Impact on the Transcriptome of Proton Beam Irradiation Targeted at Healthy Cardiac Tissue of Mice.

Proton beam therapy is considered a step forward with respect to electromagnetic radiation, thanks to the reduction in the dose delivered. Among unwanted effects to healthy tissue, cardiovascular complications are a known long-term radiotherapy complication. The transcriptional response of cardiac tissue from xenografted BALB/c nude mice obtained at 3 and 10 days after proton irradiation covering both the tumor region and the underlying healthy tissue was analyzed as a function of dose and time. Three doses were used: 2 Gy, 6 Gy, and 9 Gy. The intermediate dose had caused the greatest impact at 3 days after irradiation: at 2 Gy, 219 genes were differently expressed, many of them represented by zinc finger proteins; at 6 Gy, there were 1109, with a predominance of genes involved in energy metabolism and responses to stimuli; and at 9 Gy, there were 105, mainly represented by zinc finger proteins and molecules involved in the regulation of cardiac function. After 10 days, no significant effects were detected, suggesting that cellular repair mechanisms had defused the potential alterations in gene expression. The nonlinear dose-response curve indicates a need to update the models built on photons to improve accuracy in health risk prediction. Our data also suggest a possible role for zinc finger protein genes as markers of proton therapy efficacy.

Effectiveness of Radiomic ZOT Features in the Automated Discrimination of Oncocytoma from Clear Cell Renal Cancer.

BACKGROUND: Benign renal tumors, such as renal oncocytoma (RO), can be erroneously diagnosed as malignant renal cell carcinomas (RCC), because of their similar imaging features. Computer-aided systems leveraging radiomic features can be used to better discriminate benign renal tumors from the malignant ones. The purpose of this work was to build a machine learning model to distinguish RO from clear cell RCC (ccRCC). METHOD: We collected CT images of 77 patients, with 30 cases of RO (39%) and 47 cases of ccRCC (61%). Radiomic features were extracted both from the tumor volumes identified by the clinicians and from the tumor's zone of transition (ZOT). We used a genetic algorithm to perform feature selection, identifying the most descriptive set of features for the tumor classification. We built a decision tree classifier to distinguish between ROs and ccRCCs. We proposed two versions of the pipeline: in the first one, the feature selection was performed before the splitting of the data, while in the second one, the feature selection was performed after, i.e., on the training data only. We evaluated the efficiency of the two pipelines in cancer classification. RESULTS: The ZOT features were found to be the most predictive by the genetic algorithm. The pipeline with the feature selection performed on the whole dataset obtained an average ROC AUC score of 0.87 ± 0.09. The second pipeline, in which the feature selection was performed on the training data only, obtained an average ROC AUC score of 0.62 ± 0.17. CONCLUSIONS: The obtained results confirm the efficiency of ZOT radiomic features in capturing the renal tumor characteristics. We showed that there is a significant difference in the performances of the two proposed pipelines, highlighting how some already published radiomic analyses could be too optimistic about the real generalization capabilities of the models.

Adding pieces to the puzzle of differentiated-to-anaplastic thyroid cancer evolution: the oncogene E2F7.

Anaplastic Thyroid Cancer (ATC) is the most aggressive and de-differentiated subtype of thyroid cancer. Many studies hypothesized that ATC derives from Differentiated Thyroid Carcinoma (DTC) through a de-differentiation process triggered by specific molecular events still largely unknown. E2F7 is an atypical member of the E2F family. Known as cell cycle inhibitor and keeper of genomic stability, in specific contexts its function is oncogenic, guiding cancer progression. We performed a meta-analysis on 279 gene expression profiles, from 8 Gene Expression Omnibus patient samples datasets, to explore the causal relationship between DTC and ATC. We defined 3 specific gene signatures describing the evolution from normal thyroid tissue to DTC and ATC and validated them in a cohort of human surgically resected ATCs collected in our Institution. We identified E2F7 as a key player in the DTC-ATC transition and showed in vitro that its down-regulation reduced ATC cells' aggressiveness features. RNA-seq and ChIP-seq profiling allowed the identification of the E2F7 specific gene program, which is mainly related to cell cycle progression and DNA repair ability. Overall, this study identified a signature describing DTC de-differentiation toward ATC subtype and unveiled an E2F7-dependent transcriptional program supporting this process.

Identification of a Maturation Plasma Cell Index through a Highly Sensitive Droplet Digital PCR Assay Gene Expression Signature Validation in Newly Diagnosed Multiple Myeloma Patients.

DNA microarrays and RNA-based sequencing approaches are considered important discovery tools in clinical medicine. However, cross-platform reproducibility studies undertaken so far have highlighted that microarrays are not able to accurately measure gene expression, particularly when they are expressed at low levels. Here, we consider the employment of a digital PCR assay (ddPCR) to validate a gene signature previously identified by gene expression profile. This signature included ten Hedgehog (HH) pathways' genes able to stratify multiple myeloma (MM) patients according to their self-renewal status. Results show that the designed assay is able to validate gene expression data, both in a retrospective as well as in a prospective cohort. In addition, the plasma cells' differentiation status determined by ddPCR was further confirmed by other techniques, such as flow cytometry, allowing the identification of patients with immature plasma cells' phenotype (i.e., expressing CD19+/CD81+ markers) upregulating HH genes, as compared to others, whose plasma cells lose the expression of these markers and were more differentiated. To our knowledge, this is the first technical report of gene expression data validation by ddPCR instead of classical qPCR. This approach permitted the identification of a Maturation Index through the integration of molecular and phenotypic data, able to possibly define upfront the differentiation status of MM patients that would be clinically relevant in the future.

Automated Prediction of the Response to Neoadjuvant Chemoradiotherapy in Patients Affected by Rectal Cancer.

BACKGROUND: Rectal cancer is a malignant neoplasm of the large intestine resulting from the uncontrolled proliferation of the rectal tract. Predicting the pathologic response of neoadjuvant chemoradiotherapy at an MRI primary staging scan in patients affected by locally advanced rectal cancer (LARC) could lead to significant improvement in the survival and quality of life of the patients. In this study, the possibility of automatizing this estimation from a primary staging MRI scan, using a fully automated artificial intelligence-based model for the segmentation and consequent characterization of the tumor areas using radiomic features was evaluated. The TRG score was used to evaluate the clinical outcome. METHODS: Forty-three patients under treatment in the IRCCS Sant'Orsola-Malpighi Polyclinic were retrospectively selected for the study; a U-Net model was trained for the automated segmentation of the tumor areas; the radiomic features were collected and used to predict the tumor regression grade (TRG) score. RESULTS: The segmentation of tumor areas outperformed the state-of-the-art results in terms of the Dice score coefficient or was comparable to them but with the advantage of considering mucinous cases. Analysis of the radiomic features extracted from the lesion areas allowed us to predict the TRG score, with the results agreeing with the state-of-the-art results. CONCLUSIONS: The results obtained regarding TRG prediction using the proposed fully automated pipeline prove its possible usage as a viable decision support system for radiologists in clinical practice.

Mechanisms Involved in the Promoting Activity of Fibroblasts in HTLV-1-Mediated Lymphomagenesis: Insights into the Plasticity of Lymphomatous Cells.

Among the mechanisms leading to progression to Adult T-cell Leukaemia/Lymphoma in Human T-cell Leukaemia Virus type 1 (HTLV-1)-infected subjects, the contribution of stromal components remains poorly understood. To dissect the role of fibroblasts in HTLV-1-mediated lymphomagenesis, transcriptome studies, cytofluorimetric and qRT-PCR analyses of surface and intracellular markers linked to plasticity and stemness in coculture, and in vivo experiments were performed. A transcriptomic comparison between a more lymphomagenic (C91/III) and the parental (C91/PL) cell line evidenced hyperactivation of the PI3K/Akt pathway, confirmed by phospho-ELISA and 2-DE and WB analyses. C91/III cells also showed higher expression of mesenchymal and stemness genes. Short-term coculture with human foreskin fibroblasts (HFF) induced these features in C91/PL cells, and significantly increased not only the cancer stem cells (CSCs)-supporting CD10+GPR77+ HFF subpopulation, but also the percentage of ALDH1bright C91/PL cells. A non-cytotoxic acetylsalicylic acid treatment decreased HFF-induced ALDH1bright C91/PL cells, downregulated mesenchymal and stemness genes in cocultured cells, and delayed lymphoma growth in immunosuppressed mice, thus hindering the supportive activity of HFF on CSCs. These data suggest that crosstalk with HFF significantly intensifies the aggressiveness and plasticity of C91/PL cells, leading to the enrichment in lymphoma-initiating cells. Additional research is needed to better characterize these preliminary findings.

Challenges in the Use of Artificial Intelligence for Prostate Cancer Diagnosis from Multiparametric Imaging Data.

Many efforts have been carried out for the standardization of multiparametric Magnetic Resonance (mp-MR) images evaluation to detect Prostate Cancer (PCa), and specifically to differentiate levels of aggressiveness, a crucial aspect for clinical decision-making. Prostate Imaging-Reporting and Data System (PI-RADS) has contributed noteworthily to this aim. Nevertheless, as pointed out by the European Association of Urology (EAU 2020), the PI-RADS still has limitations mainly due to the moderate inter-reader reproducibility of mp-MRI. In recent years, many aspects in the diagnosis of cancer have taken advantage of the use of Artificial Intelligence (AI) such as detection, segmentation of organs and/or lesions, and characterization. Here a focus on AI as a potentially important tool for the aim of standardization and reproducibility in the characterization of PCa by mp-MRI is reported. AI includes methods such as Machine Learning and Deep learning techniques that have shown to be successful in classifying mp-MR images, with similar performances obtained by radiologists. Nevertheless, they perform differently depending on the acquisition system and protocol used. Besides, these methods need a large number of samples that cover most of the variability of the lesion aspect and zone to avoid overfitting. The use of publicly available datasets could improve AI performance to achieve a higher level of generalizability, exploiting large numbers of cases and a big range of variability in the images. Here we explore the promise and the advantages, as well as emphasizing the pitfall and the warnings, outlined in some recent studies that attempted to classify clinically significant PCa and indolent lesions using AI methods. Specifically, we focus on the overfitting issue due to the scarcity of data and the lack of standardization and reproducibility in every step of the mp-MR image acquisition and the classifier implementation. In the end, we point out that a solution can be found in the use of publicly available datasets, whose usage has already been promoted by some important initiatives. Our future perspective is that AI models may become reliable tools for clinicians in PCa diagnosis, reducing inter-observer variability and evaluation time.

Integrated genomic-metabolic classification of acute myeloid leukemia defines a subgroup with NPM1 and cohesin/DNA damage mutations.

Although targeting of cell metabolism is a promising therapeutic strategy in acute myeloid leukemia (AML), metabolic dependencies are largely unexplored. We aimed to classify AML patients based on their metabolic landscape and map connections between metabolic and genomic profiles. Combined serum and urine metabolomics improved AML characterization compared with individual biofluid analysis. At intracellular level, AML displayed dysregulated amino acid, nucleotide, lipid, and bioenergetic metabolism. The integration of intracellular and biofluid metabolomics provided a map of alterations in the metabolism of polyamine, purine, keton bodies and polyunsaturated fatty acids and tricarboxylic acid cycle. The intracellular metabolome distinguished three AML clusters, correlating with distinct genomic profiles: NPM1-mutated(mut), chromatin/spliceosome-mut and TP53-mut/aneuploid AML that were confirmed by biofluid analysis. Interestingly, integrated genomic-metabolic profiles defined two subgroups of NPM1-mut AML. One was enriched for mutations in cohesin/DNA damage-related genes (NPM1/cohesin-mut AML) and showed increased serum choline + trimethylamine-N-oxide and leucine, higher mutation load, transcriptomic signatures of reduced inflammatory status and better ex-vivo response to EGFR and MET inhibition. The transcriptional differences of enzyme-encoding genes between NPM1/cohesin-mut and NPM1-mut allowed in silico modeling of intracellular metabolic perturbations. This approach predicted alterations in NAD and purine metabolism in NPM1/cohesin-mut AML that suggest potential vulnerabilities, worthy of being therapeutically explored.

Classification and Personalized Prognostic Assessment on the Basis of Clinical and Genomic Features in Myelodysplastic Syndromes.

PURPOSE: Recurrently mutated genes and chromosomal abnormalities have been identified in myelodysplastic syndromes (MDS). We aim to integrate these genomic features into disease classification and prognostication. METHODS: We retrospectively enrolled 2,043 patients. Using Bayesian networks and Dirichlet processes, we combined mutations in 47 genes with cytogenetic abnormalities to identify genetic associations and subgroups. Random-effects Cox proportional hazards multistate modeling was used for developing prognostic models. An independent validation on 318 cases was performed. RESULTS: We identify eight MDS groups (clusters) according to specific genomic features. In five groups, dominant genomic features include splicing gene mutations (SF3B1, SRSF2, and U2AF1) that occur early in disease history, determine specific phenotypes, and drive disease evolution. These groups display different prognosis (groups with SF3B1 mutations being associated with better survival). Specific co-mutation patterns account for clinical heterogeneity within SF3B1- and SRSF2-related MDS. MDS with complex karyotype and/or TP53 gene abnormalities and MDS with acute leukemia-like mutations show poorest prognosis. MDS with 5q deletion are clustered into two distinct groups according to the number of mutated genes and/or presence of TP53 mutations. By integrating 63 clinical and genomic variables, we define a novel prognostic model that generates personally tailored predictions of survival. The predicted and observed outcomes correlate well in internal cross-validation and in an independent external cohort. This model substantially improves predictive accuracy of currently available prognostic tools. We have created a Web portal that allows outcome predictions to be generated for user-defined constellations of genomic and clinical features. CONCLUSION: Genomic landscape in MDS reveals distinct subgroups associated with specific clinical features and discrete patterns of evolution, providing a proof of concept for next-generation disease classification and prognosis.

Impact of concurrency on the performance of a whole exome sequencing pipeline.

BACKGROUND: Current high-throughput technologies-i.e. whole genome sequencing, RNA-Seq, ChIP-Seq, etc.-generate huge amounts of data and their usage gets more widespread with each passing year. Complex analysis pipelines involving several computationally-intensive steps have to be applied on an increasing number of samples. Workflow management systems allow parallelization and a more efficient usage of computational power. Nevertheless, this mostly happens by assigning the available cores to a single or few samples' pipeline at a time. We refer to this approach as naive parallel strategy (NPS). Here, we discuss an alternative approach, which we refer to as concurrent execution strategy (CES), which equally distributes the available processors across every sample's pipeline. RESULTS: Theoretically, we show that the CES results, under loose conditions, in a substantial speedup, with an ideal gain range spanning from 1 to the number of samples. Also, we observe that the CES yields even faster executions since parallelly computable tasks scale sub-linearly. Practically, we tested both strategies on a whole exome sequencing pipeline applied to three publicly available matched tumour-normal sample pairs of gastrointestinal stromal tumour. The CES achieved speedups in latency up to 2-2.4 compared to the NPS. CONCLUSIONS: Our results hint that if resources distribution is further tailored to fit specific situations, an even greater gain in performance of multiple samples pipelines execution could be achieved. For this to be feasible, a benchmarking of the tools included in the pipeline would be necessary. It is our opinion these benchmarks should be consistently performed by the tools' developers. Finally, these results suggest that concurrent strategies might also lead to energy and cost savings by making feasible the usage of low power machine clusters.

Prediction of vascular aging based on smartphone acquired PPG signals.

Photoplethysmography (PPG) measured by smartphone has the potential for a large scale, non-invasive, and easy-to-use screening tool. Vascular aging is linked to increased arterial stiffness, which can be measured by PPG. We investigate the feasibility of using PPG to predict healthy vascular aging (HVA) based on two approaches: machine learning (ML) and deep learning (DL). We performed data preprocessing, including detrending, demodulating, and denoising on the raw PPG signals. For ML, ridge penalized regression has been applied to 38 features extracted from PPG, whereas for DL several convolutional neural networks (CNNs) have been applied to the whole PPG signals as input. The analysis has been conducted using the crowd-sourced Heart for Heart data. The prediction performance of ML using two features (AUC of 94.7%) - the a wave of the second derivative PPG and tpr, including four covariates, sex, height, weight, and smoking - was similar to that of the best performing CNN, 12-layer ResNet (AUC of 95.3%). Without having the heavy computational cost of DL, ML might be advantageous in finding potential biomarkers for HVA prediction. The whole workflow of the procedure is clearly described, and open software has been made available to facilitate replication of the results.

Combinatorial Discriminant Analysis Applied to RNAseq Data Reveals a Set of 10 Transcripts as Signatures of Exposure of Cattle to Mycobacterium avium subsp. paratuberculosis.

Paratuberculosis or Johne's disease in cattle is a chronic granulomatous gastroenteritis caused by infection with Mycobacterium avium subspecies paratuberculosis (MAP). Paratuberculosis is not treatable; therefore, the early identification and isolation of infected animals is a key point to reduce its incidence. In this paper, we analyse RNAseq experimental data of 5 ELISA-negative cattle exposed to MAP in a positive herd, compared to 5 negative-unexposed controls. The purpose was to find a small set of differentially expressed genes able to discriminate between exposed animals in a preclinical phase from non-exposed controls. Our results identified 10 transcripts that differentiate between ELISA-negative, clinically healthy, and exposed animals belonging to paratuberculosis-positive herds and negative-unexposed animals. Of the 10 transcripts, five (TRPV4, RIC8B, IL5RA, ERF, CDC40) showed significant differential expression between the three groups while the remaining 5 (RDM1, EPHX1, STAU1, TLE1, ASB8) did not show a significant difference in at least one of the pairwise comparisons. When tested in a larger cohort, these findings may contribute to the development of a new diagnostic test for paratuberculosis based on a gene expression signature. Such a diagnostic tool could allow early interventions to reduce the risk of the infection spreading.

The Hippo pathway modulates resistance to BET proteins inhibitors in lung cancer cells.

Inhibitors of BET proteins (BETi) are anti-cancer drugs that have shown efficacy in pre-clinical settings and are currently in clinical trials for different types of cancer, including non-small cell lung cancer (NSCLC). Currently, no predictive biomarker is available to identify patients that may benefit from this treatment. To uncover the mechanisms of resistance to BETi, we performed a genome-scale CRISPR/Cas9 screening in lung cancer cells. We identified three Hippo pathway genes, LATS2, TAOK1, and NF2, as key determinants for sensitivity to BETi. The knockout of these genes induces resistance to BETi, by promoting TAZ nuclear localization and transcriptional activity. Conversely, TAZ expression promotes resistance to these drugs. We also showed that TAZ, YAP, and their partner TEAD are direct targets of BRD4 and that treatment with BETi downregulates their expression. Noticeably, molecular alterations in one or more of these genes are present in a large fraction of NSCLC patients and TAZ amplification or overexpression correlates with a worse outcome in lung adenocarcinoma. Our data define the central role of Hippo pathway in mediating resistance to BETi and provide a rationale for using BETi to counter-act YAP/TAZ-mediated pro-oncogenic activity.

Linking aberrant chromatin features in chronic lymphocytic leukemia to transcription factor networks.

In chronic lymphocytic leukemia (CLL), a diverse set of genetic mutations is embedded in a deregulated epigenetic landscape that drives cancerogenesis. To elucidate the role of aberrant chromatin features, we mapped DNA methylation, seven histone modifications, nucleosome positions, chromatin accessibility, binding of EBF1 and CTCF, as well as the transcriptome of B cells from CLL patients and healthy donors. A globally increased histone deacetylase activity was detected and half of the genome comprised transcriptionally downregulated partially DNA methylated domains demarcated by CTCF CLL samples displayed a H3K4me3 redistribution and nucleosome gain at promoters as well as changes of enhancer activity and enhancer linkage to target genes. A DNA binding motif analysis identified transcription factors that gained or lost binding in CLL at sites with aberrant chromatin features. These findings were integrated into a gene regulatory enhancer containing network enriched for B-cell receptor signaling pathway components. Our study predicts novel molecular links to targets of CLL therapies and provides a valuable resource for further studies on the epigenetic contribution to the disease.

Systems Biology approaches to cancer: towards new therapeutical strategies and personalized approaches.

Network approaches are ubiquitous, from social and ecological systems up to complex biological processes. In our recently published work we used the network framework for a Systems Medicine approach to multiple cancer types, in order to highlight similitudes and differences that can be exploited to extend existing therapeutical strategies. These approaches shed new light to oncological processes, but allow also to pose "old" questions (like the search for novel drug targets) in a "new" way.

Aneuploid acute myeloid leukemia exhibits a signature of genomic alterations in the cell cycle and protein degradation machinery.

BACKGROUND: Aneuploidy occurs in more than 20% of acute myeloid leukemia (AML) cases and correlates with an adverse prognosis. METHODS: To understand the molecular bases of aneuploid acute myeloid leukemia (A-AML), this study examined the genomic profile in 42 A-AML cases and 35 euploid acute myeloid leukemia (E-AML) cases. RESULTS: A-AML was characterized by increased genomic complexity based on exonic variants (an average of 26 somatic mutations per sample vs 15 for E-AML). The integration of exome, copy number, and gene expression data revealed alterations in genes involved in DNA repair (eg, SLX4IP, RINT1, HINT1, and ATR) and the cell cycle (eg, MCM2, MCM4, MCM5, MCM7, MCM8, MCM10, UBE2C, USP37, CK2, CK3, CK4, BUB1B, NUSAP1, and E2F) in A-AML, which was associated with a 3-gene signature defined by PLK1 and CDC20 upregulation and RAD50 downregulation and with structural or functional silencing of the p53 transcriptional program. Moreover, A-AML was enriched for alterations in the protein ubiquitination and degradation pathway (eg, increased levels of UHRF1 and UBE2C and decreased UBA3 expression), response to reactive oxygen species, energy metabolism, and biosynthetic processes, which may help in facing the unbalanced protein load. E-AML was associated with BCOR/BCORL1 mutations and HOX gene overexpression. CONCLUSIONS: These findings indicate that aneuploidy-related and leukemia-specific alterations cooperate to tolerate an abnormal chromosome number in AML, and they point to the mitotic and protein degradation machineries as potential therapeutic targets.

Molecular Aging of Human Liver: An Epigenetic/Transcriptomic Signature.

The feasibility of liver transplantation from old healthy donors suggests that this organ is able to preserve its functionality during aging. To explore the biological basis of this phenomenon, we characterized the epigenetic profile of liver biopsies collected from 45 healthy liver donors ranging from 13 to 90 years old using the Infinium HumanMethylation450 BeadChip. The analysis indicates that a large remodeling in DNA methylation patterns occurs, with 8,823 age-associated differentially methylated CpG probes. Notably, these age-associated changes tended to level off after the age of 60, as confirmed by Horvath's clock. Using stringent selection criteria, we further identified a DNA methylation signature of aging liver including 75 genomic regions. We demonstrated that this signature is specific for liver compared to other tissues and that it is able to detect biological age-acceleration effects associated with obesity. Finally, we combined DNA methylation measurements with available expression data. Although the intersection between the two omic characterizations was low, both approaches suggested a previously unappreciated role of epithelial-mesenchymal transition and Wnt-signaling pathways in the aging of human liver.

Network integration of multi-tumour omics data suggests novel targeting strategies.

We characterize different tumour types in search for multi-tumour drug targets, in particular aiming for drug repurposing and novel drug combinations. Starting from 11 tumour types from The Cancer Genome Atlas, we obtain three clusters based on transcriptomic correlation profiles. A network-based analysis, integrating gene expression profiles and protein interactions of cancer-related genes, allows us to define three cluster-specific signatures, with genes belonging to NF-κB signaling, chromosomal instability, ubiquitin-proteasome system, DNA metabolism, and apoptosis biological processes. These signatures have been characterized by different approaches based on mutational, pharmacological and clinical evidences, demonstrating the validity of our selection. Moreover, we define new pharmacological strategies validated by in vitro experiments that show inhibition of cell growth in two tumour cell lines, with significant synergistic effect. Our study thus provides a list of genes and pathways that could possibly be used, singularly or in combination, for the design of novel treatment strategies.

Integrated Molecular Characterization of Gastrointestinal Stromal Tumors (GIST) Harboring the Rare D842V Mutation in PDGFRA Gene.

Gastrointestinal stromal tumors (GIST) carrying the D842V activating mutation in the platelet-derived growth factor receptor alpha (PDGFRA) gene are a very rare subgroup of GIST (about 10%) known to be resistant to conventional tyrosine kinase inhibitors (TKIs) and to show an indolent behavior. In this study, we performed an integrated molecular characterization of D842V mutant GIST by whole-transcriptome and whole-exome sequencing coupled with protein-ligand interaction modelling to identify the molecular signature and any additional recurrent genomic event related to their clinical course. We found a very specific gene expression profile of D842V mutant tumors showing the activation of G-protein-coupled receptor (GPCR) signaling and a relative downregulation of cell cycle processes. Beyond D842V, no recurrently mutated genes were found in our cohort. Nevertheless, many private, clinically relevant alterations were found in each tumor (TP53, IDH1, FBXW7, SDH-complex). Molecular modeling of PDGFRA D842V suggests that the mutant protein binds imatinib with lower affinity with respect to wild-type structure, showing higher stability during the interaction with other type I TKIs (like crenolanib). D842V mutant GIST do not show any actionable recurrent molecular events of therapeutic significance, therefore this study supports the rationale of novel TKIs development that are currently being evaluated in clinical studies for the treatment of D842V mutant GIST.

RUNX2 expression in thyroid and breast cancer requires the cooperation of three non-redundant enhancers under the control of BRD4 and c-JUN.

Aberrant reactivation of embryonic pathways is a common feature of cancer. RUNX2 is a transcription factor crucial during embryogenesis that is aberrantly reactivated in many tumors, including thyroid and breast cancer, where it promotes aggressiveness and metastatic spreading. Currently, the mechanisms driving RUNX2 expression in cancer are still largely unknown. Here we showed that RUNX2 transcription in thyroid and breast cancer requires the cooperation of three distantly located enhancers (ENHs) brought together by chromatin three-dimensional looping. We showed that BRD4 controls RUNX2 by binding to the newly identified ENHs and we demonstrated that the anti-proliferative effects of bromodomain inhibitors (BETi) is associated with RUNX2 transcriptional repression. We demonstrated that each RUNX2 ENH is potentially controlled by a distinct set of TFs and we identified c-JUN as the principal pivot of this regulatory platform. We also observed that accumulation of genetic mutations within these elements correlates with metastatic behavior in human thyroid tumors. Finally, we identified RAINs, a novel family of ENH-associated long non-coding RNAs, transcribed from the identified RUNX2 regulatory unit. Our data provide a new model to explain how RUNX2 expression is reactivated in thyroid and breast cancer and how cancer-driving signaling pathways converge on the regulation of this gene.