Macrophage migration inhibitory factor is overproduced through EGR1 in TET2low resting monocytes.

Somatic mutation in TET2 gene is one of the most common clonal genetic events detected in age-related clonal hematopoiesis as well as in chronic myelomonocytic leukemia (CMML). In addition to being a pre-malignant state, TET2 mutated clones are associated with an increased risk of death from cardiovascular disease, which could involve cytokine/chemokine overproduction by monocytic cells. Here, we show in mice and in human cells that, in the absence of any inflammatory challenge, TET2 downregulation promotes the production of MIF (macrophage migration inhibitory factor), a pivotal mediator of atherosclerotic lesion formation. In healthy monocytes, TET2 is recruited to MIF promoter and interacts with the transcription factor EGR1 and histone deacetylases. Disruption of these interactions as a consequence of TET2-decreased expression favors EGR1-driven transcription of MIF gene and its secretion. MIF favors monocytic differentiation of myeloid progenitors. These results designate MIF as a chronically overproduced chemokine and a potential therapeutic target in patients with clonal TET2 downregulation in myeloid cells.

Integrating deep learning CT-scan model, biological and clinical variables to predict severity of COVID-19 patients.

The SARS-COV-2 pandemic has put pressure on intensive care units, so that identifying predictors of disease severity is a priority. We collect 58 clinical and biological variables, and chest CT scan data, from 1003 coronavirus-infected patients from two French hospitals. We train a deep learning model based on CT scans to predict severity. We then construct the multimodal AI-severity score that includes 5 clinical and biological variables (age, sex, oxygenation, urea, platelet) in addition to the deep learning model. We show that neural network analysis of CT-scans brings unique prognosis information, although it is correlated with other markers of severity (oxygenation, LDH, and CRP) explaining the measurable but limited 0.03 increase of AUC obtained when adding CT-scan information to clinical variables. Here, we show that when comparing AI-severity with 11 existing severity scores, we find significantly improved prognosis performance; AI-severity can therefore rapidly become a reference scoring approach.

A deep learning model to predict RNA-Seq expression of tumours from whole slide images.

Deep learning methods for digital pathology analysis are an effective way to address multiple clinical questions, from diagnosis to prediction of treatment outcomes. These methods have also been used to predict gene mutations from pathology images, but no comprehensive evaluation of their potential for extracting molecular features from histology slides has yet been performed. We show that HE2RNA, a model based on the integration of multiple data modes, can be trained to systematically predict RNA-Seq profiles from whole-slide images alone, without expert annotation. Through its interpretable design, HE2RNA provides virtual spatialization of gene expression, as validated by CD3- and CD20-staining on an independent dataset. The transcriptomic representation learned by HE2RNA can also be transferred on other datasets, even of small size, to increase prediction performance for specific molecular phenotypes. We illustrate the use of this approach in clinical diagnosis purposes such as the identification of tumors with microsatellite instability.

Predicting Survival After Hepatocellular Carcinoma Resection Using Deep Learning on Histological Slides.

BACKGROUND AND AIMS: Standardized and robust risk-stratification systems for patients with hepatocellular carcinoma (HCC) are required to improve therapeutic strategies and investigate the benefits of adjuvant systemic therapies after curative resection/ablation. APPROACH AND RESULTS: In this study, we used two deep-learning algorithms based on whole-slide digitized histological slides (whole-slide imaging; WSI) to build models for predicting survival of patients with HCC treated by surgical resection. Two independent series were investigated: a discovery set (Henri Mondor Hospital, n = 194) used to develop our algorithms and an independent validation set (The Cancer Genome Atlas [TCGA], n = 328). WSIs were first divided into small squares ("tiles"), and features were extracted with a pretrained convolutional neural network (preprocessing step). The first deep-learning-based algorithm ("SCHMOWDER") uses an attention mechanism on tumoral areas annotated by a pathologist whereas the second ("CHOWDER") does not require human expertise. In the discovery set, c-indices for survival prediction of SCHMOWDER and CHOWDER reached 0.78 and 0.75, respectively. Both models outperformed a composite score incorporating all baseline variables associated with survival. Prognostic value of the models was further validated in the TCGA data set, and, as observed in the discovery series, both models had a higher discriminatory power than a score combining all baseline variables associated with survival. Pathological review showed that the tumoral areas most predictive of poor survival were characterized by vascular spaces, the macrotrabecular architectural pattern, and a lack of immune infiltration. CONCLUSIONS: This study shows that artificial intelligence can help refine the prediction of HCC prognosis. It highlights the importance of pathologist/machine interactions for the construction of deep-learning algorithms that benefit from expert knowledge and allow a biological understanding of their output.

Deep learning-based classification of mesothelioma improves prediction of patient outcome.

Malignant mesothelioma (MM) is an aggressive cancer primarily diagnosed on the basis of histological criteria1. The 2015 World Health Organization classification subdivides mesothelioma tumors into three histological types: epithelioid, biphasic and sarcomatoid MM. MM is a highly complex and heterogeneous disease, rendering its diagnosis and histological typing difficult and leading to suboptimal patient care and decisions regarding treatment modalities2. Here we have developed a new approach-based on deep convolutional neural networks-called MesoNet to accurately predict the overall survival of mesothelioma patients from whole-slide digitized images, without any pathologist-provided locally annotated regions. We validated MesoNet on both an internal validation cohort from the French MESOBANK and an independent cohort from The Cancer Genome Atlas (TCGA). We also demonstrated that the model was more accurate in predicting patient survival than using current pathology practices. Furthermore, unlike classical black-box deep learning methods, MesoNet identified regions contributing to patient outcome prediction. Strikingly, we found that these regions are mainly located in the stroma and are histological features associated with inflammation, cellular diversity and vacuolization. These findings suggest that deep learning models can identify new features predictive of patient survival and potentially lead to new biomarker discoveries.

Unfolding the Role of Calreticulin in Myeloproliferative Neoplasm Pathogenesis.

In 2013, two seminal studies identified gain-of-function mutations in the Calreticulin (CALR) gene in a subset of JAK2/MPL-negative myeloproliferative neoplasm (MPN) patients. CALR is an endoplasmic reticulum (ER) chaperone protein that normally binds misfolded proteins in the ER and prevents their export to the Golgi and had never previously been reported mutated in cancer or to be associated with hematologic disorders. Further investigation determined that mutated CALR is able to achieve oncogenic transformation primarily through constitutive activation of the MPL-JAK-STAT signaling axis. Here we review our current understanding of the role of CALR mutations in MPN pathogenesis and how these insights can lead to innovative therapeutics approaches.

Targeting the CALR interactome in myeloproliferative neoplasms.

Mutations in the ER chaperone calreticulin (CALR) are common in myeloproliferative neoplasm (MPN) patients, activate the thrombopoietin receptor (MPL), and mediate constitutive JAK/STAT signaling. The mechanisms by which CALR mutations cause myeloid transformation are incompletely defined. We used mass spectrometry proteomics to identify CALR-mutant interacting proteins. Mutant CALR caused mislocalization of binding partners and increased recruitment of FLI1, ERP57, and CALR to the MPL promoter to enhance transcription. Consistent with a critical role for CALR-mediated JAK/STAT activation, we confirmed the efficacy of JAK2 inhibition on CALR-mutant cells in vitro and in vivo. Due to the altered interactome induced by CALR mutations, we hypothesized that CALR-mutant MPNs may be vulnerable to disruption of aberrant CALR protein complexes. A synthetic peptide designed to competitively inhibit the carboxy terminal of CALR specifically abrogated MPL/JAK/STAT signaling in cell lines and primary samples and improved the efficacy of JAK kinase inhibitors. These findings reveal what to our knowledge is a novel potential therapeutic approach for patients with CALR-mutant MPN.

Genetic and epigenetic evolution as a contributor to WT1-mutant leukemogenesis.

Genetic studies have identified recurrent somatic mutations in acute myeloid leukemia (AML) patients, including in the Wilms' tumor 1 (WT1) gene. The molecular mechanisms by which WT1 mutations contribute to leukemogenesis have not yet been fully elucidated. We investigated the role of Wt1 gene dosage in steady-state and pathologic hematopoiesis. Wt1 heterozygous loss enhanced stem cell self-renewal in an age-dependent manner, which increased stem cell function over time and resulted in age-dependent leukemic transformation. Wt1-haploinsufficient leukemias were characterized by progressive genetic and epigenetic alterations, including those in known leukemia-associated alleles, demonstrating a requirement for additional events to promote hematopoietic transformation. Consistent with this observation, we found that Wt1 depletion cooperates with Flt3-ITD mutation to induce fully penetrant AML. Our studies provide insight into mechanisms of Wt1-loss leukemogenesis and into the evolutionary events required to induce transformation of Wt1-haploinsufficient stem/progenitor cells.

Cooperative Epigenetic Remodeling by TET2 Loss and NRAS Mutation Drives Myeloid Transformation and MEK Inhibitor Sensitivity.

Mutations in epigenetic modifiers and signaling factors often co-occur in myeloid malignancies, including TET2 and NRAS mutations. Concurrent Tet2 loss and NrasG12D expression in hematopoietic cells induced myeloid transformation, with a fully penetrant, lethal chronic myelomonocytic leukemia (CMML), which was serially transplantable. Tet2 loss and Nras mutation cooperatively led to decrease in negative regulators of mitogen-activated protein kinase (MAPK) activation, including Spry2, thereby causing synergistic activation of MAPK signaling by epigenetic silencing. Tet2/Nras double-mutant leukemia showed preferential sensitivity to MAPK kinase (MEK) inhibition in both mouse model and patient samples. These data provide insights into how epigenetic and signaling mutations cooperate in myeloid transformation and provide a rationale for mechanism-based therapy in CMML patients with these high-risk genetic lesions.

Aid is a key regulator of myeloid/erythroid differentiation and DNA methylation in hematopoietic stem/progenitor cells.

Recent studies have reported that activation-induced cytidine deaminase (AID) and ten-eleven-translocation (TET) family members regulate active DNA demethylation. Genetic alterations of TET2 occur in myeloid malignancies, and hematopoietic-specific loss of Tet2 induces aberrant hematopoietic stem cell (HSC) self-renewal/differentiation, implicating TET2 as a master regulator of normal and malignant hematopoiesis. Despite the functional link between AID and TET in epigenetic gene regulation, the role of AID loss in hematopoiesis and myeloid transformation remains to be investigated. Here, we show that Aid loss in mice leads to expansion of myeloid cells and reduced erythroid progenitors resulting in anemia, with dysregulated expression of Cebpa and Gata1, myeloid/erythroid lineage-specific transcription factors. Consistent with data in the murine context, silencing of AID in human bone marrow cells skews differentiation toward myelomonocytic lineage. However, in contrast to Tet2 loss, Aid loss does not contribute to enhanced HSC self-renewal or cooperate with Flt3-ITD to induce myeloid transformation. Genome-wide transcription and differential methylation analysis uncover the critical role of Aid as a key epigenetic regulator. These results indicate that AID and TET2 share common effects on myeloid and erythroid lineage differentiation, however, their role is nonredundant in regulating HSC self-renewal and in myeloid transformation.

DNMT3A mutations promote anthracycline resistance in acute myeloid leukemia via impaired nucleosome remodeling.

Although the majority of patients with acute myeloid leukemia (AML) initially respond to chemotherapy, many of them subsequently relapse, and the mechanistic basis for AML persistence following chemotherapy has not been determined. Recurrent somatic mutations in DNA methyltransferase 3A (DNMT3A), most frequently at arginine 882 (DNMT3AR882), have been observed in AML and in individuals with clonal hematopoiesis in the absence of leukemic transformation. Patients with DNMT3AR882 AML have an inferior outcome when treated with standard-dose daunorubicin-based induction chemotherapy, suggesting that DNMT3AR882 cells persist and drive relapse. We found that Dnmt3a mutations induced hematopoietic stem cell expansion, cooperated with mutations in the FMS-like tyrosine kinase 3 gene (Flt3ITD) and the nucleophosmin gene (Npm1c) to induce AML in vivo, and promoted resistance to anthracycline chemotherapy. In patients with AML, the presence of DNMT3AR882 mutations predicts minimal residual disease, underscoring their role in AML chemoresistance. DNMT3AR882 cells showed impaired nucleosome eviction and chromatin remodeling in response to anthracycline treatment, which resulted from attenuated recruitment of histone chaperone SPT-16 following anthracycline exposure. This defect led to an inability to sense and repair DNA torsional stress, which resulted in increased mutagenesis. Our findings identify a crucial role for DNMT3AR882 mutations in driving AML chemoresistance and highlight the importance of chromatin remodeling in response to cytotoxic chemotherapy.

Genetic hierarchy and temporal variegation in the clonal history of acute myeloid leukaemia.

In acute myeloid leukaemia (AML) initiating pre-leukaemic lesions can be identified through three major hallmarks: their early occurrence in the clone, their persistence at relapse and their ability to initiate multilineage haematopoietic repopulation and leukaemia in vivo. Here we analyse the clonal composition of a series of AML through these characteristics. We find that not only DNMT3A mutations, but also TET2, ASXL1 mutations, core-binding factor and MLL translocations, as well as del(20q) mostly fulfil these criteria. When not eradicated by AML treatments, pre-leukaemic cells with these lesions can re-initiate the leukaemic process at various stages until relapse, with a time-dependent increase in clonal variegation. Based on the nature, order and association of lesions, we delineate recurrent genetic hierarchies of AML. Our data indicate that first lesions, variegation and treatment selection pressure govern the expansion and adaptive behaviour of the malignant clone, shaping AML in a time-dependent manner.

IDH1/2 mutations and BCL-2 dependence: an unexpected Chink in AML's armour.

There is a pressing need to develop novel, mechanism-based therapeutic approaches that can be used to improve therapies for genetically defined tumor subtypes. Chan and colleagues have demonstrated recently that BCL-2 inhibitors can target IDH1/2 mutant cancers through a mutant-specific dependency in metabolic regulation.

DNA hydroxymethylation profiling reveals that WT1 mutations result in loss of TET2 function in acute myeloid leukemia.

Somatic mutations in IDH1/IDH2 and TET2 result in impaired TET2-mediated conversion of 5-methylcytosine (5mC) to 5-hydroxymethylcytosine (5hmC). The observation that WT1 inactivating mutations anticorrelate with TET2/IDH1/IDH2 mutations in acute myeloid leukemia (AML) led us to hypothesize that WT1 mutations may impact TET2 function. WT1 mutant AML patients have reduced 5hmC levels similar to TET2/IDH1/IDH2 mutant AML. These mutations are characterized by convergent, site-specific alterations in DNA hydroxymethylation, which drive differential gene expression more than alterations in DNA promoter methylation. WT1 overexpression increases global levels of 5hmC, and WT1 silencing reduced 5hmC levels. WT1 physically interacts with TET2 and TET3, and WT1 loss of function results in a similar hematopoietic differentiation phenotype as observed with TET2 deficiency. These data provide a role for WT1 in regulating DNA hydroxymethylation and suggest that TET2 IDH1/IDH2 and WT1 mutations define an AML subtype defined by dysregulated DNA hydroxymethylation.

Identification of small molecule activators of BMP signaling.

Bone Morphogenetic Proteins (BMPs) are morphogens that play a major role in regulating development and homeostasis. Although BMPs are used for the treatment of bone and kidney disorders, their clinical use is limited due to the supra-physiological doses required for therapeutic efficacy causing severe side effects. Because recombinant BMPs are expensive to produce, small molecule activators of BMP signaling would be a cost-effective alternative with the added benefit of being potentially more easily deliverable. Here, we report our efforts to identify small molecule activators of BMP signaling. We have developed a cell-based assay to monitor BMP signaling by stably transfecting a BMP-responsive human cervical carcinoma cell line (C33A) with a reporter construct in which the expression of luciferase is driven by a multimerized BMP-responsive element from the Id1 promoter. A BMP-responsive clone C33A-2D2 was used to screen a bioactive library containing ∼5,600 small molecules. We identified four small molecules of the family of flavonoids all of which induced luciferase activity in a dose-dependent manner and ventralized zebrafish embryos. Two of the identified compounds induced Smad1, 5 phosphorylation (P-Smad), Id1 and Id2 expression in a dose-dependent manner demonstrating that our assays identified small molecule activators of BMP signaling.

Role of TET2 mutations in myeloproliferative neoplasms.

Recently, 5-hydroxymethylcytosine (5-hmC), the 6th base of DNA, was discovered as the product of the hydroxylation of 5-methylcytosine (5-mC) by the ten-eleven translocation (TET) oncogene family members. One of them, TET oncogene family member 2 (TET2), is mutated in a variety of myeloid malignancies, including in 15% of myeloproliferative neoplasms (MPNs). Recent studies tried to go further into the biological and epigenetic function of TET2 protein and 5-hmC marks in the pathogenesis of myeloid malignancies. Although its precise function remains partially unknown, TET2 appears to be an important regulator of hematopoietic stem cell biology. In both mouse and human cells, its inactivation leads to a dramatic deregulation of hematopoiesis that ultimately triggers blood malignancies. Understanding this leukemogenic process will provide tools to develop new epigenetic therapies against blood cancers.

Inhibition of TET2-mediated conversion of 5-methylcytosine to 5-hydroxymethylcytosine disturbs erythroid and granulomonocytic differentiation of human hematopoietic progenitors.

TET2 converts 5-methylcytosine to 5-hydroxymethylcytosine (5-hmC) in DNA and is frequently mutated in myeloid malignancies, including myeloproliferative neoplasms. Here we show that the level of 5-hmC is decreased in granulocyte DNA from myeloproliferative neoplasm patients with TET2 mutations compared with granulocyte DNA from healthy patients. Inhibition of TET2 by RNA interference decreases 5-hmC levels in both human leukemia cell lines and cord blood CD34(+) cells. These results confirm the enzymatic function of TET2 in human hematopoietic cells. Knockdown of TET2 in cord blood CD34(+) cells skews progenitor differentiation toward the granulomonocytic lineage at the expense of lymphoid and erythroid lineages. In addition, by monitoring in vitro granulomonocytic development we found a decreased granulocytic differentiation and an increase in monocytic cells. Our results indicate that TET2 disruption affects 5-hmC levels in human myeloid cells and participates in the pathogenesis of myeloid malignancies through the disturbance of myeloid differentiation.