Biology subjects

Darvish Shafighi, S.

Publications and source records attributed to Darvish Shafighi, S..

3 recordsLinked to original sources

bioRxiv2022

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

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.

cancer biology↗

bioRxiv2022

Celloscope: a probabilistic model for marker-gene-driven cell type deconvolution in spatial transcriptomics data

Spatial transcriptomics maps gene expression across tissues, posing the challenge of determining the spatial arrangement of different cell types. However, spatial transcriptomics spots contain multiple cells. Therefore, the observed signal comes from mixtures of cells of different types. Here, we propose an innovative probabilistic model, Celloscope, that utilizes established prior knowledge on marker genes for cell type deconvolution from spatial transcriptomics data. Celloscope outperformed other methods on simulated data, successfully indicated known brain structures and spatially distinguished between inhibitory and excitatory neuron types based in mouse brain tissue, and dissected large heterogeneity of immune infiltrate composition in prostate gland tissue.

bioinformatics↗

bioRxiv2020

CACTUS: integrating clonal architecture with genomic clustering and transcriptome profiling of single tumor cells

BackgroundDrawing genotype-to-phenotype maps in tumors is of paramount importance for understanding tumor heterogeneity. Assignment of single cells to their tumor clones of origin can be approached by matching the genotypes of the clones to the mutations found in RNA sequencing of the cells. The confidence of the cell-to-clone mapping can be increased by accounting for additional measurements. Follicular lymphoma, a malignancy of mature B cells that continuously acquire mutations in parallel in the exome and in B-cell receptor loci, presents a unique opportunity to align exome-derived mutations with B-cell receptor clonotypes as an independent measure for clonal evolution. ResultsHere, we propose CACTUS, a probabilistic model that leverages the information from an independent genomic clustering of cells and exploits the scarce single cell RNA sequencing data to map single cells to given imperfect genotypes of tumor clones. We apply CACTUS to two follicular lymphoma patient samples, integrating three measurements: whole exome sequencing, single cell RNA sequencing, and B-cell receptor sequencing. CACTUS outperforms a predecessor model by confidently assigning cells and B-cell receptor clonotypes to the tumor clones. ConclusionsThe integration of independent measurements increases model certainty and is the key to improving model performance in the challenging task of charting the genotype-to-phenotype maps in tumors. CACTUS opens the avenue to study the functional implications of tumor heterogeneity, and origins of resistance to targeted therapies.

cancer biology↗