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Article ; Online: Redistribution of mutation risk in cancer.

Hu, Xiaoju / De, Subhajyoti

Nature cancer

2024 Volume 5, Issue 2, Page(s) 216–217

MeSH term(s)	Humans ; Mutation ; Neoplasms/genetics
Language	English
Publishing date	2024-02-29
Publishing country	England
Document type	Journal Article
ISSN	2662-1347
ISSN (online)	2662-1347
DOI	10.1038/s43018-024-00728-x
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Article: Signatures Beyond Oncogenic Mutations in Cell-Free DNA Sequencing for Non-Invasive, Early Detection of Cancer.

De, Subhajyoti

Frontiers in genetics

2021 Volume 12, Page(s) 759832

Abstract: Early detection of cancer saves lives, but an effective detection strategy in public health settings requires a delicate balance - periodic screening should neither miss rapidly progressing disease nor fail to detect rare tumors at unusual locations; on ... ...

Abstract	Early detection of cancer saves lives, but an effective detection strategy in public health settings requires a delicate balance - periodic screening should neither miss rapidly progressing disease nor fail to detect rare tumors at unusual locations; on the other hand, even a modest false positive rate carries risks of over-diagnosis and over-treatment of relatively indolent non-malignant disease. Genomic profiling of cell-free DNA from liquid biopsy using massively parallel sequencing is emerging as an attractive, non-invasive screening platform for sensitive detection of multiple types of cancer in a single assay. Genomic data from cell-free DNA can not only identify oncogenic mutation status, but also additional molecular signatures related to potential tissue of origin, the extent of clonal growth, and malignant disease states. Utilization of the full potential of the molecular signatures from cfDNA sequencing data can guide clinical management strategies for targeted follow-ups using imaging or molecular marker-based diagnostic platforms and treatment options.
Language	English
Publishing date	2021-10-14
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2606823-0
ISSN	1664-8021
ISSN	1664-8021
DOI	10.3389/fgene.2021.759832
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Article ; Online: Hierarchical and automated cell-type annotation and inference of cancer cell of origin with Census.

Ghaddar, Bassel / De, Subhajyoti

Bioinformatics (Oxford, England)

2023 Volume 39, Issue 12

Abstract: Motivation: Cell-type annotation is a time-consuming yet critical first step in the analysis of single-cell RNA-seq data, especially when multiple similar cell subtypes with overlapping marker genes are present. Existing automated annotation methods ... ...

Abstract	Motivation: Cell-type annotation is a time-consuming yet critical first step in the analysis of single-cell RNA-seq data, especially when multiple similar cell subtypes with overlapping marker genes are present. Existing automated annotation methods have a number of limitations, including requiring large reference datasets, high computation time, shallow annotation resolution, and difficulty in identifying cancer cells or their most likely cell of origin. Results: We developed Census, a biologically intuitive and fully automated cell-type identification method for single-cell RNA-seq data that can deeply annotate normal cells in mammalian tissues and identify malignant cells and their likely cell of origin. Motivated by the inherently stratified developmental programs of cellular differentiation, Census infers hierarchical cell-type relationships and uses gradient-boosted \decision trees that capitalize on nodal cell-type relationships to achieve high prediction speed and accuracy. When benchmarked on 44 atlas-scale normal and cancer, human and mouse tissues, Census significantly outperforms state-of-the-art methods across multiple metrics and naturally predicts the cell-of-origin of different cancers. Census is pretrained on the Tabula Sapiens to classify 175 cell-types from 24 organs; however, users can seamlessly train their own models for customized applications. Availability and implementation: Census is available at Zenodo https://zenodo.org/records/7017103 and on our Github https://github.com/sjdlabgroup/Census.
MeSH term(s)	Animals ; Humans ; Mice ; Exome Sequencing ; Gene Expression Profiling/methods ; Mammals ; Neoplasms/genetics ; Sequence Analysis, RNA/methods ; Single-Cell Analysis/methods
Language	English
Publishing date	2023-11-27
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	1422668-6
ISSN	1367-4811 ; 1367-4803
ISSN (online)	1367-4811
ISSN	1367-4803
DOI	10.1093/bioinformatics/btad714
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Zs.A 2374: Show issues

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Article ; Online: Reconstructing physical cell interaction networks from single-cell data using Neighbor-seq.

Ghaddar, Bassel / De, Subhajyoti

Nucleic acids research

2022 Volume 50, Issue 14, Page(s) e82

Abstract: Cell-cell interactions are the fundamental building blocks of tissue organization and multicellular life. We developed Neighbor-seq, a method to identify and annotate the architecture of direct cell-cell interactions and relevant ligand-receptor ... ...

Abstract	Cell-cell interactions are the fundamental building blocks of tissue organization and multicellular life. We developed Neighbor-seq, a method to identify and annotate the architecture of direct cell-cell interactions and relevant ligand-receptor signaling from the undissociated cell fractions in massively parallel single cell sequencing data. Neighbor-seq accurately identifies microanatomical features of diverse tissue types such as the small intestinal epithelium, terminal respiratory tract, and splenic white pulp. It also captures the differing topologies of cancer-immune-stromal cell communications in pancreatic and skin tumors, which are consistent with the patterns observed in spatial transcriptomic data. Neighbor-seq is fast and scalable. It draws inferences from routine single-cell data and does not require prior knowledge about sample cell-types or multiplets. Neighbor-seq provides a framework to study the organ-level cellular interactome in health and disease, bridging the gap between single-cell and spatial transcriptomics.
MeSH term(s)	Cell Communication/genetics ; Humans ; Neoplasms ; Sequence Analysis, RNA/methods ; Single-Cell Analysis/methods ; Transcriptome
Language	English
Publishing date	2022-05-22
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	186809-3
ISSN	1362-4962 ; 1362-4954 ; 0301-5610 ; 0305-1048
ISSN (online)	1362-4962 ; 1362-4954
ISSN	0301-5610 ; 0305-1048
DOI	10.1093/nar/gkac333
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Zs.A 1067: Show issues

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Article: Plasma-Derived Cell-Free DNA as a Biomarker for Early Detection, Prognostication, and Personalized Treatment of Urothelial Carcinoma.

Bhalla, Sophia / Passarelli, Rachel / Biswas, Antara / De, Subhajyoti / Ghodoussipour, Saum

Journal of clinical medicine

2024 Volume 13, Issue 7

Abstract: Bladder cancer (BC) is one of the most common malignancies in the United States, with over 80,000 new cases and 16,000 deaths each year. Urothelial carcinoma (UC) is the most common histology and accounts for 90% of cases. BC management is complicated by ...

Abstract	Bladder cancer (BC) is one of the most common malignancies in the United States, with over 80,000 new cases and 16,000 deaths each year. Urothelial carcinoma (UC) is the most common histology and accounts for 90% of cases. BC management is complicated by recurrence rates of over 50% in both muscle-invasive and non-muscle-invasive bladder cancer. As such, the American Urological Association (AUA) recommends that patients undergo close surveillance during and after treatment. This surveillance is in the form of cystoscopy or imaging tests, which can be invasive and costly tests. Considering this, there have been recent pushes to find complements to bladder cancer surveillance. Cell-free DNA (CfDNA), or DNA released from dying cells, and circulating tumor DNA (ctDNA), or mutated DNA released from tumor cells, can be analyzed to detect and characterize the molecular characteristics of tumors. Research has shown promising results for ctDNA use in the BC care realm. A PubMed literature review was performed finding studies discussing cfDNA and ctDNA in BC detection, prognostication, and monitoring for recurrence. Keywords used included bladder cancer, cell-free DNA, circulating tumor DNA, urothelial carcinoma, and liquid biopsy. Studies show that ctDNA can serve as prognostic indicators of both early- and late-stage BC, aid in risk stratification prior to major surgery, assist in detection of disease progression and metastatic relapse, and can assess patients who may respond to immunotherapy. The benefit of ctDNA is not confined to BC, as studies have also suggested its promise as a biomarker for neoadjuvant chemotherapy in upper-tract UC. However, there are some limitations to ctDNA that require improvements in ctDNA-specific detection methods and BC-specific mutations before widespread utilization can be achieved. Further prospective, randomized trials are needed to elucidate the true potential ctDNA has in advancements in BC care.
Language	English
Publishing date	2024-04-02
Publishing country	Switzerland
Document type	Journal Article ; Review
ZDB-ID	2662592-1
ISSN	2077-0383
ISSN	2077-0383
DOI	10.3390/jcm13072057
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Article ; Online: Denoising sparse microbial signals from single-cell sequencing of mammalian host tissues.

Ghaddar, Bassel / Blaser, Martin J / De, Subhajyoti

Nature computational science

2023 Volume 3, Issue 9, Page(s) 741–747

Abstract: Existing genomic sequencing data can be used to study host-microbiome ecosystems, however distinguishing signals originating from truly present microbes versus contaminating species and artifacts is a substantial and often prohibitive challenge. Here we ... ...

Abstract	Existing genomic sequencing data can be used to study host-microbiome ecosystems, however distinguishing signals originating from truly present microbes versus contaminating species and artifacts is a substantial and often prohibitive challenge. Here we show that emerging sequencing technologies definitely capture reads from present microbes. We developed SAHMI, a computational resource to identify truly present microbial nucleic acids and filter contaminants and spurious false-positive taxonomic assignments from standard transcriptomic sequencing of mammalian tissues. In benchmark studies, SAHMI correctly identifies known microbial infections present in diverse tissues, and we validate SAHMI's enrichment for correctly classified, truly present species using multiple orthogonal computational experiments. The application of SAHMI to single-cell and spatial genomic data thus enables co-detection of somatic cells and microorganisms and joint analysis of host-microbiome ecosystems.
Language	English
Publishing date	2023-09-18
Publishing country	United States
Document type	Journal Article
ISSN	2662-8457
ISSN (online)	2662-8457
DOI	10.1038/s43588-023-00507-1
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Article ; Online: Drivers of dynamic intratumor heterogeneity and phenotypic plasticity.

Biswas, Antara / De, Subhajyoti

American journal of physiology. Cell physiology

2021 Volume 320, Issue 5, Page(s) C750–C760

Abstract: Cancer is a clonal disease, i.e., all tumor cells within a malignant lesion trace their lineage back to a precursor somatic cell that acquired oncogenic mutations during development and aging. And yet, those tumor cells tend to have genetic and ... ...

Abstract	Cancer is a clonal disease, i.e., all tumor cells within a malignant lesion trace their lineage back to a precursor somatic cell that acquired oncogenic mutations during development and aging. And yet, those tumor cells tend to have genetic and nongenetic variations among themselves-which is denoted as intratumor heterogeneity. Although some of these variations are inconsequential, others tend to contribute to cell state transition and phenotypic heterogeneity, providing a substrate for somatic evolution. Tumor cell phenotypes can dynamically change under the influence of genetic mutations, epigenetic modifications, and microenvironmental contexts. Although epigenetic and microenvironmental changes are adaptive, genetic mutations are usually considered permanent. Emerging reports suggest that certain classes of genetic alterations show extensive reversibility in tumors in clinically relevant timescales, contributing as major drivers of dynamic intratumor heterogeneity and phenotypic plasticity. Dynamic heterogeneity and phenotypic plasticity can confer resistance to treatment, promote metastasis, and enhance evolvability in cancer. Here, we first highlight recent efforts to characterize intratumor heterogeneity at genetic, epigenetic, and microenvironmental levels. We then discuss phenotypic plasticity and cell state transition by tumor cells, under the influence of genetic and nongenetic determinants and their clinical significance in classification of tumors and therapeutic decision-making.
MeSH term(s)	Animals ; Antineoplastic Agents/therapeutic use ; Biomarkers, Tumor/genetics ; Cell Plasticity ; Drug Resistance, Neoplasm/genetics ; Epigenesis, Genetic ; Genetic Heterogeneity ; Genetic Predisposition to Disease ; Humans ; Mutation ; Neoplasms/drug therapy ; Neoplasms/genetics ; Neoplasms/pathology ; Phenotype ; Tumor Microenvironment
Chemical Substances	Antineoplastic Agents ; Biomarkers, Tumor
Language	English
Publishing date	2021-03-03
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Review
ZDB-ID	392098-7
ISSN	1522-1563 ; 0363-6143
ISSN (online)	1522-1563
ISSN	0363-6143
DOI	10.1152/ajpcell.00575.2020
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Uc I Zs.89: Show issues

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Article ; Online: Inference on spatial heterogeneity in tumor microenvironment using spatial transcriptomics data.

Biswas, Antara / Ghaddar, Bassel / Riedlinger, Gregory / De, Subhajyoti

Computational and systems oncology

2022 Volume 2, Issue 3

Abstract: In the tumor microenvironment (TME), functional interactions among tumor, immune, and stromal cells and the extracellular matrix play key roles in tumor progression, invasion, immune modulation, and response to treatment. Intratumor heterogeneity is ... ...

Abstract	In the tumor microenvironment (TME), functional interactions among tumor, immune, and stromal cells and the extracellular matrix play key roles in tumor progression, invasion, immune modulation, and response to treatment. Intratumor heterogeneity is ubiquitous not only at the genetic and transcriptomic levels but also in the composition and characteristics of TME. However, quantitative inference on spatial heterogeneity in the TME is still limited. Here, we propose a framework to use network graph-based spatial statistical models on spatially annotated molecular data to gain insights into modularity and spatial heterogeneity in the TME. Applying the framework to spatial transcriptomics data from pancreatic ductal adenocarcinoma samples, we observed significant global and local spatially correlated patterns in the abundance score of tumor cells; in contrast, immune cell types showed dispersed patterns in the TME. Hypoxia, EMT, and inflammation signatures contributed to intra-tumor spatial variations. Spatial patterns in cell type abundance and pathway signatures in the TME potentially impact tumor growth dynamics and cancer hallmarks. Tumor biopsies are integral to the diagnosis and clinical management of cancer patients; our data suggest that owing to intra-tumor non-genetic spatial heterogeneity, individual biopsies may underappreciate the extent of clinically relevant, functional variations across geographic regions within tumors.
Language	English
Publishing date	2022-08-11
Publishing country	United States
Document type	Journal Article
ISSN	2689-9655
ISSN (online)	2689-9655
DOI	10.1002/cso2.1043
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Article ; Online: KMT2C-deficient tumors have elevated APOBEC mutagenesis and genomic instability in multiple cancers.

Hu, Xiaoju / Biswas, Antara / De, Subhajyoti

NAR cancer

2022 Volume 4, Issue 3, Page(s) zcac023

Abstract: The histone ... ...

Abstract	The histone methyltransferase
Language	English
Publishing date	2022-07-25
Publishing country	England
Document type	Journal Article
ISSN	2632-8674
ISSN (online)	2632-8674
DOI	10.1093/narcan/zcac023
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Article ; Online: Transcriptional state dynamics lead to heterogeneity and adaptive tumor evolution in urothelial bladder carcinoma.

Biswas, Antara / Sahoo, Sarthak / Riedlinger, Gregory M / Ghodoussipour, Saum / Jolly, Mohit K / De, Subhajyoti

Communications biology

2023 Volume 6, Issue 1, Page(s) 1292

Abstract: Intra-tumor heterogeneity contributes to treatment failure and poor survival in urothelial bladder carcinoma (UBC). Analyzing transcriptome from a UBC cohort, we report that intra-tumor transcriptomic heterogeneity indicates co-existence of tumor cells ... ...

Abstract	Intra-tumor heterogeneity contributes to treatment failure and poor survival in urothelial bladder carcinoma (UBC). Analyzing transcriptome from a UBC cohort, we report that intra-tumor transcriptomic heterogeneity indicates co-existence of tumor cells in epithelial and mesenchymal-like transcriptional states and bi-directional transition between them occurs within and between tumor subclones. We model spontaneous and reversible transition between these partially heritable states in cell lines and characterize their population dynamics. SMAD3, KLF4 and PPARG emerge as key regulatory markers of the transcriptional dynamics. Nutrient limitation, as in the core of large tumors, and radiation treatment perturb the dynamics, initially selecting for a transiently resistant phenotype and then reconstituting heterogeneity and growth potential, driving adaptive evolution. Dominance of transcriptional states with low PPARG expression indicates an aggressive phenotype in UBC patients. We propose that phenotypic plasticity and dynamic, non-genetic intra-tumor heterogeneity modulate both the trajectory of disease progression and adaptive treatment response in UBC.
MeSH term(s)	Humans ; Urinary Bladder ; PPAR gamma ; Urinary Bladder Neoplasms/therapy ; Carcinoma, Transitional Cell/pathology ; Disease Progression
Chemical Substances	PPAR gamma
Language	English
Publishing date	2023-12-21
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, N.I.H., Extramural
ISSN	2399-3642
ISSN (online)	2399-3642
DOI	10.1038/s42003-023-05668-3
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