Inferring Allele-Specific Copy Number Aberrations and Tumor Phylogeography from Spatially Resolved Transcriptomics

Cong Ma; Metin Balaban; Jingxian Liu; Siqi Chen; Li Ding; Benjamin J. Raphael

doi:10.1007/978-1-0716-3989-4_54

Inferring Allele-Specific Copy Number Aberrations and Tumor Phylogeography from Spatially Resolved Transcriptomics

Cong Ma, Metin Balaban, Jingxian Liu, Siqi Chen, Li Ding, Benjamin J. Raphael

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

A key challenge in cancer research is to reconstruct the somatic evolution within a tumor over time and across space. Spatially resolved transcriptomics (SRT) measures gene expression at thousands of spatial locations in a tumor, but does not directly reveal genetic aberrations. We introduce CalicoST, an algorithm to simultaneously infer allele-specific copy number aberrations (CNAs) and a spatial model of tumor evolution from SRT of tumor slices. By modeling CNA-induced perturbations in both total and allele-specific gene expression, CalicoST identifies important types of CNAs - including copy-neutral loss of heterozygosity (CNLOH) and mirrored subclonal CNAs- that are invisible to total copy number analysis. CalicoST achieves high accuracy by modeling both correlations in space with a Hidden Markov Random Field and across genomic segments with a Hidden Markov Model.

Original language	English
Title of host publication	Research in Computational Molecular Biology - 28th Annual International Conference, RECOMB 2024, Proceedings
Editors	Jian Ma
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	466-469
Number of pages	4
ISBN (Print)	9781071639887
DOIs	https://doi.org/10.1007/978-1-0716-3989-4_54
State	Published - 2024
Event	28th International Conference on Research in Computational Molecular Biology, RECOMB 2024 - Cambridge, United States Duration: Apr 29 2024 → May 2 2024

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	14758 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	28th International Conference on Research in Computational Molecular Biology, RECOMB 2024
Country/Territory	United States
City	Cambridge
Period	04/29/24 → 05/2/24

Keywords

cancer
copy number aberrations
evolution
phylogeography
spatially resolved transcriptomics

Access to Document

10.1007/978-1-0716-3989-4_54

Cite this

Ma, C., Balaban, M., Liu, J., Chen, S., Ding, L., & Raphael, B. J. (2024). Inferring Allele-Specific Copy Number Aberrations and Tumor Phylogeography from Spatially Resolved Transcriptomics. In J. Ma (Ed.), Research in Computational Molecular Biology - 28th Annual International Conference, RECOMB 2024, Proceedings (pp. 466-469). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14758 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-1-0716-3989-4_54

Ma, Cong ; Balaban, Metin ; Liu, Jingxian et al. / Inferring Allele-Specific Copy Number Aberrations and Tumor Phylogeography from Spatially Resolved Transcriptomics. Research in Computational Molecular Biology - 28th Annual International Conference, RECOMB 2024, Proceedings. editor / Jian Ma. Springer Science and Business Media Deutschland GmbH, 2024. pp. 466-469 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "A key challenge in cancer research is to reconstruct the somatic evolution within a tumor over time and across space. Spatially resolved transcriptomics (SRT) measures gene expression at thousands of spatial locations in a tumor, but does not directly reveal genetic aberrations. We introduce CalicoST, an algorithm to simultaneously infer allele-specific copy number aberrations (CNAs) and a spatial model of tumor evolution from SRT of tumor slices. By modeling CNA-induced perturbations in both total and allele-specific gene expression, CalicoST identifies important types of CNAs - including copy-neutral loss of heterozygosity (CNLOH) and mirrored subclonal CNAs- that are invisible to total copy number analysis. CalicoST achieves high accuracy by modeling both correlations in space with a Hidden Markov Random Field and across genomic segments with a Hidden Markov Model.",

keywords = "cancer, copy number aberrations, evolution, phylogeography, spatially resolved transcriptomics",

author = "Cong Ma and Metin Balaban and Jingxian Liu and Siqi Chen and Li Ding and Raphael, {Benjamin J.}",

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Ma, C, Balaban, M, Liu, J, Chen, S , Ding, L & Raphael, BJ 2024, Inferring Allele-Specific Copy Number Aberrations and Tumor Phylogeography from Spatially Resolved Transcriptomics. in J Ma (ed.), Research in Computational Molecular Biology - 28th Annual International Conference, RECOMB 2024, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 14758 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 466-469, 28th International Conference on Research in Computational Molecular Biology, RECOMB 2024, Cambridge, United States, 04/29/24. https://doi.org/10.1007/978-1-0716-3989-4_54

Inferring Allele-Specific Copy Number Aberrations and Tumor Phylogeography from Spatially Resolved Transcriptomics. / Ma, Cong; Balaban, Metin; Liu, Jingxian et al.
Research in Computational Molecular Biology - 28th Annual International Conference, RECOMB 2024, Proceedings. ed. / Jian Ma. Springer Science and Business Media Deutschland GmbH, 2024. p. 466-469 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14758 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Ding, Li

AU - Raphael, Benjamin J.

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PY - 2024

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N2 - A key challenge in cancer research is to reconstruct the somatic evolution within a tumor over time and across space. Spatially resolved transcriptomics (SRT) measures gene expression at thousands of spatial locations in a tumor, but does not directly reveal genetic aberrations. We introduce CalicoST, an algorithm to simultaneously infer allele-specific copy number aberrations (CNAs) and a spatial model of tumor evolution from SRT of tumor slices. By modeling CNA-induced perturbations in both total and allele-specific gene expression, CalicoST identifies important types of CNAs - including copy-neutral loss of heterozygosity (CNLOH) and mirrored subclonal CNAs- that are invisible to total copy number analysis. CalicoST achieves high accuracy by modeling both correlations in space with a Hidden Markov Random Field and across genomic segments with a Hidden Markov Model.

AB - A key challenge in cancer research is to reconstruct the somatic evolution within a tumor over time and across space. Spatially resolved transcriptomics (SRT) measures gene expression at thousands of spatial locations in a tumor, but does not directly reveal genetic aberrations. We introduce CalicoST, an algorithm to simultaneously infer allele-specific copy number aberrations (CNAs) and a spatial model of tumor evolution from SRT of tumor slices. By modeling CNA-induced perturbations in both total and allele-specific gene expression, CalicoST identifies important types of CNAs - including copy-neutral loss of heterozygosity (CNLOH) and mirrored subclonal CNAs- that are invisible to total copy number analysis. CalicoST achieves high accuracy by modeling both correlations in space with a Hidden Markov Random Field and across genomic segments with a Hidden Markov Model.

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Research in Computational Molecular Biology - 28th Annual International Conference, RECOMB 2024, Proceedings

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Ma C, Balaban M, Liu J, Chen S , Ding L, Raphael BJ. Inferring Allele-Specific Copy Number Aberrations and Tumor Phylogeography from Spatially Resolved Transcriptomics. In Ma J, editor, Research in Computational Molecular Biology - 28th Annual International Conference, RECOMB 2024, Proceedings. Springer Science and Business Media Deutschland GmbH. 2024. p. 466-469. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-1-0716-3989-4_54