UVB-Induced Tumor Heterogeneity Diminishes Immune Response in Melanoma.

Although clonal neo-antigen burden is associated with improved response to immune therapy, the functional basis for this remains unclear. Here we study this question in a novel controlled mouse melanoma model that enables us to explore the effects of intra-tumor heterogeneity (ITH) on tumor aggressiveness and immunity independent of tumor mutational burden. Induction of UVB-derived mutations yields highly aggressive tumors with decreased anti-tumor activity. However, single-cell-derived tumors with reduced ITH are swiftly rejected. Their rejection is accompanied by increased T cell reactivity and a less suppressive microenvironment. Using phylogenetic analyses and mixing experiments of single-cell clones, we dissect two characteristics of ITH: the number of clones forming the tumor and their clonal diversity. Our analysis of melanoma patient tumor data recapitulates our results in terms of overall survival and response to immune checkpoint therapy. These findings highlight the importance of clonal mutations in robust immune surveillance and the need to quantify patient ITH to determine the response to checkpoint blockade.

Preclinical mouse cancer models: a maze of opportunities and challenges.

Significant advances have been made in developing novel therapeutics for cancer treatment, and targeted therapies have revolutionized the treatment of some cancers. Despite the promise, only about five percent of new cancer drugs are approved, and most fail due to lack of efficacy. The indication is that current preclinical methods are limited in predicting successful outcomes. Such failure exacts enormous cost, both financial and in the quality of human life. This Primer explores the current status, promise, and challenges of preclinical evaluation in advanced mouse cancer models and briefly addresses emerging models for early-stage preclinical development.

Epigenetic therapy inhibits metastases by disrupting premetastatic niches.

Cancer recurrence after surgery remains an unresolved clinical problem1-3. Myeloid cells derived from bone marrow contribute to the formation of the premetastatic microenvironment, which is required for disseminating tumour cells to engraft distant sites4-6. There are currently no effective interventions that prevent the formation of the premetastatic microenvironment6,7. Here we show that, after surgical removal of primary lung, breast and oesophageal cancers, low-dose adjuvant epigenetic therapy disrupts the premetastatic microenvironment and inhibits both the formation and growth of lung metastases through its selective effect on myeloid-derived suppressor cells (MDSCs). In mouse models of pulmonary metastases, MDSCs are key factors in the formation of the premetastatic microenvironment after resection of primary tumours. Adjuvant epigenetic therapy that uses low-dose DNA methyltransferase and histone deacetylase inhibitors, 5-azacytidine and entinostat, disrupts the premetastatic niche by inhibiting the trafficking of MDSCs through the downregulation of CCR2 and CXCR2, and by promoting MDSC differentiation into a more-interstitial macrophage-like phenotype. A decreased accumulation of MDSCs in the premetastatic lung produces longer periods of disease-free survival and increased overall survival, compared with chemotherapy. Our data demonstrate that, even after removal of the primary tumour, MDSCs contribute to the development of premetastatic niches and settlement of residual tumour cells. A combination of low-dose adjuvant epigenetic modifiers that disrupts this premetastatic microenvironment and inhibits metastases may permit an adjuvant approach to cancer therapy.

Exploring tumor-normal cross-talk with TranNet: Role of the environment in tumor progression.

There is a growing awareness that tumor-adjacent normal tissues used as control samples in cancer studies do not represent fully healthy tissues. Instead, they are intermediates between healthy tissues and tumors. The factors that contribute to the deviation of such control samples from healthy state include exposure to the tumor-promoting factors, tumor-related immune response, and other aspects of tumor microenvironment. Characterizing the relation between gene expression of tumor-adjacent control samples and tumors is fundamental for understanding roles of microenvironment in tumor initiation and progression, as well as for identification of diagnostic and prognostic biomarkers for cancers. To address the demand, we developed and validated TranNet, a computational approach that utilizes gene expression in matched control and tumor samples to study the relation between their gene expression profiles. TranNet infers a sparse weighted bipartite graph from gene expression profiles of matched control samples to tumors. The results allow us to identify predictors (potential regulators) of this transition. To our knowledge, TranNet is the first computational method to infer such dependencies. We applied TranNet to the data of several cancer types and their matched control samples from The Cancer Genome Atlas (TCGA). Many predictors identified by TranNet are genes associated with regulation by the tumor microenvironment as they are enriched in G-protein coupled receptor signaling, cell-to-cell communication, immune processes, and cell adhesion. Correspondingly, targets of inferred predictors are enriched in pathways related to tissue remodelling (including the epithelial-mesenchymal Transition (EMT)), immune response, and cell proliferation. This implies that the predictors are markers and potential stromal facilitators of tumor progression. Our results provide new insights into the relationships between tumor adjacent control sample, tumor and the tumor environment. Moreover, the set of predictors identified by TranNet will provide a valuable resource for future investigations.

Identification of essential genes for cancer immunotherapy.

Somatic gene mutations can alter the vulnerability of cancer cells to T-cell-based immunotherapies. Here we perturbed genes in human melanoma cells to mimic loss-of-function mutations involved in resistance to these therapies, by using a genome-scale CRISPR-Cas9 library that consisted of around 123,000 single-guide RNAs, and profiled genes whose loss in tumour cells impaired the effector function of CD8+ T cells. The genes that were most enriched in the screen have key roles in antigen presentation and interferon-γ signalling, and correlate with cytolytic activity in patient tumours from The Cancer Genome Atlas. Among the genes validated using different cancer cell lines and antigens, we identified multiple loss-of-function mutations in APLNR, encoding the apelin receptor, in patient tumours that were refractory to immunotherapy. We show that APLNR interacts with JAK1, modulating interferon-γ responses in tumours, and that its functional loss reduces the efficacy of adoptive cell transfer and checkpoint blockade immunotherapies in mouse models. Our results link the loss of essential genes for the effector function of CD8+ T cells with the resistance or non-responsiveness of cancer to immunotherapies.

PhISCS-BnB: a fast branch and bound algorithm for the perfect tumor phylogeny reconstruction problem.

MOTIVATION: Recent advances in single-cell sequencing (SCS) offer an unprecedented insight into tumor emergence and evolution. Principled approaches to tumor phylogeny reconstruction via SCS data are typically based on general computational methods for solving an integer linear program, or a constraint satisfaction program, which, although guaranteeing convergence to the most likely solution, are very slow. Others based on Monte Carlo Markov Chain or alternative heuristics not only offer no such guarantee, but also are not faster in practice. As a result, novel methods that can scale up to handle the size and noise characteristics of emerging SCS data are highly desirable to fully utilize this technology. RESULTS: We introduce PhISCS-BnB (phylogeny inference using SCS via branch and bound), a branch and bound algorithm to compute the most likely perfect phylogeny on an input genotype matrix extracted from an SCS dataset. PhISCS-BnB not only offers an optimality guarantee, but is also 10-100 times faster than the best available methods on simulated tumor SCS data. We also applied PhISCS-BnB on a recently published large melanoma dataset derived from the sublineages of a cell line involving 20 clones with 2367 mutations, which returned the optimal tumor phylogeny in <4 h. The resulting phylogeny agrees with and extends the published results by providing a more detailed picture on the clonal evolution of the tumor. AVAILABILITY AND IMPLEMENTATION: https://github.com/algo-cancer/PhISCS-BnB. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A direct link between MITF, innate immunity, and hair graying.

Melanocyte stem cells (McSCs) and mouse models of hair graying serve as useful systems to uncover mechanisms involved in stem cell self-renewal and the maintenance of regenerating tissues. Interested in assessing genetic variants that influence McSC maintenance, we found previously that heterozygosity for the melanogenesis associated transcription factor, Mitf, exacerbates McSC differentiation and hair graying in mice that are predisposed for this phenotype. Based on transcriptome and molecular analyses of Mitfmi-vga9/+ mice, we report a novel role for MITF in the regulation of systemic innate immune gene expression. We also demonstrate that the viral mimic poly(I:C) is sufficient to expose genetic susceptibility to hair graying. These observations point to a critical suppressor of innate immunity, the consequences of innate immune dysregulation on pigmentation, both of which may have implications in the autoimmune, depigmenting disease, vitiligo.

Mouse models of UV-induced melanoma: genetics, pathology, and clinical relevance.

Melanocytes, a neural crest cell derivative, produce pigment to protect keratinocytes from ultraviolet radiation (UVR). Although melanocytic lesions such as nevi and cutaneous malignant melanomas are known to be associated with sun exposure, the role of UVR in oncogenesis is complex and has yet to be clearly elucidated. UVR appears to have a direct mutational role in inducing or promoting melanoma formation as well as an indirect role through microenvironmental changes. Recent advances in the modeling of human melanoma in animals have built platforms upon which prospective studies can begin to investigate these questions. This review will focus exclusively on genetically engineered mouse models of UVR-induced melanoma. The role that UVR has in mouse models depends on multiple factors, including the waveband, timing, and dose of UVR, as well as the nature of the oncogenic agent(s) driving melanomagenesis in the model. Work in the field has examined the role of neonatal and adult UVR, interactions between UVR and common melanoma oncogenes, the role of sunscreen in preventing melanoma, and the effect of UVR on immune function within the skin. Here we describe relevant mouse models and discuss how these models can best be translated to the study of human skin and cutaneous melanoma.

Genetically engineered mouse models of melanoma.

Melanoma is a complex disease that exhibits highly heterogeneous etiological, histopathological, and genetic features, as well as therapeutic responses. Genetically engineered mouse (GEM) models provide powerful tools to unravel the molecular mechanisms critical for melanoma development and drug resistance. Here, we expound briefly the basis of the mouse modeling design, the available technology for genetic engineering, and the aspects influencing the use of GEMs to model melanoma. Furthermore, we describe in detail the currently available GEM models of melanoma. Cancer 2017;123:2089-103. © 2017 American Cancer Society.

GeneAgent: Self-verification Language Agent for Gene Set Knowledge Discovery using Domain Databases.

Gene set knowledge discovery is essential for advancing human functional genomics. Recent studies have shown promising performance by harnessing the power of Large Language Models (LLMs) on this task. Nonetheless, their results are subject to several limitations common in LLMs such as hallucinations. In response, we present GeneAgent, a first-of-its-kind language agent featuring self-verification capability. It autonomously interacts with various biological databases and leverages relevant domain knowledge to improve accuracy and reduce hallucination occurrences. Benchmarking on 1,106 gene sets from different sources, GeneAgent consistently outperforms standard GPT-4 by a significant margin. Moreover, a detailed manual review confirms the effectiveness of the self-verification module in minimizing hallucinations and generating more reliable analytical narratives. To demonstrate its practical utility, we apply GeneAgent to seven novel gene sets derived from mouse B2905 melanoma cell lines, with expert evaluations showing that GeneAgent offers novel insights into gene functions and subsequently expedites knowledge discovery.

Stochastic modelling of single-cell gene expression adaptation reveals non-genomic contribution to evolution of tumor subclones.

Cancer progression is an evolutionary process driven by the selection of cells adapted to gain growth advantage. We present the first formal study on the adaptation of gene expression in subclonal evolution. We model evolutionary changes in gene expression as stochastic Ornstein-Uhlenbeck processes, jointly leveraging the evolutionary history of subclones and single-cell expression data. Applying our model to sublines derived from single cells of a mouse melanoma revealed that sublines with distinct phenotypes are underlined by different patterns of gene expression adaptation, indicating non-genetic mechanisms of cancer evolution. Interestingly, sublines previously observed to be resistant to anti-CTLA-4 treatment showed adaptive expression of genes related to invasion and non-canonical Wnt signaling, whereas sublines that responded to treatment showed adaptive expression of genes related to proliferation and canonical Wnt signaling. Our results suggest that clonal phenotypes emerge as the result of specific adaptivity patterns of gene expression.

Severus: accurate detection and characterization of somatic structural variation in tumor genomes using long reads.

Most current studies rely on short-read sequencing to detect somatic structural variation (SV) in cancer genomes. Long-read sequencing offers the advantage of better mappability and long-range phasing, which results in substantial improvements in germline SV detection. However, current long-read SV detection methods do not generalize well to the analysis of somatic SVs in tumor genomes with complex rearrangements, heterogeneity, and aneuploidy. Here, we present Severus: a method for the accurate detection of different types of somatic SVs using a phased breakpoint graph approach. To benchmark various short- and long-read SV detection methods, we sequenced five tumor/normal cell line pairs with Illumina, Nanopore, and PacBio sequencing platforms; on this benchmark Severus showed the highest F1 scores (harmonic mean of the precision and recall) as compared to long-read and short-read methods. We then applied Severus to three clinical cases of pediatric cancer, demonstrating concordance with known genetic findings as well as revealing clinically relevant cryptic rearrangements missed by standard genomic panels.

Exploring tumor-normal cross-talk with TranNet: role of the environment in tumor progression.

There is a growing awareness that tumor-adjacent normal tissues used as control samples in cancer studies do not represent fully healthy tissues. Instead, they are intermediates between healthy tissues and tumors. The factors that contribute to the deviation of such control samples from healthy state include exposure to the tumor-promoting factors, tumor-related immune response, and other aspects of tumor microenvironment. Characterizing the relation between gene expression of tumor-adjacent control samples and tumors is fundamental for understanding roles of microenvironment in tumor initiation and progression, as well as for identification of diagnostic and prognostic biomarkers for cancers. To address the demand, we developed and validated TranNet, a computational approach that utilizes gene expression in matched control and tumor samples to study the relation between their gene expression profiles. TranNet infers a sparse weighted bipartite graph from gene expression profiles of matched control samples to tumors. The results allow us to identify predictors (potential regulators) of this transition. To our knowledge, TranNet is the first computational method to infer such regulation. We applied TranNet to the data of several cancer types and their matched control samples from The Cancer Genome Atlas (TCGA). Many predictors identified by TranNet are genes associated with regulation by the tumor microenvironment as they are enriched in G-protein coupled receptor signaling, cell-to-cell communication, immune processes, and cell adhesion. Correspondingly, targets of inferred predictors are enriched in pathways related to tissue remodelling (including the epithelial-mesenchymal Transition (EMT)), immune response, and cell proliferation. This implies that the predictors are markers and potential stromal facilitators of tumor progression. Our results provide new insights for the relationships between tumor adjacent control sample, tumor and the tumor environment. Moreover, the set of predictors identified by TranNet will provide a valuable resource for future investigations. The TranNet method was implemented in python, source codes and the data sets used for and generated during this study are available at the Github site https://github.com/ncbi/TranNet .

Noncoding RNA circuitry in melanoma onset, plasticity, and therapeutic response.

Melanoma, the cancer of the melanocyte, is the deadliest form of skin cancer with an aggressive nature, propensity to metastasize and tendency to resist therapeutic intervention. Studies have identified that the re-emergence of developmental pathways in melanoma contributes to melanoma onset, plasticity, and therapeutic response. Notably, it is well known that noncoding RNAs play a critical role in the development and stress response of tissues. In this review, we focus on the noncoding RNAs, including microRNAs, long non-coding RNAs, circular RNAs, and other small RNAs, for their functions in developmental mechanisms and plasticity, which drive onset, progression, therapeutic response and resistance in melanoma. Going forward, elucidation of noncoding RNA-mediated mechanisms may provide insights that accelerate development of novel melanoma therapies.

Melanoma clonal subline analysis uncovers heterogeneity-driven immunotherapy resistance mechanisms.

Intratumoral heterogeneity (ITH) can promote cancer progression and treatment failure, but the complexity of the regulatory programs and contextual factors involved complicates its study. To understand the specific contribution of ITH to immune checkpoint blockade (ICB) response, we generated single cell-derived clonal sublines from an ICB-sensitive and genetically and phenotypically heterogeneous mouse melanoma model, M4. Genomic and single cell transcriptomic analyses uncovered the diversity of the sublines and evidenced their plasticity. Moreover, a wide range of tumor growth kinetics were observed in vivo , in part associated with mutational profiles and dependent on T cell-response. Further inquiry into melanoma differentiation states and tumor microenvironment (TME) subtypes of untreated tumors from the clonal sublines demonstrated correlations between highly inflamed and differentiated phenotypes with the response to anti-CTLA-4 treatment. Our results demonstrate that M4 sublines generate intratumoral heterogeneity at both levels of intrinsic differentiation status and extrinsic TME profiles, thereby impacting tumor evolution during therapeutic treatment. These clonal sublines proved to be a valuable resource to study the complex determinants of response to ICB, and specifically the role of melanoma plasticity in immune evasion mechanisms.

Germline modifiers of the tumor immune microenvironment implicate drivers of cancer risk and immunotherapy response.

With the continued promise of immunotherapy for treating cancer, understanding how host genetics contributes to the tumor immune microenvironment (TIME) is essential to tailoring cancer screening and treatment strategies. Here, we study 1084 eQTLs affecting the TIME found through analysis of The Cancer Genome Atlas and literature curation. These TIME eQTLs are enriched in areas of active transcription, and associate with gene expression in specific immune cell subsets, such as macrophages and dendritic cells. Polygenic score models built with TIME eQTLs reproducibly stratify cancer risk, survival and immune checkpoint blockade (ICB) response across independent cohorts. To assess whether an eQTL-informed approach could reveal potential cancer immunotherapy targets, we inhibit CTSS, a gene implicated by cancer risk and ICB response-associated polygenic models; CTSS inhibition results in slowed tumor growth and extended survival in vivo. These results validate the potential of integrating germline variation and TIME characteristics for uncovering potential targets for immunotherapy.

Preclinical therapeutic response of residual metastatic disease is distinct from its primary tumor of origin.

Cancer-related deaths are caused principally by recurrence and metastasis arising from residual disease, whose therapeutic responses has been suggested to be substantially different from primary tumors. However, experimental animal models designed for evaluating the therapeutic responses of residual disease are mostly lacking. To overcome this deficiency, we have developed a preclinical model that recapitulates the progression for advanced nonsmall cell lung cancer (NSCLC). An archived Lewis lung carcinoma mouse tumor, propagated only through serial in vivo transplantation and never adapted to cell culture, was stably labeled using lentivirus-encoded biomarkers, consistently expressed through an RNA polymerase II promoter. Labeled tumors were inoculated into syngeneic immunocompetent mice to ensure superior tumor-host interactions. Primary tumors were resected on reaching a predetermined size, followed by treatment in a setting akin to postsurgical first-line adjuvant chemotherapy and routine imaging to monitor the progression of pulmonary metastasis. We discovered that efficacious treatment, instead of reducing disease growth rates, significantly prolonged disease-free survival and overall survival. As in the clinic, cisplatin-based regimes were more effective in this model. However, the response of metastases to specific agents could not be predicted from, and often opposed, their effects on subcutaneous "primary" tumors, possibly due to their distinct growth kinetics and host interactions. We here introduce a clinically relevant model of residual metastatic disease that may more accurately predict the therapeutic response of recurrent, metastatic disease.

Making a mouse out of a molehill: how precision modeling repurposes drugs for congenital giant nevi.

Patients with congenital giant nevi (CGN), which can compromise quality of life and progress to melanoma, have limited treatment options. Choi et al. have demonstrated that topical application of a proinflammatory hapten for alopecia treatment [squaric acid dibutylester (SADBE)] caused nevus regression and prevented melanoma in an Nras mouse CGN model. Their results demonstrate the promise of repurposing drugs through precision modeling.

The Hitchhiker's Guide across a Waddington's landscape of melanoma.

Melanoma evolution may recapitulate the embryonic development of its progenitor tissue, neural crest (NC), but the exact process is unclear. In a recent issue of Nature, Karras et al. (2022) demonstrate that melanoma expansion mirrors the hierarchic process of NC differentiation, generating cell subpopulations, each with distinct function, including growth and metastasis.

A novel PD-L1-targeted shark VNAR single-domain-based CAR-T cell strategy for treating breast cancer and liver cancer.

Chimeric antigen receptor (CAR)-T cell therapy shows excellent potency against hematological malignancies, but it remains challenging to treat solid tumors, mainly because of a lack of appropriate antigenic targets and an immunosuppressive tumor microenvironment (TME). The checkpoint molecule programmed death-ligand 1 (PD-L1) is widely overexpressed in multiple tumor types, and the programmed death-ligand 1 (PD-1)/PD-L1 interaction is a crucial mediator of immunosuppression in the TME. Here we constructed a semi-synthetic shark VNAR phage library and isolated anti-PD-L1 single-domain antibodies. Among these VNARs, B2 showed cross-reactivity to human, mouse, and canine PD-L1, and it partially blocked the interaction of human PD-1 with PD-L1. CAR (B2) T cells specifically lysed human breast cancer and liver cancer cells by targeting constitutive and inducible expression of PD-L1 and hindered tumor metastasis. Combination of PD-L1 CAR (B2) T cells with CAR T cells targeted by GPC3 (a liver cancer-specific antigen) regresses liver tumors in mice. We concluded that PD-L1-targeted shark VNAR single-domain-based CAR-T therapy is a novel strategy to treat breast and liver cancer. This study provides a rationale for potential use of PD-L1 CAR-T cells as a monotherapy or in combination with a tumor-specific therapy in clinical studies.