Leveraging Single-Cell Sequencing to Classify and Characterize Tumor Subgroups in Bulk RNA-Sequencing Data
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE264680 | GEO | 2024/04/23
REPOSITORIES: GEO
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Project description:Leveraging Single-Cell Sequencing to Classify and Characterize Tumor Subgroups in Bulk RNA-Sequencing Data
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Project description:Background: The Cancer Genome Atlas identified four molecular subgroups of endometrial cancer with survival differences based on whole genome, transcriptomic, and proteomic characterization. Clinically accessible algorithms that reproduce this data are needed. Our aim was to determine if targeted sequencing alone allowed for molecular classification of endometrial cancer.Methods: Using a custom-designed 156 gene panel, we analyzed 47 endometrial cancers and matching non-tumor tissue. Variants were annotated for pathogenicity and medical records were reviewed for the clinicopathologic variables. Using molecular characteristics, tumors were classified into four subgroups. Group 1 included patients with?>?570 unfiltered somatic variants,?>?9 cytosine to adenine nucleotide substitutions per sample, and?<?1 cytosine to guanine nucleotide substitution per sample. Group 2 included patients with any somatic mutation in MSH2, MSH6, MLH1, PMS2. Group 3 included patients with TP53 mutations without mutation in mismatch repair genes. Remaining patients were classified as group 4. Analyses were performed using SAS 9.4 (SAS Institute Inc., Cary, North Carolina, USA).Results: Endometrioid endometrial cancers had more candidate variants of potential pathogenic interest (median 6 IQR 4.13 vs. 2 IQR 2.3; p?<?0.01) than uterine serous cancers. PTEN (82% vs. 15%, p?<?0.01) and PIK3CA (74% vs. 23%, p?<?0.01) mutations were more frequent in endometrioid than serous carcinomas. TP53 (18% vs. 77%, p?<?0.01) mutations were more frequent in serous carcinomas. Visual inspection of the number of unfiltered somatic variants per sample identified six grade 3 endometrioid samples with high tumor mutational burden, all of which demonstrated POLE mutations, most commonly P286R and V411L. Of the grade 3 endometrioid carcinomas, those with POLE mutations were less likely to have risk factors necessitating adjuvant treatment than those with low tumor mutational burden. Targeted sequencing was unable to assign samples to microsatellite unstable, copy number low, and copy number high subgroups.Conclusions: Targeted sequencing can predict the presence of POLE mutations based on the tumor mutational burden. However, targeted sequencing alone is inadequate to classify endometrial cancers into molecular subgroups identified by The Cancer Genome Atlas.
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Project description:Copy number variants (CNVs) are currently defined as genomic sequences that are polymorphic in copy number and range in length from 1,000 to several million base pairs. Among current array-based CNV detection platforms, long-oligonucleotide arrays promise the highest resolution. However, the performance of currently available analytical tools suffers when applied to these data because of the lower signal:noise ratio inherent in oligonucleotide-based hybridization assays. We have developed wuHMM, an algorithm for mapping CNVs from array comparative genomic hybridization (aCGH) platforms comprised of 385,000 to more than 3 million probes. wuHMM is unique in that it can utilize sequence divergence information to reduce the false positive rate (FPR). We apply wuHMM to 385K-aCGH, 2.1M-aCGH, and 3.1M-aCGH experiments comparing the 129X1/SvJ and C57BL/6J inbred mouse genomes. We assess wuHMM’s performance on the 385K platform by comparison to the higher resolution platforms and we independently validate 10 CNVs. The method requires no training data and is robust with respect to changes in algorithm parameters. At a FPR of less than 10%, the algorithm can detect CNVs with five probes on the 385K platform and three on the 2.1M and 3.1M platforms, resulting in effective resolutions of 24 kb, 2-5 kb, and 1 kb, respectively. Keywords: CNV detection algorithm development and assessment
2008-03-12 | GSE10511 | GEO
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2019-06-05 | GSE132172 | GEO
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