Single Amino Acid Variant Profiles of Subpopulations in the MCF-7 Breast Cancer Cell Line

Tan, Zhijing; Nie, Song; McDermott, Sean; Wicha, Max S.; Lubman, David M.

doi:10.1021/acs.jproteome.6b00824

Cited by 10 publications

(12 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our HLA typing agrees with the data published by Boegel et al [29]; however, there is a discrepancy between our data and the data used by the TRON Cell Line Portal (http://celllines.tron-mainz.de/) with regard to MCF-7 cell line. This discrepancy could arise if there were multiple, genetically unique subpopulations within the MCF-7 breast cancer cell line as described by Tan et al [42]. The validity of our HLA typing was also confirmed by the observation of the same HLA-typing profile in LY2 cells, as both MCF7 and LY2 cell lines were derived from the same patient [43].…”

Section: Resultssupporting

confidence: 57%

MHC class I loaded ligands from breast cancer cell lines: A potential HLA-I-typed antigen collection

Rozanov

Chiotti

et al. 2018

Journal of Proteomics

View full text Add to dashboard Cite

Breast cancer therapy based on amplifying a patient’s antitumor immune response depends on the availability of appropriate MHC class I-restricted, breast cancer-specific epitopes. To build a catalog of peptides presented by breast cancer cells, we undertook systematic MHC class I immunoprecipitation followed by elution of MHC class I-loaded peptides in breast cancer cell lines. We determined the sequence of 3,196 MHC class I-bound peptides representing 1,921 proteins from a panel of 20 breast cancer cell lines including basal, luminal, and claudin-low subtypes. The data has been deposited to the ProteomeXchange with identifier PXD006406. After removing duplicate peptides, i.e., the same peptide eluted from more than one cell line, the total number of unique peptides was 2,740. Of the unique peptides eluted, more than 1,750 had been previously identified, and of these, sixteen have been shown to be immunogenic. Importantly, only 3 of these immunogenic peptides have been identified in breast cancer cells in earlier studies. MHC class I binding probability of eluted peptides was used to plot the distribution of MHC class I allele-specific peptides in accordance with the binding score for each breast cancer cell line. We also determined that the tested breast cancer cells presented 89 mutation-containing peptides and peptides derived from aberrantly translated genes, 7 of which were shared between four or two different cell lines. Overall, the high throughput identification of MHC class I-loaded peptides is an effective strategy for systematic characterization of cancer peptides, and could be employed for design of multi-peptide anticancer vaccines.

show abstract

Section: Resultssupporting

confidence: 57%

MHC class I loaded ligands from breast cancer cell lines: A potential HLA-I-typed antigen collection

Rozanov

Chiotti

et al. 2018

Journal of Proteomics

View full text Add to dashboard Cite

show abstract

“…The number of identified proteins is much higher than in a previous study of 50 000 cells due to the strategies below. 34…”

Section: Resultsmentioning

confidence: 99%

Single Amino Acid Variant Discovery in Small Numbers of Cells

Tan

Carruthers

et al. 2018

J. Proteome Res.

Self Cite

View full text Add to dashboard Cite

We have performed deep proteomic profiling down to as few as 9 Panc-1 cells using sample fractionation, TMT multiplexing, and a carrier/reference strategy. Off line fractionation of the TMT-labeled sample pooled with TMT-labeled carrier Panc-1 whole cell proteome was achieved using alkaline reversed phase spin columns. The fractionation in conjunction with the carrier/reference (C/R) proteome allowed us to detect 47 414 unique peptides derived from 6261 proteins, which provided a sufficient coverage to search for single amino acid variants (SAAVs) related to cancer. This high sample coverage is essential in order to detect a significant number of SAAVs. In order to verify genuine SAAVs versus false SAAVs, we used the SAVControl pipeline and found a total of 79 SAAVs from the 9-cell Panc-1 sample and 174 SAAVs from the 5000-cell Panc-1 C/R proteome. The SAAVs as sorted into high confidence and low confidence SAAVs were checked manually. All the high confidence SAAVs were found to be genuine SAAVs, while half of the low confidence SAAVs were found to be false SAAVs mainly related to PTMs. We identified several cancer-related SAAVs including KRAS, which is an important oncoprotein in pancreatic cancer. In addition, we were able to detect sites involved in loss or gain of glycosylation due to the enhanced coverage available in these experiments where we can detect both sites of loss and gain of glycosylation.

show abstract

“…Several fields in proteomics are currently interested in detecting SAPs. Especially proteogenomics deals with this, where the most developed field is oncoproteogenomics since tumor mutations are of particular interest in fundamental and diagnostic cancer research [15,16,17]. On a practical note, the International Cancer Genome Consortium has established that although it should largely treat the non-cancerous sequencing data as personal data, genetic variants specific to tumor cells are nonetheless anonymous, with only rare exceptions.…”

Section: Proteomics As Sensitive Datamentioning

confidence: 99%

Beyond Genes: Re-Identifiability of Proteomic Data and Its Implications for Personalized Medicine

Boonen

Hens

Menschaert

et al. 2019

Genes

View full text Add to dashboard Cite

The increasing availability of high throughput proteomics data provides us with opportunities as well as posing new ethical challenges regarding data privacy and re-identifiability of participants. Moreover, the fact that proteomics represents a level between the genotype and the phenotype further exacerbates the situation, introducing dilemmas related to publicly available data, anonymization, ownership of information and incidental findings. In this paper, we try to differentiate proteomics from genomics data and cover the ethical challenges related to proteomics data sharing. Finally, we give an overview of the proposed solutions and the outlook for future studies.

show abstract

Single Amino Acid Variant Profiles of Subpopulations in the MCF-7 Breast Cancer Cell Line

Cited by 10 publications

References 47 publications

MHC class I loaded ligands from breast cancer cell lines: A potential HLA-I-typed antigen collection

MHC class I loaded ligands from breast cancer cell lines: A potential HLA-I-typed antigen collection

Single Amino Acid Variant Discovery in Small Numbers of Cells

Beyond Genes: Re-Identifiability of Proteomic Data and Its Implications for Personalized Medicine

Contact Info

Product

Resources

About