Faculty Bibliography

UNLABELLED:Melanoma being one of the most common and deadliest skin cancers has been increasing since the past decade. Patients at advanced stages of the disease have very poor prognoses, as opposed to at the earlier stages. Nowadays, the standard of care of advanced melanoma is resection, followed by immune checkpoint inhibition-based immunotherapy. However, a substantial proportion of patients either do not respond or develop resistance. This underscores a need for novel approaches and therapeutic targets as well as a better understanding of the mechanisms of melanoma pathogenesis. Long noncoding RNAs (lncRNA) comprise a poorly characterized class of functional players and promising targets in promoting malignancy. Certain lncRNAs have been identified to play integral roles in melanoma progression and drug resistance; however, systematic screens to uncover novel functional lncRNAs are scarce. In this study, we profile differentially expressed lncRNAs in patient-derived short-term metastatic cultures and BRAF-MEK inhibition-resistant cells. We conduct a focused growth-related CRISPR inhibition screen of overexpressed lncRNAs, validate, and functionally characterize lncRNA hits with respect to cellular growth, invasive capacities, and apoptosis in vitro as well as the transcriptomic impact of our lead candidate the novel lncRNA XLOC_030781. In sum, we extend the current knowledge of ncRNAs and their potential relevance in melanoma. SIGNIFICANCE/UNASSIGNED:LncRNAs have emerged as novel players in regulating many cellular aspects also in melanoma. The number of functional significances of most lncRNAs remains elusive. We provide a comprehensive strategy to identify functionally relevant lncRNAs in melanoma by combining expression profiling with CRISPR inhibition growths screens. Our results broaden the characterized lncRNAs as potential targets for future therapeutic applications.

Response to immune checkpoint inhibitors (ICIs) in metastatic melanoma (MM) varies among patients, and current baseline biomarkers predicting treatment outcomes are limited. As mitochondrial (MT) metabolism has emerged as an important regulator of host immune function, we explored the association of host MT genetics (MT haplogroups) with ICI efficacy in 1,225 ICI-treated patients with MM from the clinical trial CheckMate-067 and the International Germline Immuno-Oncology Melanoma Consortium. We discovered and validated significant associations of MT haplogroup T (HG-T) with resistance to anti-programmed cell death protein-1-based ICI (both single-agent and combination) and have shown that HG-T is independent from established tumor predictors. We also found that patients belonging to HG-T exhibit a unique nivolumab-resistant baseline peripheral CD8⁺ T cell repertoire compared to other MT haplogroups, providing, to our knowledge, the first link between MT inheritance, host immunity and ICI resistance. The study proposes a host blood-based biomarker with stand-alone clinical value predicting ICI efficacy and points to an ICI-resistance mechanism associated with MT metabolism, with clinical relevance in immuno-oncology.

Despite recent advances, the regulation of anticancer and antimicrobial bioactive compound (AABC) production by leukocytes remains poorly understood. Here, we demonstrate that inactivation of the DNA- and RNA-based Teazeled receptors of the Universal Receptive System in human leukocytes generated so called "Leukocyte-Tells," which showed enhanced AABC production. Comprehensive analysis of the AABCs produced by Leukocyte-Tells based on LC/MS identified 707 unique or differentially produced peptide or nonpeptide metabolites. Functional testing demonstrated that many of these metabolites exhibited increased antibacterial, antifungal, and anticancer activities. The AABCs produced by the Leukocyte-Tells were effective against different multidrug-resistant clinical isolates of fungi and gram-positive and gram-negative bacteria (including their biofilms), as well as various cancer cell lines, with >100,000-fold activity than AABCs derived from control leukocytes. Notably, the AABCs produced by the Leukocyte-Tells exhibited greater stability under adverse environmental conditions. Our findings highlight the important role of the Universal Receptive System in regulating AABC production through a process named here as cell genome-memory management, offering new insights into immune functions and suggesting potential therapeutic applications.

Chromosomal structural variants (SVs) are major contributors to cancer development. Although multiple methods exist for detecting SVs, they are limited in throughput, such as fluorescent in situ hybridization and targeted panels, and use RNA, which degrades in formalin-fixed, paraffin-embedded (FFPE) blocks and is unable to detect SVs that do not produce a fusion transcript. High-throughput chromosome conformation capture (Hi-C) is a DNA-based next-generation sequencing (NGS) method that preserves the spatial conformation of the genome, capturing long-range genetic interactions and SVs. Herein, a retrospective study analyzing 71 FFPE specimens from 10 different solid tumors was performed. Results showed high concordance (98%) with clinical fluorescent in situ hybridization and RNA NGS in detecting known SVs. Furthermore, Hi-C provided insight into the mechanism of SV formation, including chromothripsis and extrachromosomal DNA, and detected rearrangements between genes and regulatory regions, all of which are undetectable by RNA NGS. Lastly, SVs were detected in 71% of cases in which previous clinical methods failed to identify a driver. Of these, 14% were clinically actionable based on current medical guidelines, and an additional 14% were not in medical guidelines but involve targetable biomarkers. Current data suggest that Hi-C is a robust and accurate method for genome-wide SV analyses from FFPE tissue and can be incorporated into current clinical NGS workflows.

Desmosomes are transmembrane protein complexes that contribute to cell-cell adhesion in epithelia and other tissues. Here, we report the discovery of frequent genetic alterations in the desmosome in human cancers, with the strongest signal seen in cutaneous melanoma, where desmosomes are mutated in more than 70% of cases. In primary but not metastatic melanoma biopsies, the burden of coding mutations in desmosome genes is associated with a strong reduction in desmosome gene expression. Analysis by spatial transcriptomics and protein immunofluorescence suggests that these decreases in expression occur in keratinocytes in the microenvironment rather than in primary melanoma cells. In further support of a microenvironmental origin, we find that desmosome gene knockdown in keratinocytes yields markedly increased proliferation of adjacent melanoma cells in keratinocyte and melanoma cocultures. Similar increases in melanoma proliferation are observed in media preconditioned with desmosome-deficient keratinocytes. Thus, gradual accumulation of desmosome mutations in neighboring cells may prime melanoma cells for neoplastic transformation.

While the mutational landscape across early T-cell precursor acute lymphoblastic leukemia (ETP-ALL) and ETP-like leukemia is known, establishing a unified framework that activates stem cell genes characteristic of these tumors remains elusive. Using complementary mouse and human models, chromatin mapping, and enhancer profiling, we show that the coactivator ZMIZ1 promotes normal and malignant ETP population growth by inducing the transcription factor MYB in feedforward circuits to convergently activate oncogenes (MEF2C, MYCN, and BCL2) through essential enhancers. A key superenhancer, the N-Myc regulating enhancer (NMRE), drives malignant ETP population growth but is dispensable for normal lymphopoiesis. This network of stem cell superenhancers identifies treatment-resistant tumors and poor survival outcomes; unifies diverse ETP-ALLs; and contributes to cardinal features of the recently genomically identified high-risk bone marrow progenitor-like (BMP-like) ETP-ALL tumor-stem cell/myeloid gene expression, inhibited NOTCH1-induced T-cell development, aggressive clinical behavior, and venetoclax sensitivity. Since ZMIZ1 is dispensable for essential homeostasis, it might be possible to safely target this network to treat high-risk diseases.

Microsporidia are single-celled intracellular parasites that cause opportunistic diseases in humans. Encephalitozoon intestinalis is a prevalent human-infecting species that invades the small intestine. Macrophages are potential reservoirs of infection, and dissemination to other organ systems is also observed. The macrophage response to infection and the developmental trajectory of the parasite are not well studied. Here we use single cell RNA sequencing to investigate transcriptional changes in both the parasite and the host during E. intestinalis infection of human macrophages in vitro. The parasite undergoes large transcriptional changes throughout the life cycle, providing a blueprint for parasite development. While a small population of infected macrophages mount a response, most remain transcriptionally unchanged, suggesting that the majority of parasites may avoid host detection. The stealthy microsporidian lifestyle likely allows these parasites to harness macrophages for replication. Together, our data provide insights into the host response in primary human macrophages and the E. intestinalis developmental program.

Here we used a series of CTCF mutations to explore CTCF's relationship with chromatin and its contribution to gene regulation. CTCF's impact depends on the genomic context of bound sites and the unique binding properties of WT and mutant CTCF proteins. Specifically, CTCF's signal strength is linked to changes in accessibility, and the ability to block cohesin is linked to its binding stability. Multivariate modeling reveals that both CTCF and accessibility contribute independently to cohesin binding and insulation, but CTCF signal strength has a stronger effect. CTCF and chromatin have a bidirectional relationship such that at CTCF sites, accessibility is reduced in a cohesin-dependent, mutant-specific fashion. In addition, each mutant alters TF binding and accessibility in an indirect manner, changes which impart the most influence on rewiring transcriptional networks and the cell's ability to differentiate. Collectively, the mutant perturbations provide a rich resource for determining CTCF's site-specific effects.

Self-supervised learning (SSL) automates the extraction and interpretation of histopathology features on unannotated hematoxylin-eosin-stained whole slide images (WSIs). We train an SSL Barlow Twins encoder on 435 colon adenocarcinoma WSIs from The Cancer Genome Atlas to extract features from small image patches (tiles). Leiden community detection groups tiles into histomorphological phenotype clusters (HPCs). HPC reproducibility and predictive ability for overall survival are confirmed in an independent clinical trial (N = 1213 WSIs). This unbiased atlas results in 47 HPCs displaying unique and shared clinically significant histomorphological traits, highlighting tissue type, quantity, and architecture, especially in the context of tumor stroma. Through in-depth analyses of these HPCs, including immune landscape and gene set enrichment analyses, and associations to clinical outcomes, we shine light on the factors influencing survival and responses to treatments of standard adjuvant chemotherapy and experimental therapies. Further exploration of HPCs may unveil additional insights and aid decision-making and personalized treatments for colon cancer patients.

High-dimensional multiplexed imaging can reveal the spatial organization of tumour tissues at the molecular level. However, owing to the scale and information complexity of the imaging data, it is challenging to discover and thoroughly characterize the heterogeneity of tumour microenvironments. Here we show that self-supervised representation learning on data from imaging mass cytometry can be leveraged to distinguish morphological differences in tumour microenvironments and to precisely characterize distinct microenvironment signatures. We used self-supervised masked image modelling to train a vision transformer that directly takes high-dimensional multiplexed mass-cytometry images. In contrast with traditional spatial analyses relying on cellular segmentation, the vision transformer is segmentation-free, uses pixel-level information, and retains information on the local morphology and biomarker distribution. By applying the vision transformer to a lung-tumour dataset, we identified and validated a monocytic signature that is associated with poor prognosis.