Tailored Digital PCR Follow-Up of Rare Fusion Transcripts after Initial Detection through RNA Sequencing in Hematological Malignancies.

The management of hematologic malignancies has entered a new era in which minimal residual disease (MRD) monitoring plays a pivotal role. Well-established molecular targets, such as PML::RARA, CBFB::MYH11, or RUNX1::RUNX1T1, are conventionally tracked by quantitative RT-PCR. Recently, a broader landscape of fusion transcripts has been unveiled through transcriptomic analysis. These newly discovered fusion transcripts may emerge as novel molecular markers for MRD quantification. In this study, we compared a targeted RNA-sequencing (RNA-seq) approach (FusionPlex) with a whole-transcriptomic strategy (Advanta RNA-Seq XT) for fusion detection in a training set of 21 samples. We evidenced a concordance of 100% for the detection of known fusions, and showed a good correlation for gene expression quantification between the two techniques (Spearman r = 0.77). Additionally, we prospectively evaluated the identification of fusions by targeted RNA-seq in a real-life series of 126 patients with hematological malignancy. At least one fusion transcript was detected for 60 patients (48%). We designed tailored digital PCR assays for 11 rare fusions, and validated this technique for MRD quantification with a limit of detection of <0.01%. The combination of RNA-seq and tailored digital PCR may become a new standard for MRD evaluation in patients lacking conventional molecular targets.

Deep Learning for Detecting BRCA Mutations in High-Grade Ovarian Cancer Based on an Innovative Tumor Segmentation Method From Whole Slide Images.

BRCA1 and BRCA2 genes play a crucial role in repairing DNA double-strand breaks through homologous recombination. Their mutations represent a significant proportion of homologous recombination deficiency and are a reliable effective predictor of sensitivity of high-grade ovarian cancer (HGOC) to poly(ADP-ribose) polymerase inhibitors. However, their testing by next-generation sequencing is costly and time-consuming and can be affected by various preanalytical factors. In this study, we present a deep learning classifier for BRCA mutational status prediction from hematoxylin-eosin-safran-stained whole slide images (WSI) of HGOC. We constituted the OvarIA cohort composed of 867 patients with HGOC with known BRCA somatic mutational status from 2 different pathology departments. We first developed a tumor segmentation model according to dynamic sampling and then trained a visual representation encoder with momentum contrastive learning on the predicted tumor tiles. We finally trained a BRCA classifier on more than a million tumor tiles in multiple instance learning with an attention-based mechanism. The tumor segmentation model trained on 8 WSI obtained a dice score of 0.915 and an intersection-over-union score of 0.847 on a test set of 50 WSI, while the BRCA classifier achieved the state-of-the-art area under the receiver operating characteristic curve of 0.739 in 5-fold cross-validation and 0.681 on the testing set. An additional multiscale approach indicates that the relevant information for predicting BRCA mutations is located more in the tumor context than in the cell morphology. Our results suggest that BRCA somatic mutations have a discernible phenotypic effect that could be detected by deep learning and could be used as a prescreening tool in the future.

MiR-31-3p do not predict anti-EGFR efficacy in first-line therapy of RAS wild-type metastatic right-sided colon cancer.

BACKGROUND: Low miR-31-3p expression was identified as predictive of anti-EGFR efficacy in RAS-wt mCRC. Primary tumor side was also proposed as a predictive factor of anti-EGFR benefit. This retrospective multicentric study evaluated the predictive role of miR-31-3p in right-sided RAS-wt mCRC patients treated with first-line CT+anti-EGFR or CT+bevacizumab (Beva). METHODS: Seventy-two right-sided RAS-wt mCRC patients treated in first-line with CT+anti-EGFR (n = 43) or Beva (n = 29) were included. Overall survival (OS), progression-free survival (PFS) and response rate (RR) were analyzed and stratified according to tumor miR-31-3p expression level and targeted therapy (TT). RESULTS: BRAF V600E mutation was more frequent in high vs low miR-31-3p expressers (60.6% vs 15.4%, P < 0.001). PFS was significantly longer with CT+Beva than with CT+anti-EGFR (13 vs 7 months; P = 0.024). Among low miR-31-3p expressers, PFS, OS and RR were not significantly different between the two groups, while in high miR-31-3p expressers, only PFS was longer in the CT+Beva group (11 vs 6 months; P = 0.03). In patients treated with CT+anti-EGFR, low miR-31-3p expressers had a significantly longer OS (20 vs 13 months; P = 0.02) than high miR-31-3p expressers. ORR was not significantly different between the two groups of treatment, in both low and high miR-31-3p expressers. MiR-31-3p expression status was statistically correlated between primary tumors and corresponding metastases. CONCLUSION: In this study, miR-31-3p couldn't identify a subgroup of patients with right-sided RAS-wt mCRC who might benefit from anti-EGFR and suggest that Beva is the TT of choice in first-line treatment of these patients.

A framework for validating AI in precision medicine: considerations from the European ITFoC consortium.

BACKGROUND: Artificial intelligence (AI) has the potential to transform our healthcare systems significantly. New AI technologies based on machine learning approaches should play a key role in clinical decision-making in the future. However, their implementation in health care settings remains limited, mostly due to a lack of robust validation procedures. There is a need to develop reliable assessment frameworks for the clinical validation of AI. We present here an approach for assessing AI for predicting treatment response in triple-negative breast cancer (TNBC), using real-world data and molecular -omics data from clinical data warehouses and biobanks. METHODS: The European "ITFoC (Information Technology for the Future Of Cancer)" consortium designed a framework for the clinical validation of AI technologies for predicting treatment response in oncology. RESULTS: This framework is based on seven key steps specifying: (1) the intended use of AI, (2) the target population, (3) the timing of AI evaluation, (4) the datasets used for evaluation, (5) the procedures used for ensuring data safety (including data quality, privacy and security), (6) the metrics used for measuring performance, and (7) the procedures used to ensure that the AI is explainable. This framework forms the basis of a validation platform that we are building for the "ITFoC Challenge". This community-wide competition will make it possible to assess and compare AI algorithms for predicting the response to TNBC treatments with external real-world datasets. CONCLUSIONS: The predictive performance and safety of AI technologies must be assessed in a robust, unbiased and transparent manner before their implementation in healthcare settings. We believe that the consideration of the ITFoC consortium will contribute to the safe transfer and implementation of AI in clinical settings, in the context of precision oncology and personalized care.

MITF reprograms the extracellular matrix and focal adhesion in melanoma.

The microphthalmia-associated transcription factor (MITF) is a critical regulator of melanocyte development and differentiation. It also plays an important role in melanoma where it has been described as a molecular rheostat that, depending on activity levels, allows reversible switching between different cellular states. Here, we show that MITF directly represses the expression of genes associated with the extracellular matrix (ECM) and focal adhesion pathways in human melanoma cells as well as of regulators of epithelial-to-mesenchymal transition (EMT) such as CDH2, thus affecting cell morphology and cell-matrix interactions. Importantly, we show that these effects of MITF are reversible, as expected from the rheostat model. The number of focal adhesion points increased upon MITF knockdown, a feature observed in drug-resistant melanomas. Cells lacking MITF are similar to the cells of minimal residual disease observed in both human and zebrafish melanomas. Our results suggest that MITF plays a critical role as a repressor of gene expression and is actively involved in shaping the microenvironment of melanoma cells in a cell-autonomous manner.

Incidental diagnosis of mucopolysaccharidosis type I in an infant with chronic intestinal pseudoobstruction by exome sequencing.

Chronic intestinal pseudoobstruction (CIPO) is a severe form of intestinal dysmotility, and patients often undergo iterative abdominal surgeries and require parenteral nutrition. Several genes are known to be responsible for this pathology, including ACTG2 (autosomal dominant) and MYH11 (autosomal recessive). We report the first case of unexpected trio medical exome sequencing diagnosis of mucopolysaccharidosis type I (MPS-I) in a patient with an early CIPO. There was no clinical suspicion of MPS-I at the time of the prescription. It allowed biochemical confirmation of MPS-I, expert clinical evaluation and early treatment. Enzyme replacement therapy (ERT) with laronidase was started at 9 months old, and hematopoietic stem cell transplantation was carried out at 10 months and a half. The patient also had a 1.7 mb heterozygous deletion in chromosomal region 16p13.11p12.3, comprising several genes, including MYH11, paternally inherited. Her father has no symptoms of CIPO or other digestive symptoms. One previous association of CIPO and MPS-I was reported in 1986. Moreover, the number of incidental findings of inherited metabolic disorders with therapeutic impact will inevitably increase as pangenomic analyses become cheaper and easily available.

Implementation of a molecular tumor board at a regional level to improve access to targeted therapy.

BACKGROUND: With the development of precision oncology, Molecular Tumor Boards (MTB) are developing in many institutions. However, the implementation of MTB in routine clinical practice has still not been thoroughly studied. MATERIAL AND METHODS: Since the first drugs approved for targeted therapies, patient tumor samples were centralized to genomic testing platforms. In our institution, all tumor samples have been analyzed since 2014 by Next Generation Sequencing (NGS). In 2015, we established a regional MTB to discuss patient cases with 1 or more alterations identified by NGS, in genes different from those related to drug approval. We conducted a retrospective comparative analysis to study whether our MTB increased the prescriptions of Molecular Targeted Therapies (MTT) and the inclusions of patients in clinical trials with MTT, in comparison with patients with available NGS data but no MTB discussion. RESULTS: In 2014, 86 patients had UGA, but the results were not available to clinicians and not discussed in MTB. During the years 2015 and 2016, 113 patients with an UGA (unreferenced genomic alteration) were discussed in MTB. No patients with an UGA were included in 2014 in a clinical trial, versus 2 (2%) in 2015-2016. 13 patients with an UGA (12%) were treated in 2015-2016 with a MTT whereas in 2014, no patient (p = 0.001). CONCLUSIONS: In this retrospective analysis, we showed that the association of large-scale genomic testing and MTB was feasible, and could increase the prescription of MTT. However, in routine clinical practice, the majority of patients with UGA still do not have access to MTT.

Depicting the genetic architecture of pediatric cancers through an integrative gene network approach.

The genetic etiology of childhood cancers still remains largely unknown. It is therefore essential to develop novel strategies to unravel the spectrum of pediatric cancer genes. Statistical network modeling techniques have emerged as powerful methodologies for enabling the inference of gene-disease relationship and have been performed on adult but not pediatric cancers. We performed a deep multi-layer understanding of pan-cancer transcriptome data selected from the Treehouse Childhood Cancer Initiative through a co-expression network analysis. We identified six modules strongly associated with pediatric tumor histotypes that were functionally linked to developmental processes. Topological analyses highlighted that pediatric cancer predisposition genes and potential therapeutic targets were central regulators of cancer-histotype specific modules. A module was related to multiple pediatric malignancies with functions involved in DNA repair and cell cycle regulation. This canonical oncogenic module gathered most of the childhood cancer predisposition genes and clinically actionable genes. In pediatric acute leukemias, the driver genes were co-expressed in a module related to epigenetic and post-transcriptional processes, suggesting a critical role of these pathways in the progression of hematologic malignancies. This integrative pan-cancer study provides a thorough characterization of pediatric tumor-associated modules and paves the way for investigating novel candidate genes involved in childhood tumorigenesis.

Integration of transcriptome and proteome profiles in glioblastoma: looking for the missing link.

BACKGROUND: Glioblastoma (GB) is the most common and aggressive tumor of the brain. Genotype-based approaches and independent analyses of the transcriptome or the proteome have led to progress in understanding the underlying biology of GB. Joint transcriptome and proteome profiling may reveal new biological insights, and identify pathogenic mechanisms or therapeutic targets for GB therapy. We present a comparison of transcriptome and proteome data from five GB biopsies (TZ) vs their corresponding peritumoral brain zone (PBZ). Omic analyses were performed using RNA microarray chips and the isotope-coded protein label method (ICPL). RESULTS: As described in other cancers, we found a poor correlation between transcriptome and proteome data in GB. We observed only two commonly deregulated mRNAs/proteins (neurofilament light polypeptide and synapsin 1) and 12 altered biological processes; they are related to cell communication, synaptic transmission and nervous system processes. This poor correlation may be a consequence of the techniques used to produce the omic profiles, the intrinsic properties of mRNA and proteins and/or of cancer- or GB-specific phenomena. Of interest, the analysis of the transcription factor binding sites present upstream from the open reading frames of all altered proteins identified by ICPL method shows a common binding site for the topoisomerase I and p53-binding protein TOPORS. Its expression was observed in 7/11 TZ samples and not in PBZ. Some findings suggest that TOPORS may function as a tumor suppressor; its implication in gliomagenesis should be examined in future studies. CONCLUSIONS: In this study, we showed a low correlation between transcriptome and proteome data for GB samples as described in other cancer tissues. We observed that NEFL, SYN1 and 12 biological processes were deregulated in both the transcriptome and proteome data. It will be important to analyze more specifically these processes and these two proteins to allow the identification of new theranostic markers or potential therapeutic targets for GB.

Impact of 9p deletion and p16, Cyclin D1, and Myc hyperexpression on the outcome of anaplastic oligodendrogliomas.

OBJECTIVE: To study the presence of 9p deletion and p16, cyclin D1 and Myc expression and their respective diagnostic and prognostic interest in oligodendrogliomas. METHODS: We analyzed a retrospective series of 40 consecutive anaplastic oligodendrogliomas (OIII) from a single institution and compared them to a control series of 10 low grade oligodendrogliomas (OII). Automated FISH analysis of chromosome 9p status and immunohistochemistry for p16, cyclin D1 and Myc was performed for all cases and correlated with clinical and histological data, event free survival (EFS) and overall survival (OS). RESULTS: Chromosome 9p deletion was observed in 55% of OIII (22/40) but not in OII. Deletion was highly correlated to EFS (median = 29 versus 53 months, p<0.0001) and OS (median = 48 versus 83 months, p<0.0001) in both the total cohort and the OIII population. In 9p non-deleted oligodendrogliomas, p16 hyperexpression correlated with a shorter OS (p = 0.02 in OII and p = 0.0001 in OIII) whereas lack of p16 expression was correlated to a shorter EFS and OS in 9p deleted OIII (p = 0.001 and p = 0.0002 respectively). Expression of Cyclin D1 was significantly higher in OIII (median expression 45% versus 14% for OII, p = 0.0006) and was correlated with MIB-1 expression (p<0.0001), vascular proliferation (p = 0.002), tumor necrosis (p = 0.04) and a shorter EFS in the total cohort (p = 0.05). Hyperexpression of Myc was correlated to grade (median expression 27% in OII versus 35% in OIII, p = 0.03), and to a shorter EFS in 9p non-deleted OIII (p = 0.01). CONCLUSION: Chromosome 9p deletion identifies a subset of OIII with significantly worse prognosis. The combination of 9p status and p16 expression level identifies two distinct OIII populations with divergent prognosis. Hyperexpression of Bcl1 and Myc appears highly linked to anaplasia but the prognostic value is unclear and should be investigated further.

Mutational Landscape of DDR2 Gene in Lung Squamous Cell Carcinoma Using Next-generation Sequencing.

BACKGROUND: Lung cancer represents the leading cause of cancer-related death worldwide. Despite great advances in lung cancer management with the recent emergence of molecular targeted therapies for non-squamous non-small-cell lung cancer, no dramatic improvements have been achieved in lung squamous cell carcinoma (SCC). Mutations in discoidin domain receptor 2 (DDR2) gene were recently identified as promising molecular targets in this histology. The aim of this study is to describe the DDR2 mutational landscape of lung SCC and investigate the associated clinical factors. METHODS: Next-generation sequencing of the DDR2 gene was performed on 271 samples of lung SCC. Patients followed in our institution from January 2011 to August 2014 were retrospectively selected for data collection. Other driver gene alterations (EGFR, KRAS, BRAF, HER2, and PI3KCA) were analyzed using pyrosequencing. RESULTS: A total of 11 patients harboring a DDR2 mutation was detected among the 271 sequenced lung SCC samples (4%). We describe 10 unreported mutations, comprising a novel DDR2 exon 7 splice mutant. DDR2 mutations were not mutually exclusive with other driver gene alterations. One hundred thirty-six patients were included for clinical comparison and logistic regression analysis. No difference was detected between DDR2-mutant and DDR2 wild-type lung SCC regarding clinical characteristics or survival. CONCLUSION: DDR2 mutations were observed in 4% of cases of lung SCC of European descent. DDR2-mutated tumors can exhibit other driver gene alterations. No clinical characteristics were significantly associated with DDR2 mutation.

Contribution of 1p, 19q, 9p and 10q Automated Analysis by FISH to the Diagnosis and Prognosis of Oligodendroglial Tumors According to WHO 2016 Guidelines.

OBJECTIVE: To study the feasibility and the diagnostic and prognostic interest of automated analysis of 1p, 19q, 9p and 10q status by FISH technique in oligodendroglial tumors. METHODS: We analyzed a retrospective series of 33 consecutive gliomas with oligodendroglial histology (originally diagnosed as 24 oligodendrogliomas and 9 oligoastrocytomas). For all cases, automated FISH analysis of 1p, 19q, 9p and 10q status were performed and compared to clinical and histological data, ATRX, IDH1R132H and alpha-internexin status (studied by immunohistochemistry) and overall survival (OS). Manual analysis of 9p and 10q status were also performed and compared to automated analysis to verify the concordance of the two methods. RESULTS: The 33 gliomas were reclassified into 13 low-grade oligodendrogliomas (OII), 10 anaplastic oligodendrogliomas (OIII), 3 diffuse astrocytomas (AII), 3 anaplastic astrocytomas (AIII) and 4 glioblastomas (GBM) according to the WHO 2016 histological criteria. The 1p and/or 19q imbalanced status were restricted to astrocytomas with no correlation to their grade or their OS. Chromosome 9p deletion was restricted to OIII (70%) and GBM (100%) and was correlated with a shorter OS in the total cohort (p = 0.0007), the oligodendroglioma cohort (p = 0.03) and the astrocytoma cohort (p = 0.001). Concordance between 9p manual and automated analysis was satisfactory (81%, κ = 0.69). Chromosome 10q deletion was restricted to GBMs (50%) and was correlated with a poor OS in both the total cohort (p = 0.003) and the astrocytoma (AS) cohort (p = 0.04). Concordance between manual and automated analysis was satisfactory (79%, κ = 0.62). CONCLUSION: Automated analysis of 1p, 19q, 9p and 10q status by FISH is a reliable technique which allows for refined classification of oligodendroglial tumors. 1p and/or 19q imbalanced status is evidence of astrocytic differentiation. 9p deletion is found in high grade oligodendrogliomas and astrocytomas with a poor OS. 10q is related to GBM status and a poor OS.

Automated Analysis of 1p/19q Status by FISH in Oligodendroglial Tumors: Rationale and Proposal of an Algorithm.

OBJECTIVE: To propose a new algorithm facilitating automated analysis of 1p and 19q status by FISH technique in oligodendroglial tumors with software packages available in the majority of institutions using this technique. METHODS: We documented all green/red (G/R) probe signal combinations in a retrospective series of 53 oligodendroglial tumors according to literature guidelines (Algorithm 1) and selected only the most significant combinations for a new algorithm (Algorithm 2). This second algorithm was then validated on a prospective internal series of 45 oligodendroglial tumors and on an external series of 36 gliomas. RESULTS: Algorithm 2 utilizes 24 G/R combinations which represent less than 40% of combinations observed with Algorithm 1. The new algorithm excludes some common G/R combinations (1/1, 3/2) and redefines the place of others (defining 1/2 as compatible with normal and 3/3, 4/4 and 5/5 as compatible with imbalanced chromosomal status). The new algorithm uses the combination + ratio method of signal probe analysis to give the best concordance between manual and automated analysis on samples of 100 tumor cells (91% concordance for 1p and 89% concordance for 19q) and full concordance on samples of 200 tumor cells. This highlights the value of automated analysis as a means to identify cases in which a larger number of tumor cells should be studied by manual analysis. Validation of this algorithm on a second series from another institution showed a satisfactory concordance (89%, κ = 0.8). CONCLUSION: Our algorithm can be easily implemented on all existing FISH analysis software platforms and should facilitate multicentric evaluation and standardization of 1p/19q assessment in gliomas with reduction of the professional and technical time required.

Mouse genetic background impacts both on iron and non-iron metals parameters and on their relationships.

Iron is reported to interact with other metals. In addition, it has been shown that genetic background may impact iron metabolism. Our objective was to characterize, in mice of three genetic backgrounds, the links between iron and several non-iron metals. Thirty normal mice (C57BL/6, Balb/c and DBA/2; n = 10 for each group), fed with the same diet, were studied. Quantification of iron, zinc, cobalt, copper, manganese, magnesium and rubidium was performed by ICP/MS in plasma, erythrocytes, liver and spleen. Transferrin saturation was determined. Hepatic hepcidin1 mRNA level was evaluated by quantitative RT-PCR. As previously reported, iron parameters were modulated by genetic background with significantly higher values for plasma iron parameters and liver iron concentration in DBA/2 and Balb/c strains. Hepatic hepcidin1 mRNA level was lower in DBA/2 mice. No iron parameter was correlated with hepcidin1 mRNA levels. Principal component analysis of the data obtained for non-iron metals indicated that metals parameters stratified the mice according to their genetic background. Plasma and tissue metals parameters that are dependent or independent of genetic background were identified. Moreover, relationships were found between plasma and tissue content of iron and some other metals parameters. Our data: (i) confirms the impact of the genetic background on iron parameters, (ii) shows that genetic background may also play a role in the metabolism of non-iron metals, (iii) identifies links between iron and other metals parameters which may have implications in the understanding and, potentially, the modulation of iron metabolism.

From the core to beyond the margin: a genomic picture of glioblastoma intratumor heterogeneity.

Glioblastoma (GB) is a highly invasive primary brain tumor that almost systematically recurs despite aggressive therapies. One of the most challenging problems in therapy of GB is its extremely complex and heterogeneous molecular biology. To explore this heterogeneity, we performed a genome-wide integrative screening of three molecular levels: genome, transcriptome, and methylome. We analyzed tumor biopsies obtained by neuro-navigation in four distinct areas for 10 GB patients (necrotic zone, tumor zone, interface, and peripheral brain zone). We classified samples and deciphered a key genes signature of intratumor heterogeneity by Principal Component Analysis and Weighted Gene Co-expression Network Analysis. At the genome level, we identified common GB copy number alterations and but a strong interindividual molecular heterogeneity. Transcriptome analysis highlighted a pronounced intratumor architecture reflecting the surgical sampling plan of the study and identified gene modules associated with hallmarks of cancer. We provide a signature of key cancer-heterogeneity genes highly associated with the intratumor spatial gradient and show that it is enriched in genes with correlation between methylation and expression levels. Our study confirms that GBs are molecularly highly diverse and that a single tumor can harbor different transcriptional GB subtypes depending on its spatial architecture.

Characterizing the peritumoral brain zone in glioblastoma: a multidisciplinary analysis.

Glioblastoma (GB) is the most frequent and aggressive type of primary brain tumor. Recurrences are mostly located at the margin of the resection cavity in the peritumoral brain zone (PBZ). Although it is widely believed that infiltrative tumor cells in this zone are responsible for GB recurrence, few studies have examined this zone. In this study, we analyzed PBZ left after surgery with a variety of techniques including radiology, histopathology, flow cytometry, genomic, transcriptomic, proteomic, and primary cell cultures. The resulting PBZ profiles were compared with those of the GB tumor zone and normal brain samples to identify characteristics specific to the PBZ. We found that tumor cell infiltration detected by standard histological analysis was present in almost one third of PBZ taken from an area that was considered normal both on standard MRI and by the neurosurgeon under an operating microscope. The panel of techniques used in this study show that the PBZ, similar to the tumor zone itself, is characterized by substantial inter-patient heterogeneity, which makes it difficult to identify representative markers. Nevertheless, we identified specific alterations in the PBZ such as the presence of selected tumor clones and stromal cells with tumorigenic and angiogenic properties. The study of GB-PBZ is a growing field of interest and this region needs to be characterized further. This will facilitate the development of new, targeted therapies for patients with GB and the development of approaches to refine the per-operative evaluation of the PBZ to optimize the surgical resection of the tumor.

Genome-wide association study identifies TF as a significant modifier gene of iron metabolism in HFE hemochromatosis.

BACKGROUND & AIMS: Hereditary hemochromatosis (HH) is the most common form of genetic iron loading disease. It is mainly related to the homozygous C282Y/C282Y mutation in the HFE gene that is, however, a necessary but not a sufficient condition to develop clinical and even biochemical HH. This suggests that modifier genes are likely involved in the expressivity of the disease. Our aim was to identify such modifier genes. METHODS: We performed a genome-wide association study (GWAS) using DNA collected from 474 unrelated C282Y homozygotes. Associations were examined for both quantitative iron burden indices and clinical outcomes with 534,213 single nucleotide polymorphisms (SNP) genotypes, with replication analyses in an independent sample of 748 C282Y homozygotes from four different European centres. RESULTS: One SNP met genome-wide statistical significance for association with transferrin concentration (rs3811647, GWAS p value of 7×10(-9) and replication p value of 5×10(-13)). This SNP, located within intron 11 of the TF gene, had a pleiotropic effect on serum iron (GWAS p value of 4.9×10(-6) and replication p value of 3.2×10(-6)). Both serum transferrin and iron levels were associated with serum ferritin levels, amount of iron removed and global clinical stage (p<0.01). Serum iron levels were also associated with fibrosis stage (p<0.0001). CONCLUSIONS: This GWAS, the largest one performed so far in unselected HFE-associated HH (HFE-HH) patients, identified the rs3811647 polymorphism in the TF gene as the only SNP significantly associated with iron metabolism through serum transferrin and iron levels. Because these two outcomes were clearly associated with the biochemical and clinical expression of the disease, an indirect link between the rs3811647 polymorphism and the phenotypic presentation of HFE-HH is likely.

DGKI methylation status modulates the prognostic value of MGMT in glioblastoma patients treated with combined radio-chemotherapy with temozolomide.

BACKGROUND: Consistently reported prognostic factors for glioblastoma (GBM) are age, extent of surgery, performance status, IDH1 mutational status, and MGMT promoter methylation status. We aimed to integrate biological and clinical prognostic factors into a nomogram intended to predict the survival time of an individual GBM patient treated with a standard regimen. In a previous study we showed that the methylation status of the DGKI promoter identified patients with MGMT-methylated tumors that responded poorly to the standard regimen. We further evaluated the potential prognostic value of DGKI methylation status. METHODS: 399 patients with newly diagnosed GBM and treated with a standard regimen were retrospectively included in this study. Survival modelling was performed on two patient populations: intention-to-treat population of all included patients (population 1) and MGMT-methylated patients (population 2). Cox proportional hazard models were fitted to identify the main prognostic factors. A nomogram was developed for population 1. The prognostic value of DGKI promoter methylation status was evaluated on population 1 and population 2. RESULTS: The nomogram-based stratification of the cohort identified two risk groups (high/low) with significantly different median survival. We validated the prognostic value of DGKI methylation status for MGMT-methylated patients. We also demonstrated that the DGKI methylation status identified 22% of poorly responding patients in the low-risk group defined by the nomogram. CONCLUSIONS: Our results improve the conventional MGMT stratification of GBM patients receiving standard treatment. These results could help the interpretation of published or ongoing clinical trial outcomes and refine patient recruitment in the future.