scholarly journals Path to Clonal Theranostics in Luminal Breast Cancers

2022 ◽  
Vol 11 ◽  
Author(s):  
Nawale Hajjaji ◽  
Soulaimane Aboulouard ◽  
Tristan Cardon ◽  
Delphine Bertin ◽  
Yves-Marie Robin ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Drug Discovery ◽  
Drug Interactions ◽  
Anticancer Drugs ◽  
Cytoskeletal Proteins ◽  
Breast Cancers ◽  
Label Free ◽  
Cancer Resistance ◽  
Protein Targets ◽  
Tcga Dataset

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.

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

2021 ◽  
Author(s):  
N. Hajjaji ◽  
S. Aboulouard ◽  
T. Cardon ◽  
D. Bertin ◽  
YM. Robin ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Mass Spectrometry ◽  
Drug Interactions ◽  
Anticancer Drugs ◽  
Mass Spectrometry Imaging ◽  
Maldi Mass Spectrometry ◽  
Breast Cancers ◽  
Discovery Process ◽  
Spatially Resolved ◽  
Tcga Dataset

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.

scholarly journals Drug-Induced Resistance and Phenotypic Switch in Triple-Negative Breast Cancer Can Be Controlled via Resolution and Targeting of Individualized Signaling Signatures

Cancers ◽  
2021 ◽  
Vol 13 (19) ◽  
pp. 5009
Author(s):  
Swetha Vasudevan ◽  
Ibukun A. Adejumobi ◽  
Heba Alkhatib ◽  
Sangita Roy Chowdhury ◽  
Shira Stefansky ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Triple Negative Breast Cancer ◽  
Triple Negative ◽  
Growth Factor Receptor ◽  
Patient Specific ◽  
Network Structures ◽  
Breast Cancers ◽  
Drug Induced ◽  
Tcga Dataset

Triple-negative breast cancer (TNBC) is an aggressive subgroup of breast cancers which is treated mainly with chemotherapy and radiotherapy. Epidermal growth factor receptor (EGFR) was considered to be frequently expressed in TNBC, and therefore was suggested as a therapeutic target. However, clinical trials of EGFR inhibitors have failed. In this study, we examine the relationship between the patient-specific TNBC network structures and possible mechanisms of resistance to anti-EGFR therapy. Using an information-theoretical analysis of 747 breast tumors from the TCGA dataset, we resolved individualized protein network structures, namely patient-specific signaling signatures (PaSSS) for each tumor. Each PaSSS was characterized by a set of 1–4 altered protein–protein subnetworks. Thirty-one percent of TNBC PaSSSs were found to harbor EGFR as a part of the network and were predicted to benefit from anti-EGFR therapy as long as it is combined with anti-estrogen receptor (ER) therapy. Using a series of single-cell experiments, followed by in vivo support, we show that drug combinations which are not tailored accurately to each PaSSS may generate evolutionary pressure in malignancies leading to an expansion of the previously undetected or untargeted subpopulations, such as ER+ populations. This corresponds to the PaSSS-based predictions suggesting to incorporate anti-ER drugs in certain anti-TNBC treatments. These findings highlight the need to tailor anti-TNBC targeted therapy to each PaSSS to prevent diverse evolutions of TNBC tumors and drug resistance development.

scholarly journals Identification of the anti-breast cancer targets of triterpenoids in Liquidambaris Fructus and the hints for its traditional applications

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Ping Qian ◽  
Xiao-Ting Mu ◽  
Bing Su ◽  
Lu Gao ◽  
Dong-Fang Zhang
Keyword(s):  
Breast Cancer ◽  
Signaling Pathways ◽  
Cancer Progression ◽  
Tyrosine Kinases ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Breast Disease ◽  
Breast Cancers ◽  
Protein Targets ◽  
Protein Protein Interaction

Abstract Background Liquidambaris Fructus is the infructescences of Liquidambar formosana Hance and it has been used to treat some breast disease in Traditional Chinese Medicine. In the previous study we found the anti-breast cancer effect of triterpenoid in Liquidambaris Fructus. This study is a further investigation of the triterpenoids in Liquidambaris Fructus and aims to identify their anti-breast cancer targets, meanwhile, to estimate the rationality of the traditional applications of Liquidambaris Fructus. Methods Triterpenoids in Liquidambaris Fructus were isolated and their structures were identified by NMR spectrums. Potential targets of these triterpenoids were predicted using a reverse pharmacophore mapping strategy. Associations between these targets and the therapeutic targets of breast cancer were analyzed by constructing protein-protein interaction network, and targets played important roles in the network were identified using Molecular Complex Detection method. Binding affinity between the targets and triterpenoids was studied using molecular docking method. Gene ontology enrichment analysis was conducted to reveal the biological process and signaling pathways that the identified targets were involved in. Results Thirteen triterpenoids were identified and 6 of them were the first time isolated from Liquidambaris Fructus. Predicted ADME properties revealed a good druggability of these triterpenoids. We identified 18 protein targets which were closely related to breast cancer progression, especially triple-negative, basal-like or advanced stage breast cancers. The triterpenoids could bind with these targets as their inhibitors: hydrophobic skeleton is a favorable factor for them to stabilize at binding site and polar C17- or C3- substituent was necessary for binding. GO enrichment analysis indicated that inhibition of protein tyrosine kinases autophosphorylation might be the primary mechanism for the anti-breast cancer effect of the triterpenoids, and ErbB4 and EGFR were the most relevant targets. Conclusions The study revealed that triterpenoids from Liquidambaris Fructus might exert anti-breast cancer effect by directly inhibit multiple protein targets and signaling pathways, especially ErbB4 and EGFR and related pathways. This study also brings up another hint that the traditional applications of Liquidambaris Fructus on hypogalactia should be reassessed systematically because it might suppress rather than promote lactation by inhibiting the activity of ErbB4.

A novel prognostic signature based on centrosome amplification-based genes to predict clinical outcomes in breast tumors.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. 11604-11604
Author(s):  
Angela Ogden ◽  
Padmashree C.G. Rida ◽  
Ritu Aneja
Keyword(s):  
Breast Cancer ◽  
Clinical Outcomes ◽  
Chromosomal Instability ◽  
Prognostic Value ◽  
Breast Tumors ◽  
Centrosome Amplification ◽  
Age At Diagnosis ◽  
Breast Cancers ◽  
Cox Models ◽  
Tcga Dataset

11604 Background: A majority of breast tumors exhibit centrosome amplification (CA), which imparts aggressive phenotypes like chromosomal instability and invasive behavior. Nevertheless, it is unclear whether CA is associated with poor clinical outcomes after adjusting for potentially confounding factors, like stage and age at diagnosis. Methods: We developed a twenty-gene signature, “CA20,” composed of genes related to centrosome structure and/or whose dysregulation induces CA and tested its prognostic value compared with that of CIN25, a chromosomal instability (CIN) signature, in combined multivariable Cox models using the METABRIC and TCGA microarray breast datasets. The n = 1,969 primary breast cancers of the METABRIC dataset were split randomly and approximately equally into training and validation sets, unlike the n = 524 primary invasive breast cancers of the TCGA dataset, which could not be split to preserve power ≈ 0.80, so bootstrapping was instead used. CA20 and CIN25 were dichotomized by average scores and optimal cutpoints based on the log-rank test. Results: In both discovery and validation METRABRIC sets, CA20 was a significant independent predictor of worse breast cancer-specific survival (HR = 2.9, p < 0.001 and 2.4, p < 0.001, respectively, using average scores as cutpoints; similar results obtained using optimal cutpoints) in multivariable Cox models, unlike CIN25. CA20 score was highly correlated with CIN25 score (ρ = 0.93, p < 10-6). In the TCGA dataset, high CA20 score was associated with 3.8- and 3.7-fold worse overall survival (bootstrap-p = 0.001 and 0.002, respectively, for average and optimal cutpoints) after adjusting for tumor stage and age at diagnosis, unlike CIN25. Also in the TCGA dataset, CA20 correlated very strongly with CIN25 (ρ = 0.95, p < 10-6). Finally, using the TCGA dataset, we identified processes and pathways enriched in the CA20-high group (q < 0.05) that may be potential therapeutic targets, such as DNA repair processes, the DNA integrity checkpoint, and regulation of microtubule dynamics. Conclusions: CA20 is a novel signature with robust prognostic value in breast cancer and identifies patients who might respond to centrosome declustering drugs.

Sign in / Sign up

Export Citation Format

Share Document