Project description:Clones in the cancer tissue exhibit different genotypes and phenotypes, which can be linked with their evolution, future progression, and possible treatment methods. The development of single-cell RNA sequencing allowed for the measurement of single-cell phenotypes, but without a genotype-phenotype map the phenotypes of the clones cannot be obtained. We introduce CaClust, a probabilistic graphical model that integrates whole exome, ultra-deep single-cell RNA and B-cell receptor sequencing data, to infer clonal genotypes, cell-to-clone mapping, and single-cell genotyping, enabling the combined study of clonal genotypes and phenotypes. CaClust outperforms a state-of-the-art model on simulated and experimental datasets of follicular lymphoma patients. CaClust results on patient data give insights into effects of driver mutations, follicular lymphoma evolution, and possible therapeutic targets. CaClust single-cell genotyping agrees with genotypes observed in an independent targeted resequencing experiment. In short, CaClust enables the first study of genotype-to-phenotype links in follicular lymphoma of such depth and scale.
Project description:Clones in the cancer tissue exhibit different genotypes and phenotypes, which can be linked with their evolution, future progression, and possible treatment methods. The development of single-cell RNA sequencing allowed for the measurement of single-cell phenotypes, but without a genotype-phenotype map the phenotypes of the clones cannot be obtained. We introduce CaClust, a probabilistic graphical model that integrates whole exome, ultra-deep single-cell RNA and B-cell receptor sequencing data, to infer clonal genotypes, cell-to-clone mapping, and single-cell genotyping, enabling the combined study of clonal genotypes and phenotypes. CaClust outperforms a state-of-the-art model on simulated and experimental datasets of follicular lymphoma patients. CaClust results on patient data give insights into effects of driver mutations, follicular lymphoma evolution, and possible therapeutic targets. CaClust single-cell genotyping agrees with genotypes observed in an independent targeted resequencing experiment. In short, CaClust enables the first study of genotype-to-phenotype links in follicular lymphoma of such depth and scale.
Project description:Clones in the cancer tissue exhibit different genotypes and phenotypes, which can be linked with their evolution, future progression, and possible treatment methods. The development of single-cell RNA sequencing allowed for the measurement of single-cell phenotypes, but without a genotype-phenotype map the phenotypes of the clones cannot be obtained. We introduce CaClust, a probabilistic graphical model that integrates whole exome, ultra-deep single-cell RNA and B-cell receptor sequencing data, to infer clonal genotypes, cell-to-clone mapping, and single-cell genotyping, enabling the combined study of clonal genotypes and phenotypes. CaClust outperforms a state-of-the-art model on simulated and experimental datasets of follicular lymphoma patients. CaClust results on patient data give insights into effects of driver mutations, follicular lymphoma evolution, and possible therapeutic targets. CaClust single-cell genotyping agrees with genotypes observed in an independent targeted resequencing experiment. In short, CaClust enables the first study of genotype-to-phenotype links in follicular lymphoma of such depth and scale.
Project description:Clones in the cancer tissue exhibit different genotypes and phenotypes, which can be linked with their evolution, future progression, and possible treatment methods. The development of single-cell RNA sequencing allowed for the measurement of single-cell phenotypes, but without a genotype-phenotype map the phenotypes of the clones cannot be obtained. We introduce CaClust, a probabilistic graphical model that integrates whole exome, ultra-deep single-cell RNA and B-cell receptor sequencing data, to infer clonal genotypes, cell-to-clone mapping, and single-cell genotyping, enabling the combined study of clonal genotypes and phenotypes. CaClust outperforms a state-of-the-art model on simulated and experimental datasets of follicular lymphoma patients. CaClust results on patient data give insights into effects of driver mutations, follicular lymphoma evolution, and possible therapeutic targets. CaClust single-cell genotyping agrees with genotypes observed in an independent targeted resequencing experiment. In short, CaClust enables the first study of genotype-to-phenotype links in follicular lymphoma of such depth and scale.
Project description:Follicular lymphoma is the most common indolent non-Hodgkin's lymphoma involving germinal centre B cells, with a subset of patients undergoing transformation to a diffuse large B-cell lymphoma (DLBCL) morphology for which the clinical outcomes are poor. To elucidate the differences in copy number profiles between FL and tFL groups, we performed Affymetrix SNP 6.0 Array analysis on 31 paired FL-tFL cases. We wanted to identify and compare recurrent somatic copy number alterations (CNAs) between the two groups (FL vs. tFL). In addition, the concordance and discordance in the copy neutral loss of heterozygosity (cnLOH) between the two groups were also investigated to identify recurrent target gene regions. Affymetrix SNP arrays were performed according to the manufacturer's directions on DNA extracted from follicular lymphoma (FL), transformed follicular lymphoma (tFL) and matching germline (GL) sample (if available). Copy number analysis of Affymetrix SNP 6.0 Array were performed on 91 DNA samples, consisting of 31 patients. Among the 31 patients, 19 had matching germline samples, while 12 had no germline samples. The Log R Ratio (LRR) values and the B Allele Frequency (BAF) values were subsequently calculated to search for copy number aberrations and copy neutral (CN)-LOH in the FL and tFL samples. Paired and unpaired analyses were performed accordingly.
Project description:Cutaneous T-cell lymphoma (CTCL) is a malignancy of skin-homing T cells. A subgroup of patients develops large cell transformation with progression to an aggressive lymphoma and with poor survival. We aimed to study the transformed CTCL (tCTCL) ecosystem using integrative approaches spanning whole-exome sequencing (WES), single-cell RNAseq, and immune profiling in a unique cohort of 56 patients with tCTCL
Project description:Comparison of gene expression profiles from diagnostic samples of diffuse large B-cell lymphoma (DLBCL) and follicular lymphoma (FL) to a patient case withsamples of primary and relapsed transformed FL.
Project description:Comparison of gene expression profiles from diagnostic samples of diffuse large B-cell lymphoma (DLBCL) and follicular lymphoma (FL) to a patient case withsamples of primary and relapsed transformed FL