doi: 10.1021/acs.jproteome.1c00243.
Epub 2021 Apr 15.
Proteogenomic Workflow Reveals Molecular Phenotypes Related to Breast Cancer Mammographic Appearance
Affiliations
- PMID: 33855848
- PMCID: PMC8155562
- DOI: 10.1021/acs.jproteome.1c00243
Item in Clipboard
Proteogenomic Workflow Reveals Molecular Phenotypes Related to Breast Cancer Mammographic Appearance
J Proteome Res.
.
Abstract
Proteogenomic approaches have enabled the generat̲ion of novel information levels when compared to single omics studies although burdened by extensive experimental efforts. Here, we improved a data-independent acquisition mass spectrometry proteogenomic workflow to reveal distinct molecular features related to mammographic appearances in breast cancer. Our results reveal splicing processes detectable at the protein level and highlight quantitation and pathway complementarity between RNA and protein data. Furthermore, we confirm previously detected enrichments of molecular pathways associated with estrogen receptor-dependent activity and provide novel evidence of epithelial-to-mesenchymal activity in mammography-detected spiculated tumors. Several transcript-protein pairs displayed radically different abundances depending on the overall clinical properties of the tumor. These results demonstrate that there are differentially regulated protein networks in clinically relevant tumor subgroups, which in turn alter both cancer biology and the abundance of biomarker candidates and drug targets.
Keywords: breast cancer; data-independent acquisition; proteogenomics; proteomics; transcriptomics.
Conflict of interest statement
The authors declare no competing financial interest.
Figures
Experimental workflow of this study. A total of 21 samples derived
from a larger cohort (set 1, N = 172, see Experimental Procedures) and a second set of 24
tumors from a larger study (set 2, N = 109, see Experimental Procedures) were employed (A). Panel
(B) shows examples of nonspiculated and spiculated tumor masses. Panel
(C) displays the overlap between the molecular (ER status) and appearance
features evaluated in this study, for which no association was found
(set 1: Fisher exact p-value = 0.665, set 2: Fisher
exact p-value = 0.283, (D)). Tumor specimens were
processed as whole tissue lysates (WTL, MS-only analysis) and ALLPREP
flow-throughs (FT, RNA-seq and MS analyses). Panel (E) displays the
experimental workflow of our RNA and MS (DDA and DIA) analyses: tumor
tissues were cut into slices and processed by ALLPREP. RNA and protein
fractions were extracted and processed from ALLPREP sample preparation
for downstream RNA-sequencing and DDA/DIA MS, respectively. Tissue
slices were prepared only for downstream MS (DDA/DIA). Samples for
DDA were fractionated using strong anion exchange columns (SAX, six
fractions) to enable higher proteome coverage. DDA data (i) was submitted
to with MaxQuant processing to derive protein abundances and (ii)
to the MakeGTL workflow to generate a spectral library for downstream
DIA search. RNA-seq data was processed using the standard DESeq2 workflow
(see Experimental Procedures). Abbreviations:
DDA: data-dependent acquisition, DIA: data-independent acquisition,
ER: estrogen receptor, FDR: false discovery rate, FT: flow-through,
MS: mass spectrometry, RT: retention time, WTL: whole tissue lysate.
Overall comparison between transcriptomic and proteomic data layers.
Panel (A) displays the dynamic range (presented as relative abundance
over total signal) of transcript and protein intensities of matching
identifications in our RNA (green), DDA (red), and DIA (blue) MS data
(examples of transcript–protein pairs displaying similar abundances
across data layers are labeled). Distributions of Spearman correlations
between matching transcript and protein (DDA: top, DIA: bottom) abundances
are displayed in panel (B) (gray: nonsignificant, light blue: significant),
while examples of consistent positive and negative correlation between
protein levels (DDA and DIA) and RNA abundance are depicted in panel
(C). Panels (D) and (E) display the distribution of transcript–protein
correlations for significant (q-value < 0.15,
see Experimental Procedures for details) GOBP
pathways out of our DDA and DIA MS analyses, respectively. Color gradient
is representative of the low (pink) and high (dark red) median transcript–protein
correlation for each GOBP term. Acronyms: DDA: data-dependent acquisition,
DIA: data-independent acquisition, ER: estrogen receptor, GOBP: gene
ontology biological process.
Comparison between transcriptomic and proteomic data in the context
of the estrogen receptor and appearance statuses. Panels (A) and (B)
display all transcript–protein pairs scaled Log2Ratios for
the ER status (A) and appearance ((B); DDA: left, DIA: right). Significant
differential expression at the RNA level is marked by full dots and
in bigger size; concordance and discordance between RNA and protein
layers are shown in green and purple, respectively). Most significant
genes (top 5% quantile) are shown in labels. GSEA analyses were performed
on all data layers (RNA, DDA, and DIA) for ER and spiculation statuses
using the Hallmark database. Pathways are ranked based on the RNA-level
enrichment score. Panel (C) displays the overlap of GSEA analyses
for the ER status, while panel (D) shows the results of analysis of
appearance features (i.e., spiculation vs no spiculation).
Significant pathways in each data layer (RNA: green, DDA: red, DIA:
blue) are marked in full color, while transparent ones did not pass
the false discovery rate (FDR < 0.25) cutoff. Positive scores mark
enrichment in ER-positive and spiculated tumors, respectively, while
negative scores define enrichments in ER-negative and nonspiculated
samples. Acronyms: DDA: data-dependent acquisition, DIA: data-independent
acquisition, ER: estrogen receptor, FDR: false discovery rate, GSEA:
gene set enrichment analysis.
Pathway-level comparison of transcript–protein pairs. The
figure displays transcript–protein-wise comparison within significant
pathways out of GSEA analyses for the ER status (estrogen response
early, (A)) and appearance (epithelial mesenchymal transition, (B)).
Left panels display Log2Ratios of each transcript/protein (ranked
by RNA expression) between ER-positive/negative and spiculated/nonspiculated
tumors, while center panels display the corresponding enrichment scores
in each data layer (RNA: green, DDA: red, DIA: blue). Right panels
show distribution of enrichment scores for core-enriched (red) and
noncore-enriched (gray) transcript/proteins. Left and center plots
background color denotes enrichment in ER-positive (blue) and ER-negative
(red) groups and spiculated (orange) and nonspiculated (purple) tumor
groups. Abbreviations: DDA: data-dependent acquisition, DIA: data-independent
acquisition, ER: estrogen receptor, FDR: false discovery rate, GSEA:
gene set enrichment analysis.
Evaluation of differential transcript usage and single amino acid
variant detection at the proteomic level. We employed transcriptomic
data information to search our DIA data for DTU (A–C) and SAAVs
(D, E). For DTU analysis, we employed the BANDITs workflow to define
transcript differential expression to then generate an isoform-aware
spectral library with which to search our DIA MS data. Panel (A) displays
detected DTU at the protein (DIA MS) level and their expression compared
to transcript levels. Examples of transcript (left) and (when detected)
their specific peptide (right) expression are shown in panel (B) (ER
status) and (C) (appearance). t Test p-value is shown for box-plots (peptide level). For SAAV detection,
nonsynonymous SNVs detected at the RNA level in breast tumors and
healthy breast tissues derived from reconstruction surgery were employed
to define a variant-specific library against which the DIA data was
searched. Panel (D) shows in which samples (healthy breast tissue
and cancer) each variant was detected (Numbers in brackets represent
peptide charge). Abbreviations: DIA: data-independent acquisition,
DTU: differential transcript usage, MS: mass spectrometry, SAAV: single
amino acid variant, SNV: single nucleotide variant.
Protein cluster regulation dependent on the estrogen receptor status.
Co-regulated protein clusters in ER-positive (left) and ER-negative
(right) tumors (see Figure S15 ) were extracted
from the DIA data, annotated with GOBP terms, condensed, and visualized
in Cytoscape (A). Edge thickness and length relate to the cluster
distance (Euclidean), the node color relates to the scaled mean intensity
of all proteins in each cluster, and the node size depends on the
number of proteins in each cluster. Panel (B) shows the correlation
to mRNA of each protein per cluster for ER-positive and ER-negative
tumors. Panel (C) displays differences in correlation to RNA between
ER-positive and ER-negative (i.e., ER positive–ER negative)
tumor groups within showcased co-regulation clusters for FDA drug
targets. Abbreviations: DIA: data-independent acquisition, ER: estrogen
receptor, FDA: Food and Drug Administration, GOBP: gene ontology biological
process, MS: mass spectrometry.
References
-
- Fachal L.; Aschard H.; Beesley J.; Barnes D. R.; Allen J.; Kar S.; Pooley K. A.; Dennis J.; Michailidou K.; Turman C.; Soucy P.; Lemaçon A.; Lush M.; Tyrer J. P.; Ghoussaini M.; Marjaneh M. M.; Jiang X.; Agata S.; Aittomäki K.; Alonso M. R.; Andrulis I. L.; Anton-Culver H.; Antonenkova N. N.; Arason A.; Arndt V.; Aronson K. J.; Arun B. K.; Auber B.; Auer P. L.; Azzollini J.; Balmaña J.; Barkardottir R. B.; Barrowdale D.; Beeghly-Fadiel A.; Benitez J.; Bermisheva M.; Białkowska K.; Blanco A. M.; Blomqvist C.; Blot W.; Bogdanova N. V.; Bojesen S. E.; Bolla M. K.; Bonanni B.; Borg A.; Bosse K.; Brauch H.; Brenner H.; Briceno I.; Brock I. W.; Brooks-Wilson A.; Brüning T.; Burwinkel B.; Buys S. S.; Cai Q.; Caldés T.; Caligo M. A.; Camp N. J.; Campbell I.; Canzian F.; Carroll J. S.; Carter B. D.; Castelao J. E.; Chiquette J.; Christiansen H.; Chung W. K.; Claes K. B. M.; Clarke C. L.; Collée J. M.; Cornelissen S.; Couch F. J.; Cox A.; Cross S. S.; Cybulski C.; Czene K.; Daly M. B.; de la Hoya M.; Devilee P.; Diez O.; Ding Y. C.; Dite G. S.; Domchek S. M.; Dörk T.; Dos-Santos-Silva I.; Droit A.; Dubois S.; Dumont M.; Duran M.; Durcan L.; Dwek M.; Eccles D. M.; Engel C.; Eriksson M.; Evans D. G.; Fasching P. A.; Fletcher O.; Floris G.; Flyger H.; Foretova L.; Foulkes W. D.; Friedman E.; Fritschi L.; Frost D.; Gabrielson M.; Gago-Dominguez M.; Gambino G.; Ganz P. A.; Gapstur S. M.; Garber J.; García-Sáenz J. A.; Gaudet M. M.; Georgoulias V.; Giles G. G.; Glendon G.; Godwin A. K.; Goldberg M. S.; Goldgar D. E.; González-Neira A.; Tibiletti M. G.; Greene M. H.; Grip M.; Gronwald J.; Grundy A.; Guénel P.; Hahnen E.; Haiman C. A.; Håkansson N.; Hall P.; Hamann U.; Harrington P. A.; Hartikainen J. M.; Hartman M.; He W.; Healey C. S.; Heemskerk-Gerritsen B. A. M.; Heyworth J.; Hillemanns P.; Hogervorst F. B. L.; Hollestelle A.; Hooning M. J.; Hopper J. L.; Howell A.; Huang G.; Hulick P. J.; Imyanitov E. N.; Isaacs C.; Iwasaki M.; Jager A.; Jakimovska M.; Jakubowska A.; James P. A.; Janavicius R.; Jankowitz R. C.; John E. M.; Johnson N.; Jones M. E.; Jukkola-Vuorinen A.; Jung A.; Kaaks R.; Kang D.; Kapoor P. M.; Karlan B. Y.; Keeman R.; Kerin M. J.; Khusnutdinova E.; Kiiski J. I.; Kirk J.; Kitahara C. M.; Ko Y.-D.; Konstantopoulou I.; Kosma V.-M.; Koutros S.; Kubelka-Sabit K.; Kwong A.; Kyriacou K.; Laitman Y.; Lambrechts D.; Lee E.; Leslie G.; Lester J.; Lesueur F.; Lindblom A.; Lo W.-Y.; Long J.; Lophatananon A.; Loud J. T.; Lubiński J.; MacInnis R. J.; Maishman T.; Makalic E.; Mannermaa A.; Manoochehri M.; Manoukian S.; Margolin S.; Martinez M. E.; Matsuo K.; Maurer T.; Mavroudis D.; Mayes R.; McGuffog L.; McLean C.; Mebirouk N.; Meindl A.; Miller A.; Miller N.; Montagna M.; Moreno F.; Muir K.; Mulligan A. M.; Muñoz-Garzon V. M.; Muranen T. A.; Narod S. A.; Nassir R.; Nathanson K. L.; Neuhausen S. L.; Nevanlinna H.; Neven P.; Nielsen F. C.; Nikitina-Zake L.; Norman A.; Offit K.; Olah E.; Olopade O. I.; Olsson H.; Orr N.; Osorio A.; Pankratz V. S.; Papp J.; Park S. K.; Park-Simon T.-W.; Parsons M. T.; Paul J.; Pedersen I. S.; Peissel B.; Peshkin B.; Peterlongo P.; Peto J.; Plaseska-Karanfilska D.; Prajzendanc K.; Prentice R.; Presneau N.; Prokofyeva D.; Pujana M. A.; Pylkäs K.; Radice P.; Ramus S. J.; Rantala J.; Rau-Murthy R.; Rennert G.; Risch H. A.; Robson M.; Romero A.; Rossing M.; Saloustros E.; Sánchez-Herrero E.; Sandler D. P.; Santamariña M.; Saunders C.; Sawyer E. J.; Scheuner M. T.; Schmidt D. F.; Schmutzler R. K.; Schneeweiss A.; Schoemaker M. J.; Schöttker B.; Schürmann P.; Scott C.; Scott R. J.; Senter L.; Seynaeve C. M.; Shah M.; Sharma P.; Shen C.-Y.; Shu X.-O.; Singer C. F.; Slavin T. P.; Smichkoska S.; Southey M. C.; Spinelli J. J.; Spurdle A. B.; Stone J.; Stoppa-Lyonnet D.; Sutter C.; Swerdlow A. J.; Tamimi R. M.; Tan Y. Y.; Tapper W. J.; Taylor J. A.; Teixeira M. R.; Tengström M.; Teo S. H.; Terry M. B.; Teulé A.; Thomassen M.; Thull D. L.; Tischkowitz M.; Toland A. E.; Tollenaar R. A. E. M.; Tomlinson I.; Torres D.; Torres-Mejía G.; Troester M. A.; Truong T.; Tung N.; Tzardi M.; Ulmer H.-U.; Vachon C. M.; van Asperen C. J.; van der Kolk L. E.; van Rensburg E. J.; Vega A.; Viel A.; Vijai J.; Vogel M. J.; Wang Q.; Wappenschmidt B.; Weinberg C. R.; Weitzel J. N.; Wendt C.; Wildiers H.; Winqvist R.; Wolk A.; Wu A. H.; Yannoukakos D.; Zhang Y.; Zheng W.; Hunter D.; Pharoah P. D. P.; Chang-Claude J.; García-Closas M.; Schmidt M. K.; Milne R. L.; Kristensen V. N.; French J. D.; Edwards S. L.; Antoniou A. C.; Chenevix-Trench G.; Simard J.; Easton D. F.; Kraft P.; Dunning A. M. Fine-Mapping of 150 Breast Cancer Risk Regions Identifies 191 Likely Target Genes. Nat. Genet. 2020, 52, 56–73. 10.1038/s41588-019-0537-1. - DOI - PMC - PubMed
-
- Perou C. M.; Sørlie T.; Eisen M. B.; van de Rijn M.; Jeffrey S. S.; Rees C. a.; Pollack J. R.; Ross D. T.; Johnsen H.; Akslen L. A.; Fluge O.; Pergamenschikov A.; Williams C.; Zhu S. X.; Lønning P. E.; Børresen-Dale A. L.; Brown P. O.; Botstein D. Molecular Portraits of Human Breast Tumours. Nature 2000, 406, 747–752. 10.1038/35021093. - DOI - PubMed
-
- Coates A. S.; Winer E. P.; Goldhirsch A.; Gelber R. D.; Gnant M.; Piccart-Gebhart M.; Thürlimann B.; Senn H.-J. Panel Members. Tailoring Therapies--Improving the Management of Early Breast Cancer: St Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2015. Ann. Oncol. Off. J. Eur. Soc. Med. Oncol. 2015, 26, 1533–1546. 10.1093/annonc/mdv221. - DOI - PMC - PubMed
Publication types
MeSH terms
LinkOut - more resources
Full Text Sources
Other Literature Sources
Medical
Molecular Biology Databases
