Single-cell spatial multi-omics and deep learning dissect enhancer-driven gene regulatory networks in liver zonation.

In the mammalian liver, hepatocytes exhibit diverse metabolic and functional profiles based on their location within the liver lobule. However, it is unclear whether this spatial variation, called zonation, is governed by a well-defined gene regulatory code. Here, using a combination of single-cell multiomics, spatial omics, massively parallel reporter assays and deep learning, we mapped enhancer-gene regulatory networks across mouse liver cell types. We found that zonation affects gene expression and chromatin accessibility in hepatocytes, among other cell types. These states are driven by the repressors TCF7L1 and TBX3, alongside other core hepatocyte transcription factors, such as HNF4A, CEBPA, FOXA1 and ONECUT1. To examine the architecture of the enhancers driving these cell states, we trained a hierarchical deep learning model called DeepLiver. Our study provides a multimodal understanding of the regulatory code underlying hepatocyte identity and their zonation state that can be used to engineer enhancers with specific activity levels and zonation patterns.

Somatic evolution of marine transmissible leukemias in the common cockle, Cerastoderma edule.

Transmissible cancers are malignant cell lineages that spread clonally between individuals. Several such cancers, termed bivalve transmissible neoplasia (BTN), induce leukemia-like disease in marine bivalves. This is the case of BTN lineages affecting the common cockle, Cerastoderma edule, which inhabits the Atlantic coasts of Europe and northwest Africa. To investigate the evolution of cockle BTN, we collected 6,854 cockles, diagnosed 390 BTN tumors, generated a reference genome and assessed genomic variation across 61 tumors. Our analyses confirmed the existence of two BTN lineages with hemocytic origins. Mitochondrial variation revealed mitochondrial capture and host co-infection events. Mutational analyses identified lineage-specific signatures, one of which likely reflects DNA alkylation. Cytogenetic and copy number analyses uncovered pervasive genomic instability, with whole-genome duplication, oncogene amplification and alkylation-repair suppression as likely drivers. Satellite DNA distributions suggested ancient clonal origins. Our study illuminates long-term cancer evolution under the sea and reveals tolerance of extreme instability in neoplastic genomes.

System-wide mapping of peptide-GPCR interactions in C. elegans.

Neuropeptides and peptide hormones are ancient, widespread signaling molecules that underpin almost all brain functions. They constitute a broad ligand-receptor network, mainly by binding to G protein-coupled receptors (GPCRs). However, the organization of the peptidergic network and roles of many peptides remain elusive, as our insight into peptide-receptor interactions is limited and many peptide GPCRs are still orphan receptors. Here we report a genome-wide peptide-GPCR interaction map in Caenorhabditis elegans. By reverse pharmacology screening of over 55,384 possible interactions, we identify 461 cognate peptide-GPCR couples that uncover a broad signaling network with specific and complex combinatorial interactions encoded across and within single peptidergic genes. These interactions provide insights into peptide functions and evolution. Combining our dataset with phylogenetic analysis supports peptide-receptor co-evolution and conservation of at least 14 bilaterian peptidergic systems in C. elegans. This resource lays a foundation for system-wide analysis of the peptidergic network.

Single-cell somatic copy number variants in brain using different amplification methods and reference genomes.

The presence of somatic mutations, including copy number variants (CNVs), in the brain is well recognized. Comprehensive study requires single-cell whole genome amplification, with several methods available, prior to sequencing. We compared PicoPLEX with two recent adaptations of multiple displacement amplification (MDA): primary template-directed amplification (PTA) and droplet MDA, across 93 human brain cortical nuclei. We demonstrated different properties for each, with PTA providing the broadest amplification, PicoPLEX the most even, and distinct chimeric profiles. Furthermore, we performed CNV calling on two brains with multiple system atrophy and one control brain using different reference genomes. We found that 38% of brain cells have at least one Mb-scale CNV, with some supported by bulk sequencing or single-cells from other brain regions. Our study highlights the importance of selecting whole genome amplification method and reference genome for CNV calling, while supporting the existence of somatic CNVs in healthy and diseased human brain.

The Clinicogenomic Landscape of Induction Failure in Childhood and Young Adult T-Cell Acute Lymphoblastic Leukemia.

PURPOSE: Failure to respond to induction chemotherapy portends a poor outcome in childhood acute lymphoblastic leukemia (ALL) and is more frequent in T-cell ALL (T-ALL) than B-cell ALL. We aimed to address the limited understanding of clinical and genetic factors that influence outcome in a cohort of patients with T-ALL induction failure (IF). METHODS: We studied all cases of T-ALL IF on two consecutive multinational randomized trials, UKALL2003 and UKALL2011, to define risk factors, treatment, and outcomes. We performed multiomic profiling to characterize the genomic landscape. RESULTS: IF occurred in 10.3% of cases and was significantly associated with increasing age, occurring in 20% of patients age 16 years and older. Five-year overall survival (OS) rates were 52.1% in IF and 90.2% in responsive patients (P < .001). Despite increased use of nelarabine-based chemotherapy consolidated by hematopoietic stem-cell transplant in UKALL2011, there was no improvement in outcome. Persistent end-of-consolidation molecular residual disease resulted in a significantly worse outcome (5-year OS, 14.3% v 68.5%; HR, 4.10; 95% CI, 1.35 to 12.45; P = .0071). Genomic profiling revealed a heterogeneous picture with 25 different initiating lesions converging on 10 subtype-defining genes. There was a remarkable abundance of TAL1 noncoding lesions, associated with a dismal outcome (5-year OS, 12.5%). Combining TAL1 lesions with mutations in the MYC and RAS pathways produces a genetic stratifier that identifies patients highly likely to fail conventional therapy (5-year OS, 23.1% v 86.4%; HR, 6.84; 95% CI, 2.78 to 16.78; P < .0001) and who should therefore be considered for experimental agents. CONCLUSION: The outcome of IF in T-ALL remains poor with current therapy. The lack of a unifying genetic driver suggests alternative approaches, particularly using immunotherapy, are urgently needed.

Evolutionary characterization of lung adenocarcinoma morphology in TRACERx.

Lung adenocarcinomas (LUADs) display a broad histological spectrum from low-grade lepidic tumors through to mid-grade acinar and papillary and high-grade solid, cribriform and micropapillary tumors. How morphology reflects tumor evolution and disease progression is poorly understood. Whole-exome sequencing data generated from 805 primary tumor regions and 121 paired metastatic samples across 248 LUADs from the TRACERx 421 cohort, together with RNA-sequencing data from 463 primary tumor regions, were integrated with detailed whole-tumor and regional histopathological analysis. Tumors with predominantly high-grade patterns showed increased chromosomal complexity, with higher burden of loss of heterozygosity and subclonal somatic copy number alterations. Individual regions in predominantly high-grade pattern tumors exhibited higher proliferation and lower clonal diversity, potentially reflecting large recent subclonal expansions. Co-occurrence of truncal loss of chromosomes 3p and 3q was enriched in predominantly low-/mid-grade tumors, while purely undifferentiated solid-pattern tumors had a higher frequency of truncal arm or focal 3q gains and SMARCA4 gene alterations compared with mixed-pattern tumors with a solid component, suggesting distinct evolutionary trajectories. Clonal evolution analysis revealed that tumors tend to evolve toward higher-grade patterns. The presence of micropapillary pattern and 'tumor spread through air spaces' were associated with intrathoracic recurrence, in contrast to the presence of solid/cribriform patterns, necrosis and preoperative circulating tumor DNA detection, which were associated with extra-thoracic recurrence. These data provide insights into the relationship between LUAD morphology, the underlying evolutionary genomic landscape, and clinical and anatomical relapse risk.

Genomic-transcriptomic evolution in lung cancer and metastasis.

Intratumour heterogeneity (ITH) fuels lung cancer evolution, which leads to immune evasion and resistance to therapy1. Here, using paired whole-exome and RNA sequencing data, we investigate intratumour transcriptomic diversity in 354 non-small cell lung cancer tumours from 347 out of the first 421 patients prospectively recruited into the TRACERx study2,3. Analyses of 947 tumour regions, representing both primary and metastatic disease, alongside 96 tumour-adjacent normal tissue samples implicate the transcriptome as a major source of phenotypic variation. Gene expression levels and ITH relate to patterns of positive and negative selection during tumour evolution. We observe frequent copy number-independent allele-specific expression that is linked to epigenomic dysfunction. Allele-specific expression can also result in genomic-transcriptomic parallel evolution, which converges on cancer gene disruption. We extract signatures of RNA single-base substitutions and link their aetiology to the activity of the RNA-editing enzymes ADAR and APOBEC3A, thereby revealing otherwise undetected ongoing APOBEC activity in tumours. Characterizing the transcriptomes of primary-metastatic tumour pairs, we combine multiple machine-learning approaches that leverage genomic and transcriptomic variables to link metastasis-seeding potential to the evolutionary context of mutations and increased proliferation within primary tumour regions. These results highlight the interplay between the genome and transcriptome in influencing ITH, lung cancer evolution and metastasis.

Estimation of tumor cell total mRNA expression in 15 cancer types predicts disease progression.

Single-cell RNA sequencing studies have suggested that total mRNA content correlates with tumor phenotypes. Technical and analytical challenges, however, have so far impeded at-scale pan-cancer examination of total mRNA content. Here we present a method to quantify tumor-specific total mRNA expression (TmS) from bulk sequencing data, taking into account tumor transcript proportion, purity and ploidy, which are estimated through transcriptomic/genomic deconvolution. We estimate and validate TmS in 6,590 patient tumors across 15 cancer types, identifying significant inter-tumor variability. Across cancers, high TmS is associated with increased risk of disease progression and death. TmS is influenced by cancer-specific patterns of gene alteration and intra-tumor genetic heterogeneity as well as by pan-cancer trends in metabolic dysregulation. Taken together, our results indicate that measuring cell-type-specific total mRNA expression in tumor cells predicts tumor phenotypes and clinical outcomes.

Primary mediastinal large B-cell lymphoma is characterized by large-scale copy-neutral loss of heterozygosity.

Development of primary mediastinal B-cell lymphoma (PMBL) is driven by cumulative genomic aberrations. We discovered a unique copy-neutral loss of heterozygosity (CN-LOH) landscape of PMBL which distinguishes this tumor from other B-cell malignancies, including the biologically related diffuse large B-cell lymphoma. Using single nucleotide polymorphism array analysis we identified large-scale CN-LOH lesions in 91% (30/33) of diagnostic PMBLs and both investigated PMBL-derived cell lines. Altogether, the cohort showed 157 extra-large (25.3-248.4 Mb) CN-LOH lesions affecting up to 14 chromosomes per case (mean of 4.4) and resulting in a reduction of heterozygosity an average of 9.9% (range 1.3-51%) of the genome. Predominant involvement of terminal chromosomal segments suggests the implication of B-cell specific crossover events in the pathogenesis of PMBL. Notably, CN-LOH stretches non-randomly clustered on 6p (60%), 15 (37.2%), and 17q (40%), and frequently co-occurred with homozygous mutations in the MHC I (6p21), B2M (15q15), and GNA13 (17q23) genes, respectively, as shown by preliminary whole-exome/genome sequencing data. Altogether, our findings implicate CN-LOH as a novel and distinct mutational process contributing to the molecular pathogenesis of PMBL. The aberration acting as "second hit" in the Knudson hypothesis, ranks as the major mechanism converting to homozygosity the PMBL-related driver genes. Screening of the cohort of 199 B cell leukemia/lymphoma whole-genomes revealed significant differences in the CN-LOH landscape of PMBL and other B-cell malignancies, including the biologically related diffuse large B-cell lymphoma.

Biallelic mutations in cancer genomes reveal local mutational determinants.

The infinite sites model of molecular evolution posits that every position in the genome is mutated at most once1. By restricting the number of possible mutation histories, haplotypes and alleles, it forms a cornerstone of tumor phylogenetic analysis2 and is often implied when calling, phasing and interpreting variants3,4 or studying the mutational landscape as a whole5. Here we identify 18,295 biallelic mutations, where the same base is mutated independently on both parental copies, in 559 (21%) bulk sequencing samples from the Pan-Cancer Analysis of Whole Genomes study. Biallelic mutations reveal ultraviolet light damage hotspots at E26 transformation-specific (ETS) and nuclear factor of activated T cells (NFAT) binding sites, and hypermutable motifs in POLE-mutant and other cancers. We formulate recommendations for variant calling and provide frameworks to model and detect biallelic mutations. These results highlight the need for accurate models of mutation rates and tumor evolution, as well as their inference from sequencing data.

Analysis of long and short enhancers in melanoma cell states.

Understanding how enhancers drive cell-type specificity and efficiently identifying them is essential for the development of innovative therapeutic strategies. In melanoma, the melanocytic (MEL) and the mesenchymal-like (MES) states present themselves with different responses to therapy, making the identification of specific enhancers highly relevant. Using massively parallel reporter assays (MPRAs) in a panel of patient-derived melanoma lines (MM lines), we set to identify and decipher melanoma enhancers by first focusing on regions with state-specific H3K27 acetylation close to differentially expressed genes. An in-depth evaluation of those regions was then pursued by investigating the activity of overlapping ATAC-seq peaks along with a full tiling of the acetylated regions with 190 bp sequences. Activity was observed in more than 60% of the selected regions, and we were able to precisely locate the active enhancers within ATAC-seq peaks. Comparison of sequence content with activity, using the deep learning model DeepMEL2, revealed that AP-1 alone is responsible for the MES enhancer activity. In contrast, SOX10 and MITF both influence MEL enhancer function with SOX10 being required to achieve high levels of activity. Overall, our MPRAs shed light on the relationship between long and short sequences in terms of their sequence content, enhancer activity, and specificity across melanoma cell states.

A ubiquitous disordered protein interaction module orchestrates transcription elongation.

During eukaryotic transcription elongation, RNA polymerase II (RNAP2) is regulated by a chorus of factors. Here, we identified a common binary interaction module consisting of TFIIS N-terminal domains (TNDs) and natively unstructured TND-interacting motifs (TIMs). This module was conserved among the elongation machinery and linked complexes including transcription factor TFIIS, Mediator, super elongation complex, elongin, IWS1, SPT6, PP1-PNUTS phosphatase, H3K36me3 readers, and other factors. Using nuclear magnetic resonance, live-cell microscopy, and mass spectrometry, we revealed the structural basis for these interactions and found that TND-TIM sequences were necessary and sufficient to induce strong and specific colocalization in the crowded nuclear environment. Disruption of a single TIM in IWS1 induced robust changes in gene expression and RNAP2 elongation dynamics, which underscores the functional importance of TND-TIM surfaces for transcription elongation.

Aberrant integration of Hepatitis B virus DNA promotes major restructuring of human hepatocellular carcinoma genome architecture.

Most cancers are characterized by the somatic acquisition of genomic rearrangements during tumour evolution that eventually drive the oncogenesis. Here, using multiplatform sequencing technologies, we identify and characterize a remarkable mutational mechanism in human hepatocellular carcinoma caused by Hepatitis B virus, by which DNA molecules from the virus are inserted into the tumour genome causing dramatic changes in its configuration, including non-homologous chromosomal fusions, dicentric chromosomes and megabase-size telomeric deletions. This aberrant mutational mechanism, present in at least 8% of all HCC tumours, can provide the driver rearrangements that a cancer clone requires to survive and grow, including loss of relevant tumour suppressor genes. Most of these events are clonal and occur early during liver cancer evolution. Real-time timing estimation reveals some HBV-mediated rearrangements occur as early as two decades before cancer diagnosis. Overall, these data underscore the importance of characterising liver cancer genomes for patterns of HBV integration.

A pan-cancer landscape of somatic mutations in non-unique regions of the human genome.

A substantial fraction of the human genome displays high sequence similarity with at least one other genomic sequence, posing a challenge for the identification of somatic mutations from short-read sequencing data. Here we annotate genomic variants in 2,658 cancers from the Pan-Cancer Analysis of Whole Genomes (PCAWG) cohort with links to similar sites across the human genome. We train a machine learning model to use signals distributed over multiple genomic sites to call somatic events in non-unique regions and validate the data against linked-read sequencing in an independent dataset. Using this approach, we uncover previously hidden mutations in ~1,700 coding sequences and in thousands of regulatory elements, including in known cancer genes, immunoglobulins and highly mutated gene families. Mutations in non-unique regions are consistent with mutations in unique regions in terms of mutation burden and substitution profiles. The analysis provides a systematic summary of the mutation events in non-unique regions at a genome-wide scale across multiple human cancers.

Evolutionary predictability of genetic versus nongenetic resistance to anticancer drugs in melanoma.

Therapy resistance arises from heterogeneous drug-tolerant persister cells or minimal residual disease (MRD) through genetic and nongenetic mechanisms. A key question is whether specific molecular features of the MRD ecosystem determine which of these two distinct trajectories will eventually prevail. We show that, in melanoma exposed to mitogen-activated protein kinase therapeutics, emergence of a transient neural crest stem cell (NCSC) population in MRD concurs with the development of nongenetic resistance. This increase relies on a glial cell line-derived neurotrophic factor-dependent signaling cascade, which activates the AKT survival pathway in a focal adhesion kinase (FAK)-dependent manner. Ablation of the NCSC population through FAK inhibition delays relapse in patient-derived tumor xenografts. Strikingly, all tumors that ultimately escape this treatment exhibit resistance-conferring genetic alterations and increased sensitivity to extracellular signal-regulated kinase inhibition. These findings identify an approach that abrogates the nongenetic resistance trajectory in melanoma and demonstrate that the cellular composition of MRD deterministically imposes distinct drug resistance evolutionary paths.

Interpretation of allele-specific chromatin accessibility using cell state-aware deep learning.

Genomic sequence variation within enhancers and promoters can have a significant impact on the cellular state and phenotype. However, sifting through the millions of candidate variants in a personal genome or a cancer genome, to identify those that impact cis-regulatory function, remains a major challenge. Interpretation of noncoding genome variation benefits from explainable artificial intelligence to predict and interpret the impact of a mutation on gene regulation. Here we generate phased whole genomes with matched chromatin accessibility, histone modifications, and gene expression for 10 melanoma cell lines. We find that training a specialized deep learning model, called DeepMEL2, on melanoma chromatin accessibility data can capture the various regulatory programs of the melanocytic and mesenchymal-like melanoma cell states. This model outperforms motif-based variant scoring, as well as more generic deep learning models. We detect hundreds to thousands of allele-specific chromatin accessibility variants (ASCAVs) in each melanoma genome, of which 15%-20% can be explained by gains or losses of transcription factor binding sites. A considerable fraction of ASCAVs are caused by changes in AP-1 binding, as confirmed by matched ChIP-seq data to identify allele-specific binding of JUN and FOSL1. Finally, by augmenting the DeepMEL2 model with ChIP-seq data for GABPA, the TERT promoter mutation, as well as additional ETS motif gains, can be identified with high confidence. In conclusion, we present a new integrative genomics approach and a deep learning model to identify and interpret functional enhancer mutations with allelic imbalance of chromatin accessibility and gene expression.

Characterizing genetic intra-tumor heterogeneity across 2,658 human cancer genomes.

Intra-tumor heterogeneity (ITH) is a mechanism of therapeutic resistance and therefore an important clinical challenge. However, the extent, origin, and drivers of ITH across cancer types are poorly understood. To address this, we extensively characterize ITH across whole-genome sequences of 2,658 cancer samples spanning 38 cancer types. Nearly all informative samples (95.1%) contain evidence of distinct subclonal expansions with frequent branching relationships between subclones. We observe positive selection of subclonal driver mutations across most cancer types and identify cancer type-specific subclonal patterns of driver gene mutations, fusions, structural variants, and copy number alterations as well as dynamic changes in mutational processes between subclonal expansions. Our results underline the importance of ITH and its drivers in tumor evolution and provide a pan-cancer resource of comprehensively annotated subclonal events from whole-genome sequencing data.

Transcriptional Profiling of STAT1 Gain-of-Function Reveals Common and Mutation-Specific Fingerprints.

STAT1 gain-of-function (GOF) is a primary immunodeficiency typically characterized by chronic mucocutaneous candidiasis (CMC), recurrent respiratory infections, and autoimmunity. Less commonly, also immunodysregulation polyendocrinopathy enteropathy X-linked (IPEX)-like syndromes with CMC, and combined immunodeficiency without CMC have been described. Recently, our group and others have shown that different mutation-specific mechanisms underlie STAT1 GOF in vitro, including faster nuclear accumulation (R274W), and reduced mobility (R321, N574I) to near immobility in the nucleus (T419R) upon IFNγ stimulation. In this work, we evaluated the transcriptomic fingerprint of the aforementioned STAT1 GOF mutants (R274W, R321S, T419R, and N574I) relative to STAT1 wild-type upon IFNγ stimulation in an otherwise isogenic cell model. The majority of genes up-regulated in wild-type STAT1 cells were significantly more up-regulated in cells expressing GOF mutants, except for T419R. In addition to the common interferon regulated genes (IRG), STAT1 GOF mutants up-regulated an additional set of genes, that were in part shared with other GOF mutants or mutation-specific. Overall, R274W and R321S transcriptomes clustered with STAT1 WT, while T419R and N574I had a more distinct fingerprint. We observed reduced frequency of canonical IFNγ activation site (GAS) sequences in promoters of genes up-regulated by all the STAT1 GOF mutants, suggesting loss of DNA binding specificity for the canonical GAS consensus. Interestingly, the T419R mutation, expected to directly increase the affinity for DNA, showed the most pronounced effects on the transcriptome. T419R STAT1 dysregulated more non-IRG than the other GOF mutants and fewer GAS or degenerate GAS promotor sequences could be found in the promoter regions of these genes. In conclusion, our work confirms hyperactivation of common sets of IFNγ-induced genes in STAT1 GOF with additional dysregulation of mutation-specific genes, in line with the earlier observed mutation-specific mechanisms. Binding to more degenerate GAS sequences is proposed as a mechanism toward transcriptional dysregulation in R274W, R321S, and N574I. For T419R, an increased interaction with the DNA is suggested to result in a broader and less GAS-specific response. Our work indicates that multiple routes leading to STAT1 GOF are associated with common and private transcriptomic fingerprints, which may contribute to the phenotypic variation observed in vivo.