scholarly journals Path to drugging functional clones of luminal breast cancers using in-depth proteomics with spatially resolved mass spectrometry guided by MALDI imaging

Author(s):  
N. Hajjaji ◽  
S. Aboulouard ◽  
T. Cardon ◽  
D. Bertin ◽  
YM. Robin ◽  
...  

AbstractIntegrating tumor heterogeneity in the drug discovery process is a key challenge to tackle breast cancer resistance. Identifying protein targets for functionally distinct tumor clones is particularly important to tailor therapy to the heterogeneous tumor subpopulations. For this purpose, we performed an unsupervised, label-free, spatially resolved shotgun proteogenomic guided by MALDI mass spectrometry imaging (MSI) on 124 selected tumor clonal areas from early luminal breast cancers, tumor stroma, and breast cancer metastases. 2868 proteins were identified. The main protein classes found in the clonal proteome dataset were enzymes, cytoskeletal proteins, membrane-traffic, translational or scaffold proteins, or transporters. As a comparison, gene-specific transcriptional regulators, chromatin related proteins or transmembrane signal receptor were more abundant in the TCGA dataset. Moreover, 26 mutated proteins have been identified. Similarly, expanding the search to alternative proteins databases retrieved 126 alternative proteins in the clonal proteome dataset. The majority of these alternative proteins were coded mainly from non-coding RNA. To fully understand the molecular information brought by our approach and its relevance to drug target discovery, the clonal proteomic dataset was further compared to the TCGA breast cancer database and two transcriptomic panels, BC360 (nanoString®) and CDx (Foundation One®). We retrieved 139 pathways in the clonal proteome dataset. Only 55% of these pathways were also present in the TCGA dataset, 68% in BC360 and 50% in CDx. Seven of these pathways have been suggested as candidate for drug targeting, 22 have been associated with breast cancer in experimental or clinical reports, the remaining 19 pathways have been understudied in breast cancer. Among the anticancer drugs, 35 drugs matched uniquely with the clonal proteome dataset, with only 7 of them already approved in breast cancer. The number of target and drug interactions with non-anticancer drugs (such as agents targeting the cardiovascular system, metabolism, the musculoskeletal or the nervous systems) was higher in the clonal proteome dataset (540 interactions) compared to TCGA (83 interactions), BC360 (419 interactions), or CDx (172 interactions). Thus, we described the non-redundant knowledge brought by this approach compared to TCGA or transcriptomic panels, the targetable proteins identified in the clonal proteome dataset, and the potential of this approach for drug discovery and repurposing through drug interactions with antineoplastic agents and non-anticancer drugs.SignificanceSpatially resolved mass spectrometry guided by MALDI MS imaging is a precision oncology tool to map and profile breast cancer proteomic clones with the aim of integrating tumor heterogeneity in the target discovery process to develop clone-tailored therapeutic strategies in breast cancer.HighlightsSpatially resolved mass spectrometry guided by MALDI mass spectrometry imaging allows an in-depth proteomic profiling of breast cancer functional clones.This unsupervised and unlabeled technology performed on intact tumors provides a multidimensional analysis of the clonal proteome including conventional proteins, mutated proteins, and alternative proteins.The rich clonal proteomic information generated was not redundant with TCGA or transcriptomic panels, and showed pathways exclusively found in the proteomic analysis.A large proportion of the proteins in the clonal proteome dataset were druggable with both antineoplastic agents and non-anticancer drugs, showing the potential application to drug repurposing.A significant number of the proteins detected had partially or not yet known drug interactions, showing the potential for discovery.

2022 ◽  
Vol 11 ◽  
Author(s):  
Nawale Hajjaji ◽  
Soulaimane Aboulouard ◽  
Tristan Cardon ◽  
Delphine Bertin ◽  
Yves-Marie Robin ◽  
...  

Integrating tumor heterogeneity in the drug discovery process is a key challenge to tackle breast cancer resistance. Identifying protein targets for functionally distinct tumor clones is particularly important to tailor therapy to the heterogeneous tumor subpopulations and achieve clonal theranostics. For this purpose, we performed an unsupervised, label-free, spatially resolved shotgun proteomics guided by MALDI mass spectrometry imaging (MSI) on 124 selected tumor clonal areas from early luminal breast cancers, tumor stroma, and breast cancer metastases. 2868 proteins were identified. The main protein classes found in the clonal proteome dataset were enzymes, cytoskeletal proteins, membrane-traffic, translational or scaffold proteins, or transporters. As a comparison, gene-specific transcriptional regulators, chromatin related proteins or transmembrane signal receptor were more abundant in the TCGA dataset. Moreover, 26 mutated proteins have been identified. Similarly, expanding the search to alternative proteins databases retrieved 126 alternative proteins in the clonal proteome dataset. Most of these alternative proteins were coded mainly from non-coding RNA. To fully understand the molecular information brought by our approach and its relevance to drug target discovery, the clonal proteomic dataset was further compared to the TCGA breast cancer database and two transcriptomic panels, BC360 (nanoString®) and CDx (Foundation One®). We retrieved 139 pathways in the clonal proteome dataset. Only 55% of these pathways were also present in the TCGA dataset, 68% in BC360 and 50% in CDx. Seven of these pathways have been suggested as candidate for drug targeting, 22 have been associated with breast cancer in experimental or clinical reports, the remaining 19 pathways have been understudied in breast cancer. Among the anticancer drugs, 35 drugs matched uniquely with the clonal proteome dataset, with only 7 of them already approved in breast cancer. The number of target and drug interactions with non-anticancer drugs (such as agents targeting the cardiovascular system, metabolism, the musculoskeletal or the nervous systems) was higher in the clonal proteome dataset (540 interactions) compared to TCGA (83 interactions), BC360 (419 interactions), or CDx (172 interactions). Many of the protein targets identified and drugs screened were clinically relevant to breast cancer and are in clinical trials. Thus, we described the non-redundant knowledge brought by this clone-tailored approach compared to TCGA or transcriptomic panels, the targetable proteins identified in the clonal proteome dataset, and the potential of this approach for drug discovery and repurposing through drug interactions with antineoplastic agents and non-anticancer drugs.


2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e13063-e13063
Author(s):  
Nawale Hajjaji ◽  
Mira Abbouchi ◽  
Lan Anh Nguyen ◽  
Samuel Charles ◽  
Sarah Leclercq ◽  
...  

e13063 Background: Breast cancer mortality is expected to rise by almost 30% by 2030 worldwide, mainly due to the occurrence of distant metastases. The development of drugs specifically targeted at tumor drivers has not yet curbed resistance to treatment, which prevents metastases curability. There is a need for new molecular approaches to tackle metastases complex biology, particularly tumor heterogeneity, a main determinant of resistance. The aim of this study was to use a proteomic mass spectrometry-based approach to reveal functionally heterogeneous’ tumor subpopulations in breast cancer metastases, and identify clone specific drug targets. Methods: Metastasis biopsies (n = 21) were collected retrospectively from patients with advanced breast cancer treated at Oscar Lambret Cancer Center (Lille, France). Tumor heterogeneity was analyzed directly on FFPE tissue sections using MALDI mass spectrometry imaging (MSI) on a RapifleX Tissuetyper. Unsupervised spatial segmentation was performed to reveal tumor subpopulations with distinct proteomic profiles within each metastasis. The full proteomic characterization of these tumor clones was further performed with spatially resolved proteomic mass spectrometry. Results: MSI revealed that breast cancer metastases contained 2 to 5 functionally distinct tumor clones (proteomic clones). Although the clone profiles within a metastasis were correlated, unsupervised hierarchical clustering showed a clear distinction between them and specific proteomic signatures. Enrichment analysis showed that differentially expressed proteins were involved in a variety of biological processes or pathways including regulation of histone acetylation, extracellular matrix degradation, DNA repair, NOTCH pathway, estrogen-responsive target genes or exocytosis. The evolution of the proteomic clones profile during disease progression was also determined by comparison of paired biopsies. To identify the candidate treatments best fitted to metastasis heterogeneity, the specific proteomic signatures of the clones were matched against a druggable genome database. It was possible to unveil candidate drug targets personalized to each metastasis functional clone. Conclusions: MALDI mass spectrometry imaging combined with spatially resolved proteomics has the potential to tackle breast cancer metastases heterogeneity, and identify candidate drug targets specific to functional clones to personalize treatments.


2019 ◽  
Vol 37 (15_suppl) ◽  
pp. 3077-3077
Author(s):  
Nawale Hajjaji ◽  
Soulaimane Aboulouard ◽  
Yves-Marie Robin ◽  
Delphine Bertin ◽  
Isabelle Fournier ◽  
...  

3077 Background: Breast cancer is a heterogeneous disease with a wide range of outcomes. Among the intrinsic breast cancer subtypes, luminal A tumors are considered to have a favorable prognosis. However, molecular studies characterizing the genomic landscape of luminal A tumors revealed a molecular heterogeneity within this subtype, which also translated to variability in survival. A better understanding of the biology of this tumor subgroup is therefore needed to determine the appropriate therapeutic strategy. The aims of the study were to determine the frequency of high-risk luminal A tumors in a real life cohort of early breast cancers and provide a proteomic characterization of this subgroup using a mass spectrometry approach. Methods: 222 early breast cancer patients with hormone receptor positive and HER2 negative tumors treated at our institution had a PAM50-based genomic assay Prosigna to estimate their risk of recurrence. This assay assigned each tumor sample to an intrinsic molecular subtype of breast cancer. Luminal A and B tumors were analyzed with MALDI mass spectrometry imaging combined with microproteomics, a spatially-resolved on-tissue shotgun proteomic technology, to determine the proteomic profiles of both cancer cells and stroma. Results: Among the 129 luminal A breast cancers identified in our cohort, 67 (51%) had a risk of distant recurrence of 10% or more (32% had a 10% to 15% risk, and 19% a risk greater than 15%). High-risk luminal A tumors had a distinctive proteomic profile compared to low-risk luminal A or to luminal B tumors. Overexpression of the methionine biosynthesis pathway was the main differential protein expression observed in cancer cells and stroma of high-risk luminal A. Inflammation mediated by chemokine and cytokine signaling pathway and integrin signaling were also overexpressed in high risk luminal A compared to luminal B. In the stroma of luminal B tumors, EGR signaling, Ras and FGF pathways and angiogenesis were overexpressed compared to high-risk luminal A tumors. Conclusions: Real life data showed a significant proportion of high-risk luminal A breast cancers. MALDI mass spectrometry proteomics revealed distinctive tumor and microenvironment profiles in this breast cancer subgroup.


Theranostics ◽  
2020 ◽  
Vol 10 (16) ◽  
pp. 7070-7082 ◽  
Author(s):  
Chenglong Sun ◽  
Fukai Wang ◽  
Yang Zhang ◽  
Jinqian Yu ◽  
Xiao Wang

2020 ◽  
Vol 58 (6) ◽  
pp. 914-929 ◽  
Author(s):  
Klára Ščupáková ◽  
Benjamin Balluff ◽  
Caitlin Tressler ◽  
Tobi Adelaja ◽  
Ron M.A. Heeren ◽  
...  

AbstractMass spectrometry (MS) is the workhorse of metabolomics, proteomics and lipidomics. Mass spectrometry imaging (MSI), its extension to spatially resolved analysis of tissues, is a powerful tool for visualizing molecular information within the histological context of tissue. This review summarizes recent developments in MSI and highlights current challenges that remain to achieve molecular imaging at the cellular level of clinical specimens. We focus on matrix-assisted laser desorption/ionization (MALDI)-MSI. We discuss the current status of each of the analysis steps and remaining challenges to reach the desired level of cellular imaging. Currently, analyte delocalization and degradation, matrix crystal size, laser focus restrictions and detector sensitivity are factors that are limiting spatial resolution. New sample preparation devices and laser optic systems are being developed to push the boundaries of these limitations. Furthermore, we review the processing of cellular MSI data and images, and the systematic integration of these data in the light of available algorithms and databases. We discuss roadblocks in the data analysis pipeline and show how technology from other fields can be used to overcome these. Finally, we conclude with curative and community efforts that are needed to enable contextualization of the information obtained.


Author(s):  
Ákos Végvári ◽  
Alexander S. Shavkunov ◽  
Thomas E. Fehniger ◽  
Dorthe Grabau ◽  
Emma Niméus ◽  
...  

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