HELLS regulates transcription in T-cell lymphomas by reducing unscheduled R-loops and by facilitating RNAPII progression.

Chromatin modifiers are emerging as major determinants of many types of cancers, including Anaplastic Large Cell Lymphomas (ALCL), a family of highly heterogeneous T-cell lymphomas for which therapeutic options are still limited. HELLS is a multifunctional chromatin remodeling protein that affects genomic instability by participating in the DNA damage response. Although the transcriptional function of HELLS has been suggested, no clues on how HELLS controls transcription are currently available. In this study, by integrating different multi-omics and functional approaches, we characterized the transcriptional landscape of HELLS in ALCL. We explored the clinical impact of its transcriptional program in a large cohort of 44 patients with ALCL. We demonstrated that HELLS, loaded at the level of intronic regions of target promoters, facilitates RNA Polymerase II (RNAPII) progression along the gene bodies by reducing the persistence of co-transcriptional R-loops and promoting DNA damage resolution. Importantly, selective knockdown of HELLS sensitizes ALCL cells to different chemotherapeutic agents, showing a synergistic effect. Collectively, our work unveils the role of HELLS in acting as a gatekeeper of ALCL genome stability providing a rationale for drug design.

Long non-coding RNA mitophagy and ALK-negative anaplastic lymphoma-associated transcript: a novel regulator of mitophagy in T-cell lymphoma.

Long non-coding RNA (lncRNA) are emerging as powerful and versatile regulators of transcriptional programs and distinctive biomarkers of progression of T-cell lymphoma. Their role in the aggressive anaplastic lymphoma kinase-negative (ALK-) subtype of anaplastic large cell lymphoma (ALCL) has been elucidated only in part. Starting from our previously identified ALCL-associated lncRNA signature and performing digital gene expression profiling of a retrospective cohort of ALCL, we defined an 11 lncRNA signature able to discriminate among ALCL subtypes. We selected a not previously characterized lncRNA, MTAAT, with preferential expression in ALK- ALCL, for molecular and functional studies. We demonstrated that lncRNA MTAAT contributes to an aberrant mitochondrial turnover restraining mitophagy and promoting cellular proliferation. Functionally, lncRNA MTAAT acts as a repressor of a set of genes related to mitochondrial quality control via chromatin reorganization. Collectively, our work demonstrates the transcriptional role of lncRNA MTAAT in orchestrating a complex transcriptional program sustaining the progression of ALK- ALCL.

A Bayesian data fusion based approach for learning genome-wide transcriptional regulatory networks.

BACKGROUND: Reverse engineering of transcriptional regulatory networks (TRN) from genomics data has always represented a computational challenge in System Biology. The major issue is modeling the complex crosstalk among transcription factors (TFs) and their target genes, with a method able to handle both the high number of interacting variables and the noise in the available heterogeneous experimental sources of information. RESULTS: In this work, we propose a data fusion approach that exploits the integration of complementary omics-data as prior knowledge within a Bayesian framework, in order to learn and model large-scale transcriptional networks. We develop a hybrid structure-learning algorithm able to jointly combine TFs ChIP-Sequencing data and gene expression compendia to reconstruct TRNs in a genome-wide perspective. Applying our method to high-throughput data, we verified its ability to deal with the complexity of a genomic TRN, providing a snapshot of the synergistic TFs regulatory activity. Given the noisy nature of data-driven prior knowledge, which potentially contains incorrect information, we also tested the method's robustness to false priors on a benchmark dataset, comparing the proposed approach to other regulatory network reconstruction algorithms. We demonstrated the effectiveness of our framework by evaluating structural commonalities of our learned genomic network with other existing networks inferred by different DNA binding information-based methods. CONCLUSIONS: This Bayesian omics-data fusion based methodology allows to gain a genome-wide picture of the transcriptional interplay, helping to unravel key hierarchical transcriptional interactions, which could be subsequently investigated, and it represents a promising learning approach suitable for multi-layered genomic data integration, given its robustness to noisy sources and its tailored framework for handling high dimensional data.

Personalized Timing for Allogeneic Stem-Cell Transplantation in Hematologic Neoplasms: A Target Trial Emulation Approach Using Multistate Modeling and Microsimulation.

PURPOSE: Decision about the optimal timing of a treatment procedure in patients with hematologic neoplasms is critical, especially for cellular therapies (most including allogeneic hematopoietic stem-cell transplantation [HSCT]). In the absence of evidence from randomized trials, real-world observational data become beneficial to study the effect of the treatment timing. In this study, a framework to estimate the expected outcome after an intervention in a time-to-event scenario is developed, with the aim of optimizing the timing in a personalized manner. METHODS: Retrospective real-world data are leveraged to emulate a target trial for treatment timing using multistate modeling and microsimulation. This case study focuses on myelodysplastic syndromes, serving as a prototype for rare cancers characterized by a heterogeneous clinical course and complex genomic background. A cohort of 7,118 patients treated according to conventional available treatments/evidence across Europe and United States is analyzed. The primary clinical objective is to determine the ideal timing for HSCT, the only curative option for these patients. RESULTS: This analysis enabled us to identify the most appropriate time frames for HSCT on the basis of each patient's unique profile, defined by a combination relevant patients' characteristics. CONCLUSION: The developed methodology offers a structured framework to address a relevant clinical issue in the field of hematology. It makes several valuable contributions: (1) novel insights into how to develop decision models to identify the most favorable HSCT timing, (2) evidence to inform clinical decisions in a real-world context, and (3) the incorporation of complex information into decision making. This framework can be applied to provide medical insights for clinical issues that cannot be adequately addressed through randomized clinical trials.

BET inhibitors drive Natural Killer activation in non-small cell lung cancer via BRD4 and SMAD3.

Non-small-cell lung carcinoma (NSCLC) is the most common lung cancer and one of the pioneer tumors in which immunotherapy has radically changed patients' outcomes. However, several issues are emerging and their implementation is required to optimize immunotherapy-based protocols. In this work, we investigate the ability of the Bromodomain and Extra-Terminal protein inhibitors (BETi) to stimulate a proficient anti-tumor immune response toward NSCLC. By using in vitro, ex-vivo, and in vivo models, we demonstrate that these epigenetic drugs specifically enhance Natural Killer (NK) cell cytotoxicity. BETi down-regulate a large set of NK inhibitory receptors, including several immune checkpoints (ICs), that are direct targets of the transcriptional cooperation between the BET protein BRD4 and the transcription factor SMAD3. Overall, BETi orchestrate an epigenetic reprogramming that leads to increased recognition of tumor cells and the killing ability of NK cells. Our results unveil the opportunity to exploit and repurpose these drugs in combination with immunotherapy.

Clinical and Genomic-Based Decision Support System to Define the Optimal Timing of Allogeneic Hematopoietic Stem-Cell Transplantation in Patients With Myelodysplastic Syndromes.

PURPOSE: Allogeneic hematopoietic stem-cell transplantation (HSCT) is the only potentially curative treatment for patients with myelodysplastic syndromes (MDS). Several issues must be considered when evaluating the benefits and risks of HSCT for patients with MDS, with the timing of transplantation being a crucial question. Here, we aimed to develop and validate a decision support system to define the optimal timing of HSCT for patients with MDS on the basis of clinical and genomic information as provided by the Molecular International Prognostic Scoring System (IPSS-M). PATIENTS AND METHODS: We studied a retrospective population of 7,118 patients, stratified into training and validation cohorts. A decision strategy was built to estimate the average survival over an 8-year time horizon (restricted mean survival time [RMST]) for each combination of clinical and genomic covariates and to determine the optimal transplantation policy by comparing different strategies. RESULTS: Under an IPSS-M based policy, patients with either low and moderate-low risk benefited from a delayed transplantation policy, whereas in those belonging to moderately high-, high- and very high-risk categories, immediate transplantation was associated with a prolonged life expectancy (RMST). Modeling decision analysis on IPSS-M versus conventional Revised IPSS (IPSS-R) changed the transplantation policy in a significant proportion of patients (15% of patient candidate to be immediately transplanted under an IPSS-R-based policy would benefit from a delayed strategy by IPSS-M, whereas 19% of candidates to delayed transplantation by IPSS-R would benefit from immediate HSCT by IPSS-M), resulting in a significant gain-in-life expectancy under an IPSS-M-based policy (P = .001). CONCLUSION: These results provide evidence for the clinical relevance of including genomic features into the transplantation decision making process, allowing personalizing the hazards and effectiveness of HSCT in patients with MDS.

MOSAIC: An Artificial Intelligence-Based Framework for Multimodal Analysis, Classification, and Personalized Prognostic Assessment in Rare Cancers.

PURPOSE: Rare cancers constitute over 20% of human neoplasms, often affecting patients with unmet medical needs. The development of effective classification and prognostication systems is crucial to improve the decision-making process and drive innovative treatment strategies. We have created and implemented MOSAIC, an artificial intelligence (AI)-based framework designed for multimodal analysis, classification, and personalized prognostic assessment in rare cancers. Clinical validation was performed on myelodysplastic syndrome (MDS), a rare hematologic cancer with clinical and genomic heterogeneities. METHODS: We analyzed 4,427 patients with MDS divided into training and validation cohorts. Deep learning methods were applied to integrate and impute clinical/genomic features. Clustering was performed by combining Uniform Manifold Approximation and Projection for Dimension Reduction + Hierarchical Density-Based Spatial Clustering of Applications with Noise (UMAP + HDBSCAN) methods, compared with the conventional Hierarchical Dirichlet Process (HDP). Linear and AI-based nonlinear approaches were compared for survival prediction. Explainable AI (Shapley Additive Explanations approach [SHAP]) and federated learning were used to improve the interpretation and the performance of the clinical models, integrating them into distributed infrastructure. RESULTS: UMAP + HDBSCAN clustering obtained a more granular patient stratification, achieving a higher average silhouette coefficient (0.16) with respect to HDP (0.01) and higher balanced accuracy in cluster classification by Random Forest (92.7% ± 1.3% and 85.8% ± 0.8%). AI methods for survival prediction outperform conventional statistical techniques and the reference prognostic tool for MDS. Nonlinear Gradient Boosting Survival stands in the internal (Concordance-Index [C-Index], 0.77; SD, 0.01) and external validation (C-Index, 0.74; SD, 0.02). SHAP analysis revealed that similar features drove patients' subgroups and outcomes in both training and validation cohorts. Federated implementation improved the accuracy of developed models. CONCLUSION: MOSAIC provides an explainable and robust framework to optimize classification and prognostic assessment of rare cancers. AI-based approaches demonstrated superior accuracy in capturing genomic similarities and providing individual prognostic information compared with conventional statistical methods. Its federated implementation ensures broad clinical application, guaranteeing high performance and data protection.

Data-driven, harmonised classification system for myelodysplastic syndromes: a consensus paper from the International Consortium for Myelodysplastic Syndromes.

The WHO and International Consensus Classification 2022 classifications of myelodysplastic syndromes enhance diagnostic precision and refine decision-making processes in these diseases. However, some discrepancies still exist and potentially cause inconsistency in their adoption in a clinical setting. We adopted a data-driven approach to provide a harmonisation between these two classification systems. We investigated the importance of genomic features and their effect on the cluster assignment process to define harmonised entity labels. A panel of expert haematologists, haematopathologists, and data scientists who are members of the International Consortium for Myelodysplastic Syndromes was formed and a modified Delphi consensus process was adopted to harmonise morphologically defined categories without a distinct genomic profile. The panel held regular online meetings and participated in a two-round survey using an online voting tool. We identified nine clusters with distinct genomic features. The cluster of highest hierarchical importance was characterised by biallelic TP53 inactivation. Cluster assignment was irrespective of blast count. Individuals with monoallelic TP53 inactivation were assigned to other clusters. Hierarchically, the second most important group included myelodysplastic syndromes with del(5q). Isolated del(5q) and less than 5% of blast cells in the bone marrow were the most relevant label-defining features. The third most important cluster included myelodysplastic syndromes with mutated SF3B1. The absence of isolated del(5q), del(7q)/-7, abn3q26.2, complex karyotype, RUNX1 mutations, or biallelic TP53 were the basis for a harmonised label of this category. Morphologically defined myelodysplastic syndrome entities showed large genomic heterogeneity that was not efficiently captured by single-lineage versus multilineage dysplasia, marrow blasts, hypocellularity, or fibrosis. We investigated the biological continuum between myelodysplastic syndromes with more than 10% bone marrow blasts and acute myeloid leukaemia, and found only a partial overlap in genetic features. After the survey, myelodysplastic syndromes with low blasts (ie, less than 5%) and myelodysplastic syndromes with increased blasts (ie, 5% or more) were recognised as disease entities. Our data-driven approach can efficiently harmonise current classifications of myelodysplastic syndromes and provide a reference for patient management in a real-world setting.

An Innovative Drug Repurposing Approach to Restrain Endometrial Cancer Metastatization.

BACKGROUND: Endometrial cancer (EC) is the most common gynecologic tumor and the world's fourth most common cancer in women. Most patients respond to first-line treatments and have a low risk of recurrence, but refractory patients, and those with metastatic cancer at diagnosis, remain with no treatment options. Drug repurposing aims to discover new clinical indications for existing drugs with known safety profiles. It provides ready-to-use new therapeutic options for highly aggressive tumors for which standard protocols are ineffective, such as high-risk EC. METHODS: Here, we aimed at defining new therapeutic opportunities for high-risk EC using an innovative and integrated computational drug repurposing approach. RESULTS: We compared gene-expression profiles, from publicly available databases, of metastatic and non-metastatic EC patients being metastatization the most severe feature of EC aggressiveness. A comprehensive analysis of transcriptomic data through a two-arm approach was applied to obtain a robust prediction of drug candidates. CONCLUSIONS: Some of the identified therapeutic agents are already successfully used in clinical practice to treat other types of tumors. This highlights the potential to repurpose them for EC and, therefore, the reliability of the proposed approach.

Ex vivo mapping of enhancer networks that define the transcriptional program driving melanoma metastasis.

Mortality from vmelanoma is associated with metastatic disease, but the mechanisms leading to spreading of the cancer cells remain obscure. Spatial profiling revealed that melanoma is characterized by a high degree of heterogeneity, which is established by the ability of melanoma cells to switch between different phenotypical stages. This plasticity, likely a heritage from embryonic pathways, accounts for a relevant part of the metastatic potential of these lesions, and requires the rapid and efficient reorganization of the transcriptional landscape of melanoma cells. A large part of the non-coding genome cooperates to control gene expression, specifically through the activity of enhancers (ENHs). In this study, we aimed to identify ex vivo the network of active ENHs and to outline their cooperative interactions in supporting transcriptional adaptation during melanoma metastatic progression. We conducted a genome-wide analysis to map active ENHs distribution in a retrospective cohort of 39 melanoma patients, comparing the profiles obtained in primary (N = 19) and metastatic (N = 20) melanoma lesions. Unsupervised clustering showed that the profile for acetylated histone H3 at lysine 27 (H3K27ac) efficiently segregates lesions into three different clusters corresponding to progressive stages of the disease. We reconstructed the map of super-ENHs (SEs) and cooperative ENHs that associate with metastatic progression in melanoma, which showed that cooperation among regulatory elements is a mandatory requirement for transcriptional plasticity. We also showed that these elements carry out specialized and non-redundant functions, and indicated the existence of a hierarchical organization, with SEs on top as masterminds of the entire transcriptional program and classical ENHs as executors. By providing an innovative vision of how the chromatin landscape of melanoma works during metastatic spreading, our data also point out the need to integrate functional profiling in the analysis of cancer lesions to increase definition and improve interpretation of tumor heterogeneity.

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.

The long non-coding RNA TAZ-AS202 promotes lung cancer progression via regulation of the E2F1 transcription factor and activation of Ephrin signaling.

Long non-coding RNAs (lncRNAs) are transcripts without coding potential that are pervasively expressed from the genome and have been increasingly reported to play crucial roles in all aspects of cell biology. They have been also heavily implicated in cancer development and progression, with both oncogenic and tumor suppressor functions. In this work, we identified and characterized a novel lncRNA, TAZ-AS202, expressed from the TAZ genomic locus and exerting pro-oncogenic functions in non-small cell lung cancer. TAZ-AS202 expression is under the control of YAP/TAZ-containing transcriptional complexes. We demonstrated that TAZ-AS202 is overexpressed in lung cancer tissue, compared with surrounding lung epithelium. In lung cancer cell lines TAZ-AS202 promotes cell migration and cell invasion. TAZ-AS202 regulates the expression of a set of genes belonging to cancer-associated pathways, including WNT and EPH-Ephrin signaling. The molecular mechanism underlying TAZ-AS202 function does not involve change of TAZ expression or activity, but increases the protein level of the transcription factor E2F1, which in turn regulates the expression of a large set of target genes, including the EPHB2 receptor. Notably, the silencing of both E2F1 and EPHB2 recapitulates TAZ-AS202 silencing cellular phenotype, indicating that they are essential mediators of its activity. Overall, this work unveiled a new regulatory mechanism that, by increasing E2F1 protein, modifies the non-small cell lung cancer cells transcriptional program, leading to enhanced aggressiveness features. The TAZ-AS202/E2F1/EPHB2 axis may be the target for new therapeutic strategies.

Synthetic Data Generation by Artificial Intelligence to Accelerate Research and Precision Medicine in Hematology.

PURPOSE: Synthetic data are artificial data generated without including any real patient information by an algorithm trained to learn the characteristics of a real source data set and became widely used to accelerate research in life sciences. We aimed to (1) apply generative artificial intelligence to build synthetic data in different hematologic neoplasms; (2) develop a synthetic validation framework to assess data fidelity and privacy preservability; and (3) test the capability of synthetic data to accelerate clinical/translational research in hematology. METHODS: A conditional generative adversarial network architecture was implemented to generate synthetic data. Use cases were myelodysplastic syndromes (MDS) and AML: 7,133 patients were included. A fully explainable validation framework was created to assess fidelity and privacy preservability of synthetic data. RESULTS: We generated MDS/AML synthetic cohorts (including information on clinical features, genomics, treatment, and outcomes) with high fidelity and privacy performances. This technology allowed resolution of lack/incomplete information and data augmentation. We then assessed the potential value of synthetic data on accelerating research in hematology. Starting from 944 patients with MDS available since 2014, we generated a 300% augmented synthetic cohort and anticipated the development of molecular classification and molecular scoring system obtained many years later from 2,043 to 2,957 real patients, respectively. Moreover, starting from 187 MDS treated with luspatercept into a clinical trial, we generated a synthetic cohort that recapitulated all the clinical end points of the study. Finally, we developed a website to enable clinicians generating high-quality synthetic data from an existing biobank of real patients. CONCLUSION: Synthetic data mimic real clinical-genomic features and outcomes, and anonymize patient information. The implementation of this technology allows to increase the scientific use and value of real data, thus accelerating precision medicine in hematology and the conduction of clinical trials.

Real-World Validation of Molecular International Prognostic Scoring System for Myelodysplastic Syndromes.

PURPOSE: Myelodysplastic syndromes (MDS) are heterogeneous myeloid neoplasms in which a risk-adapted treatment strategy is needed. Recently, a new clinical-molecular prognostic model, the Molecular International Prognostic Scoring System (IPSS-M) was proposed to improve the prediction of clinical outcome of the currently available tool (Revised International Prognostic Scoring System [IPSS-R]). We aimed to provide an extensive validation of IPSS-M. METHODS: A total of 2,876 patients with primary MDS from the GenoMed4All consortium were retrospectively analyzed. RESULTS: IPSS-M improved prognostic discrimination across all clinical end points with respect to IPSS-R (concordance was 0.81 v 0.74 for overall survival and 0.89 v 0.76 for leukemia-free survival, respectively). This was true even in those patients without detectable gene mutations. Compared with the IPSS-R based stratification, the IPSS-M risk group changed in 46% of patients (23.6% and 22.4% of subjects were upstaged and downstaged, respectively).In patients treated with hematopoietic stem cell transplantation (HSCT), IPSS-M significantly improved the prediction of the risk of disease relapse and the probability of post-transplantation survival versus IPSS-R (concordance was 0.76 v 0.60 for overall survival and 0.89 v 0.70 for probability of relapse, respectively). In high-risk patients treated with hypomethylating agents (HMA), IPSS-M failed to stratify individual probability of response; response duration and probability of survival were inversely related to IPSS-M risk.Finally, we tested the accuracy in predicting IPSS-M when molecular information was missed and we defined a minimum set of 15 relevant genes associated with high performance of the score. CONCLUSION: IPSS-M improves MDS prognostication and might result in a more effective selection of candidates to HSCT. Additional factors other than gene mutations can be involved in determining HMA sensitivity. The definition of a minimum set of relevant genes may facilitate the clinical implementation of the score.

OVOL2 impairs RHO GTPase signaling to restrain mitosis and aggressiveness of Anaplastic Thyroid Cancer.

BACKGROUND: Anaplastic Thyroid Cancer (ATC) is an undifferentiated and aggressive tumor that often originates from well-Differentiated Thyroid Carcinoma (DTC) through a trans-differentiation process. Epithelial-to-Mesenchymal Transition (EMT) is recognized as one of the major players of this process. OVOL2 is a transcription factor (TF) that promotes epithelial differentiation and restrains EMT during embryonic development. OVOL2 loss in some types of cancers is linked to aggressiveness and poor prognosis. Here, we aim to clarify the unexplored role of OVOL2 in ATC. METHODS: Gene expression analysis in thyroid cancer patients and cell lines showed that OVOL2 is mainly associated with epithelial features and its expression is deeply impaired in ATC. To assess OVOL2 function, we established an OVOL2-overexpression model in ATC cell lines and evaluated its effects by analyzing gene expression, proliferation, invasion and migration abilities, cell cycle, specific protein localization through immunofluorescence staining. RNA-seq profiling showed that OVOL2 controls a complex network of genes converging on cell cycle and mitosis regulation and Chromatin Immunoprecipitation identified new OVOL2 target genes. RESULTS: Coherently with its reported function, OVOL2 re-expression restrained EMT and aggressiveness in ATC cells. Unexpectedly, we observed that it caused G2/M block, a consequent reduction in cell proliferation and an increase in cell death. This phenotype was associated to generalized abnormalities in the mitotic spindle structure and cytoskeletal organization. By RNA-seq experiments, we showed that many pathways related to cytoskeleton and migration, cell cycle and mitosis are profoundly affected by OVOL2 expression, in particular the RHO-GTPase pathway resulted as the most interesting. We demonstrated that RHO GTPase pathway is the central hub of OVOL2-mediated program in ATC and that OVOL2 transcriptionally inhibits RhoU and RhoJ. Silencing of RhoU recapitulated the OVOL2-driven phenotype pointing to this protein as a crucial target of OVOL2 in ATC. CONCLUSIONS: Collectively, these data describe the role of OVOL2 in ATC and uncover a novel function of this TF in inhibiting the RHO GTPase pathway interlacing its effects on EMT, cytoskeleton dynamics and mitosis.

KAP1 is a new non-genetic vulnerability of malignant pleural mesothelioma (MPM).

Malignant pleural mesothelioma (MPM) is a rare and incurable cancer, which incidence is increasing in many countries. MPM escapes the classical genetic model of cancer evolution, lacking a distinctive genetic fingerprint. Omics profiling revealed extensive heterogeneity failing to identify major vulnerabilities and restraining development of MPM-oriented therapies. Here, we performed a multilayered analysis based on a functional genome-wide CRISPR/Cas9 screening integrated with patients molecular and clinical data, to identify new non-genetic vulnerabilities of MPM. We identified a core of 18 functionally-related genes as essential for MPM cells. The chromatin reader KAP1 emerged as a dependency of MPM. We showed that KAP1 supports cell growth by orchestrating the expression of a G2/M-specific program, ensuring mitosis correct execution. Targeting KAP1 transcriptional function, by using CDK9 inhibitors resulted in a dramatic loss of MPM cells viability and shutdown of the KAP1-mediated program. Validation analysis on two independent MPM-patients sets, including a consecutive, retrospective cohort of 97 MPM, confirmed KAP1 as new non-genetic dependency of MPM and proved the association of its dependent gene program with reduced patients' survival probability. Overall these data: provided new insights into the biology of MPM delineating KAP1 and its target genes as building blocks of its clinical aggressiveness.

Tumor-associated macrophages and risk of recurrence in stage III colorectal cancer.

Tumor-associated macrophages (TAMs) have a unique favorable effect on the prognosis of colorectal cancer (CRC), although their association with stage-specific outcomes remains unclear. We assessed the densities of CD68+ and CD163+ TAMs at the invasive front of resected CRC stage III CRC from 236 patients, 165 of whom received post-surgical FOLFOX treatment, and their relationship with disease-free survival (DFS). Associations between macrophage mRNAs and clinical outcome were investigated in silico in 59 stage III CRC and FOLFOX-treated patients from The Cancer Genome Atlas (TCGA). Biological interactions of SW480 and HT29 cells and macrophages with FOLFOX were tested in co-culture models. Low TAM densities were associated with shorter DFS among patients receiving FOLFOX (CD68+ , p = 0.0001; CD163+ , p = 0.0008) but not among those who were untreated. By multivariate Cox analysis, only low TAM (CD68+ , p = 0.001; CD163+ , p = 0.002) and nodal status (CD68+ , p = 0.009; CD163+ , p = 0.007) maintained an independent predictive value. In the TCGA cohort, high CD68 mRNA levels were associated with better outcome (p = 0.02). Macrophages enhanced FOLFOX cytotoxicity on CRC cells (p < 0.01), and drugs oriented macrophage polarization from M2- to M1-phenotype. Low TAM densities identify stage III CRC patients at higher risk of recurrence after adjuvant therapy, and macrophages can augment the chemo-sensitivity of micro-metastases.

The DNA-helicase HELLS drives ALK- ALCL proliferation by the transcriptional control of a cytokinesis-related program.

Deregulation of chromatin modifiers, including DNA helicases, is emerging as one of the mechanisms underlying the transformation of anaplastic lymphoma kinase negative (ALK-) anaplastic large cell lymphoma (ALCL). We recently identified the DNA-helicase HELLS as central for proficient ALK-ALCL proliferation and progression. Here we assessed in detail its function by performing RNA-sequencing profiling coupled with bioinformatic prediction to identify HELLS targets and transcriptional cooperators. We demonstrated that HELLS, together with the transcription factor YY1, contributes to an appropriate cytokinesis via the transcriptional regulation of genes involved in cleavage furrow regulation. Binding target promoters, HELLS primes YY1 recruitment and transcriptional activation of cytoskeleton genes including the small GTPases RhoA and RhoU and their effector kinase Pak2. Single or multiple knockdowns of these genes reveal that RhoA and RhoU mediate HELLS effects on cell proliferation and cell division of ALK-ALCLs. Collectively, our work demonstrates the transcriptional role of HELLS in orchestrating a complex transcriptional program sustaining neoplastic features of ALK-ALCL.

Linc00941 Is a Novel Transforming Growth Factor ß Target That Primes Papillary Thyroid Cancer Metastatic Behavior by Regulating the Expression of Cadherin 6.

Background: Papillary thyroid cancers (PTCs) are common, usually indolent malignancies. Still, a small but significant percentage of patients have aggressive tumors and develop distant metastases leading to death. Currently, it is not possible to discriminate aggressive lesions due to lack of prognostic markers. Long noncoding RNAs (lncRNAs), which are selectively expressed in a context-dependent manner, are expected to represent a new landscape to search for molecular discriminants. Transforming growth factor ß (TGFß) is a multifunctional cytokine that fosters epithelial-to-mesenchymal transition and metastatic spreading. In PTCs, it triggers the expression of the metastatic marker Cadherin 6 (CDH6). Here, we investigated the TGFß-dependent lncRNAs that may cooperate to potentiate PTC aggressiveness. Methods: We used a genome-wide approach to map enhancer (ENH)-associated lncRNAs under TGFß control. Linc00941 was selected and validated using functional in vitro assays. A combined approach using bioinformatic analyses of the thyroid cancer (THCA)-the cancer genome atlas (TCGA) dataset and RNA-seq analysis was used to identify the processes in which linc00941 was involved in and the genes under its regulation. Correlation with clinical data was performed to evaluate the potential of this lncRNA and its targets as prognostic markers in THCA. Results: Linc00941 was identified as transcribed starting from one of the TGFß-induced ENHs. Linc00941 expression was significantly higher in aggressive cancer both in the TCGA dataset and in a separate validation cohort from our institution. Loss of function assays for linc00941 showed that it promotes response to stimuli and invasiveness while restraining proliferation in PTC cells, a typical phenotype of metastatic cells. From the integration of TCGA data and linc00941 knockdown RNA-seq profiling, we identified 77 genes under the regulation of this lncRNA. Among these, we found the prometastatic gene CDH6. Linc00941 knockdown partially recapitulates the effects observed upon CDH6 silencing, promoting cell cytoskeleton and membrane adhesions rearrangements and autophagy. The combined expression of CDH6 and linc00941 is a distinctive feature of highly aggressive PTC lesions. Conclusions: Our data provide new insights into the biology driving metastasis in PTCs and highlight how lncRNAs cooperate with coding transcripts to sustain these processes.