Project description:We report an integrated analysis incorporating DNA copy number analyses, somatic exon mutations, mRNA expression via RNA-sequencing, and shotgun mass spectrometry analysis of protein abundance in 108 surgically resected squamous cell lung cancers (SCC) with accompanying clinical outcome, evaluation of tumor pathology, and other clinically relevant data. We identified three major subtypes of SCC at the proteomic level, with two groups associated with inflammation/immune response or oxidation-reduction biology. Inflamed tumors could be further sub-classified based on neutrophil infiltration or antigen presentation proteomes and reflected patterns of infiltrating immune cells. No gene mutations, mRNA signatures, or proteomic subclasses were associated with outcomes; however, the presence of B-cell rich tertiary lymph node structures could be associated with better patient outcomes. By integrating our proteogenomic data with publicly available RNA interference screen data, we identified TP63, PSAT1, and AKR1C3 as vulnerabilities in SCC, particularly in the redox proteomic group. This cohort and its deep molecular data serves as an important resource to better understand biology and targets associated with SCC.
Project description:We report an integrated analysis incorporating DNA copy number analyses, somatic exon mutations, mRNA expression via RNA-sequencing, and shotgun mass spectrometry analysis of protein abundance in 108 surgically resected squamous cell lung cancers (SCC) with accompanying clinical outcome, evaluation of tumor pathology, and other clinically relevant data. We identified three major subtypes of SCC at the proteomic level, with two groups associated with inflammation/immune response or oxidation-reduction biology. Inflamed tumors could be further sub-classified based on neutrophil infiltration or antigen presentation proteomes and reflected patterns of infiltrating immune cells. No gene mutations, mRNA signatures, or proteomic subclasses were associated with outcomes; however, the presence of B-cell rich tertiary lymph node structures could be associated with better patient outcomes. By integrating our proteogenomic data with publicly available RNA interference screen data, we identified TP63, PSAT1, and AKR1C3 as vulnerabilities in SCC, particularly in the redox proteomic group. This cohort and its deep molecular data serves as an important resource to better understand biology and targets associated with SCC.
Project description:How genomic and transcriptomic alterations affect the functional proteome in lung cancer is not fully understood. Here, we integrate DNA copy number, somatic mutations, RNA-sequencing, and expression proteomics in a cohort of 108 squamous cell lung cancer (SCC) patients. We identify three proteomic subtypes, two of which (Inflamed, Redox) comprise 87% of tumors. The Inflamed subtype is enriched with neutrophils, B-cells, and monocytes and expresses more PD-1. Redox tumours are enriched for oxidation-reduction and glutathione pathways and harbor more NFE2L2/KEAP1 alterations and copy gain in the 3q2 locus. Proteomic subtypes are not associated with patient survival. However, B-cell-rich tertiary lymph node structures, more common in Inflamed, are associated with better survival. We identify metabolic vulnerabilities (TP63, PSAT1, and TFRC) in Redox. Our work provides a powerful resource for lung SCC biology and suggests therapeutic opportunities based on redox metabolism and immune cell infiltrates.
Project description:<p>To provide a detailed analysis on how genomic and transcriptomic alterations related to proteome level changes in squamous cell lung cancer (SCC), we performed an integrated analysis incorporating DNA copy number, somatic mutations, expression RNA-sequencing, and expression proteomics in 108 SCC nested within clinical outcome, tumor pathology, and other clinically relevant data.</p>
Project description:Current clinical therapy of non-small cell lung cancer depends on histo-pathological classification. This approach poorly predicts clinical outcome for individual patients. Proteogenomic characterization analysis holds promise to improve clinical stratification, thus paving the way for individualized therapy. We investigated proteogenomic characterization and performed comprehensive integrative genomic analysis of human large cell lung cancer. Here we analyzed proteomes of 29 paired normal lung tissues and large cell lung cancer, identified significantly deregulated proteins associated with large cell lung cancer.