Matthew G.; Michiels, Stefan; Kok, Marleen; Nielsen, Torsten O.; Lazar, Alexander J.; Bago-Horvath, Zsuzsanna; Kooreman, Loes F. S.; Van der Laak, Jeroen A.W. M.; Saltz, Joel; Gallas, Brandon D.; Kurkure, Uday; Barnes, Michael; Salgado, Roberto; Cooper, Lee A. D.; International Immuno-Oncology Biomarker Working Group; Pathology and Laboratory Medicine, School of Medicine
    Assessment of tumor-infiltrating lymphocytes (TILs) is increasingly recognized as an integral part of the prognostic workflow in triple-negative (TNBC) and HER2-positive breast cancer, as well as many other solid tumors. This recognition has come about thanks to standardized visual reporting guidelines, which helped to reduce inter-reader variability. Now, there are ripe opportunities to employ computational methods that extract spatio-morphologic predictive features, enabling computer-aided diagnostics. We detail the benefits of computational TILs assessment, the readiness of TILs scoring for computational assessment, and outline considerations for overcoming key barriers to clinical translation in this arena. Specifically, we discuss: 1. ensuring computational workflows closely capture visual guidelines and standards; 2. challenges and thoughts standards for assessment of algorithms including training, preanalytical, analytical, and clinical validation; 3. perspectives on how to realize the potential of machine learning models and to overcome the perceptual and practical limits of visual scoring.
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    Single-cell sequencing reveals the landscape of the human brain metastatic microenvironment
    (Springer Nature, 2023-07-21) Song, Qianqian; Ruiz, Jimmy; Xing, Fei; Lo, Hui-Wen; Craddock, Lou; Pullikuth, Ashok K.; Miller, Lance D.; Soike, Michael H.; O’Neill, Stacey S.; Watabe, Kounosuke; Chan, Michael D.; Su, Jing; Biostatistics and Health Data Science, School of Medicine
    Brain metastases is the most common intracranial tumor and account for approximately 20% of all systematic cancer cases. It is a leading cause of death in advanced-stage cancer, resulting in a five-year overall survival rate below 10%. Therefore, there is a critical need to identify effective biomarkers that can support frequent surveillance and promote efficient drug guidance in brain metastasis. Recently, the remarkable breakthroughs in single-cell RNA-sequencing (scRNA-seq) technology have advanced our insights into the tumor microenvironment (TME) at single-cell resolution, which offers the potential to unravel the metastasis-related cellular crosstalk and provides the potential for improving therapeutic effects mediated by multifaceted cellular interactions within TME. In this study, we have applied scRNA-seq and profiled 10,896 cells collected from five brain tumor tissue samples originating from breast and lung cancers. Our analysis reveals the presence of various intratumoral components, including tumor cells, fibroblasts, myeloid cells, stromal cells expressing neural stem cell markers, as well as minor populations of oligodendrocytes and T cells. Interestingly, distinct cellular compositions are observed across different samples, indicating the influence of diverse cellular interactions on the infiltration patterns within the TME. Importantly, we identify tumor-associated fibroblasts in both our in-house dataset and external scRNA-seq datasets. These fibroblasts exhibit high expression of type I collagen genes, dominate cell-cell interactions within the TME via the type I collagen signaling axis, and facilitate the remodeling of the TME to a collagen-I-rich extracellular matrix similar to the original TME at primary sites. Additionally, we observe M1 activation in native microglial cells and infiltrated macrophages, which may contribute to a proinflammatory TME and the upregulation of collagen type I expression in fibroblasts. Furthermore, tumor cell-specific receptors exhibit a significant association with patient survival in both brain metastasis and native glioblastoma cases. Taken together, our comprehensive analyses identify type I collagen-secreting tumor-associated fibroblasts as key mediators in metastatic brain tumors and uncover tumor receptors that are potentially associated with patient survival. These discoveries provide potential biomarkers for effective therapeutic targets and intervention strategies.