Multigene Panel Sequencing Identifies a Novel Germline Mutation Profile in Male Breast Cancer Patients.

Even though male breast cancer (MBC) risk encompasses both genetic and environmental aetiologies, the primary risk factor is a germline pathogenic variant (PV) or likely pathogenic variant (LPV) in BRCA2, BRCA1 and/or PALB2 genes. To identify new potential MBC-specific predisposition genes, we sequenced a panel of 585 carcinogenesis genes in an MBC cohort without BRCA1/BRCA2/PALB2 PV/LPV. We identified 14 genes carrying rare PVs/LPVs in the MBC population versus noncancer non-Finnish European men, predominantly coding for DNA repair and maintenance of genomic stability proteins. We identified for the first time PVs/LPVs in PRCC (pre-mRNA processing), HOXA9 (transcription regulation), RECQL4 and WRN (maintenance of genomic stability) as well as in genes involved in other cellular processes. To study the specificity of this MBC PV/LPV profile, we examined whether variants in the same genes could be detected in a female breast cancer (FBC) cohort without BRCA1/BRCA2/PALB2 PV/LPV. Only 5/109 women (4.6%) carried a PV/LPV versus 18/85 men (21.2%) on these genes. FBC did not carry any PV/LPV on 11 of these genes. Although 5.9% of the MBC cohort carried PVs/LPVs in PALLD and ERCC2, neither of these genes were altered in our FBC cohort. Our data suggest that in addition to BRCA1/BRCA2/PALB2, other genes involved in DNA repair/maintenance or genomic stability as well as cell adhesion may form a specific MBC PV/LPV signature.

A Multigene Signature Associated with Progression-Free Survival after Treatment for IDH Mutant and 1p/19q Codeleted Oligodendrogliomas.

BACKGROUND: IDH mutant and 1p/19q codeleted oligodendrogliomas are the gliomas associated with the best prognosis. However, despite their sensitivity to treatment, patient survival remains heterogeneous. We aimed to identify gene expressions associated with response to treatment from a national cohort of patients with oligodendrogliomas, all treated with radiotherapy +/- chemotherapy. METHODS: We extracted total RNA from frozen tumor samples and investigated enriched pathways using KEGG and Reactome databases. We applied a stability selection approach based on subsampling combined with the lasso-pcvl algorithm to identify genes associated with progression-free survival and calculate a risk score. RESULTS: We included 68 patients with oligodendrogliomas treated with radiotherapy +/- chemotherapy. After filtering, 1697 genes were obtained, including 134 associated with progression-free survival: 35 with a better prognosis and 99 with a poorer one. Eight genes (ST3GAL6, QPCT, NQO1, EPHX1, CST3, S100A8, CHI3L1, and OSBPL3) whose risk score remained statistically significant after adjustment for prognostic factors in multivariate analysis were selected in more than 60% of cases were associated with shorter progression-free survival. CONCLUSIONS: We found an eight-gene signature associated with a higher risk of rapid relapse after treatment in patients with oligodendrogliomas. This finding could help clinicians identify patients who need more intensive treatment.

Is pre-radiotherapy metabolic heterogeneity of glioblastoma predictive of progression-free survival?

BACKGROUND AND PURPOSE: All glioblastoma subtypes share the hallmark of aggressive invasion, meaning that it is crucial to identify their different components if we are to ensure effective treatment and improve survival. Proton MR spectroscopic imaging (MRSI) is a noninvasive technique that yields metabolic information and is able to identify pathological tissue with high accuracy. The aim of the present study was to identify clusters of metabolic heterogeneity, using a large MRSI dataset, and determine which of these clusters are predictive of progression-free survival (PFS). MATERIALS AND METHODS: MRSI data of 180 patients acquired in a pre-radiotherapy examination were included in the prospective SPECTRO-GLIO trial. Eight features were extracted for each spectrum: Cho/NAA, NAA/Cr, Cho/Cr, Lac/NAA, and the ratio of each metabolite to the sum of all the metabolites. Clustering of data was performed using a mini-batch k-means algorithm. The Cox model and logrank test were used for PFS analysis. RESULTS: Five clusters were identified as sharing similar metabolic information and being predictive of PFS. Two clusters revealed metabolic abnormalities. PFS was lower when Cluster 2 was the dominant cluster in patients' MRSI data. Among the metabolites, lactate (present in this cluster and in Cluster 5) was the most statistically significant predictor of poor outcome. CONCLUSION: Results showed that pre-radiotherapy MRSI can be used to reveal tumor heterogeneity. Groups of spectra, which have the same metabolic information, reflect the different tissue components representative of tumor burden proliferation and hypoxia. Clusters with metabolic abnormalities and high lactate are predictive of PFS.

Pseudoprogression in GBM versus true progression in patients with glioblastoma: A multiapproach analysis.

BACKGROUND AND PURPOSE: To investigate the feasibility of using a multiapproach analysis combining clinical data, diffusion- and perfusion-weighted imaging, and 3D magnetic resonance spectroscopic imaging to distinguish true tumor progression (TP) from pseudoprogression (PSP) in patients with glioblastoma. MATERIALS AND METHODS: Progression was suspected within 6 months of radiotherapy in 46 of the 180 patients included in the Phase-III SpectroGlio trial (NCT01507506). Choline/creatine (Cho/Cr), choline/N-acetyl aspartate (Cho/NAA) and lactate/N-acetyl aspartate (Lac/NAA) ratios were extracted. Apparent diffusion coefficient (ADC) and cerebral blood volume (CBV) maps were calculated. ADC, relative CBV values and tumor volume (TV) were collected at relapse. Differences between TP and PSP were evaluated using Mann-Whitney tests, and p values were adjusted with Bonferroni correction. RESULTS: Patients with suspected progression underwent a new MRI scan 1 month after the first one. Of these, 28 were classified as PSP, and 18 as TP. After a median follow-up of 41 months, median overall survival was higher in PSP than in TP (25.2 vs 20.3 months; p = 0.0092). Lac/NAA and Cho/Cr ratios were higher in TP than in PSP (1.2 vs 0.5; p = 0.006; and 3 vs 2.2; p = 0.021). After multivariate regression analysis, TV was the most significant predictor of TP vs PSP, and the only one retained in the model (p = 0.028). CONCLUSION: Three spectroscopic ratios could be used to differentiate PSP from TP. TV at relapse was the most predictive factor in the multivariate analysis, and overall survival was higher in PSP than in TP.

Radiomics-Based Detection of Radionecrosis Using Harmonized Multiparametric MRI.

In this study, a radiomics analysis was conducted to provide insights into the differentiation of radionecrosis and tumor progression in multiparametric MRI in the context of a multicentric clinical trial. First, the sensitivity of radiomic features to the unwanted variability caused by different protocol settings was assessed for each modality. Then, the ability of image normalization and ComBat-based harmonization to reduce the scanner-related variability was evaluated. Finally, the performances of several radiomic models dedicated to the classification of MRI examinations were measured. Our results showed that using radiomic models trained on harmonized data achieved better predictive performance for the investigated clinical outcome (balanced accuracy of 0.61 with the model based on raw data and 0.72 with ComBat harmonization). A comparison of several models based on information extracted from different MR modalities showed that the best classification accuracy was achieved with a model based on MR perfusion features in conjunction with clinical observation (balanced accuracy of 0.76 using LASSO feature selection and a Random Forest classifier). Although multimodality did not provide additional benefit in predictive power, the model based on T1-weighted MRI before injection provided an accuracy close to the performance achieved with perfusion.

Baseline SUVmax is related to tumor cell proliferation and patient outcome in follicular lymphoma.

Follicular lymphoma (FL) is the most common indolent lymphoma. Despite the clear benefit of CD20-based therapy, a subset of FL patients still progress to aggressive lymphoma. Thus, identifying early biomarkers that incorporate PET metrics could be helpful to identify patients with a high risk of treatment failure with Rituximab. We retrospectively included a total of 132 untreated FL patients separated into training and validation cohorts. Optimal threshold of baseline SUVmax was first determined in the training cohort (n=48) to predict progression-free survival (PFS). The PET results were investigated along with the tumor and immune microenvironment, which were determined by immunochemistry and transcriptome studies involving gene set enrichment analyses and immune cell deconvolution, together with the tumor mutation profile. We report that baseline SUVmax >14.5 was associated with poorer PFS than baseline SUVmax ≤14.5 (HR=0.28; p=0.00046). Neither immune T-cell infiltration nor immune checkpoint expression were associated with baseline PET metrics. By contrast, FL samples with Ki-67 staining ≥10% showed enrichment of cell cycle/DNA genes (p=0.013) and significantly higher SUVmax values (p=0.007). Despite similar oncogenic pathway alterations in both SUVmax groups of FL samples, 4 out of 5 cases harboring the infrequent FOXO1 transcription factor mutation were seen in FL patients with SUVmax >14.5. Thus, high baseline SUVmax reflects FL tumor proliferation and, together with Ki-67 proliferative index, can be used to identify patients at risk of early relapse with R-chemotherapy.

Quality control of 3D MRSI data in glioblastoma: Can we do without the experts?

PURPOSE: Proton magnetic resonance spectroscopic imaging (1H MRSI) is a noninvasive technique for assessing tumor metabolism. Manual inspection is still the gold standard for quality control (QC) of spectra, but it is both time-consuming and subjective. The aim of the present study was to assess automatic QC of glioblastoma MRSI data using random forest analysis. METHODS: Data for 25 patients, acquired prospectively in a preradiotherapy examination, were submitted to postprocessing with syngo.MR Spectro (VB40A; Siemens) or Java-based magnetic resonance user interface (jMRUI) software. A total of 28 features were extracted from each spectrum for the automatic QC. Three spectroscopists also performed manual inspections, labeling each spectrum as good or poor quality. All statistical analyses, with addressing unbalanced data, were conducted with R 3.6.1 (R Foundation for Statistical Computing; https://www.r-project.org). RESULTS: The random forest method classified the spectra with an area under the curve of 95.5%, sensitivity of 95.8%, and specificity of 81.7%. The most important feature for the classification was Residuum_Lipids_Versus_Fit, obtained with syngo.MR Spectro. CONCLUSION: The automatic QC method was able to distinguish between good- and poor-quality spectra, and can be used by radiation oncologists who are not spectroscopy experts. This study revealed a novel set of MRSI signal features that are closely correlated with spectral quality.

Nanobody-Based Quantification of GTP-Bound RHO Conformation Reveals RHOA and RHOC Activation Independent from Their Total Expression in Breast Cancer.

As key regulators of the actin cytoskeleton, RHO GTPase expression and/or activity are deregulated in tumorigenesis and metastatic progression. Nevertheless, the vast majority of experiments supporting this conclusion was conducted on cell lines but not on human tumor samples that were mostly studied at the expression level only. Up to now, the activity of RHO proteins remains poorly investigated in human tumors. In this article, we present the development of a robust nanobody-based ELISA assay, with a high selectivity that allows an accurate quantification of RHO protein GTP-bound state in the nanomolar range (1 nM; 20 µg/L), not only in cell lines after treatment but also in tumor samples. Of note, we present here a fine analysis of RHOA-like and RAC1 active state in tumor samples with the most comprehensive study of RHOA-GTP and RHOC-GTP levels performed on human breast tumor samples. We revealed increased GTP-bound RHOA and RHOC protein activities in tumors compared to normal tissue counterparts, and demonstrated that the RHO active state and RHO expression are two independent parameters among different breast cancer subtypes. Our results further highlight the regulation of RHO protein activation in tumor samples and the relevance of directly studying RHO GTPase activities involvement in molecular pathways.

Pretherapeutic Imaging for Axillary Staging in Breast Cancer: A Systematic Review and Meta-Analysis of Ultrasound, MRI and FDG PET.

BACKGROUND: This systematic review aimed at comparing performances of ultrasonography (US), magnetic resonance imaging (MRI), and fluorodeoxyglucose positron emission tomography (PET) for axillary staging, with a focus on micro- or micrometastases. METHODS: A search for relevant studies published between January 2002 and March 2018 was conducted in MEDLINE database. Study quality was assessed using the QUality Assessment of Diagnostic Accuracy Studies checklist. Sensitivity and specificity were meta-analyzed using a bivariate random effects approach; Results: Across 62 studies (n = 10,374 patients), sensitivity and specificity to detect metastatic ALN were, respectively, 51% (95% CI: 43-59%) and 100% (95% CI: 99-100%) for US, 83% (95% CI: 72-91%) and 85% (95% CI: 72-92%) for MRI, and 49% (95% CI: 39-59%) and 94% (95% CI: 91-96%) for PET. Interestingly, US detects a significant proportion of macrometastases (false negative rate was 0.28 (0.22, 0.34) for more than 2 metastatic ALN and 0.96 (0.86, 0.99) for micrometastases). In contrast, PET tends to detect a significant proportion of micrometastases (true positive rate = 0.41 (0.29, 0.54)). Data are not available for MRI. CONCLUSIONS: In comparison with MRI and PET Fluorodeoxyglucose (FDG), US is an effective technique for axillary triage, especially to detect high metastatic burden without upstaging majority of micrometastases.

The Chemokine Receptor CCR3 Is Potentially Involved in the Homing of Prostate Cancer Cells to Bone: Implication of Bone-Marrow Adipocytes.

Bone metastasis remains the most frequent and the deadliest complication of prostate cancer (PCa). Mechanisms leading to the homing of tumor cells to bone remain poorly characterized. Role of chemokines in providing navigational cues to migrating cancer cells bearing specific receptors is well established. Bone is an adipocyte-rich organ since 50 to 70% of the adult bone marrow (BM) volume comprise bone marrow adipocytes (BM-Ads), which are likely to produce chemokines within the bone microenvironment. Using in vitro migration assays, we demonstrated that soluble factors released by human primary BM-Ads are able to support the directed migration of PCa cells in a CCR3-dependent manner. In addition, we showed that CCL7, a chemokine previously involved in the CCR3-dependent migration of PCa cells outside of the prostate gland, is released by human BM-Ads. These effects are amplified by obesity and ageing, two clinical conditions known to promote aggressive and metastatic PCa. In human tumors, we found an enrichment of CCR3 in bone metastasis vs. primary tumors at mRNA levels using Oncomine microarray database. In addition, immunohistochemistry experiments demonstrated overexpression of CCR3 in bone versus visceral metastases. These results underline the potential importance of BM-Ads in the bone metastatic process and imply a CCR3/CCL7 axis whose pharmacological interest needs to be evaluated.

Neutral Sphingomyelinase 2 Heightens Anti-Melanoma Immune Responses and Anti-PD-1 Therapy Efficacy.

Dysregulation of lipid metabolism affects the behavior of cancer cells, but how this happens is not completely understood. Neutral sphingomyelinase 2 (nSMase2), encoded by SMPD3, catalyzes the breakdown of sphingomyelin to produce the anti-oncometabolite ceramide. We found that this enzyme was often downregulated in human metastatic melanoma, likely contributing to immune escape. Overexpression of nSMase2 in mouse melanoma reduced tumor growth in syngeneic wild-type but not CD8-deficient mice. In wild-type mice, nSMase2-overexpressing tumors showed accumulation of both ceramide and CD8+ tumor-infiltrating lymphocytes, and this was associated with increased level of transcripts encoding IFNγ and CXCL9. Overexpressing the catalytically inactive nSMase2 failed to alter tumor growth, indicating that the deleterious effect nSMase2 has on melanoma growth depends on its enzymatic activity. In vitro, small extracellular vesicles from melanoma cells overexpressing wild-type nSMase2 augmented the expression of IL12, CXCL9, and CCL19 by bone marrow-derived dendritic cells, suggesting that melanoma nSMase2 triggers T helper 1 (Th1) polarization in the earliest stages of the immune response. Most importantly, overexpression of wild-type nSMase2 increased anti-PD-1 efficacy in murine models of melanoma and breast cancer, and this was associated with an enhanced Th1 response. Therefore, increasing SMPD3 expression in melanoma may serve as an original therapeutic strategy to potentiate Th1 polarization and CD8+ T-cell-dependent immune responses and overcome resistance to anti-PD-1.

Characterization of Macrophages and Osteoclasts in the Osteosarcoma Tumor Microenvironment at Diagnosis: New Perspective for Osteosarcoma Treatment?

Biological and histopathological techniques identified osteoclasts and macrophages as targets of zoledronic acid (ZA), a therapeutic agent that was detrimental for patients in the French OS2006 trial. Conventional and multiplex immunohistochemistry of microenvironmental and OS cells were performed on biopsies of 124 OS2006 patients and 17 surgical ("OSNew") biopsies respectively. CSF-1R (common osteoclast/macrophage progenitor) and TRAP (osteoclast activity) levels in serum of 108 patients were correlated to response to chemotherapy and to prognosis. TRAP levels at surgery and at the end of the protocol were significantly lower in ZA+ than ZA- patients (padj = 0.0011; 0.0132). For ZA+-patients, an increase in the CSF-1R level between diagnosis and surgery and a high TRAP level in the serum at biopsy were associated with a better response to chemotherapy (p = 0.0091; p = 0.0251). At diagnosis, high CD163+ was associated with good prognosis, while low TRAP activity was associated with better overall survival in ZA- patients only. Multiplex immunohistochemistry demonstrated remarkable bipotent CD68+/CD163+ macrophages, homogeneously distributed throughout OS regions, aside osteoclasts (CD68+/CD163-) mostly residing in osteolytic territories and osteoid-matrix-associated CD68-/CD163+ macrophages. We demonstrate that ZA not only acts on harmful osteoclasts but also on protective macrophages, and hypothesize that the bipotent CD68+/CD163+ macrophages might present novel therapeutic targets.

VEGF-Related Germinal Polymorphisms May Identify a Subgroup of Breast Cancer Patients with Favorable Outcome under Bevacizumab-Based Therapy-A Message from COMET, a French Unicancer Multicentric Study.

The prospective multicenter COMET trial followed a cohort of 306 consecutive metastatic breast cancer patients receiving bevacizumab and paclitaxel as first-line chemotherapy. This study was intended to identify and validate reliable biomarkers to better predict bevacizumab treatment outcomes and allow for a more personalized use of this antiangiogenic agent. To that end, we aimed to establish risk scores for survival prognosis dichotomization based on classic clinico-pathological criteria combined or not with single nucleotide polymorphisms (SNPs). The genomic DNA of 306 patients was extracted and a panel of 13 SNPs, covering seven genes previously documented to be potentially involved in drug response, were analyzed by means of high-throughput genotyping. In receiver operating characteristic (ROC) analyses, the hazard model based on a triple-negative cancer phenotype variable, combined with specific SNPs in VEGFA (rs833061), VEGFR1 (rs9582036) and VEGFR2 (rs1870377), had the highest predictive value. The overall survival hazard ratio of patients assigned to the poor prognosis group based on this model was 3.21 (95% CI (2.33-4.42); p < 0.001). We propose that combining this pharmacogenetic approach with classical clinico-pathological characteristics could markedly improve clinical decision-making for breast cancer patients receiving bevacizumab-based therapy.

Comparison of Variable Selection Methods for Time-to-Event Data in High-Dimensional Settings.

Over the last decades, molecular signatures have become increasingly important in oncology and are opening up a new area of personalized medicine. Nevertheless, biological relevance and statistical tools necessary for the development of these signatures have been called into question in the literature. Here, we investigate six typical selection methods for high-dimensional settings and survival endpoints, including LASSO and some of its extensions, component-wise boosting, and random survival forests (RSF). A resampling algorithm based on data splitting was used on nine high-dimensional simulated datasets to assess selection stability on training sets and the intersection between selection methods. Prognostic performances were evaluated on respective validation sets. Finally, one application on a real breast cancer dataset has been proposed. The false discovery rate (FDR) was high for each selection method, and the intersection between lists of predictors was very poor. RSF selects many more variables than the other methods and thus becomes less efficient on validation sets. Due to the complex correlation structure in genomic data, stability in the selection procedure is generally poor for selected predictors, but can be improved with a higher training sample size. In a very high-dimensional setting, we recommend the LASSO-pcvl method since it outperforms other methods by reducing the number of selected genes and minimizing FDR in most scenarios. Nevertheless, this method still gives a high rate of false positives. Further work is thus necessary to propose new methods to overcome this issue where numerous predictors are present. Pluridisciplinary discussion between clinicians and statisticians is necessary to ensure both statistical and biological relevance of the predictors included in molecular signatures.

Comparison of unsupervised machine-learning methods to identify metabolomic signatures in patients with localized breast cancer.

Genomics and transcriptomics have led to the widely-used molecular classification of breast cancer (BC). However, heterogeneous biological behaviors persist within breast cancer subtypes. Metabolomics is a rapidly-expanding field of study dedicated to cellular metabolisms affected by the environment. The aim of this study was to compare metabolomic signatures of BC obtained by 5 different unsupervised machine learning (ML) methods. Fifty-two consecutive patients with BC with an indication for adjuvant chemotherapy between 2013 and 2016 were retrospectively included. We performed metabolomic profiling of tumor resection samples using liquid chromatography-mass spectrometry. Here, four hundred and forty-nine identified metabolites were selected for further analysis. Clusters obtained using 5 unsupervised ML methods (PCA k-means, sparse k-means, spectral clustering, SIMLR and k-sparse) were compared in terms of clinical and biological characteristics. With an optimal partitioning parameter k = 3, the five methods identified three prognosis groups of patients (favorable, intermediate, unfavorable) with different clinical and biological profiles. SIMLR and K-sparse methods were the most effective techniques in terms of clustering. In-silico survival analysis revealed a significant difference for 5-year predicted OS between the 3 clusters. Further pathway analysis using the 449 selected metabolites showed significant differences in amino acid and glucose metabolism between BC histologic subtypes. Our results provide proof-of-concept for the use of unsupervised ML metabolomics enabling stratification and personalized management of BC patients. The design of novel computational methods incorporating ML and bioinformatics techniques should make available tools particularly suited to improving the outcome of cancer treatment and reducing cancer-related mortalities.

hnRNP H/F drive RNA G-quadruplex-mediated translation linked to genomic instability and therapy resistance in glioblastoma.

RNA G-quadruplexes (RG4s) are four-stranded structures known to control mRNA translation of cancer relevant genes. RG4 formation is pervasive in vitro but not in cellulo, indicating the existence of poorly characterized molecular machinery that remodels RG4s and maintains them unfolded. Here, we performed a quantitative proteomic screen to identify cytosolic proteins that interact with a canonical RG4 in its folded and unfolded conformation. Our results identified hnRNP H/F as important components of the cytoplasmic machinery modulating the structural integrity of RG4s, revealed their function in RG4-mediated translation and uncovered the underlying molecular mechanism impacting the cellular stress response linked to the outcome of glioblastoma.

Resistance of melanoma to immune checkpoint inhibitors is overcome by targeting the sphingosine kinase-1.

Immune checkpoint inhibitors (ICIs) have dramatically modified the prognosis of several advanced cancers, however many patients still do not respond to treatment. Optimal results might be obtained by targeting cancer cell metabolism to modulate the immunosuppressive tumor microenvironment. Here, we identify sphingosine kinase-1 (SK1) as a key regulator of anti-tumor immunity. Increased expression of SK1 in tumor cells is significantly associated with shorter survival in metastatic melanoma patients treated with anti-PD-1. Targeting SK1 markedly enhances the responses to ICI in murine models of melanoma, breast and colon cancer. Mechanistically, SK1 silencing decreases the expression of various immunosuppressive factors in the tumor microenvironment to limit regulatory T cell (Treg) infiltration. Accordingly, a SK1-dependent immunosuppressive signature is also observed in human melanoma biopsies. Altogether, this study identifies SK1 as a checkpoint lipid kinase that could be targeted to enhance immunotherapy.

Translation reprogramming by eIF3 linked to glioblastoma resistance.

Intrinsic resistance to current therapies, leading to dismal clinical outcomes, is a hallmark of glioblastoma multiforme (GBM), the most common and aggressive brain tumor. Understanding the underlying mechanisms of such malignancy is, therefore, an urgent medical need. Deregulation of the protein translation machinery has been shown to contribute to cancer initiation and progression, in part by driving selective translational control of specific mRNA transcripts involved in distinct cancer cell behaviors. Here, we focus on eIF3, a multimeric complex with a known role in the initiation of translation and that is frequently deregulated in cancer. Our results show that the deregulated expression of eIF3e, the e subunit of eIF3, in specific GBM regions could impinge on selective protein synthesis impacting the GBM outcome. In particular, eIF3e restricts the expression of proteins involved in the response to cellular stress and increases the expression of key functional regulators of cell stemness. Such a translation program can therefore serve as a double-edged sword promoting GBM tumor growth and resistance to radiation.