Tumour evolution and microenvironment interactions in 2D and 3D space.

To study the spatial interactions among cancer and non-cancer cells1, we here examined a cohort of 131 tumour sections from 78 cases across 6 cancer types by Visium spatial transcriptomics (ST). This was combined with 48 matched single-nucleus RNA sequencing samples and 22 matched co-detection by indexing (CODEX) samples. To describe tumour structures and habitats, we defined 'tumour microregions' as spatially distinct cancer cell clusters separated by stromal components. They varied in size and density among cancer types, with the largest microregions observed in metastatic samples. We further grouped microregions with shared genetic alterations into 'spatial subclones'. Thirty five tumour sections exhibited subclonal structures. Spatial subclones with distinct copy number variations and mutations displayed differential oncogenic activities. We identified increased metabolic activity at the centre and increased antigen presentation along the leading edges of microregions. We also observed variable T cell infiltrations within microregions and macrophages predominantly residing at tumour boundaries. We reconstructed 3D tumour structures by co-registering 48 serial ST sections from 16 samples, which provided insights into the spatial organization and heterogeneity of tumours. Additionally, using an unsupervised deep-learning algorithm and integrating ST and CODEX data, we identified both immune hot and cold neighbourhoods and enhanced immune exhaustion markers surrounding the 3D subclones. These findings contribute to the understanding of spatial tumour evolution through interactions with the local microenvironment in 2D and 3D space, providing valuable insights into tumour biology.

M3NetFlow: A novel multi-scale multi-hop graph AI model for integrative multi-omic data analysis.

Multi-omic data-driven studies, characterizing complex disease signaling system from multiple levels, are at the forefront of precision medicine and healthcare. The integration and interpretation of multi-omic data are essential for identifying molecular targets and deciphering core signaling pathways of complex diseases. However, it remains an open problem due the large number of biomarkers and complex interactions among them. In this study, we propose a novel Multi-scale Multi-hop Multi-omic graph model, M3NetFlow, to facilitate generic multi-omic data analysis to rank targets and infer core signaling flows/pathways. To evaluate M3NetFlow, we applied it in two independent multi-omic case studies: 1) uncovering mechanisms of synergistic drug combination response (defined as anchor-target guided learning), and 2) identifying biomarkers and pathways of Alzheimer 's disease (AD). The evaluation and comparison results showed M3NetFlow achieves the best prediction accuracy (accurate), and identifies a set of essential targets and core signaling pathways (interpretable). The model can be directly applied to other multi-omic data-driven studies. The code is publicly accessible at: https://github.com/FuhaiLiAiLab/M3NetFlow.

Plasma Ceramide C24:0/C16:0 Ratio is Associated with Improved Survival in Patients with Pancreatic Ductal Adenocarcinoma.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) has a high fatality rate, with surgery as the only curative treatment. Identification of new biomarkers related to survival may help guide discovery of new pathophysiologic pathways and potential therapeutic targets. As long-chain ceramides have been linked to tumor proliferation, we sought to determine if ceramide levels were prognostic in PDAC. METHODS: Patients from two phase I studies of PDAC were followed for all-cause mortality. Ceramide levels (C24:0, C22:0, and C16:0) were quantified before treatment and at study intervals. Multivariable Cox regression models assessed the association of ceramide levels and mortality after adjusting for other univariable predictors, including time-dependent tumor resection. The ability of repeated ceramide measures to discriminate patients at risk for mortality was also assessed using multivariable modeling and the c-statistic. RESULTS: Higher plasma C16:0 concentration was associated with higher all-cause mortality in univariable and multivariable analysis (adjusted hazard ratio [aHR] 1.41, 95% confidence interval [CI] 1.09-1.82; p < 0.01). In contrast, a higher plasma C24:0/C16:0 ratio was associated with lower all-cause mortality in multivariable analysis (aHR 0.69, 95% CI 0.49-0.97; p = 0.032). Discrimination of mortality was significantly improved with the addition of either plasma C16:0 or C24:0/C16:0 levels, with optimal discrimination occurring using repeated measures of the C24:0/C16:0 ratio (c-statistic 0.73 vs. c-statistic 0.66; p < 0.001). CONCLUSIONS: Higher plasma C16:0 and lower C24:0/C16:0 ratios are independently associated with mortality in PDAC and show an ability to improve discrimination of mortality in this deadly disease. Further studies are needed to confirm this association and evaluate this novel pathway for potential therapeutic targets.

Treatment resistance to melanoma therapeutics on a single cell level.

Therapy targeting the BRAF-MEK cascade created a treatment revolution for patients with BRAF mutant advanced melanoma. Unfortunately, 80% patients treated will progress by 5 years follow-up. Thus, it is imperative we study mechanisms of melanoma progression and therapeutic resistance. We created a scRNA (single cell RNA) atlas of 128,230 cells from 18 tumors across the treatment spectrum, discovering melanoma cells clustered strongly by transcriptome profiles of patients of origins. Our cell-level investigation revealed gains of 1q and 7q as likely early clonal events in metastatic melanomas. By comparing patient tumors and their derivative cell lines, we observed that PD1 responsive tumor fraction disappears when cells are propagated in vitro. We further established three anti-BRAF-MEK treatment resistant cell lines using three BRAF mutant tumors. ALDOA and PGK1 were found to be highly expressed in treatment resistant cell populations and metformin was effective in targeting the resistant cells. Our study suggests that the investigation of patient tumors and their derivative lines is essential for understanding disease progression, treatment response and resistance.

Development of an artificial intelligence-based model to predict early recurrence of neuroendocrine liver metastasis after resection.

PURPOSE: We sought to develop an artificial intelligence (AI)-based model to predict early recurrence (ER) after curative-intent resection of neuroendocrine liver metastases (NELMs). METHODS: Patients with NELM who underwent resection were identified from a multi-institutional database. ER was defined as recurrence within 12 months of surgery. Different AI-based models were developed to predict ER using 10 clinicopathologic factors. RESULTS: Overall, 473 patients with NELM were included. Among 284 patients with recurrence (60.0%), 118 patients (41.5%) developed an ER. An ensemble AI model demonstrated the highest area under receiver operating characteristic curves of 0.763 and 0.716 in the training and testing cohorts, respectively. Maximum diameter of the primary neuroendocrine tumor, NELM radiologic tumor burden score, and bilateral liver involvement were the factors most strongly associated with risk of NELM ER. Patients predicted to develop ER had worse 5-year recurrence-free survival and overall survival (21.4% vs 37.1% [P = .002] and 61.6% vs 90.3% [P = .03], respectively) than patients not predicted to recur. An easy-to-use tool was made available online: (https://altaf-pawlik-nelm-earlyrecurrence-calculator.streamlit.app/). CONCLUSION: An AI-based model demonstrated excellent discrimination to predict ER of NELM after resection. The model may help identify patients who can benefit the most from curative-intent resection, risk stratify patients according to prognosis, as well as guide tailored surveillance and treatment decisions including consideration of nonsurgical treatment options.

NCCN Guidelines® Insights: Melanoma: Cutaneous, Version 2.2024.

The NCCN Guidelines for Cutaneous Melanoma (termed Melanoma: Cutaneous) provide multidisciplinary recommendations for diagnostic workup, staging, and treatment of patients. These NCCN Guidelines Insights focus on the update to neoadjuvant systemic therapy options and summarize the new clinical data evaluated by the NCCN panel for the recommended therapies in Version 2.2024 of the NCCN Guidelines for Cutaneous Melanoma.

A Pan-Cancer Patient-Derived Xenograft Histology Image Repository with Genomic and Pathologic Annotations Enables Deep Learning Analysis.

Patient-derived xenografts (PDX) model human intra- and intertumoral heterogeneity in the context of the intact tissue of immunocompromised mice. Histologic imaging via hematoxylin and eosin (H&E) staining is routinely performed on PDX samples, which could be harnessed for computational analysis. Prior studies of large clinical H&E image repositories have shown that deep learning analysis can identify intercellular and morphologic signals correlated with disease phenotype and therapeutic response. In this study, we developed an extensive, pan-cancer repository of >1,000 PDX and paired parental tumor H&E images. These images, curated from the PDX Development and Trial Centers Research Network Consortium, had a range of associated genomic and transcriptomic data, clinical metadata, pathologic assessments of cell composition, and, in several cases, detailed pathologic annotations of neoplastic, stromal, and necrotic regions. The amenability of these images to deep learning was highlighted through three applications: (i) development of a classifier for neoplastic, stromal, and necrotic regions; (ii) development of a predictor of xenograft-transplant lymphoproliferative disorder; and (iii) application of a published predictor of microsatellite instability. Together, this PDX Development and Trial Centers Research Network image repository provides a valuable resource for controlled digital pathology analysis, both for the evaluation of technical issues and for the development of computational image-based methods that make clinical predictions based on PDX treatment studies. Significance: A pan-cancer repository of >1,000 patient-derived xenograft hematoxylin and eosin-stained images will facilitate cancer biology investigations through histopathologic analysis and contributes important model system data that expand existing human histology repositories.

Memory-like differentiation enhances NK cell responses against colorectal cancer.

Metastatic (m) colorectal cancer (CRC) is an incurable disease with a poor prognosis and thus remains an unmet clinical need. Immune checkpoint blockade (ICB)-based immunotherapy is effective for mismatch repair-deficient (dMMR)/microsatellite instability-high (MSI-H) mCRC patients, but it does not benefit the majority of mCRC patients. NK cells are innate lymphoid cells with potent effector responses against a variety of tumor cells but are frequently dysfunctional in cancer patients. Memory-like (ML) NK cells differentiated after IL-12/IL-15/IL-18 activation overcome many challenges to effective NK cell anti-tumor responses, exhibiting enhanced recognition, function, and in vivo persistence. We hypothesized that ML differentiation enhances the NK cell responses to CRC. Compared to conventional (c) NK cells, ML NK cells displayed increased IFN-γ production against both CRC cell lines and primary patient-derived CRC spheroids. ML NK cells also exhibited improved killing of CRC target cells in vitro in short-term and sustained cytotoxicity assays, as well as in vivo in NSG mice. Mechanistically, enhanced ML NK cell responses were dependent on the activating receptor NKG2D as its blockade significantly decreased ML NK cell functions. Compared to cNK cells, ML NK cells exhibited greater antibody-dependent cytotoxicity when targeted against CRC by cetuximab. ML NK cells from healthy donors and mCRC patients exhibited increased anti-CRC responses. Collectively, our findings demonstrate that ML NK cells exhibit enhanced responses against CRC targets, warranting further investigation in clinical trials for mCRC patients, including those who have failed ICB.

Senescence Defines a Distinct Subset of Myofibroblasts That Orchestrates Immunosuppression in Pancreatic Cancer.

Pancreatic ductal adenocarcinoma (PDAC) therapeutic resistance is largely attributed to a unique tumor microenvironment embedded with an abundance of cancer-associated fibroblasts (CAF). Distinct CAF populations were recently identified, but the phenotypic drivers and specific impact of CAF heterogeneity remain unclear. In this study, we identify a subpopulation of senescent myofibroblastic CAFs (SenCAF) in mouse and human PDAC. These SenCAFs are a phenotypically distinct subset of myofibroblastic CAFs that localize near tumor ducts and accumulate with PDAC progression. To assess the impact of endogenous SenCAFs in PDAC, we used an LSL-KRASG12D;p53flox;p48-CRE;INK-ATTAC (KPPC-IA) mouse model of spontaneous PDAC with inducible senescent cell depletion. Depletion of senescent stromal cells in genetic and pharmacologic PDAC models relieved immune suppression by macrophages, delayed tumor progression, and increased responsiveness to chemotherapy. Collectively, our findings demonstrate that SenCAFs promote PDAC progression and immune cell dysfunction. Significance: CAF heterogeneity in PDAC remains poorly understood. In this study, we identify a novel subpopulation of senescent CAFs that promotes PDAC progression and immunosuppression. Targeting CAF senescence in combination therapies could increase tumor vulnerability to chemo or immunotherapy. See related article by Ye et al., p. 1302.

Natural history of undifferentiated pleomorphic sarcoma: Experience from the US Sarcoma Collaborative.

BACKGROUND: Undifferentiated pleomorphic sarcoma (UPS) is a relatively rare but aggressive neoplasm. We sought to utilize a multi-institutional US cohort of sarcoma patients to examine predictors of survival and recurrence patterns after resection of UPS. METHODS: From 2000 to 2016, patients with primary UPS undergoing curative-intent surgical resection at seven academic institutions were retrospectively reviewed. Epidemiologic and clinicopathologic factors were reviewed by site of origin. Overall survival (OS), recurrence-free survival (RFS), time-to-locoregional (TTLR), time-to-distant recurrence (TTDR), and patterns of recurrence were analyzed. RESULTS: Of the 534 UPS patients identified, 53% were female, with a median age of 60 and median tumor size of 8.5 cm. The median OS, RFS, TTLR, and TTDR for the entire cohort were 109, 49, 86, and 46 months, respectively. There were no differences in these survival outcomes between extremity and truncal UPS. Compared with truncal, extremity UPS were more commonly amenable to R0 resection (87% vs. 75%, p = 0.017) and less commonly associated with lymph node metastasis (1% vs. 6%, p = 0.031). R0 resection and radiation treatment, but not site of origin (extremity vs. trunk) were independent predictors of OS and RFS. TTLR recurrence was shorter for UPS resected with a positive margin and for tumors not treated with radiation. CONCLUSION: For patients with resected extremity and truncal UPS, tumor size >5 cm and positive resection margin are associated with worse survival OS and RFS, irrespectively the site of origin. R0 surgical resection and radiation treatment may help improve these survival outcomes.

Mapping single-cell developmental potential in health and disease with interpretable deep learning.

Single-cell RNA sequencing (scRNA-seq) has transformed our understanding of cell fate in developmental systems. However, identifying the molecular hallmarks of potency - the capacity of a cell to differentiate into other cell types - has remained challenging. Here, we introduce CytoTRACE 2, an interpretable deep learning framework for characterizing potency and differentiation states on an absolute scale from scRNA-seq data. Across 31 human and mouse scRNA-seq datasets encompassing 28 tissue types, CytoTRACE 2 outperformed existing methods for recovering experimentally determined potency levels and differentiation states covering the entire range of cellular ontogeny. Moreover, it reconstructed the temporal hierarchy of mouse embryogenesis across 62 timepoints; identified pan-tissue expression programs that discriminate major potency levels; and facilitated discovery of cellular phenotypes in cancer linked to survival and immunotherapy resistance. Our results illuminate a fundamental feature of cell biology and provide a broadly applicable platform for delineating single-cell differentiation landscapes in health and disease.

Combined KRAS-MAPK pathway inhibitors and HER2-directed drug conjugate is efficacious in pancreatic cancer.

Targeting the mitogen-activated protein kinase (MAPK) cascade in pancreatic ductal adenocarcinoma (PDAC) remains clinically unsuccessful. We aim to develop a MAPK inhibitor-based therapeutic combination with strong preclinical efficacy. Utilizing a reverse-phase protein array, we observe rapid phospho-activation of human epidermal growth factor receptor 2 (HER2) in PDAC cells upon pharmacological MAPK inhibition. Mechanistically, MAPK inhibitors lead to swift proteasomal degradation of dual-specificity phosphatase 6 (DUSP6). The carboxy terminus of HER2, containing a TEY motif also present in extracellular signal-regulated kinase 1/2 (ERK1/2), facilitates binding with DUSP6, enhancing its phosphatase activity to dephosphorylate HER2. In the presence of MAPK inhibitors, DUSP6 dissociates from the protective effect of the RING E3 ligase tripartite motif containing 21, resulting in its degradation. In PDAC patient-derived xenograft (PDX) models, combining ERK and HER inhibitors slows tumour growth and requires cytotoxic chemotherapy to achieve tumour regression. Alternatively, MAPK inhibitors with trastuzumab deruxtecan, an anti-HER2 antibody conjugated with cytotoxic chemotherapy, lead to sustained tumour regression in most tested PDXs without causing noticeable toxicity. Additionally, KRAS inhibitors also activate HER2, supporting testing the combination of KRAS inhibitors and trastuzumab deruxtecan in PDAC. This study identifies a rational and promising therapeutic combination for clinical testing in PDAC patients.

Survival disparities in rural versus urban patients with pancreatic neuroendocrine tumor: A multi-institutional study from the US neuroendocrine tumor study group.

BACKGROUND: Pancreatic Neuroendocrine Tumors (PNETs) are indolent malignancies that often have a prolonged clinical course. This study assesses disparities in outcomes between PNET patients who live in urban (UA) and rural areas (RA). METHODS: A retrospective cohort study was performed using the US Neuroendocrine Tumor Study Group database. PNET patients with a home zip code recorded were included and categorized as RA or UA according to the Federal Office of Rural Health Policy. Overall survival (OS) was analyzed by Kaplan-Meier method, log-rank test, and logistical regression. RESULTS: Of the 1176 PNET patients in the database, 1126 (96%) had zip code recorded. While 837 (74%) lived in UA, 289 (26%) lived in RA. RA patients had significantly shorter median OS following primary PNET resection (122 vs 149 months, p â€‹= â€‹0.01). After controlling for income, local healthcare access, distance from treatment center, ASA class, BMI, and T/N/M stage, living in a RA remained significantly associated with worse OS (HR 1.60, 95%CI 1.08-2.39, p â€‹= â€‹0.02). CONCLUSION: Rural patients have significantly shorter OS following PNET resection compared to their urban counterparts.

Intratumoral T-cell receptor repertoire composition predicts overall survival in patients with pancreatic ductal adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is a lethal malignancy that is refractory to immune checkpoint inhibitor therapy. However, intratumoral T-cell infiltration correlates with improved overall survival (OS). Herein, we characterized the diversity and antigen specificity of the PDAC T-cell receptor (TCR) repertoire to identify novel immune-relevant biomarkers. Demographic, clinical, and TCR-beta sequencing data were collated from 353 patients across three cohorts that underwent surgical resection for PDAC. TCR diversity was calculated using Shannon Wiener index, Inverse Simpson index, and "True entropy." Patients were clustered by shared repertoire specificity. TCRs predictive of OS were identified and their associated transcriptional states were characterized by single-cell RNAseq. In multivariate Cox regression models controlling for relevant covariates, high intratumoral TCR diversity predicted OS across multiple cohorts. Conversely, in peripheral blood, high abundance of T-cells, but not high diversity, predicted OS. Clustering patients based on TCR specificity revealed a subset of TCRs that predicts OS. Interestingly, these TCR sequences were more likely to encode CD8+ effector memory and CD4+ T-regulatory (Tregs) T-cells, all with the capacity to recognize beta islet-derived autoantigens. As opposed to T-cell abundance, intratumoral TCR diversity was predictive of OS in multiple PDAC cohorts, and a subset of TCRs enriched in high-diversity patients independently correlated with OS. These findings emphasize the importance of evaluating peripheral and intratumoral TCR repertoires as distinct and relevant biomarkers in PDAC.

sc2MeNetDrug: A computational tool to uncover inter-cell signaling targets and identify relevant drugs based on single cell RNA-seq data.

Single-cell RNA sequencing (scRNA-seq) is a powerful technology to investigate the transcriptional programs in stromal, immune, and disease cells, like tumor cells or neurons within the Alzheimer's Disease (AD) brain or tumor microenvironment (ME) or niche. Cell-cell communications within ME play important roles in disease progression and immunotherapy response and are novel and critical therapeutic targets. Though many tools of scRNA-seq analysis have been developed to investigate the heterogeneity and sub-populations of cells, few were designed for uncovering cell-cell communications of ME and predicting the potentially effective drugs to inhibit the communications. Moreover, the data analysis processes of discovering signaling communication networks and effective drugs using scRNA-seq data are complex and involve a set of critical analysis processes and external supportive data resources, which are difficult for researchers who have no strong computational background and training in scRNA-seq data analysis. To address these challenges, in this study, we developed a novel open-source computational tool, sc2MeNetDrug (https://fuhaililab.github.io/sc2MeNetDrug/). It was specifically designed using scRNA-seq data to identify cell types within disease MEs, uncover the dysfunctional signaling pathways within individual cell types and interactions among different cell types, and predict effective drugs that can potentially disrupt cell-cell signaling communications. sc2MeNetDrug provided a user-friendly graphical user interface to encapsulate the data analysis modules, which can facilitate the scRNA-seq data-based discovery of novel inter-cell signaling communications and novel therapeutic regimens.

Female-specific pancreatic cancer survival from CT imaging of visceral fat implicates glutathione metabolism in solid tumors.

RATIONALE AND OBJECTIVES: To identify if body composition, assessed with preoperative CT-based visceral fat ratio quantification as well as tumor metabolic gene expression, predicts sex-dependent overall survival (OS) in patients with pancreatic ductal adenocarcinoma (PDAC). MATERIALS AND METHODS: This was a retrospective analysis of preoperative CT in 98 male and 107 female patients with PDAC. Relative visceral fat (rVFA; visceral fat normalized to total fat) was measured automatically using software and corrected manually. Median and optimized rVFA thresholds were determined according to published methods. Kaplan Meier and log-rank tests were used to estimate OS. Multivariate models were developed to identify interactions between sex, rVFA, and OS. Unsupervised gene expression analysis of PDAC tumors from The Cancer Genome Atlas (TCGA) was performed to identify metabolic pathways with similar survival patterns to rVFA. RESULTS: Optimized preoperative rVFA threshold of 38.9% predicted significantly different OS in females with a median OS of 15 months (above threshold) vs 24 months (below threshold; p = 0.004). No significant threshold was identified in males. This female-specific significance was independent of age, stage, and presence of chronic pancreatitis (p = 0.02). Tumor gene expression analysis identified female-specific stratification from a five-gene signature of glutathione S-transferases. This was observed for PDAC as well as clear cell renal carcinoma and glioblastoma. CONCLUSION: CT-based assessments of visceral fat can predict pancreatic cancer OS in females. Glutathione S-transferase expression in tumors predicts female-specific OS in a similar fashion.

Combining the Tyrosine Kinase Inhibitor Cabozantinib and the mTORC1/2 Inhibitor Sapanisertib Blocks ERK Pathway Activity and Suppresses Tumor Growth in Renal Cell Carcinoma.

Current treatment approaches for renal cell carcinoma (RCC) face challenges in achieving durable tumor responses due to tumor heterogeneity and drug resistance. Combination therapies that leverage tumor molecular profiles could offer an avenue for enhancing treatment efficacy and addressing the limitations of current therapies. To identify effective strategies for treating RCC, we selected ten drugs guided by tumor biology to test in six RCC patient-derived xenograft (PDX) models. The multitargeted tyrosine kinase inhibitor (TKI) cabozantinib and mTORC1/2 inhibitor sapanisertib emerged as the most effective drugs, particularly when combined. The combination demonstrated favorable tolerability and inhibited tumor growth or induced tumor regression in all models, including two from patients who experienced treatment failure with FDA-approved TKI and immunotherapy combinations. In cabozantinib-treated samples, imaging analysis revealed a significant reduction in vascular density, and single-nucleus RNA sequencing (snRNA-seq) analysis indicated a decreased proportion of endothelial cells in the tumors. SnRNA-seq data further identified a tumor subpopulation enriched with cell-cycle activity that exhibited heightened sensitivity to the cabozantinib and sapanisertib combination. Conversely, activation of the epithelial-mesenchymal transition pathway, detected at the protein level, was associated with drug resistance in residual tumors following combination treatment. The combination effectively restrained ERK phosphorylation and reduced expression of ERK downstream transcription factors and their target genes implicated in cell-cycle control and apoptosis. This study highlights the potential of the cabozantinib plus sapanisertib combination as a promising treatment approach for patients with RCC, particularly those whose tumors progressed on immune checkpoint inhibitors and other TKIs. SIGNIFICANCE: The molecular-guided therapeutic strategy of combining cabozantinib and sapanisertib restrains ERK activity to effectively suppress growth of renal cell carcinomas, including those unresponsive to immune checkpoint inhibitors.

Epigenetic regulation during cancer transitions across 11 tumour types.

Chromatin accessibility is essential in regulating gene expression and cellular identity, and alterations in accessibility have been implicated in driving cancer initiation, progression and metastasis1-4. Although the genetic contributions to oncogenic transitions have been investigated, epigenetic drivers remain less understood. Here we constructed a pan-cancer epigenetic and transcriptomic atlas using single-nucleus chromatin accessibility data (using single-nucleus assay for transposase-accessible chromatin) from 225 samples and matched single-cell or single-nucleus RNA-sequencing expression data from 206 samples. With over 1 million cells from each platform analysed through the enrichment of accessible chromatin regions, transcription factor motifs and regulons, we identified epigenetic drivers associated with cancer transitions. Some epigenetic drivers appeared in multiple cancers (for example, regulatory regions of ABCC1 and VEGFA; GATA6 and FOX-family motifs), whereas others were cancer specific (for example, regulatory regions of FGF19, ASAP2 and EN1, and the PBX3 motif). Among epigenetically altered pathways, TP53, hypoxia and TNF signalling were linked to cancer initiation, whereas oestrogen response, epithelial-mesenchymal transition and apical junction were tied to metastatic transition. Furthermore, we revealed a marked correlation between enhancer accessibility and gene expression and uncovered cooperation between epigenetic and genetic drivers. This atlas provides a foundation for further investigation of epigenetic dynamics in cancer transitions.

Functional analysis of recurrent CDC20 promoter variants in human melanoma.

Small nucleotide variants in non-coding regions of the genome can alter transcriptional regulation, leading to changes in gene expression which can activate oncogenic gene regulatory networks. Melanoma is heavily burdened by non-coding variants, representing over 99% of total genetic variation, including the well-characterized TERT promoter mutation. However, the compendium of regulatory non-coding variants is likely still functionally under-characterized. We developed a pipeline to identify hotspots, i.e. recurrently mutated regions, in melanoma containing putatively functional non-coding somatic variants that are located within predicted melanoma-specific regulatory regions. We identified hundreds of statistically significant hotspots, including the hotspot containing the TERT promoter variants, and focused on a hotspot in the promoter of CDC20. We found that variants in the promoter of CDC20, which putatively disrupt an ETS motif, lead to lower transcriptional activity in reporter assays. Using CRISPR/Cas9, we generated an indel in the CDC20 promoter in human A375 melanoma cell lines and observed decreased expression of CDC20, changes in migration capabilities, increased growth of xenografts, and an altered transcriptional state previously associated with a more proliferative and less migratory state. Overall, our analysis prioritized several recurrent functional non-coding variants that, through downregulation of CDC20, led to perturbation of key melanoma phenotypes.