SB Driver Analysis: a Sleeping Beauty cancer driver analysis framework for identifying and prioritizing experimentally actionable oncogenes and tumor suppressors

Application of a high-throughput swarm-based deep neural network Algorithm reveals SPAG5 downregulation as a potential therapeutic target in adult AML

Gene‒gene interactions play pivotal roles in disease pathogenesis and are fundamental in the development of targeted therapeutics, particularly through the elucidation of oncogenic gene drivers in cancer. The systematic analysis of pathways and gene interactions is critical in the drug discovery process for various cancer subtypes. SPAG5, known for its role in spindle formation during cell division, has been identified as an oncogene in several cancers, although its specific impact on AML remains underexplored. This study leverages a high-throughput swarm-based deep neural network (SDNN) and transcriptomic data-an approach that enhances predictive accuracy and robustness through collective intelligence-to augment, model, and enhance the understanding of the TP53 pathway in AML cohorts. Our integrative systems biology approach identified SPAG5 as a uniquely downregulated driver in adult AML, underscoring its potential as a novel therapeutic target. The interaction of SPAG5 with key hub genes such as MDM2 and CDK1 not only reinforces its role in tumour suppression through negative regulation but also highlights its potential in moderating the phenotypic and genomic alterations associated with AML progression. This study of the role and interaction dynamics of SPAG5 sets the stage for future research aimed at developing targeted and personalized treatment approaches for AML, utilizing the capabilities of genetic interventions.

Transmicron: accurate prediction of insertion probabilities improves detection of cancer driver genes from transposon mutagenesis screens

Transposon screens are powerful in vivo assays used to identify loci driving carcinogenesis. These loci are identified as Common Insertion Sites (CISs), i.e. regions with more transposon insertions than expected by chance. However, the identification of CISs is affected by biases in the insertion behaviour of transposon systems. Here, we introduce Transmicron, a novel method that differs from previous methods by (i) modelling neutral insertion rates based on chromatin accessibility, transcriptional activity and sequence context and (ii) estimating oncogenic selection for each genomic region using Poisson regression to model insertion counts while controlling for neutral insertion rates. To assess the benefits of our approach, we generated a dataset applying two different transposon systems under comparable conditions. Benchmarking for enrichment of known cancer genes showed improved performance of Transmicron against state-of-the-art methods. Modelling neutral insertion rates allowed for better control of false positives and stronger agreement of the results between transposon systems. Moreover, using Poisson regression to consider intra-sample and inter-sample information proved beneficial in small and moderately-sized datasets. Transmicron is open-source and freely available. Overall, this study contributes to the understanding of transposon biology and introduces a novel approach to use this knowledge for discovering cancer driver genes.

SB Digestor: a tailored driver gene identification tool for dissecting heterogeneous Sleeping Beauty transposon-induced tumors

Sleeping Beauty (SB) insertional mutagenesis has been widely used for genome-wide functional screening in mouse models of human cancers, however, intertumor heterogeneity can be a major obstacle in identifying common insertion sites (CISs). Although previous algorithms have been successful in defining some CISs, they also miss CISs in certain situations. A major common characteristic of these previous methods is that they do not take tumor heterogeneity into account. However, intertumoral heterogeneity directly influences the sequence read number for different tumor samples and then affects CIS identification. To precisely detect and define cancer driver genes, we developed SB Digestor, a computational algorithm that overcomes biological heterogeneity to identify more potential driver genes. Specifically, we define the relationship between the sequenced read number and putative gene number to deduce the depth cutoff for each tumor, which can reduce tumor complexity and precisely reflect intertumoral heterogeneity. Using this new tool, we re-analyzed our previously published SB-based screening dataset and identified many additional potent drivers involved in Brca1-related tumorigenesis, including Arhgap42, Tcf12, and Fgfr2. SB Digestor not only greatly enhances our ability to identify and prioritize cancer drivers from SB tumors but also substantially deepens our understanding of the intrinsic genetic basis of cancer.

Transposon mutagenesis identifies cooperating genetic drivers during keratinocyte transformation and cutaneous squamous cell carcinoma progression

The systematic identification of genetic events driving cellular transformation and tumor progression in the absence of a highly recurrent oncogenic driver mutation is a challenge in cutaneous oncology. In cutaneous squamous cell carcinoma (cuSCC), the high UV-induced mutational burden poses a hurdle to achieve a complete molecular landscape of this disease. Here, we utilized the Sleeping Beauty transposon mutagenesis system to statistically define drivers of keratinocyte transformation and cuSCC progression in vivo in the absence of UV-IR, and identified both known tumor suppressor genes and novel oncogenic drivers of cuSCC. Functional analysis confirms an oncogenic role for the ZMIZ genes, and tumor suppressive roles for KMT2C, CREBBP and NCOA2, in the initiation or progression of human cuSCC. Taken together, our in vivo screen demonstrates an extremely heterogeneous genetic landscape of cuSCC initiation and progression, which can be harnessed to better understand skin oncogenic etiology and prioritize therapeutic candidates.

Non-melanoma skin cancers, the most common cancers in the US, are caused by UV skin exposure. Nearly 1 million cases of cutaneous squamous cell carcinoma (cuSCC) are diagnosed in the US each year. While most cuSCCs are highly treatable, more than twice as many individuals die from this disease as from melanoma. The high burden of UV-induced DNA damage in human skin poses a challenge for identifying initiating and cooperating mutations that promote cuSCC development and for defining potential therapeutic targets. Here, we describe a genetic screen in mice using a DNA transposon system to mutagenize the genome of keratinocytes and drive squamous cell carcinoma in the absence of UV. By sequencing where the transposons selectively integrated in the genomes of normal skin, skin with pre-cancerous lesions and skin with fully developed cuSCCs from our mouse model, we were able to identify frequently mutated genes likely important for this disease. Our analysis also defined cooperation between sets of genes not previously appreciated in cuSCC. Our mouse model and ensuing data provide a framework for understanding the genetics of cuSCC and for defining the molecular changes that may lead to the future therapies for patients.

Modeling fusion gene-associated sarcoma: Advantages for understanding sarcoma biology and pathology

Disease-specific gene fusions are reportedly major driver mutations in approximately 30% of bone and soft tissue sarcomas. Most fusion genes encode transcription factors or co-factors that regulate downstream target genes, altering cell growth, lineage commitment, and differentiation. Given the limitations of investigating their functions in vitro, the generation of mouse models expressing fusion genes in the appropriate cellular lineages is pivotal. Therefore, we generated a series of mouse models by introducing fusion genes into embryonic mesenchymal progenitors. This review describes mouse models of Ewing, synovial, alveolar soft part, and CIC-rearranged sarcomas. Furthermore, we describe the similarities between these models and their human counterparts. These models provide remarkable advantages to identify cells-of-origin, specific collaborators of fusion genes, angiogenesis key factors, or diagnostic biomarkers. Finally, we discuss the relationship between fusion proteins and the epigenetic background as well as the possible role of the super-enhancers.

Promoterless Transposon Mutagenesis Drives Solid Cancers via Tumor Suppressor Inactivation

A central challenge in cancer genomics is the systematic identification of single and cooperating tumor suppressor gene mutations driving cellular transformation and tumor progression in the absence of oncogenic driver mutation(s). Multiple in vitro and in vivo gene inactivation screens have enhanced our understanding of the tumor suppressor gene landscape in various cancers. However, these studies are limited to single or combination gene effects, specific organs, or require sensitizing mutations. In this study, we developed and utilized a Sleeping Beauty transposon mutagenesis system that functions only as a gene trap to exclusively inactivate tumor suppressor genes. Using whole body transposon mobilization in wild type mice, we observed that cumulative gene inactivation can drive tumorigenesis of solid cancers. We provide a quantitative landscape of the tumor suppressor genes inactivated in these cancers and show that, despite the absence of oncogenic drivers, these genes converge on key biological pathways and processes associated with cancer hallmarks.