Publications

e15036 Background: Previous studies have shown that assessment of promoter hypermethylation of a limited number of genes in tumor biopsies may identify all colorectal tumors analyzed. The aim of the present study was to assess the clinical usefulness of a panel of methylation biomarkers in stool DNA in the diagnosis of colorectal tumors using Methylation Curve (MC) analyses, a technique that simultaneously analyze all CpG residues within a promoter. Methods: Promoter methylation status of 5 tumor-related genes (RARB2, p16 INK4a , MGMT, p14 ARF and APC) was analyzed in DNA stool samples and corresponding tissues in an initial set of 12 newly diagnosed patients with primary colorectal carcinomas and 20 with colorectal adenomas using Methylation-specific PCR (MSP). Results were validated in a set of 88 patients (20 healthy subjects, 17 inflammatory bowel disease, 23 adenomas, 28 carcinomas) using MC analyses. Median age for every group was 63, 51, 66 and 67 y respectively. Results: In the initial set, the majority [10 of 12 (83%) carcinomas and 18 of 20 (90%) adenomas] of biopsies were positive for at least one marker. In stool DNA prevalence was 75% for carcinomas (9 of 12) and 60% for adenomas (12 of 20) with no false positive in stools. In the validation set MC was used. Analytical sensitivity of MC was 5% of methylated alleles for p16 INK4a , p14 ARF , RARB2 and APC and 10% for MGMT. In the validation set MC analyses of biopsies showed that at least one marker was positive in 22 of 28 (79%) carcinomas and 16 of 23 (70%) adenomas. In stool DNA, these percentages were 64% (18 of 28) for carcinomas and 42% (9 of 23) for adenomas. No aberrant methylation was observed in healthy subjects and in 2 of 15 (13%) of IBD patients aberrant RARB2 methylation was detected. Conclusions: Melting Curve analysis of a panel of methylation markers in stool DNA is a good alternative for the early non-invasive diagnosis of colorectal tumors. [Table: see text]

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Abstract Relapse is the main cause of treatment failure in T‐cell acute lymphoblastic leukemia (T‐ALL). Despite this, data from adult T‐ALL patients treated with specific chemotherapeutic regimens that examine predictive markers and describe relapse mechanisms are scarce. In this study, we studied 74 paired diagnosis‐relapse samples from 37 patients homogeneously treated with three consecutive measurable residual disease‐oriented trials to identify genetic determinants involved in relapse in adult T‐ALL. Analysis of single‐nucleotide variants and copy number alterations consistently found N/KRAS mutations (20% relapsed cases) at diagnosis and at relapse (resistance profile). N/KRAS mut patients frequently relapse early during consolidation treatment. Relapse‐specific mutations in NT5C2, NR3C1 , SMARCA4 , and TP53 (40% relapse cases) were not detected at diagnosis by conventional molecular techniques (relapse profile). However, single‐cell‐based analysis revealed a very minor clone containing the NT5C2(p.R367Q) variant at diagnosis. Patients with the NT5C2(p.R367Q) variant mostly relapse later during maintenance treatment. Tracking the NT5C2 variant by digital PCR confirm the expansion of the NT5C2 clone at maintenance treatment. Overall, our exploratory analysis suggests a role for these genetic events, most of which have already been described in pediatric cases, driving resistance associated to specific chemotherapeutic agents, contributing to the relapse of a high proportion of adult T‐ALL patients (60%).

In cancer, epigenetic states are deregulated and thought to be of significance in cancer development and progression. We explored DNA methylation‐based signatures in association with breast cancer subtypes to assess their impact on clinical presentation and patient prognosis. DNA methylation was analyzed using Infinium 450K arrays in 40 tumors and 17 normal breast samples, together with DNA copy number changes and subtype‐specific markers by tissue microarrays. The identified methylation signatures were validated against a cohort of 212 tumors annotated for breast cancer subtypes by the PAM50 method (The Cancer Genome Atlas). Selected markers were pyrosequenced in an independent validation cohort of 310 tumors and analyzed with respect to survival, clinical stage and grade. The results demonstrate that DNA methylation patterns linked to the luminal‐B subtype are characterized by CpG island promoter methylation events. In contrast, a large fraction of basal‐like tumors are characterized by hypomethylation events occurring within the gene body. Based on these hallmark signatures, we defined two DNA methylation‐based subtypes, Epi‐LumB and Epi‐Basal, and show that they are associated with unfavorable clinical parameters and reduced survival. Our data show that distinct mechanisms leading to changes in CpG methylation states are operative in different breast cancer subtypes. Importantly, we show that a few selected proxy markers can be used to detect the distinct DNA methylation‐based subtypes thereby providing valuable information on disease prognosis.

Cryptic deletions at chromosome 6q are common cytogenetic abnormalities in T-cell lymphoblastic leukemia/lymphoma (T-LBL), but the target genes have not been formally identified. Our results build on detection of specific chromosomal losses in a mouse model of γ-radiation-induced T-LBLs and provide interesting clues for new putative susceptibility genes in a region orthologous to human 6q15–6q16.3. Among these, Epha7 emerges as a bona fide candidate tumor suppressor gene because it is inactivated in practically all the T-LBLs analyzed (100% in mouse and 95.23% in human). We provide evidence showing that Epha7 downregulation may occur, at least in part, by loss of heterozygosity (19.35% in mouse and 12.5% in human) or promoter hypermethylation (51.61% in mouse and 43.75% in human) or a combination of both mechanisms (12.90% in mouse and 6.25% in human). These results indicate that EPHA7 might be considered a new tumor suppressor gene for 6q deletions in T-LBLs. Notably, this gene is located in 6q16.1 proximal to GRIK2 and CASP8AP2 , other candidate genes identified in this region. Thus, del6q seems to be a complex region where inactivation of multiple genes may cooperatively contribute to the onset of T-cell lymphomas.

<p>Characteristics of patients with the studied MDS</p>

Glioblastoma multiforme (GBM) is the most frequent and devastating primary brain tumor in adults. The presence of epigenetic lesions, like hypermethylation of known tumor suppressor genes such as MGMT, has been widely described in GBM, but to our knowledge, a genome-wide profile of DNA methylation changes in these lethal tumors is not yet available. In the present analysis, we have quantified the DNA methylation level of 1,505 CpG dinucleotides (807 genes) in 87 consecutive GBMs using universal BeadArrays. Supervised cluster analyses identified 25 and seven genes that were respectively hypermethylated and hypomethylated in more than 20% of the cases studied. The most frequently hypermethylated genes were HOXA11, CD81, PRKCDBP, TES, MEST, TNFRSF10A and FZD9, being involved in more than half of the cases. Studying the biological features of hypermethylated genes, we found that the group of genes hypermethylated in GBM was highly enriched (41%, p < 0.001) for targets of the PRC2 (Polycomb repressive complex 2) in embryonic stem cells. This suggests that GBM might be derived from precursor cells with stem cell-like features. DNA methylation profiles were associated with overall survival in GBM, and we confirmed the favorable prognostic impact of MGMT methylation in patients treated with alkylating agents. Furthermore, we identified that promoter hypermethylation of the transcription factor gene GATA6 (occurring in 30% of GBM) was significantly associated with unfavorable patient survival.

&lt;div&gt;Abstract&lt;p&gt;Cancer evolution is a process that is still poorly understood because of the lack of versatile &lt;i&gt;in vivo&lt;/i&gt; longitudinal studies. By generating murine non–small cell lung cancer (NSCLC) orthoallobanks and paired primary cell lines, we provide a detailed description of an &lt;i&gt;in vivo&lt;/i&gt;, time-dependent cancer malignization process. We identify the acquisition of metastatic dissemination potential, the selection of co-driver mutations, and the appearance of naturally occurring intratumor heterogeneity, thus recapitulating the stochastic nature of human cancer development. This approach combines the robustness of genetically engineered cancer models with the flexibility of allograft methodology. We have applied this tool for the preclinical evaluation of therapeutic approaches. This system can be implemented to improve the design of future treatments for patients with NSCLC. &lt;i&gt;Cancer Res; 74(21); 5978–88. ©2014 AACR&lt;/i&gt;.&lt;/p&gt;&lt;/div&gt;

Abstract Spatial biology experiments integrate the molecular and histological landscape of tissues to provide a previously inaccessible view of tissue biology, unlocking the architecture of complex multicellular tissues. Within spatial biology, spatial transcriptomics platforms are among the most advanced, allowing researchers to characterize the expression of thousands of genes across space. These new technologies are transforming our understanding of how cells are organized in space and communicate with each other to determine emergent phenotypes with unprecedented granularity. This is particularly important in cancer research, as it is becoming evident that tumor evolution is shaped not only by the genetic properties of cancer cells but also by how they interact with the tumor microenvironment and their spatial organization. While many platforms can generate spatial transcriptomics profiles, it is still unclear in which context each platform better suits the needs of its users. Here we compare the results obtained using 4 different spatial transcriptomics (VISIUM, VISIUM CytAssist, Xenium and CosMx) and one spatial proteomics (VISIUM CytAssist) platforms across serial sections of 6 FFPE samples from primary human tumors covering some of the most common forms of the disease (lung, breast, colorectal, bladder, lymphoma and ovary). We observed that the VISIUM platform with CytAssist chemistry yielded superior data quality. Xenium consistently produced more reliable results for in situ platforms, with better gene clustering and fewer false positives than CosMx. Interestingly, these platform-based variations didn’t significantly affect cell type identification. Finally, by comparing VISIUM protein profiles with the spatial transcriptomics data from all four platforms on each sample, we identified several genes with mismatched RNA and protein expression patterns, highlighting the importance of multi-omics profiling to reveal the true biology of human tumors.

&lt;p&gt;Epigenetic and expression analysis based on histological subtypes from primary tissues of the TCGA database.&lt;/p&gt;

&lt;p&gt;Supplementary Table 1&lt;/p&gt;