NG2 is a target gene of MLL-AF4 and underlies glucocorticoid resistance in MLLr B-ALL by regulating NR3C1 expression.

B-cell acute lymphoblastic leukemia (B-ALL) is the most common pediatric cancer, with long-term overall survival rates of ∼85%. However, B-ALL harboring rearrangements of the MLL gene (also known as KMT2A), referred to as MLLr B-ALL, is common in infants and is associated with poor 5-year survival, relapses, and refractoriness to glucocorticoids (GCs). GCs are an essential part of the treatment backbone for B-ALL, and GC resistance is a major clinical predictor of poor outcome.

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.

Converging and evolving immuno-genomic routes toward immune escape in breast cancer.

The interactions between tumor and immune cells along the course of breast cancer progression remain largely unknown. Here, we extensively characterize multiple sequential and parallel multiregion tumor and blood specimens of an index patient and a cohort of metastatic triple-negative breast cancers. We demonstrate that a continuous increase in tumor genomic heterogeneity and distinct molecular clocks correlated with resistance to treatment, eventually allowing tumors to escape from immune control.

Chromosomal instability in aneuploid acute lymphoblastic leukemia associates with disease progression.

Chromosomal instability (CIN) lies at the core of cancer development leading to aneuploidy, chromosomal copy-number heterogeneity (chr-CNH) and ultimately, unfavorable clinical outcomes. Despite its ubiquity in cancer, the presence of CIN in childhood B-cell acute lymphoblastic leukemia (cB-ALL), the most frequent pediatric cancer showing high frequencies of aneuploidy, remains unknown. Here, we elucidate the presence of CIN in aneuploid cB-ALL subtypes using single-cell whole-genome sequencing of primary cB-ALL samples and by generating and functionally characterizing patient-derived xenograft models (cB-ALL-PDX).

Formation of an invasion-permissive matrix requires TGFβ/SNAIL1-regulated alternative splicing of fibronectin.

Background: As in most solid cancers, the emergence of cells with oncogenic mutations in the mammary epithelium alters the tissue homeostasis. Some soluble factors, such as TGFβ, potently modify the behavior of healthy stromal cells. A subpopulation of cancer-associated fibroblasts expressing a TGFβ target, the SNAIL1 transcription factor, display myofibroblastic abilities that rearrange the stromal architecture.

GPRC5C drives branched-chain amino acid metabolism in leukemogenesis.

Leukemia stem cells (LSCs) share numerous features with healthy hematopoietic stem cells (HSCs). G-protein coupled receptor family C group 5 member C (GPRC5C) is a regulator of HSC dormancy. However, GPRC5C functionality in acute myeloid leukemia (AML) is yet to be determined.

β-Catenin activity induces an RNA biosynthesis program promoting therapy resistance in T-cell acute lymphoblastic leukemia.

Understanding the molecular mechanisms that contribute to the appearance of chemotherapy resistant cell populations is necessary to improve cancer treatment. We have now investigated the role of β-catenin/CTNNB1 in the evolution of T-cell Acute Lymphoblastic Leukemia (T-ALL) patients and its involvement in therapy resistance. We have identified a specific gene signature that is directly regulated by β-catenin, TCF/LEF factors and ZBTB33/Kaiso in T-ALL cell lines, which is highly and significantly represented in five out of six refractory patients from a cohort of 40 children with T-ALL.

Clonal heterogeneity and rates of specific chromosome gains are risk predictors in childhood high-hyperdiploid B-cell acute lymphoblastic leukemia.

B-cell acute lymphoblastic leukemia (B-ALL) is the commonest childhood cancer. High hyperdiploidy (HHD) identifies the most frequent cytogenetic subgroup in childhood B-ALL. Although hyperdiploidy represents an important prognostic factor in childhood B-ALL, the specific chromosome gains with prognostic value in HHD-B-ALL remain controversial, and the current knowledge about the hierarchy of chromosome gains, clonal heterogeneity and chromosomal instability in HHD-B-ALL remains very limited.

CD34+CD19-CD22+ B-cell progenitors may underlie phenotypic escape in patients treated with CD19-directed therapies.

CD19-directed immunotherapies have revolutionized the treatment of advanced B-cell acute lymphoblastic leukemia (B-ALL). Despite initial impressive rates of complete remission (CR) many patients ultimately relapse. Patients with B-ALL successfully treated with CD19-directed T cells eventually relapse, which, coupled with the early onset of CD22 expression during B-cell development, suggests that preexisting CD34+CD22+CD19- (pre)-leukemic cells represent an "early progenitor origin-related" mechanism underlying phenotypic escape to CD19-directed immunotherapies.

A single-cell tumor immune atlas for precision oncology.

The tumor immune microenvironment is a main contributor to cancer progression and a promising therapeutic target for oncology. However, immune microenvironments vary profoundly between patients, and biomarkers for prognosis and treatment response lack precision. A comprehensive compendium of tumor immune cells is required to pinpoint predictive cellular states and their spatial localization.

ISOTOPE: ISOform-guided prediction of epiTOPEs in cancer.

Immunotherapies provide effective treatments for previously untreatable tumors and identifying tumor-specific epitopes can help elucidate the molecular determinants of therapy response. Here, we describe a pipeline, ISOTOPE (ISOform-guided prediction of epiTOPEs In Cancer), for the comprehensive identification of tumor-specific splicing-derived epitopes. Using RNA sequencing and mass spectrometry for MHC-I associated proteins, ISOTOPE identified neoepitopes from tumor-specific splicing events that are potentially presented by MHC-I complexes.

Immune cell profiling of the cerebrospinal fluid enables the characterization of the brain metastasis microenvironment.

Brain metastases are the most common tumor of the brain with a dismal prognosis. A fraction of patients with brain metastasis benefit from treatment with immune checkpoint inhibitors (ICI) and the degree and phenotype of the immune cell infiltration has been used to predict response to ICI. However, the anatomical location of brain lesions limits access to tumor material to characterize the immune phenotype.

Impaired condensin complex and Aurora B kinase underlie mitotic and chromosomal defects in hyperdiploid B-cell ALL.

B-cell acute lymphoblastic leukemia (ALL; B-ALL) is the most common pediatric cancer, and high hyperdiploidy (HyperD) identifies the most common subtype of pediatric B-ALL. Despite HyperD being an initiating oncogenic event affiliated with childhood B-ALL, the mitotic and chromosomal defects associated with HyperD B-ALL (HyperD-ALL) remain poorly characterized. Here, we have used 54 primary pediatric B-ALL samples to characterize the cellular-molecular mechanisms underlying the mitotic/chromosome defects predicated to be early pathogenic contributors in HyperD-ALL.

Genome Sequencing and RNA-Motif Analysis Reveal Novel Damaging Noncoding Mutations in Human Tumors.

A major challenge in cancer research is to determine the biological and clinical significance of somatic mutations in noncoding regions. This has been studied in terms of recurrence, functional impact, and association to individual regulatory sites, but the combinatorial contribution of mutations to common RNA regulatory motifs has not been explored. Therefore, we developed a new method, MIRA (mutation identification for RNA alterations), to perform an unbiased and comprehensive study of significantly mutated regions (SMR) affecting binding sites for RNA-binding proteins (RBP) in cancer.

SUPPA2: fast, accurate, and uncertainty-aware differential splicing analysis across multiple conditions.

Despite the many approaches to study differential splicing from RNA-seq, many challenges remain unsolved, including computing capacity and sequencing depth requirements. Here we present SUPPA2, a new method that addresses these challenges, and enables streamlined analysis across multiple conditions taking into account biological variability. Using experimental and simulated data, we show that SUPPA2 achieves higher accuracy compared to other methods, especially at low sequencing depth and short read length.

The prognostic potential of alternative transcript isoforms across human tumors.

Background: Phenotypic changes during cancer progression are associated with alterations in gene expression, which can be exploited to build molecular signatures for tumor stage identification and prognosis. However, it is not yet known whether the relative abundance of transcript isoforms may be informative for clinical stage and survival.

Methods: Using information theory and machine learning methods, we integrated RNA sequencing and clinical data from The Cancer Genome Atlas project to perform the first systematic analysis of the prognostic potential of transcript isoforms in 12 solid tumors to build new signatures for stage and prognosis.

Leveraging transcript quantification for fast computation of alternative splicing profiles.

Alternative splicing plays an essential role in many cellular processes and bears major relevance in the understanding of multiple diseases, including cancer. High-throughput RNA sequencing allows genome-wide analyses of splicing across multiple conditions. However, the increasing number of available data sets represents a major challenge in terms of computation time and storage requirements.