Biology subjects

Rodig, S.

Publications and source records attributed to Rodig, S..

9 recordsLinked to original sources

bioRxiv2025

Genome-scale spatial mapping of the Hodgkin lymphoma microenvironment identifies tumor cell survival factors

A key challenge in cancer research is to identify the secreted factors that contribute to tumor cell survival. Nowhere is this more evident than in Hodgkin lymphoma, where malignant Hodgkin Reed Sternberg (HRS) cells comprise only 1-5% of the tumor mass, the remainder being infiltrating immune cells that presumably are required for the survival of the HRS cells. Until now, there has been no way to characterize the complex Hodgkin lymphoma tumor microenvironment at genome scale. Here, we performed genome-wide transcriptional profiling with spatial and single-cell resolution. We show that the neighborhood surrounding HRS cells forms a distinct niche involving 31 immune and stromal cell types and is enriched in CD4+ T cells, myeloid and follicular dendritic cells, while being depleted of plasma cells. Moreover, we used machine learning to nominate ligand-receptor pairs enriched in the HRS cell niche. Specifically, we identified IL13 as a candidate survival factor. In support of this hypothesis, recombinant IL13 augmented the proliferation of HRS cells in vitro. In addition, genome-wide CRISPR/Cas9 loss-of-function studies across more than 1,000 human cancer cell lines showed that IL4R and IL13RA1, the heterodimeric partners that constitute the IL13 receptor, were uniquely required for the survival of HRS cells. Moreover, monoclonal antibodies targeting either IL4R or IL13R phenocopied the genetic loss of function studies. IL13-targeting antibodies are already FDA-approved for atopic dermatitis, suggesting that clinical trials testing such agents should be explored in patients with Hodgkin lymphoma.

cancer biology↗

bioRxiv2024

Cross-domain information fusion for enhanced cell population delineation in single-cell spatial-omics data

Cell population delineation and identification is an essential step in single-cell and spatial-omics studies. Spatial-omics technologies can simultaneously measure information from three complementary domains related to this task: expression levels of a panel of molecular biomarkers at single-cell resolution, relative positions of cells, and images of tissue sections, but existing computational methods for performing this task on single-cell spatial-omics datasets often relinquish information from one or more domains. The additional reliance on the availability of "atlas" training or reference datasets limits cell type discovery to well-defined but limited cell population labels, thus posing major challenges for using these methods in practice. Successful integration of all three domains presents an opportunity for uncovering cell populations that are functionally stratified by their spatial contexts at cellular and tissue levels: the key motivation for employing spatial-omics technologies in the first place. In this work, we introduce Cell Spatio- and Neighborhood-informed Annotation and Patterning (CellSNAP), a self-supervised computational method that learns a representation vector for each cell in tissue samples measured by spatial-omics technologies at the single-cell or finer resolution. The learned representation vector fuses information about the corresponding cell across all three aforementioned domains. By applying CellSNAP to datasets spanning both spatial proteomic and spatial transcriptomic modalities, and across different tissue types and disease settings, we show that CellSNAP markedly enhances de novo discovery of biologically relevant cell populations at fine granularity, beyond current approaches, by fully integrating cells molecular profiles with cellular neighborhood and tissue image information.

bioinformatics↗

bioRxiv2023

A cellular and spatial atlas of TP53-associated tissue remodeling in lung adenocarcinoma

TP53 is the most frequently mutated gene across many cancers and is associated with shorter survival in lung adenocarcinoma (LUAD). To define how TP53 mutations affect the LUAD tumor microenvironment (TME), we constructed a multi-omic cellular and spatial tumor atlas of 23 treatment-naive human lung tumors. We found that TP53-mutant (TP53mut) malignant cells lose alveolar identity and upregulate highly proliferative and entropic gene expression programs consistently across resectable LUAD patient tumors, genetically engineered mouse models, and cell lines harboring a wide spectrum of TP53 mutations. We further identified a multicellular tumor niche composed of SPP1+ macrophages and collagen-expressing fibroblasts that coincides with hypoxic, pro-metastatic expression programs in TP53mut tumors. Spatially correlated angiostatic and immune checkpoint interactions, including CD274-PDCD1 and PVR-TIGIT, are also enriched in TP53mut LUAD tumors, which may influence response to checkpoint blockade therapy. Our methodology can be further applied to investigate mutation-specific TME changes in other cancers.

cancer biology↗

bioRxiv2023

Spatially aware deep learning reveals tumor heterogeneity patterns that encode distinct kidney cancer states

Background Abstract Background Results Discussion Methods Data Availability Code Availability Author Contributions Competing Interests Extended Data References Renal cell carcinoma (RCC) is among the 10 most common cancers worldwide and is comprised of several histological subtypes1. The clear cell histological subtype (ccRCC) is the most common form of RCC and accounts for the vast majority (75-80%) of metastatic cases1. In addition to highly recurrent mutations in hypoxia (VHL) and chromatin regulator genes (e.g. PBRM1, BAP1, SETD2), ccRCC exhibits extensive genomic intratumoral heterogeneity (ITH)2, which wa ...

cancer biology↗

bioRxiv2022

SlideCNA: Spatial copy number alteration detection from Slide-seq-like spatial transcriptomics data

Solid tumors are spatially heterogeneous in their genetic, molecular and cellular composition, and this variation can be meaningful for diagnosis, prognosis and therapy. Recent spatial profiling studies have mostly charted genetic and RNA variation in tumors separately. To leverage the potential of RNA to identify copy number alterations (CNAs), we developed SlideCNA, a computational tool to extract sparse spatial CNA signals from spatial transcriptomics data, using expression-aware spatial binning. We test SlideCNA on simulated and real Slide-seq data of metastatic breast cancer (MBC) and demonstrate its potential for spatial sub-clone detection.

bioinformatics↗

bioRxiv2022

Intertumoral lineage diversity and immunosuppressive transcriptional programs in well-differentiated gastroenteropancreatic neuroendocrine tumors

Neuroendocrine tumors (NETs) are rare cancers that may arise in the gastrointestinal tract and pancreas. The fundamental mechanisms driving gastroenteropancreatic (GEP) NET growth remain incompletely elucidated; however, the heterogeneous clinical behavior of GEP-NETs suggests that both cellular lineage dynamics and tumor microenvironment influence tumor pathophysiology. Here, we investigated the single-cell transcriptomes of tumor and immune cells from patients with gastroenteropancreatic NETs. Malignant GEP-NET cells expressed genes and regulons associated with normal, gastrointestinal endocrine cell differentiation and fate determination stages. While tumor and lymphoid compartments sparsely expressed immunosuppressive targets, infiltrating myeloid cells were enriched for alternative immunotherapy pathways including VSIR, Tim3/Gal9, and SIGLEC10. Finally, analysis of paired primary and metastatic tissue specimens from small intestinal NETs demonstrated transcriptional transformation between the primary tumor and its distant metastasis. Our findings highlight the transcriptomic heterogeneity that distinguishes the cellular landscapes of GEP-NET anatomic subtypes and reveal potential avenues for future precision medicine therapeutics.

cancer biology↗

bioRxiv2020

Expansion Sequencing: Spatially Precise In Situ Transcriptomics in Intact Biological Systems

AbstractMethods for highly multiplexed RNA imaging are limited in spatial resolution, and thus in their ability to localize transcripts to nanoscale and subcellular compartments. We adapt expansion microscopy, which physically expands biological specimens, for long-read untargeted and targeted in situ RNA sequencing. We applied untargeted expansion sequencing (ExSeq) to mouse brain, yielding readout of thousands of genes, including splice variants and novel transcripts. Targeted ExSeq yielded nanoscale-resolution maps of RNAs throughout dendrites and spines in neurons of the mouse hippocampus, revealing patterns across multiple cell types; layer-specific cell types across mouse visual cortex; and the organization and position-dependent states of tumor and immune cells in a human metastatic breast cancer biopsy. Thus ExSeq enables highly multiplexed mapping of RNAs, from nanoscale to system scale. One Sentence SummaryIn situ sequencing of physically expanded specimens enables multiplexed mapping of RNAs at nanoscale, subcellular resolution.

genomics↗

bioRxiv2019

Pathognomonic and epistatic genetic alterations in B-cell non-Hodgkin lymphoma

B-cell non-Hodgkins lymphoma (B-NHL) encompasses multiple clinically and phenotypically distinct subtypes of malignancy with unique molecular etiologies. Common subtypes of B-NHL such as diffuse large B-cell lymphoma (DLBCL) have been comprehensively interrogated at the genomic level. But rarer subtypes such as mantle cell lymphoma (MCL) remain sparsely characterized. Furthermore, multiple B-NHL subtypes have thus far not been comprehensively compared using the same methodology to identify conserved or subtype-specific patterns of genomic alterations. Here, we employed a large targeted hybrid-capture sequencing approach encompassing 380 genes to interrogate the genomic landscapes of 685 B-NHL tumors at high depth; including DLBCL, MCL, follicular lymphoma (FL), and Burkitt lymphoma (BL). We identified conserved hallmarks of B-NHL that were deregulated in the majority of tumor from each subtype, including the frequent genetic deregulation of the ubiquitin proteasome system (UPS). In addition, we identified subtype-specific patterns of genetic alterations, including clusters of co-occurring mutations and DNA copy number alterations. The cumulative burden of mutations within a single cluster were more discriminatory of B-NHL subtypes than individual mutations, implicating likely patterns of genetic cooperation that contribute to disease etiology. We therefore provide the first cross-sectional analysis of mutations and DNA copy number alterations across major B-NHL subtypes and a framework of co-occurring genetic alterations that deregulate genetic hallmarks and likely cooperate in lymphomagenesis.

genomics↗

bioRxiv2017

DNA copy number gains of TCF4 (E2-2) are associated with poor outcome and the activated B-cell-like subtype of diffuse large B-cell lymphoma

The activated B-cell (ABC) subtype of diffuse large B-cell lymphoma (DLBCL) is characterized by the chronic activation of signaling initiated by immunoglobulin- (IgM). By analyzing DNA copy profiles of 1,000 DLBCLs, we identified gains of 18q21.2 as the most frequent genetic alteration in ABC-like DLBCL. We show that these alterations target the TCF4 (E2-2) transcription factor, and that over-expression of TCF4 leads to its occupancy on immunoglobulin gene enhancers and increased expression of IgM at the transcript and protein level. The TCF4 gene is one of the top BRD4-regulated genes in DLBCL. Using a BET proteolysis-targeting chimera (PROTAC) we show that TCF4 and IgM expression can be extinguished, and ABC-like DLBCL cells can be killed in vitro and in vivo. This highlights a novel genetic mechanism for promoting immunoglobulin signaling in ABC-like DLBCL and provides a functional rationale for the use of BET inhibitors in this disease.

genomics↗