Showing papers by "Sridhar Hannenhalli published in 2022"

Big data in basic and translational cancer research

Abstract: Historically, the primary focus of cancer research has been molecular and clinical studies of a few essential pathways and genes. Recent years have seen the rapid accumulation of large-scale cancer omics data catalysed by breakthroughs in high-throughput technologies. This fast data growth has given rise to an evolving concept of ‘big data’ in cancer, whose analysis demands large computational resources and can potentially bring novel insights into essential questions. Indeed, the combination of big data, bioinformatics and artificial intelligence has led to notable advances in our basic understanding of cancer biology and to translational advancements. Further advances will require a concerted effort among data scientists, clinicians, biologists and policymakers. Here, we review the current state of the art and future challenges for harnessing big data to advance cancer research and treatment. The increasing size of cancer datasets requires new ways of thinking for analysing and integrating these data. In this Review, Jiang et al. discuss considerations and strategies for wielding ‘big data’ ― large, information-rich datasets ― in basic research and for translational applications such as identifying biomarkers, informing clinical trials and developing new assays and treatments.

Immune cell gene expression signatures in diffuse glioma are associated with IDH mutation status, patient outcome and malignant cell state, and highlight the importance of specific cell subsets in glioma biology

Abstract: The tumor micro-environment (TME) plays an important role in various cancers, including gliomas. We estimated immune cell type-specific gene expression profiles in 3 large clinically annotated glioma datasets using CIBERSORTx and LM22/LM10 blood-based immune signatures and found that the proportions and estimated gene expression patterns of specific immune cells significantly varied according to IDH mutation status. When IDH-WT and IDH-MUT tumors were considered separately, cluster-of-cluster analyses of immune cell gene expression identified groups with distinct survival outcomes. We confirmed and extended these findings by applying a signature matrix derived from single-cell RNA-sequencing data derived from 19 glioma tumor samples to the bulk profiling data, validating findings from the LM22/LM10 results. To link immune cell signatures with outcomes in checkpoint therapy, we then showed a significant association of monocytic lineage cell gene expression clusters with patient survival and with mesenchymal gene expression scores. Integrating immune cell-based gene expression with previously described malignant cell states in glioma demonstrated that macrophage M0 abundance significantly correlated with mesenchymal state in IDH-WT gliomas, with evidence of a previously implicated role of the Oncostatin-M receptor and macrophages in the mesenchymal state. Among IDH-WT tumors that were enriched for the mesenchymal cell state, the estimated M0 macrophage expression signature coordinately also trended to a mesenchymal signature. We also examined IDH-MUT tumors stratified by 1p/19q status, showing that a mesenchymal gene expression signature the M0 macrophage fraction was enriched in IDH-MUT, non-codeleted tumors. Overall, these results highlight the biological and clinical significance of the immune cell environment related to IDH mutation status, patient prognosis and the mesenchymal state in diffuse gliomas.

Immune cell gene expression signatures in diffuse glioma are associated with IDH mutation status, patient outcome and malignant cell state, and highlight the importance of specific cell subsets in glioma biology

Abstract: The tumor micro-environment (TME) plays an important role in various cancers, including gliomas. We estimated immune cell type-specific gene expression profiles in 3 large clinically annotated glioma datasets using CIBERSORTx and LM22/LM10 blood-based immune signatures and found that the proportions and estimated gene expression patterns of specific immune cells significantly varied according to IDH mutation status. When IDH-WT and IDH-MUT tumors were considered separately, cluster-of-cluster analyses of immune cell gene expression identified groups with distinct survival outcomes. We confirmed and extended these findings by applying a signature matrix derived from single-cell RNA-sequencing data derived from 19 glioma tumor samples to the bulk profiling data, validating findings from the LM22/LM10 results. To link immune cell signatures with outcomes in checkpoint therapy, we then showed a significant association of monocytic lineage cell gene expression clusters with patient survival and with mesenchymal gene expression scores. Integrating immune cell-based gene expression with previously described malignant cell states in glioma demonstrated that macrophage M0 abundance significantly correlated with mesenchymal state in IDH-WT gliomas, with evidence of a previously implicated role of the Oncostatin-M receptor and macrophages in the mesenchymal state. Among IDH-WT tumors that were enriched for the mesenchymal cell state, the estimated M0 macrophage expression signature coordinately also trended to a mesenchymal signature. We also examined IDH-MUT tumors stratified by 1p/19q status, showing that a mesenchymal gene expression signature the M0 macrophage fraction was enriched in IDH-MUT, non-codeleted tumors. Overall, these results highlight the biological and clinical significance of the immune cell environment related to IDH mutation status, patient prognosis and the mesenchymal state in diffuse gliomas.

A path-based analysis of infected cell line and COVID-19 patient transcriptome reveals novel potential targets and drugs against SARS-CoV-2

Abstract: Most transcriptomic studies of SARS-CoV-2 infection have focused on differentially expressed genes, which do not necessarily reveal the genes mediating the transcriptomic changes. In contrast, exploiting curated biological network, our PathExt tool identifies central genes from the differentially active paths mediating global transcriptomic response. Here we apply PathExt to multiple cell line infection models of SARS-CoV-2 and other viruses, as well as to COVID-19 patient-derived PBMCs. The central genes mediating SARS-CoV-2 response in cell lines were uniquely enriched for ATP metabolic process, G1/S transition, leukocyte activation and migration. In contrast, PBMC response reveals dysregulated cell-cycle processes. In PBMC, the most frequently central genes are associated with COVID-19 severity. Importantly, relative to differential genes, PathExt-identified genes show greater concordance with several benchmark anti-COVID-19 target gene sets. We propose six novel anti-SARS-CoV-2 targets ADCY2, ADSL, OCRL, TIAM1, PBK, and BUB1, and potential drugs targeting these genes, such as Bemcentinib, Phthalocyanine, and Conivaptan.

A Path-Based Analysis of Infected Cell Line and COVID-19 Patient Transcriptome Reveals Novel Potential Targets and Drugs Against SARS-CoV-2

Abstract: Most transcriptomic studies of SARS-CoV-2 infection have focused on differentially expressed genes, which do not necessarily reveal the genes mediating the transcriptomic changes. In contrast, exploiting curated biological network, our PathExt tool identifies central genes from the differentially active paths mediating global transcriptomic response. Here we apply PathExt to multiple cell line infection models of SARS-CoV-2 and other viruses, as well as to COVID-19 patient-derived PBMCs. The central genes mediating SARS-CoV-2 response in cell lines were uniquely enriched for ATP metabolic process, G1/S transition, leukocyte activation and migration. In contrast, PBMC response reveals dysregulated cell-cycle processes. In PBMC, the most frequently central genes are associated with COVID-19 severity. Importantly, relative to differential genes, PathExt-identified genes show greater concordance with several benchmark anti-COVID-19 target gene sets. We propose six novel anti-SARS-CoV-2 targets ADCY2, ADSL, OCRL, TIAM1, PBK, and BUB1, and potential drugs targeting these genes, such as Bemcentinib, Phthalocyanine, and Conivaptan.

Cross-species identification of cancer resistance–associated genes that may mediate human cancer risk

Abstract: Cancer is a predominant disease across animals. We applied a comparative genomics approach to systematically characterize genes whose conservation levels correlate positively (PC) or negatively (NC) with cancer resistance estimates across 193 vertebrates. Pathway analysis reveals that NC genes are enriched for metabolic functions and PC genes in cell cycle regulation, DNA repair, and immune response, pointing to their corresponding roles in mediating cancer risk. We find that PC genes are less tolerant to loss-of-function (LoF) mutations, are enriched in cancer driver genes, and are associated with germline mutations that increase human cancer risk. Their relevance to cancer risk is further supported via the analysis of mouse functional genomics and cancer mortality of zoo mammals’ data. In sum, our study describes a cross-species genomic analysis pointing to candidate genes that may mediate human cancer risk.

Comprehensive single-cell transcriptomic analysis of embryonic melanoblasts uncovers lineage-specific mechanisms of melanoma metastasis and therapy resistance

Abstract: Across cancers, tumor cells can resemble embryonic cell states that may allow them to metastasize and evade therapies. Melanoma is a cancer of the melanocyte that exhibits a wide range of transcriptional states characterized by alterations in embryonic melanocyte gene expression patterns. How these states and their functions are related to the embryonic precursors of melanocytes, the melanoblasts, is unknown. Here, we present the first high-resolution single-cell RNA-seq profiles of embryonic melanocytic lineages in mice. We discover a diverse array of transcriptional cell states in this lineage and confirm, for the first time at the single-cell level, that melanocytes arise from Schwann-cell precursors (SCPs), a highly plastic cell population, via a newly described intermediate mesenchymal-like state. Via novel computational strategies to map these developmental cell states to metastatic melanoma, we find that SCP-resembling tumors are associated with exclusion of the immune cells and non-response to immune checkpoint blockade. In contrast, a higher mesenchymal profile underlies immune dysfunction and resistance to BRAF-inhibition therapy. We also carry out the first time-resolved single-cell RNA-seq study of early melanoma metastatic colonization, demonstrating that melanoma cells activate a SCP program transiently during early metastatic colonization. Finally, we discover a hybrid lineage state that resembles multiple melanocytic lineages simultaneously and is enriched in melanoma cells during metastatic seeding and in therapy resistance. Our work reveals that the lineage-specific mechanisms underlie melanoma progression/evolution, including early metastatic colonization and therapeutic resistance.

Transcription factors organize into functional groups on the linear genome and in 3D chromatin

Abstract: Transcription factors (TFs) and their binding sites have evolved to interact cooperatively or competitively with each other. Here we examine in detail, across multiple cell lines, such cooperation or competition among TFs both in sequential and spatial proximity (using chromatin conformation capture assays) on one hand, and based on both in vivo binding as well as TF binding motifs on the other. We ascertain significantly co-occurring (“attractive”) or avoiding (“repulsive”) TF pairs using robust randomized models that retain the essential characteristics of the experimental data. Across human cell lines TFs organize into two groups, with intra-group attraction and inter-group repulsion. This is true for both sequential and spatial proximity, as well as for both in vivo binding and motifs. Attractive TF pairs exhibit significantly more physical interactions suggesting an underlying mechanism. The two TF groups differ significantly in their genomic and network properties, as well in their function—while one group regulates housekeeping function, the other potentially regulates lineage-specific functions, that are disrupted in cancer. We also show that weaker binding sites tend to occur in spatially interacting regions of the genome. Our results suggest a complex pattern of spatial cooperativity of TFs that has evolved along with the genome to support housekeeping and lineage-specific functions.