Dataset Information

Human Lung cancer proteomics and proteogenomics LC/MS&MS

ABSTRACT: In this project, a cohort of early stage NSCLC samples (141) were analyzed by MS-based proteomics in order to identify molecular subtypes at the phenotype level. In total, close to 14 000 proteins were identified and quantified across samples by HiRIEF-LC-MS and TMT based relative quantification. Further, six identified NSCLC proteome subtypes were investigated in relation to cancer driver pathways, immune phenotypes and neoantigen expression based on the generated MS-data.

INSTRUMENT(S): Q Exactive HF

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Lung

DISEASE(S): Lung Cancer

SUBMITTER: Georgios Mermelekas

LAB HEAD: Janne Lehtiö

PROVIDER: PXD020191 | Pride | 2021-12-15

REPOSITORIES: Pride

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Publications

Proteogenomics of non-small cell lung cancer reveals molecular subtypes associated with specific therapeutic targets and immune evasion mechanisms.

Lehtiö Janne J Arslan Taner T Siavelis Ioannis I Pan Yanbo Y Socciarelli Fabio F Berkovska Olena O Umer Husen M HM Mermelekas Georgios G Pirmoradian Mohammad M Jönsson Mats M Brunnström Hans H Brustugun Odd Terje OT Purohit Krishna Pinganksha KP Cunningham Richard R Foroughi Asl Hassan H Isaksson Sofi S Arbajian Elsa E Aine Mattias M Karlsson Anna A Kotevska Marija M Gram Hansen Carsten C Drageset Haakensen Vilde V Helland Åslaug Å Tamborero David D Johansson Henrik J HJ Branca Rui M RM Planck Maria M Staaf Johan J Orre Lukas M LM

Nature cancer 20211122 11

Despite major advancements in lung cancer treatment, long-term survival is still rare, and a deeper understanding of molecular phenotypes would allow the identification of specific cancer dependencies and immune evasion mechanisms. Here we performed in-depth mass spectrometry (MS)-based proteogenomic analysis of 141 tumors representing all major histologies of non-small cell lung cancer (NSCLC). We identified six distinct proteome subtypes with striking differences in immune cell composition and ...[more]

PMID: 34870237

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Project description:This study constructed a non-gapped bacterial artificial chromosomes (BAC) array containing 3604 BAC clones covering 18 lung cancer-related chromosome imbalance hotspot regions. Using this specialized array, DNA from tumor and normal tissues of 40 Asian and 20 Caucasian non-small cell lung cancer (NSLCL) patients was analyzed by array-comparative genomic hybridization (array-CGH). Block-wise normalization and a Bayes regression approach were used to refine the chromosomal imbalance regions identified by array-CGH. The array-CGH results then analyzed by MetaCore software to identify potential cancer-related genes. Finally, 273 genes showing significantly associated with molecular pathway, cancer biomarker, and gene ontology database, such as ZNF322A on 6p22.1, ARHGAP19 on 10q24.1, FRAT2 on 10q24.1, and PAFAH1B1 on 17p13.3 functioning in MAPK, Rho GTPase, and Wnt, and motility control pathways with frequent copy number gain were selected. This study mapped concisely the novel oncogenes or tumor suppressor genes in lung cancer and revealed insights of difference on chromosomal imbalance between lung cancer from Asian and Caucasian. Clinical samples preparation and DNA extraction: Tissues were collected after obtaining appropriate institutional review board permission and informed consent from the recruited patients. Surgically resected tumor tissue and corresponding normal tissue were collected from 40 patients diagnosed with primary NSCLC admitted to Taipei Veterans General Hospital, Taiwan and 20 Caucasian NSCLC tumor tissues from University of Chicago, USA. Histological classification was determined according to the WHO classification and the tumor-node-metastasis system. Information on the age, sex, tumor type, tumor stage and smoking history of the patients was obtained from hospital records. The genomic DNA was prepared using proteinase K digestion and phenol-chloroform extraction. Array-CGH: The genomic microarray used containing 3604 BAC clones representing the human genome at 18 non-gap chromosome regions. Clone mapping was analyzed using the BAC end pairs database of UCSC Genome Bioinformatics (http://genome.ucsc.edu/). The BAC library used was RP-11, which was provided from Genome Research Center, University of Yang-Ming. The extracted BAC DNA was amplified to 10 μg by Phi29 DNA polymerase reaction (TempliPhi DNA Sequencing Template Amplification Kit, Amersham, Princeton, NJ), and digested by DNaseI (New England Biolabs, Ipswich, MA), and purified by MultiScreen-PCR Plates (Millipore, Danvers, MA). After digestion, the DNA fragments were resolved by gel electrophoresis and the fragment size were optimized to within 0.5–3 kb. Finally, the purified BAC DNA fragments were dotted triplicate in 72 blocks on the glass slides (Corning, NY, USA) with spotting solution (50% DMSO, 50% D2H2O). In brief, approximately 500 ng of tumor genomic DNA and reference DNA (for Asian samples: 28 matched-normal genomic DNA pool; for Caucasian samples: commercial human genomic DNA, Invitrogen) were labeled by random priming using BioPrime DNA Labeling System (Invitrogen) with Cy3-dCTP (tumor) or Cy5-dCTP (reference). Labeled tumor and reference DNA were combined together with 100 μg human Cot-1 DNA (Invitrogen). Hybridization was performed using MAUI hybridization system (BioMcro Systems, Salt Lake City, UT), agitating the hybridization solution for 50 hours at 50℃. The arrays were scanned using scanner GenePix4000B (Axon Instruments, Foster City, CA) and the images were segmented and transformed to Cy3-Cy5 log ratio using GenePix software by Axon Instruments, Inc. (http://www.axon.com). Further data processing, a normalization method called “block-wise normalization”, which normalized the array-CGH data by the log ratio of the self-self hybridization of reference DNA according to the mean and the standard deviations of each array block, was established.

Dataset Information

Human Lung cancer proteomics and proteogenomics LC/MS&MS

Publications

Proteogenomics of non-small cell lung cancer reveals molecular subtypes associated with specific therapeutic targets and immune evasion mechanisms.

Similar Datasets

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