Proteomics of tumor and serum samples from isocitrate dehydrogenase-wildtype glioblastoma patients: is the detoxification of reactive oxygen species associated with shorter survival?

Proteomics has been little used for the identification of novel prognostic and/or therapeutic markers in isocitrate dehydrogenase (IDH)-wildtype glioblastoma (GB). In this study, we analyzed 50 tumor and 30 serum samples from short- and long-term survivors of IDH-wildtype GB (STS and LTS, respectively) by data-independent acquisition mass spectrometry (DIA-MS)-based proteomics, with the aim of identifying such markers. DIA-MS identified 5422 and 826 normalized proteins in tumor and serum samples, respectively, with only three tumor proteins and 26 serum proteins displaying significant differential expression between the STS and LTS groups. These dysregulated proteins were principally associated with the detoxification of reactive oxygen species (ROS). In particular, GB patients in the STS group had high serum levels of malate dehydrogenase 1 (MDH1) and ribonuclease inhibitor 1 (RNH1) and low tumor levels of fatty acid-binding protein 7 (FABP7), which may have enabled them to maintain low ROS levels, counteracting the effects of the first-line treatment with radiotherapy plus concomitant and adjuvant temozolomide. A blood score built on the levels of MDH1 and RNH1 expression was found to be an independent prognostic factor for survival based on the serum proteome data for a cohort of 96 IDH-wildtype GB patients. This study highlights the utility of circulating MDH1 and RNH1 biomarkers for determining the prognosis of patients with IDH-wildtype GB. Furthermore, the pathways driven by these biomarkers, and the tumor FABP7 pathway, may constitute promising therapeutic targets for blocking ROS detoxification to overcome resistance to chemoradiotherapy in potential GB STS.

Mesenchymal-like immune-altered is the fourth robust triple-negative breast cancer molecular subtype.

BACKGROUND: Robust molecular subtyping of triple-negative breast cancer (TNBC) is a prerequisite for the success of precision medicine. Today, there is a clear consensus on three TNBC molecular subtypes: luminal androgen receptor (LAR), basal-like immune-activated (BLIA), and basal-like immune-suppressed (BLIS). However, the debate about the robustness of other subtypes is still open. METHODS: An unprecedented number (n = 1942) of TNBC patient data was collected. Microarray- and RNAseq-based cohorts were independently investigated. Unsupervised analyses were conducted using k-means consensus clustering. Clusters of patients were then functionally annotated using different approaches. Prediction of response to chemotherapy and targeted therapies, immune checkpoint blockade, and radiotherapy were also screened for each TNBC subtype. RESULTS: Four TNBC subtypes were identified in the cohort: LAR (19.36%); mesenchymal stem-like (MSL/MES) (17.35%); BLIA (31.06%); and BLIS (32.23%). Regarding the MSL/MES subtype, we suggest renaming it to mesenchymal-like immune-altered (MLIA) to emphasize its specific histological background and nature of immune response. Treatment response prediction results show, among other things, that despite immune activation, immune checkpoint blockade is probably less or completely ineffective in MLIA, possibly caused by mesenchymal background and/or an enrichment in dysfunctional cytotoxic T lymphocytes. TNBC subtyping results were included in the bc-GenExMiner v5.0 webtool ( http://bcgenex.ico.unicancer.fr ). CONCLUSION: The mesenchymal TNBC subtype is characterized by an exhausted and altered immune response, and resistance to immune checkpoint inhibitors. Consensus for molecular classification of TNBC subtyping and prediction of cancer treatment responses helps usher in the era of precision medicine for TNBC patients.

Influence of inputs for bone lesion segmentation in longitudinal 18F-FDG PET/CT imaging studies.

In metastatic breast cancer, bone metastases are prevalent and associated with multiple complications. Assessing their response to treatment is therefore crucial. Most deep learning methods segment or detect lesions on a single acquisition while only a few focus on longitudinal studies. In this work, 45 patients with baseline (BL) and follow-up (FU) images recruited in the context of the EPICUREseinmeta study were analyzed. The aim was to determine if a network trained for a particular timepoint can generalize well to another one, and to explore different improvement strategies. Four networks based on the same 3D U-Net framework to segment bone lesions on BL and FU images were trained with different strategies and compared. These four networks were trained 1) only with BL images 2) only with FU images 3) with both BL and FU images 4) only with FU images but with BL images and bone lesion segmentations registered as input channels. With the obtained segmentations, we computed the PET Bone Index (PBI) which assesses the bone metastases burden of patients and we analyzed its potential for treatment response evaluation. Dice scores of 0.53, 0.55, 0.59 and 0.62 were respectively obtained on FU acquisitions. The under-performance of the first and third networks may be explained by the lower SUV uptake due to treatment response in FU images compared to BL images. The fourth network gives better results than the second network showing that the addition of BL PET images and bone lesion segmentations as prior knowledge has its importance. With an AUC of 0.86, the difference of PBI between two acquisitions could be used to assess treatment response. Clinical relevance- To assess the response to treatment of bone metastases, it is crucial to detect and segment them on several acquisitions from a same patient. We proposed a completely automatic method to detect and segment these metastases on longitudinal 18F-FDG PET/CT images in the context of metastatic breast cancer. We also proposed an automatic PBI to quantitatively assess the evolution of the bone metastases burden of patient and to automatically evaluate their response to treatment.

Deformable image registration with deep network priors: a study on longitudinal PET images.

Objective.This paper proposes a novel approach for the longitudinal registration of PET imaging acquired for the monitoring of patients with metastatic breast cancer. Unlike with other image analysis tasks, the use of deep learning (DL) has not significantly improved the performance of image registration. With this work, we propose a new registration approach to bridge the performance gap between conventional and DL-based methods: medical image registration method regularized by architecture (MIRRBA).Approach.MIRRBAis a subject-specific deformable registration method which relies on a deep pyramidal architecture to parametrize the deformation field. Diverging from the usual deep-learning paradigms,MIRRBAdoes not require a learning database, but only a pair of images to be registered that is used to optimize the network's parameters. We appliedMIRRBAon a private dataset of 110 whole-body PET images of patients with metastatic breast cancer. We used different architecture configurations to produce the deformation field and studied the results obtained. We also compared our method to several standard registration approaches: two conventional iterative registration methods (ANTs and Elastix) and two supervised DL-based models (LapIRN and Voxelmorph). Registration accuracy was evaluated using the Dice score, the target registration error, the average Hausdorff distance and the detection rate, while the realism of the registration obtained was evaluated using Jacobian's determinant. The ability of the different methods to shrink disappearing lesions was also computed with the disappearing rate.Main results.MIRRBA significantly improved all metrics when compared to DL-based approaches. The organ and lesion Dice scores of Voxelmorph improved by 6% and 52% respectively, while the ones of LapIRN increased by 5% and 65%. Regarding conventional approaches, MIRRBA presented comparable results showing the feasibility of our method.Significance.In this paper, we also demonstrate the regularizing power of deep architectures and present new elements to understand the role of the architecture in DL methods used for registration.

Impact of HER2 Status on Pathological Response after Neoadjuvant Chemotherapy in Early Triple-Negative Breast Cancer.

PURPOSE: Investigates the link between HER2 status and histological response after neoadjuvant chemotherapy in patients with early TNBC. METHODS: We retrieved clinical and anatomopathological data retrospectively from 449 patients treated for the first time with standard neoadjuvant chemotherapy for early unilateral BC between 2005 and 2020. The primary endpoint was pathological complete response (pCR, i.e., ypT0 ypN0), according to HER2 status. Secondary endpoints included invasive disease-free survival (I-DFS) and overall survival (OS). RESULTS: 437 patients were included, and 121 (27.7%) patients had HER2-low tumours. The pCR rate was not significantly different between the HER2-low group vs. the HER2-0 group (35.7% versus 41.8%, p = 0.284) in either univariate analysis or multivariate analysis adjusted for TNM classification and grade (odds ratio [OR] = 0.70, confidence interval [CI] 95% 0.45-1.08). With a median follow-up of 72.9 months, no significant survival differences were observed between patients with HER2-low tumours vs. patients with HER2-0 tumours in terms of I-DFS (p = 0.487) and OS (p = 0.329). CONCLUSIONS: In our cohort, HER2 status was not significantly associated with pCR in a manner consistent with data published recently on TNBC. However, the prognostic impact of HER2-low expression among TNBC patients warrants further evaluation.

Prognostic Value of Metabolic, Volumetric and Textural Parameters of Baseline [18F]FDG PET/CT in Early Triple-Negative Breast Cancer.

(1) Background: triple-negative breast cancer (TNBC) remains a clinical and therapeutic challenge primarily affecting young women with poor prognosis. TNBC is currently treated as a single entity but presents a very diverse profile in terms of prognosis and response to treatment. Positron emission tomography/computed tomography (PET/CT) with 18F-fluorodeoxyglucose ([18F]FDG) is gaining importance for the staging of breast cancers. TNBCs often show high [18F]FDG uptake and some studies have suggested a prognostic value for metabolic and volumetric parameters, but no study to our knowledge has examined textural features in TNBC. The objective of this study was to evaluate the association between metabolic, volumetric and textural parameters measured at the initial [18F]FDG PET/CT and disease-free survival (DFS) and overall survival (OS) in patients with nonmetastatic TBNC. (2) Methods: all consecutive nonmetastatic TNBC patients who underwent a [18F]FDG PET/CT examination upon diagnosis between 2012 and 2018 were retrospectively included. The metabolic and volumetric parameters (SUVmax, SUVmean, SUVpeak, MTV, and TLG) and the textural features (entropy, homogeneity, SRE, LRE, LGZE, and HGZE) of the primary tumor were collected. (3) Results: 111 patients were enrolled (median follow-up: 53.6 months). In the univariate analysis, high TLG, MTV and entropy values of the primary tumor were associated with lower DFS (p = 0.008, p = 0.006 and p = 0.025, respectively) and lower OS (p = 0.002, p = 0.001 and p = 0.046, respectively). The discriminating thresholds for two-year DFS were calculated as 7.5 for MTV, 55.8 for TLG and 2.6 for entropy. The discriminating thresholds for two-year OS were calculated as 9.3 for MTV, 57.4 for TLG and 2.67 for entropy. In the multivariate analysis, lymph node involvement in PET/CT was associated with lower DFS (p = 0.036), and the high MTV of the primary tumor was correlated with lower OS (p = 0.014). (4) Conclusions: textural features associated with metabolic and volumetric parameters of baseline [18F]FDG PET/CT have a prognostic value for identifying high-relapse-risk groups in early TNBC patients.

Brain Neural Progenitors are New Predictive Biomarkers for Breast Cancer Hormonotherapy.

Heterogeneity of the tumor microenvironment (TME) is one of the major causes of treatment resistance in breast cancer. Among TME components, nervous system role in clinical outcome has been underestimated. Identifying neuronal signatures associated with treatment response will help to characterize neuronal influence on tumor progression and identify new treatment targets. The search for hormonotherapy-predictive biomarkers was implemented by supervised machine learning (ML) analysis on merged transcriptomics datasets from public databases. ML-derived genes were investigated by pathway enrichment analysis, and potential gene signatures were curated by removing the variables that were not strictly nervous system specific. The predictive and prognostic abilities of the generated signatures were examined by Cox models, in the initial cohort and seven external cohorts. Generated signature performances were compared with 14 other published signatures, in both the initial and external cohorts. Underlying biological mechanisms were explored using deconvolution tools (CIBERSORTx and xCell). Our pipeline generated two nervous system-related signatures of 24 genes and 97 genes (NervSign24 and NervSign97). These signatures were prognostic and hormonotherapy-predictive, but not chemotherapy-predictive. When comparing their predictive performance with 14 published risk signatures in six hormonotherapy-treated cohorts, NervSign97 and NervSign24 were the two best performers. Pathway enrichment score and deconvolution analysis identified brain neural progenitor presence and perineural invasion as nervous system-related mechanisms positively associated with NervSign97 and poor clinical prognosis in hormonotherapy-treated patients. Transcriptomic profiling has identified two nervous system-related signatures that were validated in clinical samples as hormonotherapy-predictive signatures, meriting further exploration of neuronal component involvement in tumor progression. Significance: The development of personalized and precision medicine is the future of cancer therapy. With only two gene expression signatures approved by FDA for breast cancer, we are in need of new ones that can reliably stratify patients for optimal treatment. This study provides two hormonotherapy-predictive and prognostic signatures that are related to nervous system in TME. It highlights tumor neuronal components as potential new targets for breast cancer therapy.

Rucaparib in patients presenting a metastatic breast cancer with homologous recombination deficiency, without germline BRCA1/2 mutation.

BACKGROUND: Breast cancer may present genomic alterations leading to homologous recombination deficiency (HRD). PARP inhibitors have proven their efficacy in patients with HER2-negative (HER2-) metastatic breast cancer (mBC) harbouring germline (g) BRCA1/2 mutations in 3 phases III trials. The single-arm phase II RUBY trial included 42 patients, 40 of whom received at least one dose of rucaparib. RUBY study assessed the efficacy of rucaparib in HER2-mBC with either high genomic loss of heterozygosity (LOH) score or non-germline BRCA1/2 mutation. PATIENTS AND METHODS: The primary objective was the clinical benefit rate (CBR), and the study was powered to see 20% CBR using a 2-stage Simon design. RESULTS: The primary-end point was not reached with a CBR of 13.5%. Two LOH-high patients, without somatic BRCA1/2 mutation, presented a complete and durable response (12 and 28.5 months). Whole-genome analysis was performed on 24 samples, including 5 patients who presented a clinical benefit from rucaparib. HRDetect tended to be associated with response to rucaparib, without reaching statistical significance (median HRDetect responders versus non-responders: 0.465 versus 0.040; p = 0.2135). Finally, 220 of 711 patients with mBC screened for LOH upstream from RUBY presented a high LOH score associated with a higher likelihood of death (hazard ratio = 1.39; 95% CI: 1.11-1.75; p = 0.005). CONCLUSION: Our data suggest that a small subset of patients with high LOH scores without germline BRCA1/2 mutation could derive benefit from PARP inhibitors. However, the RUBY study underlines the need to develop additional biomarkers to identify selectively potential responders.

[Interest of the bc-GenExMiner web tool in oncology]. / Intérêt de l'outil web bc-GenExMiner en oncologie.

We are taking advantage of the launch of the latest version (v4.6) of our web-based data mining tool "breast cancer gene-expression miner" (bc-GenExMiner) to take stock of its position within the oncology research landscape and to present an activity report ten years after its establishment (http://bcgenex.ico.unicancer.fr). bc-GenExMiner is an open-access, user-friendly tool for statistical mining on breast tumor transcriptomes, annotated with more than 20 clinicopathologic and molecular characteristics. The database comprises more than 16,000 patients from 64 cohorts - including TCGA, METABRIC and SCAN-B - for whom several thousands of genes have been quantified by microarrays or RNA-seq. Correlation, expression and prognostic analyses are available for targeted, exhaustive or customized explorations of queried genes. bc-GenExMiner facilitates the validation, investigation, and prioritization of discoveries and hypotheses on genes of interest. It allows users to analyse large databases, create data visualizations, and obtain robust statistical analysis, thereby accelerating biomarker discovery. Ten years after its launch, judging by the number of visits, analyses, and scientific citations of bc-GenExMiner, we conclude that this web resource serves its purpose in the international scientific community working in breast cancer research, with a never-ending rise in its use.

The EPICURE study: a pilot prospective cohort study of heterogeneous and massive data integration in metastatic breast cancer patients.

BACKGROUND: Breast cancer is the most common cancer in women and the first cancer concerning mortality. Metastatic breast cancer remains a disease with a poor prognosis and about 30% of women diagnosed with an early stage will have a secondary progression. Metastatic breast cancer is an incurable disease despite significant therapeutic advances in both supportive cares and targeted specific therapies. In the management of a metastatic patient, each clinician follows a highly complex and strictly personal decision making process. It is based on a number of objective and subjective parameters which guides therapeutic choice in the most individualized or adapted manner. METHODS/DESIGN: The main objective is to integrate massive and heterogeneous data concerning the patient's environment, personal and familial history, clinical and biological data, imaging, histological results (with multi-omics data), and microbiota analysis. These characteristics are multiple and in dynamic interaction overtime. With the help of mathematical units with biological competences and scientific collaborations, our project is to improve the comprehension of treatment response, based on health clinical and molecular heterogeneous big data investigation. DISCUSSION: Our project is to prove feasibility of creation of a clinico-biological database prospectively by collecting epidemiological, socio-economic, clinical, biological, pathological, multi-omic data and to identify characteristics related to the overall survival status before treatment and within 15 years after treatment start from a cohort of 300 patients with a metastatic breast cancer treated in the institution. TRIAL REGISTRATION: ClinicalTrials.gov identifier (NCT number): NCT03958136 . Registration 21st of May, 2019; retrospectively registered.

bc-GenExMiner 4.5: new mining module computes breast cancer differential gene expression analyses.

'Breast cancer gene-expression miner' (bc-GenExMiner) is a breast cancer-associated web portal (http://bcgenex.ico.unicancer.fr). Here, we describe the development of a new statistical mining module, which permits several differential gene expression analyses, i.e. 'Expression' module. Sixty-two breast cancer cohorts and one healthy breast cohort with their corresponding clinicopathological information are included in bc-GenExMiner v4.5 version. Analyses are based on microarray or RNAseq transcriptomic data. Thirty-nine differential gene expression analyses, grouped into 13 categories, according to clinicopathological and molecular characteristics ('Targeted' and 'Exhaustive') and gene expression ('Customized'), have been developed. Output results are visualized in four forms of plots. This new statistical mining module offers, among other things, the possibility to compare gene expression in healthy (cancer-free), tumour-adjacent and tumour tissues at once and in three triple-negative breast cancer subtypes (i.e. C1: molecular apocrine tumours; C2: basal-like tumours infiltrated by immune suppressive cells and C3: basal-like tumours triggering an ineffective immune response). Several validation tests showed that bioinformatics process did not alter the pathobiological information contained in the source data. In this work, we developed and demonstrated that bc-GenExMiner 'Expression' module can be used for exploratory and validation purposes. Database URL: http://bcgenex.ico.unicancer.fr.

Random forest of perfect trees: concept, performance, applications and perspectives.

MOTIVATION: The principle of Breiman's random forest (RF) is to build and assemble complementary classification trees in a way that maximizes their variability. We propose a new type of random forest that disobeys Breiman's principles and involves building trees with no classification errors in very large quantities. We used a new type of decision tree that uses a neuron at each node as well as an in-innovative half Christmas tree structure. With these new RFs, we developed a score, based on a family of ten new statistical information criteria, called Nguyen information criteria (NICs), to evaluate the predictive qualities of features in three dimensions. RESULTS: The first NIC allowed the Akaike information criterion to be minimized more quickly than data obtained with the Gini index when the features were introduced in a logistic regression model. The selected features based on the NICScore showed a slight advantage compared to the support vector machines-recursive feature elimination (SVM-RFE) method. We demonstrate that the inclusion of artificial neurons in tree nodes allows a large number of classifiers in the same node to be taken into account simultaneously and results in perfect trees without classification errors. AVAILABILITY AND IMPLEMENTATION: The methods used to build the perfect trees in this article were implemented in the 'ROP' R package, archived at https://cran.r-project.org/web/packages/ROP/index.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Development of an absolute assignment predictor for triple-negative breast cancer subtyping using machine learning approaches.

Triple-negative breast cancer (TNBC) heterogeneity represents one of the main obstacles to precision medicine for this disease. Recent concordant transcriptomics studies have shown that TNBC could be divided into at least three subtypes with potential therapeutic implications. Although a few studies have been conducted to predict TNBC subtype using transcriptomics data, the subtyping was partially sensitive and limited by batch effect and dependence on a given dataset, which may penalize the switch to routine diagnostic testing. Therefore, we sought to build an absolute predictor (i.e., intra-patient diagnosis) based on machine learning algorithms with a limited number of probes. To that end, we started by introducing probe binary comparison for each patient (indicators). We based the predictive analysis on this transformed data. Probe selection was first involved combining both filter and wrapper methods for variable selection using cross-validation. We tested three prediction models (random forest, gradient boosting [GB], and extreme gradient boosting) using this optimal subset of indicators as inputs. Nested cross-validation consistently allowed us to choose the best model. The results showed that the fifty selected indicators highlighted the biological characteristics associated with each TNBC subtype. The GB based on this subset of indicators performs better than other models.

Automatic Segmentation of Metastatic Breast Cancer Lesions on 18F-FDG PET/CT Longitudinal Acquisitions for Treatment Response Assessment.

Metastatic breast cancer patients receive lifelong medication and are regularly monitored for disease progression. The aim of this work was to (1) propose networks to segment breast cancer metastatic lesions on longitudinal whole-body PET/CT and (2) extract imaging biomarkers from the segmentations and evaluate their potential to determine treatment response. Baseline and follow-up PET/CT images of 60 patients from the EPICUREseinmeta study were used to train two deep-learning models to segment breast cancer metastatic lesions: One for baseline images and one for follow-up images. From the automatic segmentations, four imaging biomarkers were computed and evaluated: SULpeak, Total Lesion Glycolysis (TLG), PET Bone Index (PBI) and PET Liver Index (PLI). The first network obtained a mean Dice score of 0.66 on baseline acquisitions. The second network obtained a mean Dice score of 0.58 on follow-up acquisitions. SULpeak, with a 32% decrease between baseline and follow-up, was the biomarker best able to assess patients' response (sensitivity 87%, specificity 87%), followed by TLG (43% decrease, sensitivity 73%, specificity 81%) and PBI (8% decrease, sensitivity 69%, specificity 69%). Our networks constitute promising tools for the automatic segmentation of lesions in patients with metastatic breast cancer allowing treatment response assessment with several biomarkers.

STING-dependent paracriny shapes apoptotic priming of breast tumors in response to anti-mitotic treatment.

A fascinating but uncharacterized action of antimitotic chemotherapy is to collectively prime cancer cells to apoptotic mitochondrial outer membrane permeabilization (MOMP), while impacting only on cycling cell subsets. Here, we show that a proapoptotic secretory phenotype is induced by activation of cGAS/STING in cancer cells that are hit by antimitotic treatment, accumulate micronuclei and maintain mitochondrial integrity despite intrinsic apoptotic pressure. Organotypic cultures of primary human breast tumors and patient-derived xenografts sensitive to paclitaxel exhibit gene expression signatures typical of type I IFN and TNFα exposure. These cytokines induced by cGAS/STING activation trigger NOXA expression in neighboring cells and render them acutely sensitive to BCL-xL inhibition. cGAS/STING-dependent apoptotic effects are required for paclitaxel response in vivo, and they are amplified by sequential, but not synchronous, administration of BH3 mimetics. Thus anti-mitotic agents propagate apoptotic priming across heterogeneously sensitive cancer cells through cytosolic DNA sensing pathway-dependent extracellular signals, exploitable by delayed MOMP targeting.

OLFM4 Expression in Ductal Carcinoma In Situ and in Invasive Breast Cancer Cohorts by a SWATH-Based Proteomic Approach.

Human olfactomedin-4 (OLFM4) is a secreted protein involved in a variety of cellular functions including proliferation, differentiation, apoptosis, and cell adhesion. OLFM4 expression has been studied in several tumor types including gastric, colorectal, lung, and endometrioid cancers where it has been suggested to be an independent favorable or unfavorable prognostic marker. For breast cancer, the clinical significance of OLFM4 is still unclear. In the present study, SWATH-MS is used as a tool for the robust identification and quantification of breast tissue proteins. SWATH-MS data show that OLFM4 expression is higher in DCIS than in invasive breast cancer. In-depth analysis of the breast tumor proteome show that OLFM4 is a favorable pronostic marker. Serum OLFM4 levels in peripheral blood are also analyzed by ELISA in 825 cases, including 94 cases of healthy individuals, 61 cases of non-invasive breast tumor (DCIS) and 670 cases of breast cancer (BC). It is found that serum OLFM4 levels are significantly higher in the DCIS cohort and in the breast cancer cohort compared with the healthy controls. This result suggests that circulating OLFM4 could be an interesting biomarker of early breast cancer. Data are available via ProteomeXchange with identifier PXD014194.

Identification of three subtypes of triple-negative breast cancer with potential therapeutic implications.

BACKGROUND: Heterogeneity and lack of targeted therapies represent the two main impediments to precision treatment of triple-negative breast cancer (TNBC), and therefore, molecular subtyping and identification of therapeutic pathways are required to optimize medical care. The aim of the present study was to define robust TNBC subtypes with clinical relevance. METHODS: Gene expression profiling by means of DNA chips was conducted in an internal TNBC cohort composed of 238 patients. In addition, external data (n = 257), obtained by using the same DNA chip, were used for validation. Fuzzy clustering was followed by functional annotation of the clusters. Immunohistochemistry was used to confirm transcriptomics results: CD138 and CD20 were used to test for plasma cell and B lymphocyte infiltrations, respectively; MECA79 and CD31 for tertiary lymphoid structures; and UCHL1/PGP9.5 and S100 for neurogenesis. RESULTS: We identified three molecular clusters within TNBC: one molecular apocrine (C1) and two basal-like-enriched (C2 and C3). C2 presented pro-tumorigenic immune response (immune suppressive), high neurogenesis (nerve infiltration), and high biological aggressiveness. In contrast, C3 exhibited adaptive immune response associated with complete B cell differentiation that occurs in tertiary lymphoid structures, and immune checkpoint upregulation. External cohort subtyping by means of the same approach proved the robustness of these results. Furthermore, plasma cell and B lymphocyte infiltrates, tertiary lymphoid structures, and neurogenesis were validated at the protein levels by means of histological evaluation and immunohistochemistry. CONCLUSION: Our work showed that TNBC can be subcategorized in three different subtypes characterized by marked biological features, some of which could be targeted by specific therapies.

iTRAQ-Based Quantitative Proteomic Analysis Strengthens Transcriptomic Subtyping of Triple-Negative Breast Cancer Tumors.

Heterogeneity and lack of targeted therapies represent the two main impediments to precision treatment of triple-negative breast cancer (TNBC). Therefore, molecular subtyping and identification of therapeutic pathways are required to optimize medical care. The aim of the present study is to define robust TNBC subtypes with clinical relevance by means of proteomics and transcriptomics. As a first step, unsupervised analyses are conducted in parallel on proteomics and transcriptomics data of 83 TNBC tumors. Proteomics data unsupervised analysis did not permit separation of TNBC into different subtypes, whereas transcriptomics data are able to clearly and robustly identify three subtypes: molecular apocrine (C1), basal-like immune-suppressed (C2), and basal-like immune response (C3). Supervised analysis of proteomics data are then conducted based on transcriptomics subtyping. Thirty out of 62 proteins differentially expressed between C1, C2, and C3 belonged to biological categories which characterized these TNBC clusters: luminal and androgen-regulated proteins (C1), basal, invasion, and extracellular matrix (C2), and basal and immune response (interferon pathway and immunoglobulins) (C3). Although proteomics unsupervised analysis of TNBC tumors is unsuccessful at identifying clusters, the integrated approach is promising. Identification and measurement of 30 proteins strengthen subtyping of TNBC based on robust transcriptomics unsupervised analysis.