Integrating Functional Proteomics and Next Generation Sequencing Reveals Potential Therapeutic Targets for Taiwanese Breast Cancer

doi:10.21203/rs.3.rs-4864575/v1

Download PDF

Research Article

Integrating Functional Proteomics and Next Generation Sequencing Reveals Potential Therapeutic Targets for Taiwanese Breast Cancer

https://doi.org/10.21203/rs.3.rs-4864575/v1

This work is licensed under a CC BY 4.0 License

You are reading this latest preprint version

Integrating functional proteomics and next-generation sequencing (NGS) offers a comprehensive approach to unraveling the molecular intricacies of breast cancer. This study investigates the functional interplay between genomic alterations and protein expression in Taiwanese breast cancer patients. By analyzing 61 breast cancer samples using tandem mass tag (TMT) labeling and mass spectrometry, coupled with whole-exome sequencing (WES) or targeted sequencing, we identified key genetic mutations and their impact on protein expression. Notably, pathogenic variants in BRCA1, BRCA2, PTEN, and PIK3CA were found to be clinically relevant, potentially guiding targeted therapy decisions. Additionally, we discovered trans correlations between specific gene alterations (FANCA, HRAS, PIK3CA, MAP2K1, JAK2) and the expression of 22 proteins, suggesting potential molecular mechanisms underlying breast cancer development and progression. These findings highlight the power of integrating proteomics and NGS to identify potential therapeutic targets and enhance personalized medicine strategies for Taiwanese breast cancer patients.

Breast cancer is the most prevalent cancer among women in Taiwan [1]. While the mortality rate is decreasing in the West, it is increasing in Asian countries, including Taiwan [2]. The average age of onset for breast cancer in Taiwanese women is also younger than in Western women [3]. Despite advancements in breast cancer treatment, there are still unmet clinical needs. These include identifying specific genetic mutations in Taiwanese breast cancer patients to develop more effective targeted therapies [4]. Addressing these unmet needs is crucial to improving the quality of life and survival rates for Taiwanese women with breast cancer.

Proteomics and Next-Generation Sequencing (NGS) are powerful tools revolutionizing breast cancer research. Proteomics is the large-scale study of proteins, crucial for understanding the molecular mechanisms of breast cancer. It helps identify biomarkers for early detection, prognosis, and prediction of treatment response [5–6]. Mass spectrometry (MS)-based proteomics enables comprehensive analysis of protein expression and modifications, shedding light on tumor heterogeneity and potential therapeutic targets. Spatial proteomics, such as imaging mass spectrometry, allows visualizing protein distribution within tissues, providing insights into tumor microenvironment and disease progression [7].

Next generation sequencing (NGS) enables rapid sequencing of DNA and RNA, providing valuable information on genetic mutations, gene expression patterns, and tumor evolution [8]. Integrating NGS data with proteomics (proteogenomics) enhances our understanding of how genetic alterations translate into protein changes, driving breast cancer development and progression [9]. Together, proteomics and NGS are accelerating the development of personalized medicine for breast cancer patients. We initiated an approach integrating functional proteomics and NGS, in an effort to understand the molecular alternations underpinning breast cancer development, and to identify potential therapeutics directing to future personalized medicine.

Study population

Pre-operative and treatment-naive core needle biopsy samples stored as formalin-fixed paraffin-embedded (FFPE) pathological archives were used for NGS and MS analyses [9]. Inclusion criteria were newly diagnosed early breast cancers (stage 0 to III) while exclusion criteria were de novo stage IV disease and insufficient samples. The whole project had been reviewed and approved by the Institutional Review Board with singed informed consent from all participants.

NGS: whole exome sequencing

The procedure of whole exome sequencing (WES) for both tumor and adjacent normal tissue had been described previously [10–11]. Exome capture and library preparation was conducted using the Agilent SureSelect XT Reagent Kit and SureSelect XT Human All Exon Version 6 (60 Mb) probe set (Agilent Technologies, Inc.). 1000 ng of genomic DNA was used for library construction. Sequencing was conducted using the Illumina Hiseq4000 with 150 bp paired-end protocol (Illumina, Inc., San Diego, CA, USA). Reads were mapped to human reference genome (hg19) using BWA-MEM [12]. Picard tools were used for sorting BAM files and marking duplicates. GATK Best Practices workflow was used for indel realignment and base quality score recalibration [13]. For variant calling, MuTect2 was used for calling somatic SNVs and indels [14]. Various filters were applied to reduce false positives and variants were annotated using Variant Effect Predictor [15].

NGS: targeted sequencing

The procedure of targeted sequencing had also been described [4]. Tumor DNA was extracted from 10-µm sections using a High Pure FFPET DNA Isolation Kit (Roche Applied Science, Indianapolis, IN, USA) with contaminated RNA removed by RNase. The Agilent HaloPlex Target Enrichment System was used for library preparation. The circularized target DNA-HaloPlex probe hybrids were captured on streptavidin beads (HaloPlex Magnetic Beads, Agilent Technologies Inc.) and added DNA ligase to close nicks in the hybrids. Target libraries were amplified through 22 cycles of PCR, and all samples were sequenced on Illumina NextSeq500 (Illumina, Inc.) using 150PE protocol. The qualified reads data then went through a genomic alignment against hg19 using BWA to obtain basic sequence information [12].

Protein extraction and functional proteomics

Proteins were extracted from the FFPE samples while trypsin was digested [16]. Harvested peptides were desalted and quantitated by bicinchoninic acid protein assay (Thermo Fisher Scientific, Waltham, MA, USA). The TMTsixplex Isobaric Label Reagents (Thermo Fisher Scientific) were used for tandem mass tag (TMT) labeling. All samples were randomly divided into 12 TMT batches, each batch contains 5 samples and one supermix control, which was prepared by mixing equal amounts of the desalted peptides from 61 cancer samples as control.

Nanoscale liquid chromatography with tandem mass spectrometry (nanoLC MS/MS)

NanoLC-MS/MS analyses were conducted with a nanoAcquity UPLC system (Waters, Milford, MA, USA) connected to the Orbitrap Elite hybrid mass spectrometer (Thermo Fisher Scientific). Peptide mixtures in 0.1% formic acid (FA) were loaded onto a C18 BEH column (75 µm ID X 25 cm) packed with 1.7-µm particles at a pore with of 130 Å (Waters, Milford, MA). The peptides were separated using a segmented gradient in 60 min from 5–35% solvent B (acetonitrile with 0.1% FA) at a flow rate of 300 nL/min and a column temperature of 35°C. Solvent A was 0.1% FA in water. The MS was operated in the data-dependent mode. In brief, survey full scan MS spectra were acquired in the orbitrap (m/z 350–1600) with the resolution set to 60 K at m/z 400 and automatic gain control (AGC) target at 106. The 15 most intense ions were sequentially isolated for HCD MS/MS fragmentation and detection in the orbitrap with previously selected ions dynamically excluded for 60 s. For MS/MS, we used a resolution of 15,000, an isolation window of 2 m/z and an AGC target value of 50,000 ions, with the maximal accumulation time of 200 ms. MS/MS fragmentation was performed with normalized collision energy of 35% and an activation time of 0.1 ms. Ions with singly and unrecognized charge state were excluded from MS/MS fragmentation. In this study, two technical repeats and three independent biological batches for each breast cancer sample were analyzed.

Protein identification was carried out using the Andromeda search engine, which was incorporated in MaxQuant (v.1.6.12.0) against the SWISSPROT human sequence database (canonical + isoforms) downloaded in Oct 2019 [17–18]. The enzyme specificity was trypsin with up to two missed cleavages. Cysteine carbamidomethylation was set as a fixed modification. N-acetylation of proteins, oxidation of methionine, and formylation of lysine were set as variable modifications. The minimum peptide length was set to seven amino acids. False discovery rates (FDRs) at the peptide and protein levels were fixed at 1%.

Proteogenomic analysis

Protein quantitation was determined by MaxQuant. PSM-level normalization in MaxQuant was turned on, with PIF filter > 0.75 and “weighted ratio to reference channel" normalization method selected, the latter referred to the reference sample (control group, which was the mixed average of all cancerous samples) in each TMT batch [19]. To further minimize the impact of technical factors on the quantitation, a median-median-normalization was applied [20].

Due to discrepancy in the genomic regions of interest, only common mutant genes between WES and targeted sequencing were interrogated, namely AKT1, ATM, BRCA1, BRCA2, EGFR, FGFR3, FNACA, JAK2, MAP2K2, MET, NOTCH1, RET, SMO and TP53. We only consider somatic mutations from WES as no germline controls from targeted sequencing. Both cis and trans correlations between genomic alterations and protein expression, with Pearson correlation coefficients and accompanied P-value reported. The Bonferroni correction was used to address the issue of multiple comparisons and reduce type I error (false positive) with an induced alpha-level of 5x10^− 4 [21]. All variants, regardless of functionality, were used for proteogenomic correlation analysis while oncogenicity of reported variants was ascertained by the OncoKB database [22–23].

Study population

The study population contained 61 Taiwanese breast cancers with functional proteomics data. Table 1 details TMT batch, controls and immunohistochemistry (IHC) subtypes based on hormone receptor (HR) and human epidermal growth factor receptor II (HER2) status. There were 29 HR+/HER2-, 16 HR+/HER2+, 9 HR-/HER2 + and 7 HR-/HER2- cases.

Table 1

Tandem mass tag (TMT) and immunohistochemistry (IHC) subtypes of the study population.
Batch	TMT	Sample ID	IHC subtype	Batch	TMT	Sample ID	IHC subtype
1	126	SA33	HR+/HER2-	7	126	SA23	HR+/HER2-
1	127	SA28	HR-/HER2-	7	127	SA05	HR-/HER2+
1	128	SA10	HR+/HER2-	7	128	Control7	Control
1	129	SA36	HR+/HER2+	7	129	SA17	HR+/HER2-
1	130	Control1	Control	7	130	SA34	HR+/HER2-
1	131	SA06	HR+/HER2+	7	131	SA44	HR+/HER2+
2	126	SA48	HR-/HER2-	8	126	SA18	HR+/HER2-
2	127	Control2	Control	8	127	SA35	HR-/HER2+
2	128	SA30	HR-/HER2+	8	128	SA11	HR+/HER2+
2	129	SA27	HR+/HER2-	8	129	SA45	HR-/HER2-
2	130	SA13	HR-/HRE2+	8	130	Control8	Control
2	131	SA14	HR-/HER2+	8	131	SA21	HR+/HER2-
3	126	SA03	HR-/HER2-	9	126	SA20	HR+/HER2-
3	127	SA29	HR+/HER2+	9	127	Control9	Control
3	128	SA16	HR+/HER2+	9	128	SA04	HR+/HER2+
3	129	Control3	Control	9	129	SA32	HR+/HER2-
3	130	SA37	HR+/HER2-	9	130	SA15	HR+/HER2-
3	131	SA38	HR-/HER2+	9	131	SA01	HR+/HER2+
4	126	Control4	Control	10	126	SA59	HR-/HER2-
4	127	SA22	HR+/HER2-	10	127	SA55	HR-/HER2-
4	128	SA02	HR+/HER2-	10	128	Control10	Control
4	129	SA07	HR-/HER2-	10	129	SA61	HR-/HER2+
4	130	SA42	HR-/HER2+	10	130	SA39	HR-/HER2+
4	131	SA09	HR-/HER2+	10	131	SA52	HR-/HER2-
5	126	SA43	HR+/HER2+	11	126	SA57	HR+/HER2-
5	127	Control5	Control	11	127	Control11	Control
5	128	SA40	HR-/HER2+	11	128	SA53	HR+/HER2-
5	129	SA47	HR-/HER2+	11	129	SA56	HR+/HER2+
5	130	SA41	HR+/HER2-	11	130	SA31	HR+/HER2+
5	131	SA19	HR+/HER2-	11	131	SA51	HR-/HER2-
6	126	SA12	HR+/HER2-	12	126	SA54	HR+/HER2-
6	127	SA46	HR+/HER2-	12	127	SA60	HR+/HER2-
6	128	SA24	HR-/HER2-	12	128	SA08	HR+/HER2+
6	129	SA26	HR-/HER2-	12	129	SA58	HR+/HER2-
6	130	SA49	HR+/HER2-	12	130	SA50	HR+/HER2-
6	131	Control6	Control	12	131	Control12	Control
(HR: hormone receptor, HER2: human epidermal growth factor receptor II)

NGS experiments

Among 61 Taiwanese breast cancers, 40 underwent WES and the remaining underwent targeted sequencing. Figure 1 shows mutational landscape of common altered genes between both NGS platforms. Pathogenic variants were observed in 19 (31%) of the study cohort, with BRCA1 as the most prevalence (13%) and TP53 (10%). Supplementary Table 1 details genomic alterations of Taiwanese breast cancers, with and without functional pathogenicity.

Proteogenomic analyses

No cis correlation was observed in this study while trans correlations between genomic alteration and protein expression were pronounced after controlling for false discovery (Table 2 and Fig. 2). Negative correlations were noted between FANCA alterations and protein expression of GBAS, SFXN3, TWF1, CPPED1, EIF3L, COPE, PRPS1/PRPS1L1, PHB; HRAS alterations and NEB, FLNC, LRP1 protein expression; PIK3CA alterations and TPM3, ANXA6, EEF1D, SSR3, HMGB1, HMGB1P1 protein expression; MAP2K1 alteration and PPP1CC expression while positive correlations between PIK3CA alterations and SAMM50, RANGAP1 expression as well as JAK2 alteration and ITGA11 expression.

Table 2

Correlations between genomic alteration and protein expression among 61 Taiwanese breast cancers.
Gene	Protein	Correlation	P-value
FANCA	GBAS	-0.5114828	0.0001
FANCA	SFXN3	-0.5107265	0.0001
HRAS	NEB	-0.5698695	0.0001
PIK3CA	SAMM50	0.57097525	0.0001
PIK3CA	TPM3	-0.4831809	0.0001
PIK3CA	ANXA6	-0.4811268	0.0001
PIK3CA	EEF1D	-0.483507	0.0001
FANCA	TWF1	-0.470296	0.0002
FANCA	CPPED1	-0.4775763	0.0002
HRAS	FLNC	-0.4880356	0.0002
MAP2K1	PPP1CC	-0.5575056	0.0002
PIK3CA	SSR3	-0.5012579	0.0002
FANCA	EIF3L	-0.477688	0.0003
FANCA	COPE	-0.4861374	0.0003
HRAS	LRP1	-0.4535422	0.0003
JAK2	ITGA11	0.5481589	0.0003
PIK3CA	RANGAP1	0.45661621	0.0003
FANCA	PRPS1;PRPS1L1	-0.4879296	0.0004
FANCA	PHB	-0.4476773	0.0004
PIK3CA	HMGB1;HMGB1P1	-0.4505585	0.0004

To further understand the molecular interplay between genomic alterations and protein expression, we put all 27 candidates, including 5 altered genes (FANCA, HRAS

PIK3CA, MAP2K1, JAK2) and 22 impacted proteins into the STRING database using default setting, and Fig. 3 displays network view summaring the predicted associations for these proteogenomic candidates. The protein-protein interaction (PPI) enrichment p-value was 0.00126, indicating input proteins have more interactions among themselves than what would be expected for a random set of proteins of the same size and degree distribution drawn from the genome. Such an enrichment indicates that the proteins are at least partially biologically connected, as a group. Functional enrichments with more than 2 of strength in terms of log₁₀(observed / expected) included ribose phosphate diphosphokinase activity (molecular function), ribose phosphate diphosphokinase complex (cellular component), 5-Phosphoribose 1-diphosphate biosynthesis, MAPK3 (ERK1) activation, signaling by FGFR4 in disease, signaling by PDGFRA extracellular domain mutants, signaling by PDGFRA transmembrane, juxtamembrane and kinase domain mutants (Reactome, ref. 25), PDGFR-beta pathway (WikiPathways) and N-terminal domain of ribose phosphate pyrophosphokinase (protein domains, SMART, ref.26). Supplementary Table 1 details enrichment analysis results.

Functional proteomics is a powerful approach for studying breast cancer that focuses on analyzing protein expression, interactions, and functions. Key aspects include identifying biomarker panels for prognosis and treatment prediction, profiling protein expression in large cohorts of breast cancer specimens to link proteomic data with clinical outcomes and exploring the complexity of breast cancer proteomes to identify potential therapeutic targets and improve patient stratification [27–29]. Functional proteomics has significant potential to enhance breast cancer management by providing insights into disease mechanisms, identifying novel biomarkers, and guiding personalized treatment strategies.

NGS, coupled with matched treatment, has shown to enhance clinical outcomes [30].

NGS testing allows for molecular-guided treatment decisions, offering new targeted therapy options. With identification of actionable mutations, NGS helps detect mutations that can be targeted with specific therapies. Combining NGS with functional proteomics offers several benefits for breast cancer research and clinical applications such as comprehensive molecular profiling, enhanced heterogeneity analysis as both genomic and proteomic levels are deciphered.

In current study, we took advantage of 61 Taiwanese breast cancers who underwent functional proteomics coupled with either WES or targeted sequencing. A couple of pathogenic mutations were identified and were clinically relevant: namely BRCA1/2, PTEN and PIK3CA. Both BRCA1 and BRCA2 are well-known tumor suppressors while Poly (ADP-ribose) polymerase (PARP) inhibitors are FDA-approved for patients with germline BRCA1/2 mutant ovarian and breast cancers [31]. PTEN is a tumor suppressor that is one of the most frequently mutated genes in human cancer, and the pan-AKT kinase inhibitor capivasertib in combination with the selective estrogen receptor degrader (SERD) fulvestrant is FDA-approved for the treatment of patients with PTEN-mutant HR+/HER2- metastatic breast cancer [32]. PIK3CA, the catalytic subunit of PI3-kinase, is frequently mutated in a diverse range of cancers including breast, endometrial and cervical cancers, and the alpha-isoform selective PI(3)-kinase inhibitor alpelisib and the pan-AKT kinase inhibitor capivasertib, each in combination with fulvestrant, are FDA-approved for the treatment of patients with PIK3CA mutant ER+/HER2- metastatic breast cancer [32–33]. Other pathogenic mutations are currently not actionable for breast cancer treatment.

Five genes, including FANCA, HRAS, PIK3CA, MAP2K1 and JAK2, were shown to impact distant (trans) protein expressions, with most being negatively correlated. FANCA is a tumor suppressor and DNA repair protein with germline mutations of FANCA associated with the cancer predisposition syndrome Fanconi Anemia. HRAS, a GTPase, is commonly mutant in head and neck, thyroid, and bladder cancer. The Ras proto-oncogene family (HRAS, NRAS and KRAS) is the upstream of pro-proliferative and anti-apoptotic signal transduction pathways, including the mitogen activated protein kinase (MAPK) and PI3 kinase (PI3K) pathways [34]. MAP2K1 (MEK1), which is infrequently mutated in melanoma, colon and lung cancer, is involved in the RAS/MAPK signaling pathway, influencing various cellular processes such as growth, proliferation, and survival [35]. JAK2, a non-receptor tyrosine kinase, is commonly mutant in hematologic malignancies such as myeloproliferative neoplasm [36]. Except PIK3CA, none of these impacting genes were breast cancer actionable.

Twenty-two proteins were impacted by 5 altered genes, with most being negatively. GBAS (NIPSNAP2) a positive regulator of L-type calcium channels, belonging to the NipSnap family. SFXN3 is a mitochondrial protein that functions as a serine transporter, facilitating the transport of serine into the mitochondria, and is also involved in iron transport [37]. NEB is involved in maintaining the structural integrity of sarcomeres and the membrane system associated with the myofibrils. SAMM50 maintains the structure of mitochondrial cristae and the proper assembly of the mitochondrial respiratory chain complexes. TPM3 gene encodes the slow muscle alpha (α)-tropomyosin protein, which belongs to the tropomyosin family, a group of actin-binding proteins [38]. ANXA6 is associated with CD21 and regulates the release of Ca²⁺ from intracellular stores [39]. As a subunit of elongation factor-1 complex (EEF1), EEF1D is essential for protein synthesis, delivering aminoacyl tRNAs to ribosome, and short isoforms through alternative splicing may be pathogenic [40]. TWF1 gene encodes an actin monomer-binding protein, which is essential for cytoskeletal remodeling, myogenic differentiation and cancer progression [41]. CPPED1 belongs to the calcineurin-like phosphoesterase domain family and has protein phosphatase activity, specifically targeting serine and threonine residues. FLNC gene encodes Filamin-C, a protein that plays a key role in the structure and function of muscles. Protein phosphatase 1 catalytic subunit gamma (PP1-gamma), encoded by PPP1CC, opposes the action of kinases and phosphorylases and is involved in signal transduction. SSR3 gene encodes the gamma subunit of the signal sequence receptor (SSR), which is a protein complex involved in the recognition and targeting of proteins to the endoplasmic reticulum for further processing and secretion. Eukaryotic translation initiation factor 3 (eIF-3) complex is encoded by EIF3L, while this complex is essential for protein synthesis. COPE gene encodes a a subunit of the coatomer protein complex, which plays a vital role in intracellular protein transport, specifically in the retrograde transport of proteins from the Golgi apparatus back to the endoplasmic reticulum. Low-density lipoprotein receptor-related protein 1, encoded by LRP1 gene, is a large endocytic cell surface receptor involved in cell adhesion, signaling, trafficking and degradation of ligands. ITGA11 gene encodes integrin subunit alpha 11, a protein that forms part of the integrin, which is a cell adhesion molecule. RANGAP1 associates with the nuclear pore complex and acts as a GTPase activator for Ran, a small GTPase that plays a crucial role in nucleo-cytoplasmic transport converts GTP-bound Ran to GDP-bound Ran, which is essential for the directionality and fidelity of nuclear transport [42]. Phosphoribosyl pyrophosphate synthetase 1 (PRPP synthetase 1), encoded by PRPS1 gene, plays a crucial role in the production of phosphoribosyl pyrophosphate (PRPP), a molecule involved in the synthesis of purine and pyrimidine nucleotides, which are the building blocks of DNA and RNA. Prohibitin protein (encoded by PHB) plays a role in cellular senescence and tumor suppression in humans [43]. High Mobility Group Box 1 is a protein encoded by HMGB1 gene and is a non-histone chromosomal protein, functioning in DNA binding and chromatin architecture, inflammatory response, and autophagy. A diverse set of proteins were correlated with altered genes, and most of which were negatively correlated. Despite this, we could still categorize these proteins into cellular processes and signaling (SFXN3, SAMM50, EEF1D, TWF1, CPPED1, PPP1CC, SSR3, EIF3L, COPE, RANGAP1, PRPS1, PHB, HMGB1), muscle and structure proteins (NEB, TPM3, FLNC, ITGA11) and others (GBAS, ANXA6, LRP1, PRPS1L1, HMGB1P1).

It deserves notice that several studies evaluating the association between PHB expression and breast cancer [44–45]. In our study, altered FANCA was associated with a reduced PHB protein expression. TWF1 has also been linked to breast cancer progression, while our study indicated that FANCA alterations negatively impacted TWF1 expression [46]. SFXN3 and ANXA6 had also been reported to be over-expressed in breast cancer [37, 39]. To ascertain that our findings were not spurious, public domain GDC TCGA Breast Cancer (BRCA) database was consulted and elevated TWF1 mRNA expression did show a trend toward poor overall survival (Supplementary Fig. 1) while PIK3CA alterations did impact ANXA6 mRNA expression (Supplementary Fig. 2).

By combining NGS and functional proteomics, researchers and clinicians can gain a more holistic view of breast cancer, potentially improving diagnosis, prognosis, and treatment outcomes.

There were some limitations of the study. First, limited sample size might compromise the generalizability and external validation of the findings, and more samples in future studies are warranted. Second, NGS was performed with either WES or targeted sequencing, and the discrepancy in sequencing technologies and genomic regions inevitable introduced bias. Third, distinct molecular aberrations might exist across breast cancer subtypes, future studies should focus on subtype-specific proteogenomic interplays with more samples assayed.

Author Contributions: Conceptualization, WCK and CCH; methodology WCK and CYL; formal analysis, CJH; investigation, CCL and YCH; resources, CCH; writing—original draft preparation, CCH; writing—review and editing, WCK; supervision, LMT; funding acquisition, CCH and LMT. All authors have read and agreed to the published version of the manuscript.

Funding: This work was supported in part by Fu-Jen Catholic University (grant number: PL-201812001-T), Taipei Veterans General Hospital (grant numbers: V110E-005-3, V111E-006-3, V112E-004-3 and V112C-013), Ministry of Health and Welfare (MOHW111-TDU-B-222-124016, MOHW112-TDU-B-222-124016, MOHW113-TDU-B-222-124016) and National Science and Technology Council (grant number: NSTC 111-2314-B-075-063-MY3).

Institutional Review Board Statement: The whole study protocol had been reviewed and approved by the Institutional Review Board of Taipei Veterans General Hospital with the approved number: 2022-01-009C.

Informed Consent Statement: Written informed consent was obtained from all participating patients before enrollment.

Data Availability Statement: All nanoLC-MS/MS raw files and MaxQuant-generated result data have been deposited to the ProteomeXchange Consortium (http://proteomecentral.proteomexchange.org) via the PRIDE partner repository with the dataset identifier PXD038543.

Acknowledgments: The authors wound like to thank the Taiwan Clinical Oncology Research Foundation, Melissa Lee Cancer Foundation, and Dr. Morris Chang for their kind assistance during the study. The authors also thanked the Mass Spectrometry Laboratory of Tzong Jwo Jang, College of Medicine, Fu Jen Catholic University, New Taipei, Taiwan, as well as Academia Sinica Common Mass Spectrometry Facilities for Proteomics and Protein Modification Analysis located at the Institute of Biological Chemistry, Academia Sinica, Taipei, Taiwan, for the assistance on all nanoLC-MS/MS analyses.

Conflicts of Interest: The authors declare no conflicts of interest.” Authors must identify and declare any personal circumstances or interest that may be perceived as inappropriately influencing the representation or interpretation of reported research results.

Chou CY, Shen TT, Wang WC, Wu MP. Favorable breast cancer mortality-to-incidence ratios of countries with good human development index rankings and high health expenditures. Taiwan J Obstet Gynecol. 2024;63:527–31.
Chen YC, Su SY, Jhuang JR, Chiang CJ, Yang YW, Wu CC, Lin LJ, Lee WC. Forecast of a future leveling of the incidence trends of female breast cancer in Taiwan: an age-period-cohort analysis. Sci Rep. 2022;12:12481.
Neagu AN, Whitham D, Buonanno E, Jenkins A, Alexa-Stratulat T, Tamba BI, Darie CC. Proteomics and its applications in breast cancer. Am J Cancer Res. 2021;11:4006–49.
Huang CS, Liu CY, Lu TP, Huang CJ, Chiu JH, Tseng LM, Huang CC. Targeted Sequencing of Taiwanese Breast Cancer with Risk Stratification by the Concurrent Genes Signature: A Feasibility Study. J Pers Med. 2021;11:613.
Rossi C, Cicalini I, Cufaro MC, Consalvo A, Upadhyaya P, Sala G, Antonucci I, Del Boccio P, Stuppia L, De Laurenzi V. Breast cancer in the era of integrating Omics approaches. Oncogenesis. 2022;11:17.
Brožová K, Hantusch B, Kenner L, Kratochwill K. Spatial Proteomics for the Molecular Characterization of Breast Cancer. Proteomes. 2023;11:17.
Lu M, Zhan X. The crucial role of multiomic approach in cancer research and clinically relevant outcomes. EPMA J. 2018;9:77–102.
Zhu Z, Jiang L, Ding X. Advancing Breast Cancer Heterogeneity Analysis: Insights from Genomics, Transcriptomics and Proteomics at Bulk and Single-Cell Levels. Cancers (Basel). 2023;15:4164.
Huang CC, Ku WC, Huang CJ, Tseng LM. Abstract 7098: Identifying of potential therapeutic targets of Taiwanese breast cancer by functional proteomics. Cancer Res. 2024;84(6Supplement):7098.
Wu CH, Hsieh CS, Chang YC, Huang CC, Yeh HT, Hou MF, Chung YC, Tu SH, Chang KJ, Chattopadhyay A, Lai LC, Lu TP, Li YH, Tsai MH, Chuang EY. Differential whole-genome doubling and homologous recombination deficiencies across breast cancer subtypes from the Taiwanese population. Commun Biol. 2021;4:1052.
Wu CH, Yeh HT, Hsieh CS, Huang CC, Chattopadhyay A, Chung YC, Tu SH, Li YH, Lu TP, Lai LC, Hou MF, Chang KJ, Tsai MH, Chuang EY. Evolutionary Trajectories and Genomic Divergence in Localized Breast Cancers after Ipsilateral Breast Tumor Recurrence. Cancers (Basel). 2021;13:1821.
Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics. 2009;25:1754–60.
DePristo MA, Banks E, Poplin R, Garimella KV, Maguire JR, Hartl C, Philippakis AA, del Angel G, Rivas MA, Hanna M, McKenna A, Fennell TJ, Kernytsky AM, Sivachenko AY, Cibulskis K, Gabriel SB, Altshuler D, Daly MJ. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat Genet. 2011;43:491–8.
Cibulskis K, Lawrence MS, Carter SL, Sivachenko A, Jaffe D, Sougnez C, Gabriel S, Meyerson M, Lander ES, Getz G. Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nat Biotechnol. 2013;31:213–9.
McLaren W, Gil L, Hunt SE, Riat HS, Ritchie GR, Thormann A, Flicek P, Cunningham F. The Ensembl Variant Effect Predictor. Genome Biol. 2016;17:122.
Wakabayashi M, Yoshihara H, Masuda T, Tsukahara M, Sugiyama N, Ishihama Y. Phosphoproteome analysis of formalin-fixed and paraffin-embedded tissue sections mounted on microscope slides. J Proteome Res. 2014;13:915–24.
Cox J, Mann M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat Biotechnol. 2008;26:1367–72.
Cox J, Neuhauser N, Michalski A, Scheltema RA, Olsen JV, Mann M. Andromeda: a peptide search engine integrated into the MaxQuant environment. J Proteome Res. 2011;10:1794–805.
Yu SH, Kyriakidou P, Cox J. Isobaric Matching between Runs and Novel PSM-Level Normalization in MaxQuant Strongly Improve Reporter Ion-Based Quantification. J Proteome Res. 2020;19:3945–54.
Gesell Salazar M, Neugebauer S, Kacprowski T, Michalik S, Ahnert P, Creutz P, Rosolowski M, PROGRESS Study Group, Löffler M, Bauer M, Suttorp N, Kiehntopf M, Völker U. Association of proteome and metabolome signatures with severity in patients with community-acquired pneumonia. J Proteom. 2020;214:103627.
Armstrong RA. When to use the Bonferroni correction. Ophthalmic Physiol Opt. 2014;34:502–8.
Chakravarty D, Gao J, Phillips SM, Kundra R, Zhang H, Wang J, Rudolph JE, Yaeger R, Soumerai T, Nissan MH, Chang MT, Chandarlapaty S, Traina TA, Paik PK, Ho AL, Hantash FM, Grupe A, Baxi SS, Callahan MK, Snyder A, Chi P, Danila D, Gounder M, Harding JJ, Hellmann MD, Iyer G, Janjigian Y, Kaley T, Levine DA, Lowery M, Omuro A, Postow MA, Rathkopf D, Shoushtari AN, Shukla N, Voss M, Paraiso E, Zehir A, Berger MF, Taylor BS, Saltz LB, Riely GJ, Ladanyi M, Hyman DM, Baselga J, Sabbatini P, Solit DB, Schultz N. OncoKB: A Precision Oncology Knowledge Base. JCO Precis Oncol. 2017;2017:PO.17.00011.
Suehnholz SP, Nissan MH, Zhang H, Kundra R, Nandakumar S, Lu C, Carrero S, Dhaneshwar A, Fernandez N, Xu BW, Arcila ME, Zehir A, Syed A, Brannon AR, Rudolph JE, Paraiso E, Sabbatini PJ, Levine RL, Dogan A, Gao J, Ladanyi M, Drilon A, Berger MF, Solit DB, Schultz N, Chakravarty D. Quantifying the Expanding Landscape of Clinical Actionability for Patients with Cancer. Cancer Discov. 2024;14:49–65.
Szklarczyk D, Kirsch R, Koutrouli M, Nastou K, Mehryary F, Hachilif R, Gable AL, Fang T, Doncheva NT, Pyysalo S, Bork P, Jensen LJ, von Mering C. The STRING database in 2023: protein-protein association networks and functional enrichment analyses for any sequenced genome of interest. Nucleic Acids Res. 2023;51(D1):D638–46.
Milacic M, Beavers D, Conley P, Gong C, Gillespie M, Griss J, Haw R, Jassal B, Matthews L, May B, Petryszak R, Ragueneau E, Rothfels K, Sevilla C, Shamovsky V, Stephan R, Tiwari K, Varusai T, Weiser J, Wright A, Wu G, Stein L, Hermjakob H. D'Eustachio P. The Reactome Pathway Knowledgebase 2024. Nucleic Acids Res. 2024;52:D672–8.
Letunic I, Khedkar S, Bork P. SMART: recent updates, new developments and status in 2020. Nucleic Acids Res. 2021;49(D1):D458–60.
Gonzalez-Angulo AM, Hennessy BT, Meric-Bernstam F, Sahin A, Liu W, Ju Z, Carey MS, Myhre S, Speers C, Deng L, Broaddus R, Lluch A, Aparicio S, Brown P, Pusztai L, Symmans WF, Alsner J, Overgaard J, Borresen-Dale AL, Hortobagyi GN, Coombes KR, Mills GB. Functional proteomics can define prognosis and predict pathologic complete response in patients with breast cancer. Clin Proteom. 2011;8:11.
Asleh K, Negri GL, Spencer Miko SE, Colborne S, Hughes CS, Wang XQ, Gao D, Gilks CB, Chia SKL, Nielsen TO, Morin GB. Proteomic analysis of archival breast cancer clinical specimens identifies biological subtypes with distinct survival outcomes. Nat Commun. 2022;13:896.
Gámez-Pozo A, Trilla-Fuertes L, Berges-Soria J, Selevsek N, López-Vacas R, Díaz-Almirón M, Nanni P, Arevalillo JM, Navarro H, Grossmann J, Gayá Moreno F, Gómez, Rioja R, Prado-Vázquez G, Zapater-Moros A, Main P, Feliú J, Martínez Del Prado P, Zamora P, Ciruelos E, Espinosa E. Fresno Vara JÁ. Functional proteomics outlines the complexity of breast cancer molecular subtypes. Sci Rep. 2017;7:10100.
Bruzas S, Kuemmel S, Harrach H, Breit E, Ataseven B, Traut A, Rüland A, Kostara A, Chiari O, Dittmer-Grabowski C, Reinisch M. Next-Generation Sequencing-Directed Therapy in Patients with Metastatic Breast Cancer in Routine Clinical Practice. Cancers (Basel). 2021;13:4564.
Kaufman B, Shapira-Frommer R, Schmutzler RK, Audeh MW, Friedlander M, Balmaña J, Mitchell G, Fried G, Stemmer SM, Hubert A, Rosengarten O, Steiner M, Loman N, Bowen K, Fielding A, Domchek SM. Olaparib monotherapy in patients with advanced cancer and a germline BRCA1/2 mutation. J Clin Oncol. 2015;33:244–50.
Turner NC, Oliveira M, Howell SJ, Dalenc F, Cortes J, Gomez Moreno HL, Hu X, Jhaveri K, Krivorotko P, Loibl S, Morales Murillo S, Okera M, Park YH, Sohn J, Toi M, Tokunaga E, Yousef S, Zhukova L, de Bruin EC, Grinsted L, Schiavon G, Foxley A, Rugo HS. CAPItello-291 Study Group. Capivasertib in Hormone Receptor-Positive Advanced Breast Cancer. N Engl J Med. 2023;388:2058–70.
André F, Ciruelos E, Rubovszky G, Campone M, Loibl S, Rugo HS, Iwata H, Conte P, Mayer IA, Kaufman B, Yamashita T, Lu YS, Inoue K, Takahashi M, Pápai Z, Longin AS, Mills D, Wilke C, Hirawat S, Juric D. SOLAR-1 Study Group. Alpelisib for PIK3CA-Mutated, Hormone Receptor-Positive Advanced Breast Cancer. N Engl J Med. 2019;380:1929–40.
Malumbres M, Barbacid M. RAS oncogenes: the first 30 years. Nat Rev Cancer. 2003;3:459–65.
Zhao Y, Du SS, Zhao CY, Li TL, Tong SC, Zhao L. Mechanism of Abnormal Activation of MEK1 Induced by Dehydroalanine Modification. Int J Mol Sci. 2024;25:7482.
Babon JJ, Lucet IS, Murphy JM, Nicola NA, Varghese LN. The molecular regulation of Janus kinase (JAK) activation. Biochem J. 2014;462:1–13.
Yuan D, Liu J, Sang W, Li Q. Comprehensive analysis of the role of SFXN family in breast cancer. Open Med (Wars). 2023;18:20230685.
Ledahawsky LM, Terzenidou ME, Edwards R, Kline RA, Graham LC, Eaton SL, van der Hoorn D, Chaytow H, Huang YT, Groen EJN, Motyl AAL, Lamont DJ, Tokatlidis K, Wishart TM, Gillingwater TH. The mitochondrial protein Sideroflexin 3 (SFXN3) influences neurodegeneration pathways in vivo. FEBS J. 2022;289:3894–914.
Williams SD, Smith TM, Stewart LV, Sakwe AM. Hypoxia-Inducible Expression of Annexin A6 Enhances the Resistance of Triple-Negative Breast Cancer Cells to EGFR and AR Antagonists. Cells. 2022;11:3007.
Xu H, Yu S, Peng K, Gao L, Chen S, Shen Z, Han Z, Chen M, Lin J, Chen S, Kang M. The role of EEF1D in disease pathogenesis: a narrative review. Ann Transl Med. 2021;9:1600.
Zhai K, Jiang N, Wen JF, Zhang X, Liu T, Long KJ, Ke XX, Xu G, Chen C. Overexpression of TWF1 promotes lung adenocarcinoma progression and is associated with poor prognosis in cancer patients through the MMP1 signaling pathway. J Thorac Dis. 2023;15:2644–58.
Zhang F, Yang J, Cheng Y. Impact of RANGAP1 SUMOylation on Smad4 nuclear export by bioinformatic analysis and cell assays. Biomol Biomed. 2024. 10.17305/bb.2024.10443.
Aslam A, Masood F, Perveen K, Berger MR, Pervaiz A, Zepp M, Klika KD, Yasin T, Hameed A. Preparation, characterization and evaluation of HPβCD-PTX/PHB nanoparticles for pH-responsive, cytotoxic and apoptotic properties. Int J Biol Macromol. 2024;270(Pt 2):132268.
Yang J, Li B, He QY. Significance of prohibitin domain family in tumorigenesis and its implication in cancer diagnosis and treatment. Cell Death Dis. 2018;9:580.
Zhou XY, Li Y, Liu J, Lu W, He Q, Li J, Liu S. Pan-cancer Analysis Combined with Experiments Deciphers PHB Regulation for Breast Cancer Cell Survival and Predicts Biomarker Function. Comb Chem High Throughput Screen. 2023. 10.2174/0113862073266248231024113533.
Zhou XY, Li Y, Liu J, Lu W, He Q, Li J, Liu S. Pan-cancer Analysis Combined with Experiments Deciphers PHB Regulation for Breast Cancer Cell Survival and Predicts Biomarker Function. Comb Chem High Throughput Screen. 2023. 10.2174/0113862073266248231024113533.
Hagar SM, Mohammed MN, Iman HI. Role of actin binding protein Twinfilin 1 in breast cancer. Azhar Int J Pharm Med Sci. 2022;2:52–9.
Cancer Genome Atlas Network. Comprehensive molecular portraits of human breast tumours. Nature. 2012;490:61–70.

No competing interests reported.

SupplementaryFigures.docx

Download PDF

Editorial decision: Revision requested
10 Oct, 2024
Reviews received at journal
01 Oct, 2024
Reviews received at journal
16 Sep, 2024
Reviewers agreed at journal
05 Sep, 2024
Reviewers agreed at journal
02 Sep, 2024
Reviewers invited by journal
02 Sep, 2024
Editor assigned by journal
06 Aug, 2024
Submission checks completed at journal
06 Aug, 2024
First submitted to journal
05 Aug, 2024

You are reading this latest preprint version

Integrating Functional Proteomics and Next Generation Sequencing Reveals Potential Therapeutic Targets for Taiwanese Breast Cancer

Status:

Version 1

Abstract

Figures

Introduction

Materials and methods

Results

Discussion

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1