Spatially resolved clonal copy number alterations in benign and malignant tissue

Erickson, Andrew; He, Mengxiao; Berglund, Emelie; Marklund, Maja; Mirzazadeh, Reza; Schultz, Niklas; Kvastad, Linda; Andersson, Alma; Bergenstråhle, Ludvig; Bergenstråhle, Joseph; Larsson, Ludvig; Galicia, Leire Alonso; Shamikh, Alia; Basmaci, Elisa; Ståhl, Teresita Díaz de; Rajakumar, Timothy; Doultsinos, Dimitrios; Thrane, Kim; Ji, Andrew L.; Khavari, Paul A.; Tarish, Firaz; Tanoglidi, Anna; Maaskola, Jonas; Colling, Richard; Mirtti, Tuomas; Hamdy, Freddie C.; Woodcock, Dan J.; Helleday, Thomas; Mills, Ian G.; Lamb, Alastair; Lundeberg, Joakim

doi:10.1038/s41586-022-05023-2

Cited by 141 publications

(147 citation statements)

References 47 publications

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“…Copy number inference helped delineate tumour regions from non-malignant areas but also revealed subclonal heterogeneity in five of the eight samples studied. Our work mirrors a recent report of spatially restricted subclones observed via spatial inferred CNVs (siCNVs) 62 . For one sample, microdissection and ultra-low-pass whole genome sequencing confirmed the presence of subclone-specific CNAs.…”

Section: Discussionsupporting

confidence: 89%

Spatial transcriptomics reveals ovarian cancer subclones with distinct tumour microenvironments

Denisenko

Kock

Tan³

et al. 2022

Preprint

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High-grade serous ovarian carcinoma (HGSOC) is characterised by recurrence, chemotherapy resistance and overall poor prognosis. Genetic heterogeneity of tumour cells and the microenvironment of the tumour have been hypothesised as key determinants of treatment resistance and relapse. Here, using a combination of spatial and single cell transcriptomics (10x Visium and Chromium platforms), we examine tumour genetic heterogeneity and infiltrating populations of HGSOC samples from eight patients with variable response to neoadjuvant chemotherapy. By inferring gross copy number alterations (CNAs), we identified distinct tumour subclones co-existing within individual tumour sections. These tumour subclones have unique CNA profiles and spatial locations within each tumour section, which were further validated by ultra-low-pass whole genome sequencing. Differential expression analysis between subclones within the same section identified both tumour cell intrinsic expression differences and markers indicative of different infiltrating cell populations. The gene sets differentially expressed between subclones were significantly enriched for genes encoding plasma membrane and secreted proteins, indicative of subclone-specific microenvironments. Furthermore, we identified tumour derived ligands with variable expression levels between subclones that correlated or anticorrelated with various non-malignant cell infiltration patterns. We highlight several of these that are potentially direct tumour-stroma/immune cell relationships as the non-malignant cell type expresses a cognate receptor for the tumour derived ligand. These include predictions of CXCL10-CXCR3 mediated recruitment of T and B cells to associate with the subclones of one patient and CD47-SIRPA mediated exclusion of macrophages from association with subclones of another. Finally, we show that published HGSOC molecular subtype signatures associated with prognosis are heterogeneously expressed across tumour sections and that areas containing different tumour subclones with different infiltration patterns can match different subtypes. Our study highlights the high degree of intratumoural subclonal and infiltrative heterogeneity in HGSOC which will be critical to better understand resistance and relapse.

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Section: Discussionsupporting

confidence: 89%

Spatial transcriptomics reveals ovarian cancer subclones with distinct tumour microenvironments

Denisenko

Kock

Tan³

et al. 2022

Preprint

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“…UMAP or tSNE, to visualize the full repertoire of factors in a single image. The full model is described elsewhere 53 .…”

Section: Introductionmentioning

confidence: 99%

Spatio-temporal analysis of prostate tumors in situ suggests pre-existence of treatment-resistant clones

et al. 2022

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The molecular mechanisms underlying lethal castration-resistant prostate cancer remain poorly understood, with intratumoral heterogeneity a likely contributing factor. To examine the temporal aspects of resistance, we analyze tumor heterogeneity in needle biopsies collected before and after treatment with androgen deprivation therapy. By doing so, we are able to couple clinical responsiveness and morphological information such as Gleason score to transcriptome-wide data. Our data-driven analysis of transcriptomes identifies several distinct intratumoral cell populations, characterized by their unique gene expression profiles. Certain cell populations present before treatment exhibit gene expression profiles that match those of resistant tumor cell clusters, present after treatment. We confirm that these clusters are resistant by the localization of active androgen receptors to the nuclei in cancer cells post-treatment. Our data also demonstrates that most stromal cells adjacent to resistant clusters do not express the androgen receptor, and we identify differentially expressed genes for these cells. Altogether, this study shows the potential to increase the power in predicting resistant tumors.

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“…Besides, Erickson et al . [35] developed a method to infer genome-wide copy number variations (CNVs) from spatially resolved mRNA profiles in situ that reveals distinct CNV based clonal patterns within tumors. In contrast to Tumoroscope, however, both these methods do not directly address the problem of deconvoluting the mixture of cancer clones per each spot.…”

Section: Discussionmentioning

confidence: 99%

“…The recently developed method STARCH [34] combines RNA-sequencing of ST spots and DNAsequencing from neighboring tissues in the same tumor sample to infer the spatial arrangement of clones based on their copy number profiles. Besides, Erickson et al [35] developed a method to infer genome-wide copy number variations (CNVs) from spatially resolved mRNA profiles in situ that reveals distinct CNV based clonal patterns within tumors. In contrast to Tumoroscope, however, both these methods do not directly address the problem of deconvoluting the mixture of cancer clones per each spot.…”

Section: Discussionmentioning

confidence: 99%

Tumoroscope: a probabilistic model for mapping cancer clones in tumor tissues

Shafighi

Geras

Jurzysta

et al. 2022

Preprint

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Spatial and genomic heterogeneity of tumors is the key for cancer progression, treatment, and survival. However, a technology for direct mapping the clones in the tumor tissue based on point mutations is lacking. Here, we propose Tumoroscope, the first probabilistic model that accurately infers cancer clones and their high-resolution localization by integrating pathological images, whole exome sequencing, and spatial transcriptomics data. In contrast to previous methods, Tumoroscope explicitly addresses the problem of deconvoluting the proportions of clones in spatial transcriptomics spots. Applied to a reference prostate cancer dataset and a newly generated breast cancer dataset, Tumoroscope reveals spatial patterns of clone colocalization and mutual exclusion in sub-areas of the tumor tissue. We further infer clone-specific gene expression levels and the most highly expressed genes for each clone. In summary, Tumoroscope enables an integrated study of the spatial, genomic, and phenotypic organization of tumors.

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Spatially resolved clonal copy number alterations in benign and malignant tissue

Cited by 141 publications

References 47 publications

Spatial transcriptomics reveals ovarian cancer subclones with distinct tumour microenvironments

Spatial transcriptomics reveals ovarian cancer subclones with distinct tumour microenvironments

Spatio-temporal analysis of prostate tumors in situ suggests pre-existence of treatment-resistant clones

Tumoroscope: a probabilistic model for mapping cancer clones in tumor tissues

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