Integrative single-cell expression and functional studies unravels a sensitization to cytarabine-based chemotherapy through HIF pathway inhibition in AML leukemia stem cells

Relapse remains a major challenge in the clinical management of acute myeloid leukemia (AML) and is driven by rare therapy-resistant leukemia stem cells (LSCs) that reside in specific bone marrow niches. Hypoxia signaling maintains cells in a quiescent and metabolically relaxed state, desensitizing them to chemotherapy. This suggests the hypothesis that hypoxia contributes to the chemoresistance of AML-LSCs and may represent a therapeutic target to sensitize AML-LSCs to chemotherapy. Here, we identify HIFhigh and HIFlow specific AML subgroups (inv(16)/t(8;21) and MLLr, respectively) and provide a comprehensive single-cell expression atlas of 119,000 AML cells and AML-LSCs in paired diagnostic-relapse samples from these molecular subgroups. The HIF/hypoxia pathway signature is attenuated in AML-LSCs compared with more differentiated AML cells but is more expressed than in healthy hematopoietic cells. Importantly, chemical inhibition of HIF cooperates with standard-of-care chemotherapy to impair AML growth and to substantially eliminate AML-LSCs in vitro and in vivo. These findings support the HIF pathway in the stem cell-driven drug resistance of AML and unravel avenues for combinatorial targeted and chemotherapy-based approaches to specifically eliminate AML-LSCs.

COLONOMICS - integrative omics data of one hundred paired normal-tumoral samples from colon cancer patients

Colonomics is a multi-omics dataset that includes 250 samples: 50 samples from healthy colon mucosa donors and 100 paired samples from colon cancer patients (tumor/adjacent). From these samples, Colonomics project includes data from genotyping, DNA methylation, gene expression, whole exome sequencing and micro-RNAs (miRNAs) expression. It also includes data from copy number variation (CNV) from tumoral samples. In addition, clinical data from all these samples is available. The aims of the project were to explore and integrate these datasets to describe colon cancer at molecular level and to compare normal and tumoral tissues. Also, to improve screening by finding biomarkers for the diagnosis and prognosis of colon cancer. This project has its own website including four browsers allowing users to explore Colonomics datasets. Since generated data could be reuse for the scientific community for exploratory or validation purposes, here we describe omics datasets included in the Colonomics project as well as results from multi-omics layers integration.

A convergent malignant phenotype in B-cell acute lymphoblastic leukemia involving the splicing factor SRRM1

A significant proportion of infant B-cell acute lymphoblastic leukemia (B-ALL) patients remains with a dismal prognosis due to yet undetermined mechanisms. We performed a comprehensive multicohort analysis of gene expression, gene fusions, and RNA splicing alterations to uncover molecular signatures potentially linked to the observed poor outcome. We identified 87 fusions with significant allele frequency across patients and shared functional impacts, suggesting common mechanisms across fusions. We further identified a gene expression signature that predicts high risk independently of the gene fusion background and includes the upregulation of the splicing factor SRRM1. Experiments in B-ALL cell lines provided further evidence for the role of SRRM1 on cell survival, proliferation, and invasion. Supplementary analysis revealed that SRRM1 potentially modulates splicing events associated with poor outcomes through protein-protein interactions with other splicing factors. Our findings reveal a potential convergent mechanism of aberrant RNA processing that sustains a malignant phenotype independently of the underlying gene fusion and that could potentially complement current clinical strategies in infant B-ALL.

Exome Sequencing Reveals AMER1 as a Frequently Mutated Gene in Colorectal Cancer

PURPOSE

Somatic mutations occur at early stages of adenoma and accumulate throughout colorectal cancer progression. The aim of this study was to characterize the mutational landscape of stage II tumors and to search for novel recurrent mutations likely implicated in colorectal cancer tumorigenesis.

EXPERIMENTAL DESIGN

The exomic DNA of 42 stage II, microsatellite-stable colon tumors and their paired mucosae were sequenced. Other molecular data available in the discovery dataset [gene expression, methylation, and copy number variations (CNV)] were used to further characterize these tumors. Additional datasets comprising 553 colorectal cancer samples were used to validate the discovered mutations.

RESULTS

As a result, 4,886 somatic single-nucleotide variants (SNV) were found. Almost all SNVs were private changes, with few mutations shared by more than one tumor, thus revealing tumor-specific mutational landscapes. Nevertheless, these diverse mutations converged into common cellular pathways, such as cell cycle or apoptosis. Among this mutational heterogeneity, variants resulting in early stop codons in the AMER1 (also known as FAM123B or WTX) gene emerged as recurrent mutations in colorectal cancer. Losses of AMER1 by other mechanisms apart from mutations such as methylation and copy number aberrations were also found. Tumors lacking this tumor suppressor gene exhibited a mesenchymal phenotype characterized by inhibition of the canonical Wnt pathway.

CONCLUSIONS

In silico and experimental validation in independent datasets confirmed the existence of functional mutations in AMER1 in approximately 10% of analyzed colorectal cancer tumors. Moreover, these tumors exhibited a characteristic phenotype.

miR-1269 promotes metastasis and forms a positive feedback loop with TGF-β

As patient survival drops precipitously from early-stage cancers to late-stage and metastatic cancers, microRNAs that promote relapse and metastasis can serve as prognostic and predictive markers as well as therapeutic targets for chemoprevention. Here we show that miR-1269a promotes colorectal cancer (CRC) metastasis and forms a positive feedback loop with TGF-β signalling. miR-1269a is upregulated in late-stage CRCs, and long-term monitoring of 100 stage II CRC patients revealed that miR-1269a expression in their surgically removed primary tumours is strongly associated with risk of CRC relapse and metastasis. Consistent with clinical observations, miR-1269a significantly increases the ability of CRC cells to invade and metastasize in vivo. TGF-β activates miR-1269 via Sox4, while miR-1269a enhances TGF-β signalling by targeting Smad7 and HOXD10, hence forming a positive feedback loop. Our findings suggest that miR-1269a is a potential marker to inform adjuvant chemotherapy decisions for CRC patients and a potential therapeutic target to deter metastasis.

Identification of candidate susceptibility genes for colorectal cancer through eQTL analysis

In this study, we aim to identify the genes responsible for colorectal cancer risk behind the loci identified in genome-wide association studies (GWAS). These genes may be candidate targets for developing new strategies for prevention or therapy. We analyzed the association of genotypes for 26 GWAS single nucleotide polymorphisms (SNPs) with the expression of genes within a 2 Mb region (cis-eQTLs). Affymetrix Human Genome U219 expression arrays were used to assess gene expression in two series of samples, one of healthy colonic mucosa (n = 47) and other of normal mucosa adjacent to colon cancer (n = 97, total 144). Paired tumor tissues (n = 97) were also analyzed but did not provide additional findings. Partial Pearson correlation (r), adjusted for sample type, was used for the analysis. We have found Bonferroni-significant cis-eQTLs in three loci: rs3802842 in 11q23.1 associated to C11orf53, COLCA1 (C11orf92) and COLCA2 (C11orf93; r = 0.60); rs7136702 in 12q13.12 associated to DIP2B (r = 0.63) and rs5934683 in Xp22.3 associated to SHROOM2 and GPR143 (r = 0.47). For loci in chromosomes 11 and 12, we have found other SNPs in linkage disequilibrium that are more strongly associated with the expression of the identified genes and are better functional candidates: rs7130173 for 11q23.1 (r = 0.66) and rs61927768 for 12q13.12 (r = 0.86). These SNPs are located in DNA regions that may harbor enhancers or transcription factor binding sites. The analysis of trans-eQTLs has identified additional genes in these loci that may have common regulatory mechanisms as shown by the analysis of protein-protein interaction networks.

Expression and methylation profiles associated with recurrent mutations in stage II colorectal cancer

e14613

Background: Aberration of normal genetic and epigenetic patterns occurs at early stages of colorectal cancer (CRC) and accumulates throughout cancer progression. To characterize pre-metastatic tumors, a series of stage II, microsatellite stable, colon tumors and their paired mucosa were profiled on RNA expression and DNA methylation microarrays ( www.colonomics.org ). Our aim is to define molecular subtypes based on recurrent gene mutations, methylation and expression profiles, and explore if these molecular subtypes are associated to patients’ prognosis. Methods: We have sequenced exomes (Illumina Genome Analyzer) of a subset of 42 COLONOMICS normal-tumor paired samples (21 good and 21 bad prognosis). Variants identified in normal tissue were used to filter SNPs. DNA methylation (Infinium Human Methylation 450k) and RNA expression (Affymetrix U219) data from those samples were used in this analysis. Correlation was used to assess the association between tumor mutations and differentially expressed/methylated genes. Significant genes were subsequently used to perform tumor clustering. Results: Exome analysis revealed a mean of 150 somatic mutations per sample. From these, 12 variants were recurrently mutated (KRAS, TP53, etc) in more than 3 tumors that were used to define tumor subtypes based on gene methylation/expression patterns. We obtained 12 profiles that clearly identified the cluster of mutated samples. For some profiles, the cluster only includes those tumors with the mutation. Interestingly, some clusters included the mutated samples and additional tumors showing the same phenotype despite not having the mutation. For each mutation, the overlapping between the differently methylated/expressed genes ranged from 14 to 200 common genes. When combining data from the three platforms two main CRC molecular subtypes emerge; each of which shows molecular heterogeneity but no association with prognosis. Conclusions: Mutational status is associated with gene methylation and expression patterns in CRC patients. Although none of these clusters was associated with prognosis, different groups of tumors could be related to distinctive pathways, which may reveal useful as therapeutic targets.