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212


Detection of Gene Fusions and Rearrangements in Formalin-Fixed, Paraffin-Embedded Solid Tumor Specimens Using High-Throughput Chromosome Conformation Capture

Galbraith, Kristyn; Wu, Jamin; Sikkink, Kristin; Mohamed, Hussein; Reid, Derek; Perez-Arreola, Michelle; Belton, Jon-Matthew; Nomikou, Sofia; Melnyk, Shadi; Yang, Yiying; Liechty, Benjamin L; Jour, George; Tsirigos, Aristotelis; Hermel, David J; Beck, Alyssa; Sigal, Darren; Dahl, Nathan A; Vibhakar, Rajeev; Schmitt, Anthony; Snuderl, Matija
Chromosomal structural variants (SVs) are major contributors to cancer development. Although multiple methods exist for detecting SVs, they are limited in throughput, such as fluorescent in situ hybridization and targeted panels, and use RNA, which degrades in formalin-fixed, paraffin-embedded (FFPE) blocks and is unable to detect SVs that do not produce a fusion transcript. High-throughput chromosome conformation capture (Hi-C) is a DNA-based next-generation sequencing (NGS) method that preserves the spatial conformation of the genome, capturing long-range genetic interactions and SVs. Herein, a retrospective study analyzing 71 FFPE specimens from 10 different solid tumors was performed. Results showed high concordance (98%) with clinical fluorescent in situ hybridization and RNA NGS in detecting known SVs. Furthermore, Hi-C provided insight into the mechanism of SV formation, including chromothripsis and extrachromosomal DNA, and detected rearrangements between genes and regulatory regions, all of which are undetectable by RNA NGS. Lastly, SVs were detected in 71% of cases in which previous clinical methods failed to identify a driver. Of these, 14% were clinically actionable based on current medical guidelines, and an additional 14% were not in medical guidelines but involve targetable biomarkers. Current data suggest that Hi-C is a robust and accurate method for genome-wide SV analyses from FFPE tissue and can be incorporated into current clinical NGS workflows.
PMID: 40023492
ISSN: 1943-7811
CID: 5832862

Native stem cell transcriptional circuits define cardinal features of high-risk leukemia

Wang, Qing; Boccalatte, Francesco; Xu, Jason; Gambi, Giovanni; Nadorp, Bettina; Akter, Fatema; Mullin, Carea; Melnick, Ashley F; Choe, Elizabeth; McCarter, Anna C; Jerome, Nicole A; Chen, Siyi; Lin, Karena; Khan, Sarah; Kodgule, Rohan; Sussman, Jonathan H; Pölönen, Petri; Rodriguez-Hernaez, Javier; Narang, Sonali; Avrampou, Kleopatra; King, Bryan; Tsirigos, Aristotelis; Ryan, Russell J H; Mullighan, Charles G; Teachey, David T; Tan, Kai; Aifantis, Iannis; Chiang, Mark Y
While the mutational landscape across early T-cell precursor acute lymphoblastic leukemia (ETP-ALL) and ETP-like leukemia is known, establishing a unified framework that activates stem cell genes characteristic of these tumors remains elusive. Using complementary mouse and human models, chromatin mapping, and enhancer profiling, we show that the coactivator ZMIZ1 promotes normal and malignant ETP population growth by inducing the transcription factor MYB in feedforward circuits to convergently activate oncogenes (MEF2C, MYCN, and BCL2) through essential enhancers. A key superenhancer, the N-Myc regulating enhancer (NMRE), drives malignant ETP population growth but is dispensable for normal lymphopoiesis. This network of stem cell superenhancers identifies treatment-resistant tumors and poor survival outcomes; unifies diverse ETP-ALLs; and contributes to cardinal features of the recently genomically identified high-risk bone marrow progenitor-like (BMP-like) ETP-ALL tumor-stem cell/myeloid gene expression, inhibited NOTCH1-induced T-cell development, aggressive clinical behavior, and venetoclax sensitivity. Since ZMIZ1 is dispensable for essential homeostasis, it might be possible to safely target this network to treat high-risk diseases.
PMCID:11837855
PMID: 39969525
ISSN: 1540-9538
CID: 5843072

Binding domain mutations provide insight into CTCF's relationship with chromatin and its contribution to gene regulation

Do, Catherine; Jiang, Guimei; Cova, Giulia; Katsifis, Christos C; Narducci, Domenic N; Sakellaropoulos, Theodore; Vidal, Raphael; Lhoumaud, Priscillia; Tsirigos, Aristotelis; Regis, Faye Fara D; Kakabadze, Nata; Nora, Elphege P; Noyes, Marcus; Hansen, Anders S; Skok, Jane A
Here we used a series of CTCF mutations to explore CTCF's relationship with chromatin and its contribution to gene regulation. CTCF's impact depends on the genomic context of bound sites and the unique binding properties of WT and mutant CTCF proteins. Specifically, CTCF's signal strength is linked to changes in accessibility, and the ability to block cohesin is linked to its binding stability. Multivariate modeling reveals that both CTCF and accessibility contribute independently to cohesin binding and insulation, but CTCF signal strength has a stronger effect. CTCF and chromatin have a bidirectional relationship such that at CTCF sites, accessibility is reduced in a cohesin-dependent, mutant-specific fashion. In addition, each mutant alters TF binding and accessibility in an indirect manner, changes which impart the most influence on rewiring transcriptional networks and the cell's ability to differentiate. Collectively, the mutant perturbations provide a rich resource for determining CTCF's site-specific effects.
PMID: 40118069
ISSN: 2666-979x
CID: 5813802

Self-supervised learning reveals clinically relevant histomorphological patterns for therapeutic strategies in colon cancer

Liu, Bojing; Polack, Meaghan; Coudray, Nicolas; Claudio Quiros, Adalberto; Sakellaropoulos, Theodore; Le, Hortense; Karimkhan, Afreen; Crobach, Augustinus S L P; van Krieken, J Han J M; Yuan, Ke; Tollenaar, Rob A E M; Mesker, Wilma E; Tsirigos, Aristotelis
Self-supervised learning (SSL) automates the extraction and interpretation of histopathology features on unannotated hematoxylin-eosin-stained whole slide images (WSIs). We train an SSL Barlow Twins encoder on 435 colon adenocarcinoma WSIs from The Cancer Genome Atlas to extract features from small image patches (tiles). Leiden community detection groups tiles into histomorphological phenotype clusters (HPCs). HPC reproducibility and predictive ability for overall survival are confirmed in an independent clinical trial (N = 1213 WSIs). This unbiased atlas results in 47 HPCs displaying unique and shared clinically significant histomorphological traits, highlighting tissue type, quantity, and architecture, especially in the context of tumor stroma. Through in-depth analyses of these HPCs, including immune landscape and gene set enrichment analyses, and associations to clinical outcomes, we shine light on the factors influencing survival and responses to treatments of standard adjuvant chemotherapy and experimental therapies. Further exploration of HPCs may unveil additional insights and aid decision-making and personalized treatments for colon cancer patients.
PMID: 40057490
ISSN: 2041-1723
CID: 5808052

Characterization of tumour heterogeneity through segmentation-free representation learning on multiplexed imaging data

Tan, Jimin; Le, Hortense; Deng, Jiehui; Liu, Yingzhuo; Hao, Yuan; Hollenberg, Michelle; Liu, Wenke; Wang, Joshua M; Xia, Bo; Ramaswami, Sitharam; Mezzano, Valeria; Loomis, Cynthia; Murrell, Nina; Moreira, Andre L; Cho, Kyunghyun; Pass, Harvey I; Wong, Kwok-Kin; Ban, Yi; Neel, Benjamin G; Tsirigos, Aristotelis; Fenyö, David
High-dimensional multiplexed imaging can reveal the spatial organization of tumour tissues at the molecular level. However, owing to the scale and information complexity of the imaging data, it is challenging to discover and thoroughly characterize the heterogeneity of tumour microenvironments. Here we show that self-supervised representation learning on data from imaging mass cytometry can be leveraged to distinguish morphological differences in tumour microenvironments and to precisely characterize distinct microenvironment signatures. We used self-supervised masked image modelling to train a vision transformer that directly takes high-dimensional multiplexed mass-cytometry images. In contrast with traditional spatial analyses relying on cellular segmentation, the vision transformer is segmentation-free, uses pixel-level information, and retains information on the local morphology and biomarker distribution. By applying the vision transformer to a lung-tumour dataset, we identified and validated a monocytic signature that is associated with poor prognosis.
PMID: 39979589
ISSN: 2157-846x
CID: 5812702

A proinflammatory response and polarized differentiation of stromal elements characterizes the murine myeloma bone marrow niche

Ghamlouch, Hussein; Gagler, Dylan C; Blaney, Patrick; Boyle, Eileen M; Wang, Yubao; Avigan, Jason; Choi, Jinyoung; Landgren, Ola; Tsirigos, Aristotelis; Maura, Francesco; Morgan, Gareth J; Davies, Faith E
BACKGROUND:The bone marrow (BM) niche contains non-hematopoietic elements including mesenchymal stromal cells (MSC) and bone marrow endothelial cells (BMEC) which provide mechanical support, and control hematopoietic cell growth and differentiation. Although it is known that multiple myeloma (MM) cells interact closely with the BM microenvironment, little is known about the impact of MM on non-hematopoietic niche-forming cells. METHODS:To address the role of the niche in MM pathogenesis, we utilized the 5TGM1 murine model. During the asymptomatic precursor stage of the model, we isolated the rare non-hematopoietic cells and performed single cell RNA sequencing. Using in-silico methods we characterized the individual cellular components of the niche, their relative abundance and differentiation state before and after exposure to MM cells as well as their intercellular interactions. RESULTS:MM engraftment increased the abundance of MSC-lineage cells, BMECs and enhanced endothelial to mesenchymal transition. An inflammatory and oxidative stress signal was identified together with polarization of MSC differentiation away from osteocyte formation towards adipocytes which provide growth factors that are known to support MM expansion. BMEC differentiation was polarized towards sinusoidal endothelial cells with a pro-angiogenic/pro-inflammatory phenotype. CONCLUSIONS:MM cells impact the BM niche by generating a pro-inflammatory microenvironment with MSC differentiation being changed to generate cell subsets that favor MM growth and survival. In order to induce remission and improve long-term outcome for MM patients these inflammatory and oxidative stress signals need to be reduced and normal niche differentiation trajectories restored.
PMCID:11866767
PMID: 40011943
ISSN: 2162-3619
CID: 5801102

Binding domain mutations provide insight into CTCF's relationship with chromatin and its contribution to gene regulation

Do, Catherine; Jiang, Guimei; Cova, Giulia; Katsifis, Christos C; Narducci, Domenic N; Sakellaropoulos, Theodore; Vidal, Raphael; Lhoumaud, Priscillia; Tsirigos, Aristotelis; Regis, Faye Fara D; Kakabadze, Nata; Nora, Elphege P; Noyes, Marcus; Hansen, Anders S; Skok, Jane A
Here we used a series of CTCF mutations to explore CTCF's relationship with chromatin and its contribution to gene regulation. CTCF's impact depends on the genomic context of bound sites and the unique binding properties of WT and mutant CTCF proteins. Specifically, CTCF's signal strength is linked to changes in accessibility, and the ability to block cohesin is linked to its binding stability. Multivariate modelling reveals that both CTCF and accessibility contribute independently to cohesin binding and insulation, however CTCF signal strength has a stronger effect. CTCF and chromatin have a bidirectional relationship such that at CTCF sites, accessibility is reduced in a cohesin-dependent, mutant specific fashion. In addition, each mutant alters TF binding and accessibility in an indirect manner, changes which impart the most influence on rewiring transcriptional networks and the cell's ability to differentiate. Collectively, the mutant perturbations provide a rich resource for determining CTCF's site-specific effects.
PMID: 38370764
ISSN: 2692-8205
CID: 5840692

Self supervised artificial intelligence predicts poor outcome from primary cutaneous squamous cell carcinoma at diagnosis

Coudray, Nicolas; Juarez, Michelle C; Criscito, Maressa C; Quiros, Adalberto Claudio; Wilken, Reason; Jackson Cullison, Stephanie R; Stevenson, Mary L; Doudican, Nicole A; Yuan, Ke; Aquino, Jamie D; Klufas, Daniel M; North, Jeffrey P; Yu, Siegrid S; Murad, Fadi; Ruiz, Emily; Schmults, Chrysalyne D; Cardona Machado, Cristian D; Cañueto, Javier; Choudhary, Anirudh; Hughes, Alysia N; Stockard, Alyssa; Leibovit-Reiben, Zachary; Mangold, Aaron R; Tsirigos, Aristotelis; Carucci, John A
Primary cutaneous squamous cell carcinoma (cSCC) is responsible for ~10,000 deaths annually in the United States. Stratification of risk of poor outcome at initial biopsy would significantly impact clinical decision-making during the initial post operative period where intervention has been shown to be most effective. Using whole-slide images (WSI) from 163 patients from 3 institutions, we developed a self supervised deep-learning model to predict poor outcomes in cSCC patients from histopathological features at initial diagnosis, and validated it using WSI from 563 patients, collected from two other academic institutions. For disease-free survival prediction, the model attained a concordance index of 0.73 in the development cohort and 0.84 in the Mayo cohort. The model's interpretability revealed that features like poor differentiation and deep invasion were strongly associated with poor prognosis. Furthermore, the model is effective in stratifying risk among BWH T2a and AJCC T2, known for outcome heterogeneity.
PMID: 39955424
ISSN: 2398-6352
CID: 5794132

AI accurately identifies targetable alterations in lung cancer histological images

Le, Hortense; Tsirigos, Aristotelis
PMID: 39930263
ISSN: 1759-4782
CID: 5793232

The common murine retroviral integration site activating Hhex marks a distal regulatory enhancer co-opted in human Early T-cell precursor leukemia

Hardwick, Joyce; Rodriguez-Hernaez, Javier; Gambi, Giovanni; Venters, Bryan J; Guo, Yan; Li, Liqi; Love, Paul E; Copeland, Neal G; Jenkins, Nancy A; Papaioannou, Dimitrios; Aifantis, Iannis; Tsirigos, Aristotelis; Ivan, Mircea; Davé, Utpal P
The Hhex gene encodes a transcription factor that is important for both embryonic and post-natal development, especially of hematopoietic tissues. Hhex is one of the most common sites of retroviral integration in mouse models. We found the most common integrations in AKXD (recombinant inbred strains) T-ALLs occur 57-61kb 3' of Hhex and activate Hhex gene expression. The genomic region of murine leukemia virus (MLV) integrations has features of a developmental stage-specific cis regulatory element (CRE), as evidenced by ATAC-seq in murine progenitor cells and high H3K27 acetylation at the syntenic CRE in human hematopoietic cell lines. With ChIP-exonuclease, we describe occupancy of LIM domain binding protein 1 (LDB1), the constitutive partner of the LIM Only-2 (LMO2), GATA1, and TAL1 transcription factors at GATA sites and a composite GATA-E box within the CRE. With virtual 4C analysis, we observed looping between this +65kb CRE and the proximal intron 1 enhancer of HHEX in primary human ETP-ALLs and in normal progenitor cells. Our results show that retroviral integrations at intergenic sites can mark and take advantage of CREs. Specifically, in the case of HHEX activation, this newly described +65kb CRE is co-opted in the pathogenesis of ETP-ALL by the LMO2/LDB1 complex.
PMID: 39880094
ISSN: 1083-351x
CID: 5780992