Many patients with advanced cancers achieve dramatic responses to a panoply of therapeutics yet retain minimal residual disease (MRD), which ultimately results in relapse. To gain insights into the biology of MRD, we applied single-cell RNA sequencing to malignant cells isolated from BRAF mutant patient-derived xenograft melanoma cohorts exposed to concurrent RAF/MEK-inhibition. We identified distinct drug-tolerant transcriptional states, varying combinations of which co-occurred within MRDs from PDXs and biopsies of patients on treatment. One of these exhibited a neural crest stem cell (NCSC) transcriptional program largely driven by the nuclear receptor RXRG. An RXR antagonist mitigated accumulation of NCSCs in MRD and delayed the development of resistance. These data identify NCSCs as key drivers of resistance and illustrate the therapeutic potential of MRD-directed therapy. They also highlight how gene regulatory network architecture reprogramming may be therapeutically exploited to limit cellular heterogeneity, a key driver of disease progression and therapy resistance.

Although common cancer hallmarks are well established, lineage-restricted oncogenes remain less understood. Here, we report an inherent dependency of melanoma cells on the small GTPase RAB7, identified within a lysosomal gene cluster that distinguishes this malignancy from over 35 tumor types. Analyses in human cells, clinical specimens, and mouse models demonstrated that RAB7 is an early-induced melanoma driver whose levels can be tuned to favor tumor invasion, ultimately defining metastatic risk. Importantly, RAB7 levels and function were independent of MITF, the best-characterized melanocyte lineage-specific transcription factor. Instead, we describe the neuroectodermal master modulator SOX10 and the oncogene MYC as RAB7 regulators. These results reveal a unique wiring of the lysosomal pathway that melanomas exploit to foster tumor progression.

The Hippo signaling pathway and its two downstream effectors, the YAP and TAZ transcriptional coactivators, are drivers of tumor growth in experimental models. Studying mouse models, we show that YAP and TAZ can also exert a tumor-suppressive function. We found that normal hepatocytes surrounding liver tumors displayed activation of YAP and TAZ and that deletion of Yap and Taz in these peritumoral hepatocytes accelerated tumor growth. Conversely, experimental hyperactivation of YAP in peritumoral hepatocytes triggered regression of primary liver tumors and melanoma-derived liver metastases. Furthermore, whereas tumor cells growing in wild-type livers required YAP and TAZ for their survival, those surrounded by Yap- and Taz-deficient hepatocytes were not dependent on YAP and TAZ. Tumor cell survival thus depends on the relative activity of YAP and TAZ in tumor cells and their surrounding tissue, suggesting that YAP and TAZ act through a mechanism of cell competition to eliminate tumor cells.

Deciphering the genomic regulatory code of enhancers is a key challenge in biology as this code underlies cellular identity. A better understanding of how enhancers work will improve the interpretation of non-coding genome variation, and empower the generation of cell type specific drivers for gene therapy. Here we explore the combination of deep learning and cross-species chromatin accessibility profiling to build explainable enhancer models. We apply this strategy to decipher the enhancer code in melanoma, a relevant case study due to the presence of distinct melanoma cell states. We trained and validated a deep learning model, called DeepMEL, using chromatin accessibility data of 26 melanoma samples across six different species. We demonstrate the accuracy of DeepMEL predictions on the CAGI5 challenge, where it significantly outperforms existing models on the melanoma enhancer of IRF4. Next, we exploit DeepMEL to analyse enhancer architectures and identify accurate transcription factor binding sites for the core regulatory complexes in the two different melanoma states, with distinct roles for each transcription factor, in terms of nucleosome displacement

No abstract

No abstract

We introduce the Haar + tree: a refined, wavelet-inspired data structure for synopsis construction. The advantages of this structure are twofold: First, it achieves higher synopsis quality at the task of summarizing data sets with sharp discontinuities than state-of-the-art histogram and Haar wavelet techniques. Second, thanks to its search space delimitation capacity, Haar + synopsis construction operates in time linear to the size of the data set for any monotonic distributive error metric. Through experimentation, we demonstrate the superiority of Haar + synopses over histogram and Haar wavelet methods in both construction time and achieved quality for representative error metrics.

Recent research studied the problem of publishing microdata without revealing sensitive information, leading to the privacy-preserving paradigms of k-anonymity and -diversity. k-anonymity protects against the identification of an individual's record. -diversity, in addition, safeguards against the association of an individual with specific sensitive information. However, existing approaches suffer from at least one of the following drawbacks: (i) -diversification is solved by techniques developed for the simpler k-anonymization problem, causing unnecessary information loss.(ii) The anonymization process is inefficient in terms of computational and I/O cost. (iii) Previous research focused exclusively on the privacy-constrained problem and ignored the equally important accuracy-constrained (or dual) anonymization problem.In this article, we propose a framework for efficient anonymization of microdata that addresses these deficiencies. First, we focus on one-dimensional (i.e., single-attribute) quasi-identifiers, and study the properties of optimal solutions under the k-anonymity and -diversity models for the privacy-constrained (i.e., direct) and the accuracy-constrained (i.e., dual) anonymization problems. Guided by these properties, we develop efficient heuristics to solve the one-dimensional problems in linear time. Finally, we generalize our solutions to multidimensional quasi-identifiers using space-mapping techniques. Extensive experimental evaluation shows that our techniques clearly outperform the existing approaches in terms of execution time and information loss.