Recent Advances in Immune-Based Therapies for Acute Myeloid Leukemia.

Despite advancements, acute myeloid leukemia (AML) remains unconquered by current therapies. Evidence of immune evasion during AML progression, such as HLA loss and T-cell exhaustion, suggests that antileukemic immune responses contribute to disease control and could be harnessed by immunotherapy. In this review, we discuss a spectrum of AML immunotherapy targets, encompassing cancer cell-intrinsic and surface antigens as well as targeting in the leukemic milieu, and how they can be tailored for personalized approaches. These targets are overviewed across major immunotherapy modalities applied to AML: immune checkpoint inhibitors, antibody-drug conjugates, therapeutic vaccines, bispecific/trispecific antibodies, and chimeric antigen receptor (CAR)-T and CAR-NK cells. Significance: Immune therapies in AML treatment show evolving promise. Ongoing research aims to customize approaches for varied patient profiles and clinical scenarios. This review covers immune surveillance mechanisms, therapy options like checkpoint inhibitors, antibodies, CAR-T/NK cells, and vaccines, as well as resistance mechanisms and microenvironment considerations.

From Cell Populations to Molecular Complexes: Multiplexed Multimodal Microscopy to Explore p53-53BP1 Molecular Interaction.

Surpassing the diffraction barrier revolutionized modern fluorescence microscopy. However, intrinsic limitations in statistical sampling, the number of simultaneously analyzable channels, hardware requirements, and sample preparation procedures still represent an obstacle to its widespread diffusion in applicative biomedical research. Here, we present a novel pipeline based on automated multimodal microscopy and super-resolution techniques employing easily available materials and instruments and completed with open-source image-analysis software developed in our laboratory. The results show the potential impact of single-molecule localization microscopy (SMLM) on the study of biomolecules' interactions and the localization of macromolecular complexes. As a demonstrative application, we explored the basis of p53-53BP1 interactions, showing the formation of a putative macromolecular complex between the two proteins and the basal transcription machinery in situ, thus providing visual proof of the direct role of 53BP1 in sustaining p53 transactivation function. Moreover, high-content SMLM provided evidence of the presence of a 53BP1 complex on the cell cytoskeleton and in the mitochondrial space, thus suggesting the existence of novel alternative 53BP1 functions to support p53 activity.

EU-LIFE charter of independent life science research institutes.

The diverse range of organizations contributing to the global research ecosystem is believed to enhance the overall quality and resilience of its output. Mid-sized autonomous research institutes, distinct from universities, play a crucial role in this landscape. They often lead the way in new research fields and experimental methods, including those in social and organizational domains, which are vital for driving innovation. The EU-LIFE alliance was established with the goal of fostering excellence by developing and disseminating best practices among European biomedical research institutes. As directors of the 15 EU-LIFE institutes, we have spent a decade comparing and refining our processes. Now, we are eager to share the insights we've gained. To this end, we have crafted this Charter, outlining 10 principles we deem essential for research institutes to flourish and achieve ground-breaking discoveries. These principles, detailed in the Charter, encompass excellence, independence, training, internationality and inclusivity, mission focus, technological advancement, administrative innovation, cooperation, societal impact, and public engagement. Our aim is to inspire the establishment of new institutes that adhere to these principles and to raise awareness about their significance. We are convinced that they should be viewed a crucial component of any national and international innovation strategies.

High-sensitivity analysis of clonal hematopoiesis reveals increased clonal complexity of potential-driver mutations in severe COVID-19 patients.

Whether Clonal Hematopoiesis (CH) represents a risk factor for severity of the COVID-19 disease remains a controversial issue. We report the first high- sensitivity analysis of CH in COVID-19 patients (threshold of detection at 0.5% vs 1 or 2% in previous studies). We analyzed 24 patients admitted to ICU for COVID-19 (COV-ICU) and 19 controls, including healthy subjects and asymptomatic SARS-CoV2-positive individuals. Despite the significantly higher numbers of CH mutations identified (80% mutations with <2% variant allele frequency, VAF), we did not find significant differences between COV-ICU patients and controls in the prevalence of CH or in the numbers, VAF or functional categories of the mutated genes, suggesting that CH is not overrepresented in patients with COVID-19. However, when considering potential drivers CH mutations (CH-PD), COV-ICU patients showed higher clonal complexity, in terms of both mutation numbers and VAF, and enrichment of variants reported in myeloid neoplasms. However, we did not score an impact of increased CH-PD on patient survival or clinical parameters associated with inflammation. These data suggest that COVID-19 influence the clonal composition of the peripheral blood and call for further investigations addressing the potential long-term clinical impact of CH on people experiencing severe COVID-19. We acknowledge that it will indispensable to perform further studies on larger patient cohorts in order to validate and generalize our conclusions. Moreover, we performed CH analysis at a single time point. It will be necessary to consider longitudinal approaches with long periods of follow-up in order to assess if the COVID-19 disease could have an impact on the evolution of CH and long-term consequences in patients that experienced severe COVID-19.

UNCAN.eu: Toward a European Federated Cancer Research Data Hub.

To enable a collective effort that generates a new level of UNderstanding CANcer (UNCAN.eu) [Cancer Discov (2022) 12 (11): OF1], the European Union supports the creation of a sustainable platform that connects cancer research across Member States. A workshop hosted in Heidelberg gathered European cancer experts to identify ongoing initiatives that may contribute to building this platform and discuss the governance and long-term evolution of a European Federated Cancer Data Hub.

Caloric restriction leads to druggable LSD1-dependent cancer stem cells expansion.

Caloric Restriction (CR) has established anti-cancer effects, but its clinical relevance and molecular mechanism remain largely undefined. Here, we investigate CR's impact on several mouse models of Acute Myeloid Leukemias, including Acute Promyelocytic Leukemia, a subtype strongly affected by obesity. After an initial marked anti-tumor effect, lethal disease invariably re-emerges. Initially, CR leads to cell-cycle restriction, apoptosis, and inhibition of TOR and insulin/IGF1 signaling. The relapse, instead, is associated with the non-genetic selection of Leukemia Initiating Cells and the downregulation of double-stranded RNA (dsRNA) sensing and Interferon (IFN) signaling genes. The CR-induced adaptive phenotype is highly sensitive to pharmacological or genetic ablation of LSD1, a lysine demethylase regulating both stem cells and dsRNA/ IFN signaling. CR + LSD1 inhibition leads to the re-activation of dsRNA/IFN signaling, massive RNASEL-dependent apoptosis, and complete leukemia eradication in ~90% of mice. Importantly, CR-LSD1 interaction can be modeled in vivo and in vitro by combining LSD1 ablation with pharmacological inhibitors of insulin/IGF1 or dual PI3K/MEK blockade. Mechanistically, insulin/IGF1 inhibition sensitizes blasts to LSD1-induced death by inhibiting the anti-apoptotic factor CFLAR. CR and LSD1 inhibition also synergize in patient-derived AML and triple-negative breast cancer xenografts. Our data provide a rationale for epi-metabolic pharmacologic combinations across multiple tumors.

Comprehensive genomic profiling on metastatic Melanoma: results from a network screening from 7 Italian Cancer Centres.

BACKGROUND: The current therapeutic algorithm for Advanced Stage Melanoma comprises of alternating lines of Targeted and Immuno-therapy, mostly via Immune-Checkpoint blockade. While Comprehensive Genomic Profiling of solid tumours has been approved as a companion diagnostic, still no approved predictive biomarkers are available for Melanoma aside from BRAF mutations and the controversial Tumor Mutational Burden. This study presents the results of a Multi-Centre Observational Clinical Trial of Comprehensive Genomic Profiling on Target and Immuno-therapy treated advanced Melanoma. METHODS: 82 samples, collected from 7 Italian Cancer Centres of FFPE-archived Metastatic Melanoma and matched blood were sequenced via a custom-made 184-gene amplicon-based NGS panel. Sequencing and bioinformatics analysis was performed at a central hub. Primary analysis was carried out via the Ion Reporter framework. Secondary analysis and Machine Learning modelling comprising of uni and multivariate, COX/Lasso combination, and Random Forest, was implemented via custom R/Python scripting. RESULTS: The genomics landscape of the ACC-mela cohort is comparable at the somatic level for Single Nucleotide Variants and INDELs aside a few gene targets. All the clinically relevant targets such as BRAF and NRAS have a comparable distribution thus suggesting the value of larger scale sequencing in melanoma. No comparability is reached at the CNV level due to biotechnological biases and cohort numerosity. Tumour Mutational Burden is slightly higher in median for Complete Responders but fails to achieve statistical significance in Kaplan-Meier survival analysis via several thresholding strategies. Mutations on PDGFRB, NOTCH3 and RET were shown to have a positive effect on Immune-checkpoint treatment Overall and Disease-Free Survival, while variants in NOTCH4 were found to be detrimental for both endpoints. CONCLUSIONS: The results presented in this study show the value and the challenge of a genomics-driven network trial. The data can be also a valuable resource as a validation cohort for Immunotherapy and Target therapy genomic biomarker research.

High-Fat Diet Promotes Acute Promyelocytic Leukemia through PPARδ-Enhanced Self-renewal of Preleukemic Progenitors.

Risk and outcome of acute promyelocytic leukemia (APL) are particularly worsened in obese-overweight individuals, but the underlying molecular mechanism is unknown. In established mouse APL models (Ctsg-PML::RARA), we confirmed that obesity induced by high-fat diet (HFD) enhances leukemogenesis by increasing penetrance and shortening latency, providing an ideal model to investigate obesity-induced molecular events in the preleukemic phase. Surprisingly, despite increasing DNA damage in hematopoietic stem cells (HSC), HFD only minimally increased mutational load, with no relevant impact on known cancer-driving genes. HFD expanded and enhanced self-renewal of hematopoietic progenitor cells (HPC), with concomitant reduction in long-term HSCs. Importantly, linoleic acid, abundant in HFD, fully recapitulates the effect of HFD on the self-renewal of PML::RARA HPCs through activation of peroxisome proliferator-activated receptor delta, a central regulator of fatty acid metabolism. Our findings inform dietary/pharmacologic interventions to counteract obesity-associated cancers and suggest that nongenetic factors play a key role. PREVENTION RELEVANCE: Our work informs interventions aimed at counteracting the cancer-promoting effect of obesity. On the basis of our study, individuals with a history of chronic obesity may still significantly reduce their risk by switching to a healthier lifestyle, a concept supported by evidence in solid tumors but not yet in hematologic malignancies. See related Spotlight, p. 47.

Inhibition of the lysine demethylase LSD1 modulates the balance between inflammatory and antiviral responses against coronaviruses.

Innate immune responses to coronavirus infections are highly cell specific. Tissue-resident macrophages, which are infected by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in patients but are inconsistently infected in vitro, exert critical but conflicting effects by secreting both antiviral type I interferons (IFNs) and tissue-damaging inflammatory cytokines. Steroids, the only class of host-targeting drugs approved for the treatment of coronavirus disease 2019 (COVID-19), indiscriminately suppress both responses, possibly impairing viral clearance. Here, we established in vitro cell culture systems that enabled us to separately investigate the cell-intrinsic and cell-extrinsic proinflammatory and antiviral activities of mouse macrophages infected with the prototypical murine coronavirus MHV-A59. We showed that the nuclear factor κB-dependent inflammatory response to viral infection was selectively inhibited by loss of the lysine demethylase LSD1, which was previously implicated in innate immune responses to cancer, with negligible effects on the antiviral IFN response. LSD1 ablation also enhanced an IFN-independent antiviral response, blocking viral egress through the lysosomal pathway. The macrophage-intrinsic antiviral and anti-inflammatory activity of Lsd1 inhibition was confirmed in vitro and in a humanized mouse model of SARS-CoV-2 infection. These results suggest that LSD1 controls innate immune responses against coronaviruses at multiple levels and provide a mechanistic rationale for potentially repurposing LSD1 inhibitors for COVID-19 treatment.

GASOLINE: detecting germline and somatic structural variants from long-reads data.

Long-read sequencing allows analyses of single nucleic-acid molecules and produces sequences in the order of tens to hundreds kilobases. Its application to whole-genome analyses allows identification of complex genomic structural-variants (SVs) with unprecedented resolution. SV identification, however, requires complex computational methods, based on either read-depth or intra- and inter-alignment signatures approaches, which are limited by size or type of SVs. Moreover, most currently available tools only detect germline variants, thus requiring separate computation of sample pairs for comparative analyses. To overcome these limits, we developed a novel tool (Germline And SOmatic structuraL varIants detectioN and gEnotyping; GASOLINE) that groups SV signatures using a sophisticated clustering procedure based on a modified reciprocal overlap criterion, and is designed to identify germline SVs, from single samples, and somatic SVs from paired test and control samples. GASOLINE is a collection of Perl, R and Fortran codes, it analyzes aligned data in BAM format and produces VCF files with statistically significant somatic SVs. Germline or somatic analysis of 30[Formula: see text] sequencing coverage experiments requires 4-5 h with 20 threads. GASOLINE outperformed currently available methods in the detection of both germline and somatic SVs in synthetic and real long-reads datasets. Notably, when applied on a pair of metastatic melanoma and matched-normal sample, GASOLINE identified five genuine somatic SVs that were missed using five different sequencing technologies and state-of-the art SV calling approaches. Thus, GASOLINE identifies germline and somatic SVs with unprecedented accuracy and resolution, outperforming currently available state-of-the-art WGS long-reads computational methods.

A Rare Subset of Primary Tumor Cells with Concomitant Hyperactivation of Extracellular Matrix Remodeling and dsRNA-IFN1 Signaling Metastasizes in Breast Cancer.

Metastatic breast cancer has a poor prognosis and is largely considered incurable. A better understanding of the molecular determinants of breast cancer metastasis could facilitate development of improved prevention and treatment strategies. We used lentiviral barcoding coupled to single-cell RNA sequencing to trace clonal and transcriptional evolution during breast cancer metastasis and showed that metastases derive from rare prometastatic clones that are underrepresented in primary tumors. Both low clonal fitness and high metastatic potential were independent of clonal origin. Differential expression and classification analyses revealed that the prometastatic phenotype was acquired by rare cells characterized by the concomitant hyperactivation of extracellular matrix remodeling and dsRNA-IFN signaling pathways. Notably, genetic silencing of key genes in these pathways (KCNQ1OT1 or IFI6, respectively) significantly impaired migration in vitro and metastasis in vivo, with marginal effects on cell proliferation and tumor growth. Gene expression signatures derived from the identified prometastatic genes predict metastatic progression in patients with breast cancer, independently of known prognostic factors. This study elucidates previously unknown mechanisms of breast cancer metastasis and provides prognostic predictors and therapeutic targets for metastasis prevention. SIGNIFICANCE: Transcriptional lineage tracing coupled with single-cell transcriptomics defined the transcriptional programs underlying metastatic progression in breast cancer, identifying prognostic signatures and prevention strategies.

Barcode demultiplexing of nanopore sequencing raw signals by unsupervised machine learning.

Introduction: Oxford Nanopore Technologies (ONT) is a third generation sequencing approach that allows the analysis of individual, full-length nucleic acids. ONT records the alterations of an ionic current flowing across a nano-scaled pore while a DNA or RNA strand is threading through the pore. Basecalling methods are then leveraged to translate the recorded signal back to the nucleic acid sequence. However, basecall generally introduces errors that hinder the process of barcode demultiplexing, a pivotal task in single-cell RNA sequencing that allows for separating the sequenced transcripts on the basis of their cell of origin. Methods: To solve this issue, we present a novel framework, called UNPLEX, designed to tackle the barcode demultiplexing problem by operating directly on the recorded signals. UNPLEX combines two unsupervised machine learning methods: autoencoders and self-organizing maps (SOM). The autoencoders extract compact, latent representations of the recorded signals that are then clustered by the SOM. Results and Discussion: Our results, obtained on two datasets composed of in silico generated ONT-like signals, show that UNPLEX represents a promising starting point for the development of effective tools to cluster the signals corresponding to the same cell.

High-resolution Nanopore methylome-maps reveal random hyper-methylation at CpG-poor regions as driver of chemoresistance in leukemias.

Aberrant DNA methylation at CpG dinucleotides is a cancer hallmark that is associated with the emergence of resistance to anti cancer treatment, though molecular mechanisms and biological significance remain elusive. Genome scale methylation maps by currently used methods are based on chemical modification of DNA and are best suited for analyses of methylation at CpG rich regions (CpG islands). We report the first high coverage whole-genome map in cancer using the long read nanopore technology, which allows simultaneous DNA-sequence and -methylation analyses on native DNA. We analyzed clonal epigenomic/genomic evolution in Acute Myeloid Leukemias (AMLs) at diagnosis and relapse, after chemotherapy. Long read sequencing coupled to a novel computational method allowed definition of differential methylation at unprecedented resolution, and showed that the relapse methylome is characterized by hypermethylation at both CpG islands and sparse CpGs regions. Most differentially methylated genes, however, were not differentially expressed nor enriched for chemoresistance genes. A small fraction of under-expressed and hyper-methylated genes at sparse CpGs, in the gene body, was significantly enriched in transcription factors (TFs). Remarkably, these few TFs supported large gene-regulatory networks including 50% of all differentially expressed genes in the relapsed AMLs and highly-enriched in chemoresistance genes. Notably, hypermethylated regions at sparse CpGs were poorly conserved in the relapsed AMLs, under-represented at their genomic positions and showed higher methylation entropy, as compared to CpG islands. Analyses of available datasets confirmed TF binding to their target genes and conservation of the same gene-regulatory networks in large patient cohorts. Relapsed AMLs carried few patient specific structural variants and DNA mutations, apparently not involved in drug resistance. Thus, drug resistance in AMLs can be mainly ascribed to the selection of random epigenetic alterations at sparse CpGs of a few transcription factors, which then induce reprogramming of the relapsing phenotype, independently of clonal genomic evolution.

Correlative Multi-Modal Microscopy: A Novel Pipeline for Optimizing Fluorescence Microscopy Resolutions in Biological Applications.

The modern fluorescence microscope is the convergence point of technologies with different performances in terms of statistical sampling, number of simultaneously analyzed signals, and spatial resolution. However, the best results are usually obtained by maximizing only one of these parameters and finding a compromise for the others, a limitation that can become particularly significant when applied to cell biology and that can reduce the spreading of novel optical microscopy tools among research laboratories. Super resolution microscopy and, in particular, molecular localization-based approaches provide a spatial resolution and a molecular localization precision able to explore the scale of macromolecular complexes in situ. However, its use is limited to restricted regions, and consequently few cells, and frequently no more than one or two parameters. Correlative microscopy, obtained by the fusion of different optical technologies, can consequently surpass this barrier by merging results from different spatial scales. We discuss here the use of an acquisition and analysis correlative microscopy pipeline to obtain high statistical sampling, high content, and maximum spatial resolution by combining widefield, confocal, and molecular localization microscopy.

Alterations induced by the PML-RARα oncogene revealed by image cross correlation spectroscopy.

The molecular mechanisms that underlie oncogene-induced genomic damage are still poorly understood. To understand how oncogenes affect chromatin architecture, it is important to visualize fundamental processes such as DNA replication and transcription in intact nuclei and quantify the alterations of their spatiotemporal organization induced by oncogenes. Here, we apply superresolution microscopy in combination with image cross correlation spectroscopy to the U937-PR9 cell line, an in vitro model of acute promyelocytic leukemia that allows us to activate the expression of the PML-RARα oncogene and analyze its effects on the spatiotemporal organization of functional nuclear processes. More specifically, we perform Tau-stimulated emission depletion imaging, a superresolution technique based on the concept of separation of photons by lifetime tuning. Tau-stimulated emission depletion imaging is combined with a robust image analysis protocol that quickly produces a value of colocalization fraction on several hundreds of single cells and allows observation of cell-to-cell variability. Upon activation of the oncogene, we detect a significant increase in the fraction of transcription sites colocalized with PML/PML-RARα. This increase of colocalization can be ascribed to oncogene-induced disruption of physiological PML bodies and the abnormal occurrence of a relatively large number of PML-RARα microspeckles. We also detect a significant cell-to-cell variability of this increase of colocalization, which can be ascribed, at least in part, to a heterogeneous response of the cells to the activation of the oncogene. These results prove that our method efficiently reveals oncogene-induced alterations in the spatial organization of nuclear processes and suggest that the abnormal localization of PML-RARα could interfere with the transcription machinery, potentially leading to DNA damage and genomic instability.

Automated multimodal fluorescence microscopy for hyperplex spatial-proteomics: Coupling microfluidic-based immunofluorescence to high resolution, high sensitivity, three-dimensional analysis of histological slides.

In situ multiplexing analysis and in situ transcriptomics are now providing revolutionary tools to achieve the comprehension of the molecular basis of cancer and to progress towards personalized medicine to fight the disease. The complexity of these tasks requires a continuous interplay among different technologies during all the phases of the experimental procedures. New tools are thus needed and their characterization in terms of performances and limits is mandatory to reach the best resolution and sensitivity. We propose here a new experimental pipeline to obtain an optimized costs-to-benefits ratio thanks to the alternate employment of automated and manual procedures during all the phases of a multiplexing experiment from sample preparation to image collection and analysis. A comparison between ultra-fast and automated immunofluorescence staining and standard staining protocols has been carried out to compare the performances in terms of antigen saturation, background, signal-to-noise ratio and total duration. We then developed specific computational tools to collect data by automated analysis-driven fluorescence microscopy. Computer assisted selection of targeted areas with variable magnification and resolution allows employing confocal microscopy for a 3D high resolution analysis. Spatial resolution and sensitivity were thus maximized in a framework where the amount of stored data and the total requested time for the procedure were optimized and reduced with respect to a standard experimental approach.

Corrigendum: Single-cell technologies to decipher the immune microenvironment in myeloid neoplasms: Perspectives and opportunities.

[This corrects the article DOI: 10.3389/fonc.2021.796477.].

From Double-Strand Break Recognition to Cell-Cycle Checkpoint Activation: High Content and Resolution Image Cytometry Unmasks 53BP1 Multiple Roles in DNA Damage Response and p53 Action.

53BP1 protein has been isolated in-vitro as a putative p53 interactor. From the discovery of its engagement in the DNA-Damage Response (DDR), its role in sustaining the activity of the p53-regulated transcriptional program has been frequently under-evaluated, even in the case of a specific response to numerous DNA Double-Strand Breaks (DSBs), i.e., exposure to ionizing radiation. The localization of 53BP1 protein constitutes a key to decipher the network of activities exerted in response to stress. We present here an automated-microscopy for image cytometry protocol to analyze the evolution of the DDR, and to demonstrate how 53BP1 moved from damaged sites, where the well-known interaction with the DSB marker γH2A.X takes place, to nucleoplasm, interacting with p53, and enhancing the transcriptional regulation of the guardian of the genome protein. Molecular interactions have been quantitatively described and spatiotemporally localized at the highest spatial resolution by a simultaneous analysis of the impairment of the cell-cycle progression. Thanks to the high statistical sampling of the presented protocol, we provide a detailed quantitative description of the molecular events following the DSBs formation. Single-Molecule Localization Microscopy (SMLM) Analysis finally confirmed the p53-53BP1 interaction on the tens of nanometers scale during the distinct phases of the response.