Stepwise-edited, human melanoma models reveal mutations' effect on tumor and microenvironment

Origins of cancer: ain't it just mature cells misbehaving?

A pervasive view is that undifferentiated stem cells are alone responsible for generating all other cells and are the origins of cancer. However, emerging evidence demonstrates fully differentiated cells are plastic, can be coaxed to proliferate, and also play essential roles in tissue maintenance, regeneration, and tumorigenesis. Here, we review the mechanisms governing how differentiated cells become cancer cells. First, we examine the unique characteristics of differentiated cell division, focusing on why differentiated cells are more susceptible than stem cells to accumulating mutations. Next, we investigate why the evolution of multicellularity in animals likely required plastic differentiated cells that maintain the capacity to return to the cell cycle and required the tumor suppressor p53. Finally, we examine an example of an evolutionarily conserved program for the plasticity of differentiated cells, paligenosis, which helps explain the origins of cancers that arise in adults. Altogether, we highlight new perspectives for understanding the development of cancer and new strategies for preventing carcinogenic cellular transformations from occurring.

Creating a multifaceted prognostic model for cutaneous melanoma: the convergence of single-cell and bulk sequencing with machine learning

Background

Cutaneous melanoma is a highly heterogeneous cancer, and understanding the role of inflammation-related genes in its progression is crucial.

Methods

The cohorts used include the TCGA cohort from TCGA database, and GSE115978, GSE19234, GSE22153 cohort, and GSE65904 cohort from GEO database. Weighted Gene Coexpression Network Analysis (WGCNA) identified key inflammatory modules. Machine learning techniques were employed to construct prognostic models, which were validated across multiple cohorts, including the TCGA cohort, GSE19234, GSE22153, and GSE65904. Immune cell infiltration, tumor mutation load, and immunotherapy response were assessed. The hub gene STAT1 was validated through cellular experiments.

Results

Single-cell analysis revealed heterogeneity in inflammation-related genes, with NK cells, T cells, and macrophages showing elevated inflammation-related scores. WGCNA identified a module highly associated with inflammation. Machine learning yielded a CoxBoost + GBM prognostic model. The model effectively stratified patients into high-risk and low-risk groups in multiple cohorts. A nomogram and Receiver Operating Characteristic (ROC) curves confirmed the model's accuracy. Low-risk patients exhibited increased immune cell infiltration, higher Tumor Mutational Burden (TMB), and potentially better immunotherapy response. Cellular experiments validated the functional role of STAT1 in melanoma progression.

Conclusion

Inflammation-related genes play a critical role in cutaneous melanoma progression. The developed prognostic model, nomogram, and validation experiments highlight the potential clinical relevance of these genes and provide a basis for further investigation into personalized treatment strategies for melanoma patients.

Beyond genetics: driving cancer with the tumour microenvironment behind the wheel

Cancer has long been viewed as a genetic disease of cumulative mutations. This notion is fuelled by studies showing that ageing tissues are often riddled with clones of complex oncogenic backgrounds coexisting in seeming harmony with their normal tissue counterparts. Equally puzzling, however, is how cancer cells harbouring high mutational burden contribute to normal, tumour-free mice when allowed to develop within the confines of healthy embryos. Conversely, recent evidence suggests that adult tissue cells expressing only one or a few oncogenes can, in some contexts, generate tumours exhibiting many of the features of a malignant, invasive cancer. These disparate observations are difficult to reconcile without invoking environmental cues triggering epigenetic changes that can either dampen or drive malignant transformation. In this Review, we focus on how certain oncogenes can launch a two-way dialogue of miscommunication between a stem cell and its environment that can rewire downstream events non-genetically and skew the morphogenetic course of the tissue. We review the cells and molecules of and the physical forces acting in the resulting tumour microenvironments that can profoundly affect the behaviours of transformed cells. Finally, we discuss possible explanations for the remarkable diversity in the relative importance of mutational burden versus tumour microenvironment and its clinical relevance.

Disrupting CD38-driven T cell dysfunction restores sensitivity to cancer immunotherapy

A central problem in cancer immunotherapy with immune checkpoint blockade (ICB) is the development of resistance, which affects 50% of patients with metastatic melanoma1,2. T cell exhaustion, resulting from chronic antigen exposure in the tumour microenvironment, is a major driver of ICB resistance3. Here, we show that CD38, an ecto-enzyme involved in nicotinamide adenine dinucleotide (NAD+) catabolism, is highly expressed in exhausted CD8+ T cells in melanoma and is associated with ICB resistance. Tumour-derived CD38hiCD8+ T cells are dysfunctional, characterised by impaired proliferative capacity, effector function, and dysregulated mitochondrial bioenergetics. Genetic and pharmacological blockade of CD38 in murine and patient-derived organotypic tumour models (MDOTS/PDOTS) enhanced tumour immunity and overcame ICB resistance. Mechanistically, disrupting CD38 activity in T cells restored cellular NAD+ pools, improved mitochondrial function, increased proliferation, augmented effector function, and restored ICB sensitivity. Taken together, these data demonstrate a role for the CD38-NAD+ axis in promoting T cell exhaustion and ICB resistance, and establish the efficacy of CD38 directed therapeutic strategies to overcome ICB resistance using clinically relevant, patient-derived 3D tumour models.

Enhanced Photodynamic Therapy Synergizing with Inhibition of Tumor Neutrophil Ferroptosis Boosts Anti-PD-1 Therapy of Gastric Cancer

For tumor treatment, the ultimate goal in tumor therapy is to eliminate the primary tumor, manage potential metastases, and trigger an antitumor immune response, resulting in the complete clearance of all malignant cells. Tumor microenvironment (TME) refers to the local biological environment of solid tumors and has increasingly become an attractive target for cancer therapy. Neutrophils within TME of gastric cancer (GC) spontaneously undergo ferroptosis, and this process releases oxidized lipids that limit T cell activity. Enhanced photodynamic therapy (PDT) mediated by di-iodinated IR780 (Icy7) significantly increases the production of reactive oxygen species (ROS). Meanwhile, neutrophil ferroptosis can be triggered by increased ROS generation in the TME. In this study, a liposome encapsulating both ferroptosis inhibitor Liproxstatin-1 and modified photosensitizer Icy7, denoted LLI, significantly inhibits tumor growth of GC. LLI internalizes into MFC cells to generate ROS causing immunogenic cell death (ICD). Simultaneously, liposome-deliver Liproxstatin-1 effectively inhibits the ferroptosis of tumor neutrophils. LLI-based immunogenic PDT and neutrophil-targeting immunotherapy synergistically boost the anti-PD-1 treatment to elicit potent TME and systemic antitumor immune response with abscopal effects. In conclusion, LLI holds great potential for GC immunotherapy.

Adaptation of a mutual exclusivity framework to identify driver mutations within oncogenic pathways

Distinguishing genomic alterations in cancer-associated genes that have functional impact on tumor growth and disease progression from the ones that are passengers and confer no fitness advantage have important clinical implications. Evidence-based methods for nominating drivers are limited by existing knowledge on the oncogenic effects and therapeutic benefits of specific variants from clinical trials or experimental settings. As clinical sequencing becomes a mainstay of patient care, applying computational methods to mine the rapidly growing clinical genomic data holds promise in uncovering functional candidates beyond the existing knowledge base and expanding the patient population that could potentially benefit from genetically targeted therapies. We propose a statistical and computational method (MAGPIE) that builds on a likelihood approach leveraging the mutual exclusivity pattern within an oncogenic pathway for identifying probabilistically both the specific genes within a pathway and the individual mutations within such genes that are truly the drivers. Alterations in a cancer-associated gene are assumed to be a mixture of driver and passenger mutations with the passenger rates modeled in relationship to tumor mutational burden. We use simulations to study the operating characteristics of the method and assess false-positive and false-negative rates in driver nomination. When applied to a large study of primary melanomas, the method accurately identifies the known driver genes within the RTK-RAS pathway and nominates several rare variants as prime candidates for functional validation. A comprehensive evaluation of MAGPIE against existing tools has also been conducted leveraging the Cancer Genome Atlas data.

TP53 to mediate immune escape in tumor microenvironment: an overview of the research progress

Increasing evidence suggests that key cancer-causing driver genes continue to exert a sustained influence on the tumor microenvironment (TME), highlighting the importance of immunotherapeutic targeting of gene mutations in governing tumor progression. TP53 is a prominent tumor suppressor that encodes the p53 protein, which controls the initiation and progression of different tumor types. Wild-type p53 maintains cell homeostasis and genomic instability through complex pathways, and mutant p53 (Mut p53) promotes tumor occurrence and development by regulating the TME. To date, it has been wildly considered that TP53 is able to mediate tumor immune escape. Herein, we summarized the relationship between TP53 gene and tumors, discussed the mechanism of Mut p53 mediated tumor immune escape, and summarized the progress of applying p53 protein in immunotherapy. This study will provide a basic basis for further exploration of therapeutic strategies targeting p53 protein.

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.

Evolution and impact of high content imaging

The field of high content imaging has steadily evolved and expanded substantially across many industry and academic research institutions since it was first described in the early 1990's. High content imaging refers to the automated acquisition and analysis of microscopic images from a variety of biological sample types. Integration of high content imaging microscopes with multiwell plate handling robotics enables high content imaging to be performed at scale and support medium- to high-throughput screening of pharmacological, genetic and diverse environmental perturbations upon complex biological systems ranging from 2D cell cultures to 3D tissue organoids to small model organisms. In this perspective article the authors provide a collective view on the following key discussion points relevant to the evolution of high content imaging: • Evolution and impact of high content imaging: An academic perspective • Evolution and impact of high content imaging: An industry perspective • Evolution of high content image analysis • Evolution of high content data analysis pipelines towards multiparametric and phenotypic profiling applications • The role of data integration and multiomics • The role and evolution of image data repositories and sharing standards • Future perspective of high content imaging hardware and software.

Adaptation of a Mutual Exclusivity Framework to Identify Driver Mutations within Biological Pathways

Distinguishing genomic alterations in cancer genes that have functional impact on tumor growth and disease progression from the ones that are passengers and confer no fitness advantage has important clinical implications. Evidence-based methods for nominating drivers are limited by existing knowledge on the oncogenic effects and therapeutic benefits of specific variants from clinical trials or experimental settings. As clinical sequencing becomes a mainstay of patient care, applying computational methods to mine the rapidly growing clinical genomic data holds promise in uncovering novel functional candidates beyond the existing knowledge-base and expanding the patient population that could potentially benefit from genetically targeted therapies. We propose a statistical and computational method (MAGPIE) that builds on a likelihood approach leveraging the mutual exclusivity pattern within an oncogenic pathway for identifying probabilistically both the specific genes within a pathway and the individual mutations within such genes that are truly the drivers. Alterations in a cancer gene are assumed to be a mixture of driver and passenger mutations with the passenger rates modeled in relationship to tumor mutational burden. A limited memory BFGS algorithm is used to facilitate large scale optimization. We use simulations to study the operating characteristics of the method and assess false positive and false negative rates in driver nomination. When applied to a large study of primary melanomas the method accurately identified the known driver genes within the RTK-RAS pathway and nominated a number of rare variants with previously unknown biological and clinical relevance as prime candidates for functional validation.

Experimental Models for Rare Melanoma Research-The Niche That Needs to Be Addressed

Melanoma, the tumor arising from the malignant transformation of pigment-producing cells-the melanocytes-represents one of the most severe cancer types. Despite their rarity compared to cutaneous melanoma, the extracutaneous subtypes such as uveal melanoma (UM), acral lentiginous melanoma (ALM), and mucosal melanoma (MM) stand out due to their increased aggressiveness and mortality rate, demanding continuous research to elucidate their specific pathological features and develop efficient therapies. Driven by the emerging progresses made in the preclinical modeling of melanoma, the current paper covers the most relevant in vitro, in vivo, and in ovo systems, providing a deeper understanding of these rare melanoma subtypes. However, the preclinical models for UM, ALM, and MM that were developed so far remain scarce, and none of them is able to completely simulate the complexity that is characteristic to these melanomas; thus, a continuous expansion of the existing library of experimental models is pivotal for driving advancements in this research field. An overview of the applicability of precision medicine in the management of rare melanoma subtypes is also provided.

Modeling the Tumor Microenvironment and Cancer Immunotherapy in Next-Generation Humanized Mice

Cancer immunotherapy has brought significant clinical benefits to numerous patients with malignant disease. However, only a fraction of patients experiences complete and durable responses to currently available immunotherapies. This highlights the need for more effective immunotherapies, combination treatments and predictive biomarkers. The molecular properties of a tumor, intratumor heterogeneity and the tumor immune microenvironment decisively shape tumor evolution, metastasis and therapy resistance and are therefore key targets for precision cancer medicine. Humanized mice that support the engraftment of patient-derived tumors and recapitulate the human tumor immune microenvironment of patients represent a promising preclinical model to address fundamental questions in precision immuno-oncology and cancer immunotherapy. In this review, we provide an overview of next-generation humanized mouse models suitable for the establishment and study of patient-derived tumors. Furthermore, we discuss the opportunities and challenges of modeling the tumor immune microenvironment and testing a variety of immunotherapeutic approaches using human immune system mouse models.

The journey from melanocytes to melanoma

Over the past decade, melanoma has led the field in new cancer treatments, with impressive gains in on-treatment survival but more modest improvements in overall survival. Melanoma presents heterogeneity and transcriptional plasticity that recapitulates distinct melanocyte developmental states and phenotypes, allowing it to adapt to and eventually escape even the most advanced treatments. Despite remarkable advances in our understanding of melanoma biology and genetics, the melanoma cell of origin is still fiercely debated because both melanocyte stem cells and mature melanocytes can be transformed. Animal models and high-throughput single-cell sequencing approaches have opened new opportunities to address this question. Here, we discuss the melanocytic journey from the neural crest, where they emerge as melanoblasts, to the fully mature pigmented melanocytes resident in several tissues. We describe a new understanding of melanocyte biology and the different melanocyte subpopulations and microenvironments they inhabit, and how this provides unique insights into melanoma initiation and progression. We highlight recent findings on melanoma heterogeneity and transcriptional plasticity and their implications for exciting new research areas and treatment opportunities. The lessons from melanocyte biology reveal how cells that are present to protect us from the damaging effects of ultraviolet radiation reach back to their origins to become a potentially deadly cancer.

Spatial cancer systems biology resolves heterotypic interactions and identifies disruption of spatial hierarchy as a pathological driver event

Spatially annotated single-cell datasets provide unprecedented opportunities to dissect cell-cell communication in development and disease. Heterotypic signaling includes interactions between different cell types and is well established in tissue development and spatial organization. Epithelial organization requires several different programs that are tightly regulated. Planar cell polarity (PCP) is the organization of epithelial cells along the planar axis, orthogonal to the apical-basal axis. Here, we investigate PCP factors and explore the implications of developmental regulators as malignant drivers. Utilizing cancer systems biology analysis, we derive a gene expression network for WNT-ligands (WNT) and their cognate frizzled (FZD) receptors in skin cutaneous melanoma. The profiles supported by unsupervised clustering of multiple-sequence alignments identify ligand-independent signaling and implications for metastatic progression based on the underpinning developmental spatial program. Omics studies and spatial biology connect developmental programs with oncological events and explain key spatial features of metastatic aggressiveness. Dysregulation of prominent PCP factors such as specific representatives of the WNT and FZD families in malignant melanoma recapitulates the development program of normal melanocytes but in an uncontrolled and disorganized fashion.

Immune infiltration associated C1q acts as a novel prognostic biomarker of cutaneous melanoma

C1q (complement C1q A chain, complement C1q B chain, and complement C1q C chain) is a recognized component of the classical complement pathway that influences the prognosis of various cancers. However, the effects of C1q on cutaneous melanoma (SKCM) outcomes and immune infiltration remain unknown. Gene expression profiling interactive analysis 2 and the human protein atlas were used to evaluate differential expression of C1q mRNA and protein. The relationship between C1q expression and clinicopathological features was also examined. The genetic alterations of C1q and their impact on survival were analyzed using the cbioportal database. The Kaplan-Meier approach was used to assess the significance of C1q in individuals with SKCM. The cluster profiler R package and the cancer single-cell state atlas database were used to investigate the function and mechanism of C1q in SKCM. The relationship between C1q and immune cell infiltration was estimated using single-sample gene set enrichment analysis. C1q expression was increased, and predicted a favorable prognosis. High C1q expression correlated with clinicopathological T stage, pathological stage, overall survival, and disease specific survival events. Moreover, C1q genetic alterations range from 2.7% to 4%, with no impact on prognosis. According to the enrichment analysis, C1q and immune-related pathways were closely connected. The link between complement C1q B chain and the functional state of inflammation was determined using the cancer single-cell state atlas database. In particular, C1q expression was significantly associated with infiltration of most immune cells and checkpoints PDCD1, CD274, and HAVCR2. The results of this study suggest that C1q is associated with prognosis and immune cell infiltration, supporting its value as a diagnostic and prognostic biomarker.

Quantifying tumor specificity using Bayesian probabilistic modeling for drug target discovery and prioritization

In diseases such as cancer, the design of new therapeutic strategies requires extensive, costly, and unfortunately sometimes deadly testing to reveal life threatening "off target" effects. A crucial first step in predicting toxicity are analyses of normal RNA and protein tissue expression, which are now possible using comprehensive molecular tissue atlases. However, no standardized approaches exist for target prioritization, which instead rely on ad-hoc thresholds and manual inspection. Such issues are compounded, given that genomic and proteomic data detection sensitivity and accuracy are often problematic. Thus, quantifiable probabilistic scores for tumor specificity that address these challenges could enable the creation of new predictive models for combinatorial drug design and correlative analyses. Here, we propose a Bayesian Tumor Specificity (BayesTS) score that can naturally account for multiple independent forms of molecular evidence derving from both RNA-Seq and protein expression while preserving the uncertainty of the inference. We applied BayesTS to 24,905 human protein-coding genes across 3,644 normal samples (GTEx and TCGA) spanning 63 tissues. These analyses demonstrate the ability of BayesTS to accurately incorporate protein, RNA and tissue distribution evidence, while effectively capturing the uncertainty of these inferences. This approach prioritized well-established drug targets, while deemphasizing those which were later found to induce toxicity. BayesTS allows for the adjustment of tissue importance weights for tissues of interest, such as reproductive and physiologically dispensable tissues (e.g., tonsil, appendix), enabling clinically translatable prioritizations. Our results show that BayesTS can facilitate novel drug target discovery and can be easily generalized to unconventional molecular targets, such as splicing neoantigens. We provide the code and inferred tumor specificity predictions as a database available online (https://github.com/frankligy/BayesTS). We envision that the widespread adoption of BayesTS will facilitate improved target prioritization for oncology drug development, ultimately leading to the discovery of more effective and safer drugs.

Spatial cancer systems biology resolves heterotypic interactions and identifies disruption of spatial hierarchy as a pathological driver event

Spatially annotated single-cell datasets provide unprecedented opportunities to dissect cell-cell communication in development and disease. Heterotypic signaling includes interactions between different cell types and is well established in tissue development and spatial organization. Epithelial organization requires several different programs that are tightly regulated. Planar cell polarity is the organization of epithelial cells along the planar axis orthogonal to the apical-basal axis. In this study, we investigate planar cell polarity factors and explore the implications of developmental regulators as malignant drivers. Utilizing cancer systems biology analysis, we derive gene expression network for WNT-ligands (WNT) and their cognate frizzled (FZD) receptors in skin cutaneous melanoma. The profiles supported by unsupervised clustering of multiple-sequence alignments identify ligand-independent signaling and implications for metastatic progression based on the underpinning developmental spatial program. Omics studies and spatial biology connect developmental programs with oncological events and explain key spatial features of metastatic aggressiveness. Dysregulation of prominent planar cell polarity factors such specific representative of the WNT and FZD families in malignant melanoma recapitulates the development program of normal melanocytes but in an uncontrolled and disorganized fashion.

The role of macrophages in non-small cell lung cancer and advancements in 3D co-cultures

Lung cancer (LC) is the leading cause of cancer-related deaths worldwide. Traditional therapeutic approaches such as chemotherapy or radiotherapy have provided only a marginal improvement in the treatment of lung carcinomas. Inhibitors targeting specific genetic aberrations present in non-small cell lung cancer (NSCLC), the most common subtype (85%), have improved the prognostic outlook, but due to the complexity of the LC mutational spectrum, only a fraction of patients benefit from these targeted molecular therapies. More recently, the realization that the immune infiltrate surrounding solid tumors can foster tumor-promoting inflammation has led to the development and implementation of anticancer immunotherapies in the clinic. In NSCLC, one of the most abundant leukocyte infiltrates is macrophages. These highly plastic phagocytes, which are part of the cellular repertoire of the innate immunity, can have a pivotal role in early NSCLC establishment, malignant progression, and tumor invasion. Emerging macrophage-targeting therapies have been focused on the re-differentiation of the macrophages toward an antitumorigenic phenotype, depletion of tumor-promoting macrophage subtypes, or combination therapies combining traditional cytotoxic treatments with immunotherapeutic agents. The most extensively used models employed for the exploration of NSCLC biology and therapy have been 2D cell lines and murine models. However, studying cancer immunology requires appropriately complex models. 3D platforms, including organoid models, are quickly advancing powerful tools to study immune cell-epithelial cell interactions within the tumor microenvironment. Co-cultures of immune cells along with NSCLC organoids allow for an in vitro observation of the tumor microenvironment dynamics closely resembling in vivo settings. Ultimately, the implementation of 3D organoid technology into tumor microenvironment-modeling platforms might facilitate the exploration of macrophage-targeted therapies in NSCLC immunotherapeutic research, thus establishing a new frontier in NSCLC treatment.

Tumor-intrinsic SIRPA promotes sensitivity to checkpoint inhibition immunotherapy in melanoma

Checkpoint inhibition immunotherapy has revolutionized cancer treatment, but many patients show resistance. Here we perform integrative transcriptomic and proteomic analyses on emerging immuno-oncology targets across multiple clinical cohorts of melanoma under anti-PD-1 treatment, on both bulk and single-cell levels. We reveal a surprising role of tumor-intrinsic SIRPA in enhancing antitumor immunity, in contrast to its well-established role as a major inhibitory immune modulator in macrophages. The loss of SIRPA expression is a marker of melanoma dedifferentiation, a key phenotype linked to immunotherapy efficacy. Inhibition of SIRPA in melanoma cells abrogates tumor killing by activated CD8+ T cells in a co-culture system. Mice bearing SIRPA-deficient melanoma tumors show no response to anti-PD-L1 treatment, whereas melanoma-specific SIRPA overexpression significantly enhances immunotherapy response. Mechanistically, SIRPA is regulated by its pseudogene, SIRPAP1. Our results suggest a complicated role of SIRPA in the tumor ecosystem, highlighting cell-type-dependent antagonistic effects of the same target on immunotherapy.