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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. Caloric restriction (CR) has been demonstrated to have a role in tumour growth and therapy response but its effects on cancer stem cells are less known. Here, in Acute Promyelocytic Leukemia, the authors show that despite initial anti-tumour effect, CR drives the selection of leukaemia-initiating cells resulting in relapse which could be prevented by ablation of LSD1. [ABSTRACT FROM AUTHOR]

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 × 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. [ABSTRACT FROM AUTHOR]

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. DNA methylation patterns from Acute Myeloid Leukemia patients highlight sparse CpG regions as drivers for chemoresistance. [ABSTRACT FROM AUTHOR]