Targeting LINC00152 activates cAMP/Ca2+/ferroptosis axis and overcomes tamoxifen resistance in ER+ breast cancer.

Tamoxifen has been the mainstay therapy to treat early, locally advanced, and metastatic estrogen receptor-positive (ER + ) breast cancer, constituting around 75% of all cases. However, the emergence of resistance is common, necessitating the identification of novel therapeutic targets. Here, we demonstrated that long-noncoding RNA LINC00152 confers tamoxifen resistance by blocking tamoxifen-induced ferroptosis, an iron-mediated cell death. Mechanistically, inhibiting LINC00152 reduces the mRNA stability of phosphodiesterase 4D (PDE4D), leading to activation of the cAMP/PKA/CREB axis and increased expression of the TRPC1 Ca2+ channel. This causes cytosolic Ca2+ overload and generation of reactive oxygen species (ROS) that is, on the one hand, accompanied by downregulation of FTH1, a member of the iron sequestration unit, thus increasing intracellular Fe2+ levels; and on the other hand, inhibition of the peroxidase activity upon reduced GPX4 and xCT levels, in part by cAMP/CREB. These ultimately restore tamoxifen-dependent lipid peroxidation and ferroptotic cell death which are reversed upon chelating Ca2+ or overexpressing GPX4 or xCT. Overexpressing PDE4D reverses LINC00152 inhibition-mediated tamoxifen sensitization by de-activating the cAMP/Ca2+/ferroptosis axis. Importantly, high LINC00152 expression is significantly correlated with high PDE4D/low ferroptosis and worse survival in multiple cohorts of tamoxifen- or tamoxifen-containing endocrine therapy-treated ER+ breast cancer patients. Overall, we identified LINC00152 inhibition as a novel mechanism of tamoxifen sensitization via restoring tamoxifen-dependent ferroptosis upon destabilizing PDE4D, increasing cAMP and Ca2+ levels, thus leading to ROS generation and lipid peroxidation. Our findings reveal LINC00152 and its effectors as actionable therapeutic targets to improve clinical outcome in refractory ER+ breast cancer.

Genomic Profiling of Metastatic Castration-Resistant Prostate Cancer Samples Resistant to Androgen Receptor Pathway Inhibitors.

PURPOSE: The androgen receptor axis inhibitors (ARPI; e.g., enzalutamide, abiraterone acetate) are administered in daily practice for men with metastatic castration-resistant prostate cancer (mCRPC). However, not all patients respond, and mechanisms of both primary and acquired resistance remain largely unknown. EXPERIMENTAL DESIGN: In the prospective trial MATCH-R (NCT02517892), 59 patients with mCRPC underwent whole-exome sequencing (WES) and/or RNA sequencing (RNA-seq) of samples collected before starting ARPI. Also, 18 patients with mCRPC underwent biopsy at time of resistance. The objectives were to identify genomic alterations associated with resistance to ARPIs as well as to describe clonal evolution. Associations of genomic and transcriptomic alterations with primary resistance were determined using Wilcoxon and Fisher exact tests. RESULTS: WES analysis indicated that no single-gene genomic alterations were strongly associated with primary resistance. RNA-seq analysis showed that androgen receptor (AR) gene alterations and expression levels were similar between responders and nonresponders. RNA-based pathway analysis found that patients with primary resistance had a higher Hedgehog pathway score, a lower AR pathway score and a lower NOTCH pathway score than patients with a response. Subclonal evolution and acquisition of new alterations in AR-related genes or neuroendocrine differentiation are associated with acquired resistance. ARPIs do not induce significant changes in the tumor transcriptome of most patients; however, programs associated with cell proliferation are enriched in resistant samples. CONCLUSIONS: Low AR activity, activation of stemness programs, and Hedgehog pathway were associated with primary ARPIs' resistance, whereas most acquired resistance was associated with subclonal evolution, AR-related events, and neuroendocrine differentiation. See related commentary by Slovin, p. 4323.

Transinteractome analysis reveals distinct niche requirements for isotype-based plasma cell subsets in the bone marrow.

Bone marrow (BM) long-lived plasma cells (PCs) are essential for long-term protection against infection, and their persistence within this organ relies on interactions with Cxcl12-expressing stromal cells that are still not clearly identified. Here, using single cell RNAseq and in silico transinteractome analyses, we identified Leptin receptor positive (LepR+ ) mesenchymal cells as the stromal cell subset most likely to interact with PCs within the BM. Moreover, we demonstrated that depending on the isotype they express, PCs may use different sets of integrins and adhesion molecules to interact with these stromal cells. Altogether, our results constitute an unprecedented characterization of PC subset stromal niches and open new avenues for the specific targeting of BM PCs based on their isotype.

Integrative Pan-Cancer Genomic and Transcriptomic Analyses of Refractory Metastatic Cancer.

Metastatic relapse after treatment is the leading cause of cancer mortality, and known resistance mechanisms are missing for most treatments administered to patients. To bridge this gap, we analyze a pan-cancer cohort (META-PRISM) of 1,031 refractory metastatic tumors profiled via whole-exome and transcriptome sequencing. META-PRISM tumors, particularly prostate, bladder, and pancreatic types, displayed the most transformed genomes compared with primary untreated tumors. Standard-of-care resistance biomarkers were identified only in lung and colon cancers-9.6% of META-PRISM tumors, indicating that too few resistance mechanisms have received clinical validation. In contrast, we verified the enrichment of multiple investigational and hypothetical resistance mechanisms in treated compared with nontreated patients, thereby confirming their putative role in treatment resistance. Additionally, we demonstrated that molecular markers improve 6-month survival prediction, particularly in patients with advanced breast cancer. Our analysis establishes the utility of the META-PRISM cohort for investigating resistance mechanisms and performing predictive analyses in cancer. SIGNIFICANCE: This study highlights the paucity of standard-of-care markers that explain treatment resistance and the promise of investigational and hypothetical markers awaiting further validation. It also demonstrates the utility of molecular profiling in advanced-stage cancers, particularly breast cancer, to improve the survival prediction and assess eligibility to phase I clinical trials. This article is highlighted in the In This Issue feature, p. 1027.

Targeting LINC00152 activates cAMP/Ca2+/ferroptosis axis and overcomes tamoxifen resistance in ER+ breast cancer.

Tamoxifen has been the mainstay therapy to treat early, locally advanced, and metastatic estrogen receptor-positive (ER+) breast cancer, constituting around 75% of all cases. However, emergence of resistance is common, necessitating the identification of novel therapeutic targets. Here, we demonstrated that long-noncoding RNA LINC00152 confers tamoxifen resistance via blocking tamoxifen-induced ferroptosis, an iron-mediated cell death. Mechanistically, inhibiting LINC00152 reduces the mRNA stability of phosphodiesterase 4D (PDE4D), leading to activation of cAMP/PKA/CREB axis and increased expression of TRPC1 Ca2+ channel. This causes cytosolic Ca2+ overload and generation of reactive oxygen species (ROS) that is, on one hand, accompanied by downregulation of FTH1, a member of the iron sequestration unit, thus increasing intracellular Fe2+ levels; and on the other hand, inhibition of the peroxidase activity upon reduced GPX4 and xCT levels. These ultimately induce lipid peroxidation and ferroptotic cell death in combination with tamoxifen. Overexpressing PDE4D rescues LINC00152 inhibition-mediated tamoxifen sensitization by de-activating the cAMP/Ca2+/ferroptosis axis. Importantly, high LINC00152 expression is significantly correlated with high PDE4D/low ferroptosis and worse survival in multiple cohorts of tamoxifen- or tamoxifen-containing endocrine therapy-treated ER+ breast cancer patients. Overall, we identified LINC00152 inhibition as a novel mechanism of ferroptosis induction and tamoxifen sensitization, thereby revealing LINC00152 and its effectors as actionable therapeutic targets to improve clinical outcome in refractory ER+ breast cancer.

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.

The contribution of uncharted RNA sequences to tumor identity in lung adenocarcinoma.

The identity of cancer cells is defined by the interplay between genetic, epigenetic transcriptional and post-transcriptional variation. A lot of this variation is present in RNA-seq data and can be captured at once using reference-free, k-mer analysis. An important issue with k-mer analysis, however, is the difficulty of distinguishing signal from noise. Here, we use two independent lung adenocarcinoma datasets to identify all reproducible events at the k-mer level, in a tumor versus normal setting. We find reproducible events in many different locations (introns, intergenic, repeats) and forms (spliced, polyadenylated, chimeric etc.). We systematically analyze events that are ignored in conventional transcriptomics and assess their value as biomarkers and for tumor classification, survival prediction, neoantigen prediction and correlation with the immune microenvironment. We find that unannotated lincRNAs, novel splice variants, endogenous HERV, Line1 and Alu repeats and bacterial RNAs each contribute to different, important aspects of tumor identity. We argue that differential RNA-seq analysis of tumor/normal sample collections would benefit from this type k-mer analysis to cast a wider net on important cancer-related events. The code is available at https://github.com/Transipedia/dekupl-lung-cancer-inter-cohort.

Multi-omics prediction in melanoma immunotherapy: A new brick in the wall.

In this issue of Cancer Cell, Newell et al. introduce whole-genome and methylome data to melanoma immunotherapy response analysis. Genome breaks are more frequent in resistant tumors, but the best response classifiers remain mutation burden and interferon-É£ signature. Clinical translation will need aggregation of many such datasets.

2-kupl: mapping-free variant detection from DNA-seq data of matched samples.

BACKGROUND: The detection of genome variants, including point mutations, indels and structural variants, is a fundamental and challenging computational problem. We address here the problem of variant detection between two deep-sequencing (DNA-seq) samples, such as two human samples from an individual patient, or two samples from distinct bacterial strains. The preferred strategy in such a case is to align each sample to a common reference genome, collect all variants and compare these variants between samples. Such mapping-based protocols have several limitations. DNA sequences with large indels, aggregated mutations and structural variants are hard to map to the reference. Furthermore, DNA sequences cannot be mapped reliably to genomic low complexity regions and repeats. RESULTS: We introduce 2-kupl, a k-mer based, mapping-free protocol to detect variants between two DNA-seq samples. On simulated and actual data, 2-kupl achieves higher accuracy than other mapping-free protocols. Applying 2-kupl to prostate cancer whole exome sequencing data, we identify a number of candidate variants in hard-to-map regions and propose potential novel recurrent variants in this disease. CONCLUSIONS: We developed a mapping-free protocol for variant calling between matched DNA-seq samples. Our protocol is suitable for variant detection in unmappable genome regions or in the absence of a reference genome.

Reference-free transcriptome signatures for prostate cancer prognosis.

BACKGROUND: RNA-seq data are increasingly used to derive prognostic signatures for cancer outcome prediction. A limitation of current predictors is their reliance on reference gene annotations, which amounts to ignoring large numbers of non-canonical RNAs produced in disease tissues. A recently introduced kind of transcriptome classifier operates entirely in a reference-free manner, relying on k-mers extracted from patient RNA-seq data. METHODS: In this paper, we set out to compare conventional and reference-free signatures in risk and relapse prediction of prostate cancer. To compare the two approaches as fairly as possible, we set up a common procedure that takes as input either a k-mer count matrix or a gene expression matrix, extracts a signature and evaluates this signature in an independent dataset. RESULTS: We find that both gene-based and k-mer based classifiers had similarly high performances for risk prediction and a markedly lower performance for relapse prediction. Interestingly, the reference-free signatures included a set of sequences mapping to novel lncRNAs or variable regions of cancer driver genes that were not part of gene-based signatures. CONCLUSIONS: Reference-free classifiers are thus a promising strategy for the identification of novel prognostic RNA biomarkers.

Applications of single-cell and bulk RNA sequencing in onco-immunology.

The rising interest for precise characterization of the tumour immune contexture has recently brought forward the high potential of RNA sequencing (RNA-seq) in identifying molecular mechanisms engaged in the response to immunotherapy. In this review, we provide an overview of the major principles of single-cell and conventional (bulk) RNA-seq applied to onco-immunology. We describe standard preprocessing and statistical analyses of data obtained from such techniques and highlight some computational challenges relative to the sequencing of individual cells. We notably provide examples of gene expression analyses such as differential expression analysis, dimensionality reduction, clustering and enrichment analysis. Additionally, we used public data sets to exemplify how deconvolution algorithms can identify and quantify multiple immune subpopulations from either bulk or single-cell RNA-seq. We give examples of machine and deep learning models used to predict patient outcomes and treatment effect from high-dimensional data. Finally, we balance the strengths and weaknesses of single-cell and bulk RNA-seq regarding their applications in the clinic.

Reference-free transcriptome exploration reveals novel RNAs for prostate cancer diagnosis.

The use of RNA-sequencing technologies held a promise of improved diagnostic tools based on comprehensive transcript sets. However, mining human transcriptome data for disease biomarkers in clinical specimens are restricted by the limited power of conventional reference-based protocols relying on unique and annotated transcripts. Here, we implemented a blind reference-free computational protocol, DE-kupl, to infer yet unreferenced RNA variations from total stranded RNA-sequencing datasets of tissue origin. As a bench test, this protocol was powered for detection of RNA subsequences embedded into putative long noncoding (lnc)RNAs expressed in prostate cancer. Through filtering of 1,179 candidates, we defined 21 lncRNAs that were further validated by NanoString for robust tumor-specific expression in 144 tissue specimens. Predictive modeling yielded a restricted probe panel enabling more than 90% of true-positive detections of cancer in an independent The Cancer Genome Atlas cohort. Remarkably, this clinical signature made of only nine unannotated lncRNAs largely outperformed PCA3, the only used prostate cancer lncRNA biomarker, in detection of high-risk tumors. This modular workflow is highly sensitive and can be applied to any pathology or clinical application.

A Dual Model for Prioritizing Cancer Mutations in the Non-coding Genome Based on Germline and Somatic Events.

We address here the issue of prioritizing non-coding mutations in the tumoral genome. To this aim, we created two independent computational models. The first (germline) model estimates purifying selection based on population SNP data. The second (somatic) model estimates tumor mutation density based on whole genome tumor sequencing. We show that each model reflects a different set of constraints acting either on the normal or tumor genome, and we identify the specific genome features that most contribute to these constraints. Importantly, we show that the somatic mutation model carries independent functional information that can be used to narrow down the non-coding regions that may be relevant to cancer progression. On this basis, we identify positions in non-coding RNAs and the non-coding parts of mRNAs that are both under purifying selection in the germline and protected from mutation in tumors, thus introducing a new strategy for future detection of cancer driver elements in the expressed non-coding genome.

New Functional Signatures for Understanding Melanoma Biology from Tumor Cell Lineage-Specific Analysis.

Molecular signatures specific to particular tumor types are required to design treatments for resistant tumors. However, it remains unclear whether tumors and corresponding cell lines used for drug development share such signatures. We developed similarity core analysis (SCA), a universal and unsupervised computational framework for extracting core molecular features common to tumors and cell lines. We applied SCA to mRNA/miRNA expression data from various sources, comparing melanoma cell lines and metastases. The signature obtained was associated with phenotypic characteristics in vitro, and the core genes CAPN3 and TRIM63 were implicated in melanoma cell migration/invasion. About 90% of the melanoma signature genes belong to an intrinsic network of transcription factors governing neural development (TFAP2A, DLX2, ALX1, MITF, PAX3, SOX10, LEF1, and GAS7) and miRNAs (211-5p, 221-3p, and 10a-5p). The SCA signature effectively discriminated between two subpopulations of melanoma patients differing in overall survival, and classified MEKi/BRAFi-resistant and -sensitive melanoma cell lines.

Mining the coding and non-coding genome for cancer drivers.

Progress in next-generation sequencing provides unprecedented opportunities to fully characterize the spectrum of somatic mutations of cancer genomes. Given the large number of somatic mutations identified by such technologies, the prioritization of cancer-driving events is a consistent bottleneck. Most bioinformatics tools concentrate on driver mutations in the coding fraction of the genome, those causing changes in protein products. As more non-coding pathogenic variants are identified and characterized, the development of computational approaches to effectively prioritize cancer-driving variants within the non-coding fraction of human genome is becoming critical. After a short summary of methods for coding variant prioritization, we here review the highly diverse non-coding elements that may act as cancer drivers and describe recent methods that attempt to evaluate the deleteriousness of sequence variation in these elements. With such tools, the prioritization and identification of cancer-implicated regulatory elements and non-coding RNAs is becoming a reality.

Acquired initiating mutations in early hematopoietic cells of CLL patients.

UNLABELLED: Appropriate cancer care requires a thorough understanding of the natural history of the disease, including the cell of origin, the pattern of clonal evolution, and the functional consequences of the mutations. Using deep sequencing of flow-sorted cell populations from patients with chronic lymphocytic leukemia (CLL), we established the presence of acquired mutations in multipotent hematopoietic progenitors. Mutations affected known lymphoid oncogenes, including BRAF, NOTCH1, and SF3B1. NFKBIE and EGR2 mutations were observed at unexpectedly high frequencies, 10.7% and 8.3% of 168 advanced-stage patients, respectively. EGR2 mutations were associated with a shorter time to treatment and poor overall survival. Analyses of BRAF and EGR2 mutations suggest that they result in deregulation of B-cell receptor (BCR) intracellular signaling. Our data propose disruption of hematopoietic and early B-cell differentiation through the deregulation of pre-BCR signaling as a phenotypic outcome of CLL mutations and show that CLL develops from a pre-leukemic phase. SIGNIFICANCE: The origin and pathogenic mechanisms of CLL are not fully understood. The current work indicates that CLL develops from pre-leukemic multipotent hematopoietic progenitors carrying somatic mutations. It advocates for abnormalities in early B-cell differentiation as a phenotypic convergence of the diverse acquired mutations observed in CLL.

Characterization of novel genomic alterations and therapeutic approaches using acute megakaryoblastic leukemia xenograft models.

Acute megakaryoblastic leukemia (AMKL) is a heterogeneous disease generally associated with poor prognosis. Gene expression profiles indicate the existence of distinct molecular subgroups, and several genetic alterations have been characterized in the past years, including the t(1;22)(p13;q13) and the trisomy 21 associated with GATA1 mutations. However, the majority of patients do not present with known mutations, and the limited access to primary patient leukemic cells impedes the efficient development of novel therapeutic strategies. In this study, using a xenotransplantation approach, we have modeled human pediatric AMKL in immunodeficient mice. Analysis of high-throughput RNA sequencing identified recurrent fusion genes defining new molecular subgroups. One subgroup of patients presented with MLL or NUP98 fusion genes leading to up-regulation of the HOX A cluster genes. A novel CBFA2T3-GLIS2 fusion gene resulting from a cryptic inversion of chromosome 16 was identified in another subgroup of 31% of non-Down syndrome AMKL and strongly associated with a gene expression signature of Hedgehog pathway activation. These molecular data provide useful markers for the diagnosis and follow up of patients. Finally, we show that AMKL xenograft models constitute a relevant in vivo preclinical screening platform to validate the efficacy of novel therapies such as Aurora A kinase inhibitors.

Genome-wide discovery and analysis of microRNAs and other small RNAs from rice embryogenic callus.

Small RNAs constitute a new and unanticipated layer of gene regulation present in the three domains of life. In plants, all organs are ultimately derived from a few pluripotent stem cells localized in specialized structures called apical meristems. The development of meristems involves a coordinated balance between undifferentiated growth and differentiation, a phenomenon requiring a tight regulation of gene expression. We used in vitro cultured embryogenic calli as a model to investigate the roles of meristem-associated small RNAs. Using high throughput sequencing, we sequenced 20 million short reads with size of 18-30 nt from rice undifferentiated and differentiated calli. We confirmed 50 known microRNA families, representing one third of annotated rice microRNAs. Using a specific computational pipeline for plant microRNA identification, we identified 24 novel microRNA families. Among them, 53 microRNA or microRNA* sequences appear to vary in expression between differentiated and undifferentiated calli, suggesting a role in meristem development. Our analysis also revealed a new class of plant small RNAs derived from 5' or 3' ends of mature tRNA analogous to the tRFs in human cancer cell. We independently verified the expression of these small RNAs from 5' end of mature tRNA using qRT-PCR.

Entropy measures quantify global splicing disorders in cancer.

Most mammalian genes are able to express several splice variants in a phenomenon known as alternative splicing. Serious alterations of alternative splicing occur in cancer tissues, leading to expression of multiple aberrant splice forms. Most studies of alternative splicing defects have focused on the identification of cancer-specific splice variants as potential therapeutic targets. Here, we examine instead the bulk of non-specific transcript isoforms and analyze their level of disorder using a measure of uncertainty called Shannon's entropy. We compare isoform expression entropy in normal and cancer tissues from the same anatomical site for different classes of transcript variations: alternative splicing, polyadenylation, and transcription initiation. Whereas alternative initiation and polyadenylation show no significant gain or loss of entropy between normal and cancer tissues, alternative splicing shows highly significant entropy gains for 13 of the 27 cancers studied. This entropy gain is characterized by a flattening in the expression profile of normal isoforms and is correlated to the level of estimated cellular proliferation in the cancer tissue. Interestingly, the genes that present the highest entropy gain are enriched in splicing factors. We provide here the first quantitative estimate of splicing disruption in cancer. The expression of normal splice variants is widely and significantly disrupted in at least half of the cancers studied. We postulate that such splicing disorders may develop in part from splicing alteration in key splice factors, which in turn significantly impact multiple target genes.