This vignette from the R package JMDplots version 1.2.19-9 shows chemical metrics for proteins that are differentially expressed in breast cancer compared to normal tissue. The analysis is described in more detail in a paper (Dick, 2021). Abbreviations:
Differences are calculated as (median value for up-regulated proteins) - (median value for down-regulated proteins). Dashed lines enclose the 50% confidence region for highest probability density.
In the table, values of ΔZC and ΔnH2O are multiplied by 1000, values of ΔMW are multiplied by 100, and negative values are shown in bold. Abbreviations:
| set | reference (description) | ndown | nup | ΔZC | ΔnH2O | ΔnAA | ΔMW |
|---|---|---|---|---|---|---|---|
| a |
AMG+08 (T / periphery)
|
56 | 128 | -10 | 5 | 126 | 47 |
| b |
CIR+10 (LCM T / N)
|
59 | 62 | -43 | 10 | -8 | 126 |
| c |
SRG+10 (invasive carcinoma / N)
|
56 | 61 | 28 | 33 | -134 | -33 |
| d |
HTP+11 (microvessels IDC / nonmalignant)
|
40 | 86 | -26 | 22 | -46 | 216 |
| e |
GTM+12 (IDC / benign)
|
27 | 166 | -17 | 62 | -112 | -19 |
| f |
GTM+12 (IDC / adjacent N)
|
71 | 122 | -2 | 73 | -64 | 7 |
| g |
LLL+13 (TNBC tumor / paraneoplastic)
|
128 | 80 | -3 | -9 | 12 | 90 |
| h |
SRS+13 (DCIS / matched N)
|
56 | 50 | -1 | 34 | -27 | -48 |
| i |
SRS+13 (IDC / matched N)
|
73 | 71 | 12 | 44 | -172 | 53 |
| j |
GSB+14 (T / distant N)
|
88 | 108 | -2 | 35 | -187 | 150 |
| k |
PPH+14 (multiple subtypes T / N)
|
54 | 40 | -1 | -23 | -254 | -71 |
| l |
CVJ+15 (TNBC T / N)
|
92 | 124 | -11 | 3 | -56 | 116 |
| m |
PGT+16 (early stage T / N)
|
21 | 38 | -11 | 27 | -72 | 94 |
| n |
PGT+16 (late stage T / N)
|
24 | 42 | -13 | 20 | -97 | 75 |
| o |
PBR+16 (FFPE T / adjacent N)
|
406 | 563 | 0 | -24 | -515 | -28 |
| p |
BST+17 (tumor epithelium / N)
|
234 | 245 | -28 | 3 | 10 | 113 |
| q |
TZD+18 (T / adjacent N all)
|
268 | 1836 | -39 | -2 | 40 | 148 |
| r |
TZD+18 (T / adjacent N basal)
|
291 | 1058 | -41 | 1 | 97 | 133 |
| s |
GCS+19 (T / contralateral N)
|
162 | 235 | -9 | 40 | 18 | 64 |
| t |
GCS+19 (T / adjacent N)
|
159 | 246 | -5 | 40 | 44 | 82 |
| u |
LLC+19 (T / adjacent N)
|
93 | 48 | -22 | 14 | -99 | 250 |
| v |
LLF+20 (TNBC grade I.II T / adjacent N)
|
556 | 309 | -15 | -9 | -50 | 42 |
| w |
LLF+20 (TNBC grade III T / adjacent N)
|
667 | 359 | -24 | 17 | -93 | 120 |
Gene names or other identifiers were converted to UniProt accession numbers using the UniProt mapping tool.
a. Extracted from Supporting Information RTF files of Alldridge et al. (2008). Proteins identified by any number of peptides in both cancer and matched periphery were excluded; of the remaining proteins those identified by at least two peptides were used. b. Table S2(a) of Cha et al. (2010) (proteins differentially abundant at or above 99% confidence level). c. Table 4 of Sutton et al. (2010). d. Tables 1 and 2 of Hill et al. (2011). e. f. Supporting Table 6 of Gormley et al. (2012). g. Table S1 of Liang et al. (2013). h. i. Table 2 of Shaheed et al. (2013). For DCIS (3 patients), proteins were classified as up/down regulated if 2 or more ratios were greater/less than 1, and no ratios were less/greater than 1. For IC (4 patients), proteins were classified as up/down regulated if 3 or more ratios were greater/less than 1, and no ratios were less/greater than 1. j. Extracted from Table S2 of Groessl et al. (2014). Values in all LFQ columns (distant, near, tumor) were quantile normalized, then the ratio between tumor and distant was calculated. Proteins with normalized LFQ ratios > 1.2 or < 1/1.2 and p-value < 0.05 were identified as differentially expressed. k. Supplementary Tables 12, 13, 14 and 15 of Panis et al. (2014), filtered to include proteins that are up- or down-regulated in all subtypes (LUM, LUMHER, HER, TN). l. Supplemental Data S3 of Campone et al. (2015), filtered to include proteins with min and max credible intervals for expression ratios that are both <1 or >1. m. n. Supplementary Table S3 of Pendharkar et al. (2016). o. Table S4A of Pozniak et al. (2016). p. Supplemental Table 4 of Braakman et al. (2017), filtered to include proteins with p-value < 2. q. r. Tables S5-1 (differentially expressed proteins for 52 tumor / non-cancerous tissue pairs) and S5-2 (13 basal-like tumor / non-cancerous tissue pairs) of Tang et al. (2018), filtered to include proteins with log2 fold change > 1 or < -1. s. t. Supplementary File S1 of Gomig et al. (2019) (PT: primary breast tissue; NCT: non tumor contralateral breast tissue; ANT: non tumor adjacent breast tissue). u. Supplementary Table 2 of Liu et al. (2019). v. w. Supplementary Tables S2 and S3 of Lin et al. (2020).
Alldridge L, Metodieva G, Greenwood C, Al-Janabi K, Thwaites L, Sauven P, Metodiev M. 2008. Proteome profiling of breast tumors by gel electrophoresis and nanoscale electrospray ionization mass spectrometry. Journal of Proteome Research 7:1458–1469. DOI: 10.1021/pr7007829.
Braakman RBH, Stingl C, Tilanus-Linthorst MMA, Deurzen CHM, Timmermans MAM, Smid M, Foekens JA, Luider TM, Martens JWM, Umar A. 2017. Proteomic characterization of microdissected breast tissue environment provides a protein-level overview of malignant transformation. Proteomics 17:1600213. DOI: 10.1002/pmic.201600213.
Campone M, Valo I, Jézéquel P, Moreau M, Boissard A, Campion L, Loussouarn D, Verriele V, Coqueret O, Guette C. 2015. Prediction of recurrence and survival for triple-negative breast cancer (TNBC) by a protein signature in tissue samples. Molecular & Cellular Proteomics 14:2936–2946. DOI: 10.1074/mcp.M115.048967.
Cha S, Imielinski MB, Rejtar T, Richardson EA, Thakur D, Sgroi DC, Karger BL. 2010. In situ proteomic analysis of human breast cancer epithelial cells using laser capture microdissection: Annotation by protein set enrichment analysis and gene ontology. Molecular & Cellular Proteomics 9:2529–2544. DOI: 10.1074/mcp.M110.000398.
Gomig THB, Cavalli IJ, Souza RLR de, Lucena ACR, Batista M, Machado KC, Marchini FK, Marchi FA, Lima RS, Andrade Urban C de, Cavalli LR, Souza Fonseca Ribeiro EM de. 2019. High-throughput mass spectrometry and bioinformatics analysis of breast cancer proteomic data. Data in Brief 25:104125. DOI: 10.1016/j.dib.2019.104125.
Gormley M, Tchafa A, Meng R, Zhong Z, Quong AA. 2012. Proteomic profiling of infiltrating ductal carcinoma reveals increased cellular interactions with tissue microenvironment. Journal of Proteome Research 11:2236–2246. DOI: 10.1021/pr201018y.
Groessl M, Slany A, Bileck A, Gloessmann K, Kreutz D, Jaeger W, Pfeiler G, Gerner C. 2014. Proteome profiling of breast cancer biopsies reveals a wound healing signature of cancer-associated fibroblasts. Journal of Proteome Research 13:4773–4782. DOI: 10.1021/pr500727h.
Hill JJ, Tremblay T-L, Pen A, Li J, Robotham AC, Lenferink AEG, Wang E, O’Connor-McCourt M, Kelly JF. 2011. Identification of vascular breast tumor markers by laser capture microdissection and label-free LC-MS. Journal of Proteome Research 10:2479–2493. DOI: 10.1021/pr101267k.
Liang J-l, Li S-j, Liu X-g, Li W-f, Hao D-y, Fan Z-m. 2013. Quantitative proteomic studies on TNBC in premenopausal patients. Chemical Research in Chinese Universities 29:500–505. DOI: 10.1007/s40242-013-2497-9.
Lin Y, Lin L, Fu F, Wang C, Hu A, Xie J, Jiang M, Wang Z, Yang L, Guo R, Yang P, Shen H. 2020. Quantitative proteomics reveals stage-specific protein regulation of triple negative breast cancer. Breast Cancer Research and Treatment. DOI: 10.1007/s10549-020-05916-8.
Liu C, Liu Y, Chen L, Zhang M, Li W, Cheng H, Zhang B. 2019. Quantitative proteome and lysine succinylome analyses provide insights into metabolic regulation in breast cancer. Breast Cancer 26:93–105. DOI: 10.1007/s12282-018-0893-1.
Panis C, Pizzatti L, Herrera AC, Corrêa S, Binato R, Abdelhay E. 2014. Label-free proteomic analysis of breast cancer molecular subtypes. Journal of Proteome Research 13:4752–4772. DOI: 10.1021/pr500676x.
Pendharkar N, Gajbhiye A, Taunk K, RoyChoudhury S, Dhali S, Seal S, Mane A, Abhang S, Santra MK, Chaudhury K, Rapole S. 2016. Quantitative tissue proteomic investigation of invasive ductal carcinoma of breast with luminal B HER2 positive and HER2 enriched subtypes towards potential diagnostic and therapeutic biomarkers. Journal of Proteomics 132:112–130. DOI: 10.1016/j.jprot.2015.11.024.
Pozniak Y, Balint-Lahat N, Rudolph JD, Lindskog C, Katzir R, Avivi C, Pontén F, Ruppin E, Barshack I, Geiger T. 2016. System-wide clinical proteomics of breast cancer reveals global remodeling of tissue homeostasis. Cell Systems 2:172–184. DOI: 10.1016/j.cels.2016.02.001.
Shaheed S-u, Rustogi N, Scally A, Wilson J, Thygesen H, Loizidou MA, Hadjisavvas A, Hanby A, Speirs V, Loadman P, Linforth R, Kyriacou K, Sutton CW. 2013. Identification of stage-specific breast markers using quantitative proteomics. Journal of Proteome Research 12:5696–5708. DOI: 10.1021/pr400662k.
Sutton CW, Rustogi N, Gurkan C, Scally A, Loizidou MA, Hadjisavvas A, Kyriacou K. 2010. Quantitative proteomic profiling of matched normal and tumor breast tissues. Journal of Proteome Research 9:3891–3902. DOI: 10.1021/pr100113a.
Tang W, Zhou M, Dorsey TH, Prieto DA, Wang XW, Ruppin E, Veenstra TD, Ambs S. 2018. Integrated proteotranscriptomics of breast cancer reveals globally increased protein-mRNA concordance associated with subtypes and survival. Genome Medicine 10:94. DOI: 10.1186/s13073-018-0602-x.