Dataset Information

Leveraging Single-Cell Sequencing to Classify and Characterize Tumor Subgroups in Bulk RNA-Sequencing Data

ABSTRACT: Leveraging Single-Cell Sequencing to Classify and Characterize Tumor Subgroups in Bulk RNA-Sequencing Data

PROVIDER: PRJNA1103795 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR28785100.fastq.gz	Fastqsanger.gz
	SRR28785200.fastq.gz	Fastqsanger.gz
	SRR28785101.fastq.gz	Fastqsanger.gz
	SRR28785201.fastq.gz	Fastqsanger.gz
	SRR28785102.fastq.gz	Fastqsanger.gz

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Project description:Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous disease, commonly described by cell-of-origin (COO) molecular subtypes. We sought to identify novel patient subgroups through an unsupervised analysis of a large public dataset of gene expression profiles from newly diagnosed de novo DLBCL patients, yielding 2 biologically distinct subgroups characterized by differences in the tumor microenvironment. Pathway analysis and immune deconvolution algorithms identified higher B-cell content and a strong proliferative signal in subgroup A and enriched T-cell, macrophage, and immune/inflammatory signals in subgroup B, reflecting similar biology to published DLBCL stratification research. A gene expression classifier, featuring 26 gene expression scores, was derived from the public dataset to discriminate subgroup A (classifier-negative, immune-low) and subgroup B (classifier-positive, immune-high) patients. Subsequent application to an independent series of diagnostic biopsies replicated the subgroups, with immune cell composition confirmed via immunohistochemistry. Avadomide, a CRL4CRBN E3 ubiquitin ligase modulator, demonstrated clinical activity in relapsed/refractory DLBCL patients, independent of COO subtypes. Given the immunomodulatory activity of avadomide and the need for a patient-selection strategy, we applied the gene expression classifier to pretreatment biopsies from relapsed/refractory DLBCL patients receiving avadomide (NCT01421524). Classifier-positive patients exhibited an enrichment in response rate and progression-free survival of 44% and 6.2 months vs 19% and 1.6 months for classifier-negative patients (hazard ratio, 0.49; 95% confidence interval, 0.280-0.86; P = .0096). The classifier was not prognostic for rituximab, cyclophosphamide, doxorubicin, vincristine, prednisone or salvage immunochemotherapy. The classifier described here discriminates DLBCL tumors based on tumor and nontumor composition and has potential utility to enrich for clinical response to immunomodulatory agents, including avadomide.

Project description:Male breast cancer (MBC) is a rare and inadequately characterized disease. The aim of the present study was to characterize MBC tumors transcriptionally, to classify them into comprehensive subgroups, and to compare them with female breast cancer (FBC). Methods: Sixty-six clinicopathologically well-annotated fresh frozen MBC tumors were analyzed using Illumina Human HT-12 bead arrays, and a tissue microarray with 220 MBC tumors was constructed for validation using immunohistochemistry. Two external gene expression datasets were used for comparison purposes; 37 MBCs and 359 FBCs. Results: Using an unsupervised approach, we classified the MBC tumors into two subgroups, luminal M1 and luminal M2, respectively, with differences in tumor biological features and outcome, and which differed from the intrinsic subgroups described in FBC. The two subgroups were recapitulated in the external MBCs dataset. Luminal M2 tumors were characterized by high expression of immune response genes and genes associated with estrogen receptor (ER) signaling. Luminal M1 tumors, on the other hand, despite being ER positive by immunohistochemistry showed a lower correlation to genes associated with ER signaling and displayed a more aggressive phenotype and worse prognosis. Validation of two of the most differentially expressed genes, class 1 human leukocyte antigen (HLA) and the metabolizing gene N-acetyltransferase-1 (NAT1), respectively, revealed significantly better survival associated with high expression of both markers (HLA, hazard ratio (HR) 3.6, P=0.002; NAT1, HR 2.5, P=0.033). Importantly, NAT1 remained significant in a multivariate analysis (HR 2.8, P=0.040) and may thus be a novel prognostic marker in MBC. Conclusions: We have detected two unique and stable subgroups of MBC with differences in tumor biological features and outcome. They differ from the widely acknowledged intrinsic subgroups of FBC. As such they may constitute two novel subgroups of breast cancer, occurring exclusively in men, and which may consequently require novel treatment approaches. Finally, we identified NAT1 as a possible prognostic biomarker for MBC, as suggested by NAT1 positivity corresponding to better outcome.

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Leveraging Single-Cell Sequencing to Classify and Characterize Tumor Subgroups in Bulk RNA-Sequencing Data

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