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Article ; Online: The Role of the Pathologist in Renal Cell Carcinoma Management.

Matar, Sayed / El Ahmar, Nourhan / Laimon, Yasmin Nabil / Ghandour, Fatme / Signoretti, Sabina

Hematology/oncology clinics of North America

2023 Volume 37, Issue 5, Page(s) 849–862

Abstract: Recent advances in our understanding of the molecular alterations underlying different types of renal cell carcinoma (RCC), as well as the implementation of immune checkpoint inhibitors in the treatment of patients with advanced disease, have ... ...

Abstract	Recent advances in our understanding of the molecular alterations underlying different types of renal cell carcinoma (RCC), as well as the implementation of immune checkpoint inhibitors in the treatment of patients with advanced disease, have significantly expanded the role of pathologists in the management of RCC patients and in the identification of predictive biomarkers that can guide patient treatment. In this chapter, we examine pathologists' evolving role in patient care and the development of precision medicine strategies for RCC.
MeSH term(s)	Humans ; Carcinoma, Renal Cell/drug therapy ; Carcinoma, Renal Cell/genetics ; Pathologists ; Biomarkers ; Kidney Neoplasms/drug therapy ; Kidney Neoplasms/genetics ; B7-H1 Antigen ; Biomarkers, Tumor/genetics
Chemical Substances	Biomarkers ; B7-H1 Antigen ; Biomarkers, Tumor
Language	English
Publishing date	2023-05-29
Publishing country	United States
Document type	Journal Article ; Review ; Research Support, N.I.H., Extramural ; Research Support, U.S. Gov't, Non-P.H.S.
ZDB-ID	93115-9
ISSN	1558-1977 ; 0889-8588
ISSN (online)	1558-1977
ISSN	0889-8588
DOI	10.1016/j.hoc.2023.04.014
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Article ; Online: Mitochondrial transporter expression patterns distinguish tumor from normal tissue and identify cancer subtypes with different survival and metabolism.

Wohlrab, Hartmut / Signoretti, Sabina / Rameh, Lucia E / DeConti, Derrick K / Hansen, Steen H

Scientific reports

2022 Volume 12, Issue 1, Page(s) 17035

Abstract: Transporters of the inner mitochondrial membrane are essential to metabolism. We demonstrate that metabolism as represented by expression of genes encoding SLC25 transporters differentiates human cancers. Tumor to normal tissue expression ratios for ... ...

Abstract	Transporters of the inner mitochondrial membrane are essential to metabolism. We demonstrate that metabolism as represented by expression of genes encoding SLC25 transporters differentiates human cancers. Tumor to normal tissue expression ratios for clear cell renal cell carcinoma, colon adenocarcinoma, lung adenocarcinoma and breast invasive carcinoma were found to be highly significant. Affinity propagation trained on SLC25 gene expression patterns from 19 human cancer types (6825 TCGA samples) and normal tissues (2322 GTEx samples) was used to generate clusters. They differentiate cancers from normal tissues. They also indicate cancer subtypes with survivals distinct from the total patient population of the cancer type. Probing the kidney, colon, lung, and breast cancer clusters, subtype pairs of cancers were identified with distinct prognoses and differing in expression of protein coding genes from among 2080 metabolic enzymes assayed. We demonstrate that SLC25 expression clusters facilitate the identification of the tissue-of-origin, essential to efficacy of most cancer therapies, of CUPs (cancer-unknown-primary) known to have poor prognoses. Different cancer types within a single cluster have similar metabolic patterns and this raises the possibility that such cancers may respond similarly to existing and new anti-cancer therapies.
MeSH term(s)	Adenocarcinoma/genetics ; Breast Neoplasms/genetics ; Carcinoma, Renal Cell/pathology ; Colonic Neoplasms/genetics ; Female ; Gene Expression Regulation, Neoplastic ; Humans ; Kidney Neoplasms/pathology ; Prognosis
Language	English
Publishing date	2022-10-11
Publishing country	England
Document type	Journal Article
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-022-21411-0
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Article ; Online: Sensitivity of

Stransky, Laura A / Vigeant, Sean M / Huang, Bofu / West, Destiny / Denize, Thomas / Walton, Emily / Signoretti, Sabina / Kaelin, William G

Proceedings of the National Academy of Sciences of the United States of America

2022 Volume 119, Issue 14, Page(s) e2120403119

Abstract: Inactivation of the VHL tumor suppressor gene is the signature initiating event in clear cell renal cell carcinoma (ccRCC), which is the most common form of kidney cancer. The VHL tumor suppressor protein marks hypoxia-inducible factor 1 (HIF1) and HIF2 ... ...

Abstract	Inactivation of the VHL tumor suppressor gene is the signature initiating event in clear cell renal cell carcinoma (ccRCC), which is the most common form of kidney cancer. The VHL tumor suppressor protein marks hypoxia-inducible factor 1 (HIF1) and HIF2 for proteasomal degradation when oxygen is present. The inappropriate accumulation of HIF2 drives tumor formation by VHL tumor suppressor protein (pVHL)–defective ccRCC. Belzutifan, a first-in-class allosteric HIF2 inhibitor, has advanced to phase 3 testing for advanced ccRCC and is approved for ccRCCs arising in patients with VHL disease, which is caused by germline VHL mutations. HIF2 can suppress p53 function in some settings and preliminary data suggested that an intact p53 pathway, as measured by activation in response to DNA damage, was necessary for HIF2 dependence. Here, we correlated HIF2 dependence and p53 status across a broader collection of ccRCC cell lines. We also genetically manipulated p53 function in ccRCC lines that were or were not previously HIF2-dependent and then assessed their subsequent sensitivity to HIF2 ablation using CRISPR-Cas9 or the HIF2 inhibitor PT2399, which is closely related to belzutifan. From these studies, we conclude that p53 status does not dictate HIF2 dependence, at least in preclinical models, and thus is unlikely to be a useful biomarker for predicting which ccRCC patients will respond to HIF2 inhibitors.
MeSH term(s)	Basic Helix-Loop-Helix Transcription Factors/antagonists & inhibitors ; Basic Helix-Loop-Helix Transcription Factors/genetics ; Basic Helix-Loop-Helix Transcription Factors/metabolism ; Carcinoma, Renal Cell/drug therapy ; Carcinoma, Renal Cell/genetics ; Carcinoma, Renal Cell/pathology ; Cell Line, Tumor ; Female ; Gene Expression Regulation, Neoplastic ; Humans ; Indans/pharmacology ; Indans/therapeutic use ; Kidney Neoplasms/drug therapy ; Kidney Neoplasms/genetics ; Kidney Neoplasms/pathology ; Male ; Sulfones/pharmacology ; Sulfones/therapeutic use ; Tumor Suppressor Protein p53/genetics ; Tumor Suppressor Protein p53/metabolism ; Von Hippel-Lindau Tumor Suppressor Protein/genetics ; Von Hippel-Lindau Tumor Suppressor Protein/metabolism
Chemical Substances	Basic Helix-Loop-Helix Transcription Factors ; Indans ; PT2399 ; Sulfones ; Tumor Suppressor Protein p53 ; endothelial PAS domain-containing protein 1 (1B37H0967P) ; Von Hippel-Lindau Tumor Suppressor Protein (EC 2.3.2.27) ; VHL protein, human (EC 6.3.2.-)
Language	English
Publishing date	2022-03-31
Publishing country	United States
Document type	Journal Article
ZDB-ID	209104-5
ISSN	1091-6490 ; 0027-8424
ISSN (online)	1091-6490
ISSN	0027-8424
DOI	10.1073/pnas.2120403119
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Article ; Online: Regulation of HHLA2 expression in kidney cancer and myeloid cells.

Shigemura, Tomonari / Perrot, Nahuel / Huang, Zimo / Bhatt, Rupal S / Sheshdeh, Aseman Bagheri / Ahmar, Nourhan El / Ghandour, Fatme / Signoretti, Sabina / McDermott, David F / Freeman, Gordon J / Mahoney, Kathleen M

BMC cancer

2023 Volume 23, Issue 1, Page(s) 1039

Abstract: Background: The immune checkpoint HERV-H LTR-associating 2 (HHLA2) is expressed in kidney cancer and various other tumor types. Therapeutics targeting HHLA2 or its inhibitory receptor KIR3DL3 are being developed for solid tumors, including renal cell ... ...

Abstract	Background: The immune checkpoint HERV-H LTR-associating 2 (HHLA2) is expressed in kidney cancer and various other tumor types. Therapeutics targeting HHLA2 or its inhibitory receptor KIR3DL3 are being developed for solid tumors, including renal cell carcinoma (RCC). However, the regulation of HHLA2 expression remains poorly understood. A better understanding of HHLA2 regulation in tumor cells and the tumor microenvironment is crucial for the successful translation of these therapeutic agents into clinical applications. Methods: Flow cytometry and quantitative real-time PCR were used to analyze HHLA2 expression in primary kidney tumors ex vivo and during in vitro culture. HHLA2 expression in A498 and 786-O ccRCC cell lines was examined in vitro and in subcutaneous tumor xenografts in NSG mice. Monocytes and dendritic cells were analyzed for HHLA2 expression. We tested a range of cytokines and culture conditions, including hypoxia, to induce HHLA2 expression. Results: Analysis of HHLA2 expression revealed that HHLA2 is expressed on tumor cells in primary kidney tumors ex vivo; however, its expression gradually diminishes during a 4-week in vitro culture period. A498 and 786-O ccRCC tumor cell lines do not express HHLA2 in vitro, but HHLA2 expression was observed when grown as subcutaneous xenografts in NSG immunodeficient mice. Induction experiments using various cytokines and culture conditions failed to induce HHLA2 expression in A498 and 786-O tumor cell lines in vitro. Analysis of HHLA2 expression in monocytes and dendritic cells demonstrated that only IL-10 and BMP4, along with IL-1β and IL-6 to a lesser extent, modestly enhanced HHLA2 protein and mRNA expression. Conclusions: HHLA2 expression is induced on kidney cancer cells in vivo by a tumor microenvironmental signal that is not present in vitro. HHLA2 expression is differentially regulated in kidney cancer epithelial cells and monocytes. Cytokines, particularly IL10, that induce HHLA2 expression in monocytes fail to upregulate HHLA2 expression in tumor cell lines in vitro. These findings underscore the importance of the interplay between tumor cell and tumor microenvironmental signals in the regulation of HHLA2. Further investigation is warranted to elucidate the mechanisms involved in HHLA2 regulation and its implications for therapeutic development.
MeSH term(s)	Humans ; Animals ; Mice ; Carcinoma, Renal Cell/genetics ; Endogenous Retroviruses/metabolism ; Kidney Neoplasms/genetics ; Cytokines/metabolism ; Myeloid Cells/metabolism ; Immunoglobulins/genetics ; Tumor Microenvironment
Chemical Substances	Cytokines ; Immunoglobulins ; HHLA2 protein, human
Language	English
Publishing date	2023-10-27
Publishing country	England
Document type	Journal Article
ZDB-ID	2041352-X
ISSN	1471-2407 ; 1471-2407
ISSN (online)	1471-2407
ISSN	1471-2407
DOI	10.1186/s12885-023-11496-9
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Article ; Online: Renal Cell Carcinoma in the Era of Precision Medicine: From Molecular Pathology to Tissue-Based Biomarkers.

Signoretti, Sabina / Flaifel, Abdallah / Chen, Ying-Bei / Reuter, Victor E

Journal of clinical oncology : official journal of the American Society of Clinical Oncology

2018 , Page(s) JCO2018792259

Abstract: Renal cell carcinoma (RCC) is not a single entity but includes various tumor subtypes that have been identified on the basis of either characteristic pathologic features or distinctive molecular changes. Clear cell RCC is the most common type of RCC and ... ...

Abstract	Renal cell carcinoma (RCC) is not a single entity but includes various tumor subtypes that have been identified on the basis of either characteristic pathologic features or distinctive molecular changes. Clear cell RCC is the most common type of RCC and is characterized by dysregulation of the von Hippel Lindau/hypoxia-inducible factor pathway. Non-clear cell RCC represents a more heterogeneous group of tumors with diverse histopathologic and molecular features. In the past two decades, the improved understanding of the molecular landscape of RCC has led to the development of more effective therapies for metastatic RCC, which include both targeted agents and immune checkpoint inhibitors. Because only subsets of patients with metastatic RCC respond to a given treatment, predictive biomarkers are needed to guide treatment selection and sequence. In this review, we describe the key histologic features and molecular alterations of RCC subtypes and discuss emerging tissue-based biomarkers of response to currently available therapies for metastatic disease.
Language	English
Publishing date	2018-10-29
Publishing country	United States
Document type	Journal Article ; Review
ZDB-ID	604914-x
ISSN	1527-7755 ; 0732-183X
ISSN (online)	1527-7755
ISSN	0732-183X
DOI	10.1200/JCO.2018.79.2259
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Zs.A 1847: Show issues			Location: Je nach Verfügbarkeit (siehe Angabe bei Bestand) bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular Jg. 1995 - 2021: Lesesall (1.OG) ab Jg. 2022: Lesesaal (EG)
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Article ; Online: Development of a Histopathology Informatics Pipeline for Classification and Prediction of Clinical Outcomes in Subtypes of Renal Cell Carcinoma.

Marostica, Eliana / Barber, Rebecca / Denize, Thomas / Kohane, Isaac S / Signoretti, Sabina / Golden, Jeffrey A / Yu, Kun-Hsing

Clinical cancer research : an official journal of the American Association for Cancer Research

2021 Volume 27, Issue 10, Page(s) 2868–2878

Abstract: Purpose: Histopathology evaluation is the gold standard for diagnosing clear cell (ccRCC), papillary, and chromophobe renal cell carcinoma (RCC). However, interrater variability has been reported, and the whole-slide histopathology images likely contain ...

Abstract	Purpose: Histopathology evaluation is the gold standard for diagnosing clear cell (ccRCC), papillary, and chromophobe renal cell carcinoma (RCC). However, interrater variability has been reported, and the whole-slide histopathology images likely contain underutilized biological signals predictive of genomic profiles. Experimental design: To address this knowledge gap, we obtained whole-slide histopathology images and demographic, genomic, and clinical data from The Cancer Genome Atlas, the Clinical Proteomic Tumor Analysis Consortium, and Brigham and Women's Hospital (Boston, MA) to develop computational methods for integrating data analyses. Leveraging these large and diverse datasets, we developed fully automated convolutional neural networks to diagnose renal cancers and connect quantitative pathology patterns with patients' genomic profiles and prognoses. Results: Our deep convolutional neural networks successfully detected malignancy (AUC in the independent validation cohort: 0.964-0.985), diagnosed RCC histologic subtypes (independent validation AUCs of the best models: 0.953-0.993), and predicted stage I ccRCC patients' survival outcomes (log-rank test Conclusions: Our results suggest that convolutional neural networks can extract histologic signals predictive of patients' diagnoses, prognoses, and genomic variations of clinical importance. Our approaches can systematically identify previously unknown relations among diverse data modalities.
MeSH term(s)	Aged ; Biomarkers, Tumor ; Carcinoma, Renal Cell/diagnosis ; Carcinoma, Renal Cell/etiology ; Carcinoma, Renal Cell/mortality ; Computational Biology/methods ; Female ; Humans ; Image Processing, Computer-Assisted ; Immunohistochemistry ; Kidney Neoplasms/diagnosis ; Kidney Neoplasms/etiology ; Kidney Neoplasms/mortality ; Machine Learning ; Male ; Middle Aged ; Mutation ; Neoplasm Grading/methods ; Neoplasm Staging/methods ; Neural Networks, Computer ; Prognosis ; ROC Curve
Chemical Substances	Biomarkers, Tumor
Language	English
Publishing date	2021-03-15
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	1225457-5
ISSN	1557-3265 ; 1078-0432
ISSN (online)	1557-3265
ISSN	1078-0432
DOI	10.1158/1078-0432.CCR-20-4119
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Article ; Online: Immune-restoring CAR-T cells display antitumor activity and reverse immunosuppressive TME in a humanized ccRCC mouse model.

Wang, Yufei / Cho, Jae-Won / Kastrunes, Gabriella / Buck, Alicia / Razimbaud, Cecile / Culhane, Aedin C / Sun, Jiusong / Braun, David A / Choueiri, Toni K / Wu, Catherine J / Jones, Kristen / Nguyen, Quang-De / Zhu, Zhu / Wei, Kevin / Zhu, Quan / Signoretti, Sabina / Freeman, Gordon J / Hemberg, Martin / Marasco, Wayne A

iScience

2024 Volume 27, Issue 2, Page(s) 108879

Abstract: One of the major barriers that have restricted successful use of chimeric antigen receptor (CAR) T cells in the treatment of solid tumors is an unfavorable tumor microenvironment (TME). We engineered CAR-T cells targeting carbonic anhydrase IX (CAIX) to ... ...

Abstract	One of the major barriers that have restricted successful use of chimeric antigen receptor (CAR) T cells in the treatment of solid tumors is an unfavorable tumor microenvironment (TME). We engineered CAR-T cells targeting carbonic anhydrase IX (CAIX) to secrete anti-PD-L1 monoclonal antibody (mAb), termed immune-restoring (IR) CAR G36-PDL1. We tested CAR-T cells in a humanized clear cell renal cell carcinoma (ccRCC) orthotopic mouse model with reconstituted human leukocyte antigen (HLA) partially matched human leukocytes derived from fetal CD34
Language	English
Publishing date	2024-01-15
Publishing country	United States
Document type	Journal Article
ISSN	2589-0042
ISSN (online)	2589-0042
DOI	10.1016/j.isci.2024.108879
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Article ; Online: Combinatorial biomarker for predicting outcomes to anti-PD-1 therapy in patients with metastatic clear cell renal cell carcinoma.

Deutsch, Julie Stein / Lipson, Evan J / Danilova, Ludmila / Topalian, Suzanne L / Jedrych, Jaroslaw / Baraban, Ezra / Ged, Yasser / Singla, Nirmish / Choueiri, Toni K / Gupta, Saurabh / Motzer, Robert J / McDermott, David / Signoretti, Sabina / Atkins, Michael / Taube, Janis M

Cell reports. Medicine

2023 Volume 4, Issue 2, Page(s) 100947

Abstract: With a rapidly developing immunotherapeutic landscape for patients with metastatic clear cell renal cell carcinoma, biomarkers of efficacy are highly desirable to guide treatment strategy. Hematoxylin and eosin (H&E)-stained slides are inexpensive and ... ...

Abstract	With a rapidly developing immunotherapeutic landscape for patients with metastatic clear cell renal cell carcinoma, biomarkers of efficacy are highly desirable to guide treatment strategy. Hematoxylin and eosin (H&E)-stained slides are inexpensive and widely available in pathology laboratories, including in resource-poor settings. Here, H&E scoring of tumor-infiltrating immune cells (TIL
MeSH term(s)	Humans ; Carcinoma, Renal Cell ; Prognosis ; Biomarkers, Tumor
Chemical Substances	Biomarkers, Tumor
Language	English
Publishing date	2023-02-23
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, N.I.H., Extramural
ISSN	2666-3791
ISSN (online)	2666-3791
DOI	10.1016/j.xcrm.2023.100947
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Article: Spatially aware deep learning reveals tumor heterogeneity patterns that encode distinct kidney cancer states.

Nyman, Jackson / Denize, Thomas / Bakouny, Ziad / Labaki, Chris / Titchen, Breanna M / Bi, Kevin / Hari, Surya Narayanan / Rosenthal, Jacob / Mehta, Nicita / Jiang, Bowen / Sharma, Bijaya / Felt, Kristen / Umeton, Renato / Braun, David A / Rodig, Scott / Choueiri, Toni K / Signoretti, Sabina / Van Allen, Eliezer M

bioRxiv : the preprint server for biology

2023

Abstract: Clear cell renal cell carcinoma (ccRCC) is molecularly heterogeneous, immune infiltrated, and selectively sensitive to immune checkpoint inhibition (ICI). Established histopathology paradigms like nuclear grade have baseline prognostic relevance for ... ...

Abstract	Clear cell renal cell carcinoma (ccRCC) is molecularly heterogeneous, immune infiltrated, and selectively sensitive to immune checkpoint inhibition (ICI). Established histopathology paradigms like nuclear grade have baseline prognostic relevance for ccRCC, although whether existing or novel histologic features encode additional heterogeneous biological and clinical states in ccRCC is uncertain. Here, we developed spatially aware deep learning models of tumor- and immune-related features to learn representations of ccRCC tumors using diagnostic whole-slide images (WSI) in untreated and treated contexts (n = 1102 patients). We discovered patterns of nuclear grade heterogeneity in WSI not achievable through human pathologist analysis, and these graph-based "microheterogeneity" structures associated with PBRM1 loss of function, adverse clinical factors, and selective patient response to ICI. Joint computer vision analysis of tumor phenotypes with inferred tumor infiltrating lymphocyte density identified a further subpopulation of highly infiltrated, microheterogeneous tumors responsive to ICI. In paired multiplex immunofluorescence images of ccRCC, microheterogeneity associated with greater PD1 activation in CD8+ lymphocytes and increased tumor-immune interactions. Thus, our work reveals novel spatially interacting tumor-immune structures underlying ccRCC biology that can also inform selective response to ICI.
Language	English
Publishing date	2023-02-20
Publishing country	United States
Document type	Preprint
DOI	10.1101/2023.01.18.524545
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Article ; Online: Spatially aware deep learning reveals tumor heterogeneity patterns that encode distinct kidney cancer states.

Cell reports. Medicine

2023 Volume 4, Issue 9, Page(s) 101189

Abstract: Clear cell renal cell carcinoma (ccRCC) is molecularly heterogeneous, immune infiltrated, and selectively sensitive to immune checkpoint inhibition (ICI). However, the joint tumor-immune states that mediate ICI response remain elusive. We develop ... ...

Abstract	Clear cell renal cell carcinoma (ccRCC) is molecularly heterogeneous, immune infiltrated, and selectively sensitive to immune checkpoint inhibition (ICI). However, the joint tumor-immune states that mediate ICI response remain elusive. We develop spatially aware deep-learning models of tumor and immune features to learn representations of ccRCC tumors using diagnostic whole-slide images (WSIs) in untreated and treated contexts (n = 1,102 patients). We identify patterns of grade heterogeneity in WSIs not achievable through human pathologist analysis, and these graph-based "microheterogeneity" structures associate with PBRM1 loss of function and with patient outcomes. Joint analysis of tumor phenotypes and immune infiltration identifies a subpopulation of highly infiltrated, microheterogeneous tumors responsive to ICI. In paired multiplex immunofluorescence images of ccRCC, microheterogeneity associates with greater PD1 activation in CD8
MeSH term(s)	Humans ; Carcinoma, Renal Cell/genetics ; Deep Learning ; Kidney Neoplasms ; Carcinoma ; Phenotype
Language	English
Publishing date	2023-09-18
Publishing country	United States
Document type	Journal Article ; Research Support, U.S. Gov't, Non-P.H.S. ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ISSN	2666-3791
ISSN (online)	2666-3791
DOI	10.1016/j.xcrm.2023.101189
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