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Metastasis, a complex, multistep process, is responsible for the overwhelming majority of cancer-related deaths. Despite its devastating consequences, it is not possible to effectively treat cancer that has spread to vital organs, the mechanisms leading to metastasis are still poorly understood, and the catalog of metastasis promoting genes is still incomprehensive. To identify new driver genes of metastasis development, we performed an in vitro Sleeping Beauty transposon-based forward genetic screen in nonmetastatic SKBR3 human breast cancer cells. Boyden chamber-based matrix invasion assays were used to harvest cells that acquired a de novo invasive phenotype. Using targeted RNA sequencing data from 18 pools of invasive cells, we carried out a gene-centric candidate gene prediction and identified established and novel metastasis driver genes. Analysis of these genes revealed their association with metastasis related processes and we further established their clinical relevance in metastatic breast cancer. Two novel candidate genes, G protein–coupled receptor kinase interacting ArfGAP 2 (GIT2) and muscle-associated receptor tyrosine kinase (MUSK), were functionally validated as metastasis driver genes in a series of in vitro and in vivo experimental metastasis models. We propose that our robust and scalable approach will be a useful addition to the toolkit of methodologic resources used to identify genes driving cancer metastasis. Implications: Novel metastasis drivers were identified in a human breast cancer cell line by performing an in vitro, Sleeping Beauty transposon-based forward genetic screen and an RNA fusion-based candidate gene prediction.

Supplementary Figure from In Vitro Insertional Mutagenesis Screen Identifies Novel Genes Driving Breast Cancer Metastasis

Breslow thickness is a major prognostic factor for melanoma. It is based on histopathological evaluation, and thus it is not available to aid clinical decision making at the time of the initial melanoma diagnosis. In this work, we assessed the efficacy of multispectral imaging (MSI) to predict Breslow thickness and developed a classification algorithm to determine optimal safety margins of the melanoma excision. First, we excluded nevi from the analysis with a novel quantitative parameter. Parameter s’ could differentiate nevi from melanomas with a sensitivity of 89.60% and specificity of 88.11%. Following this step, we have categorized melanomas into three different subgroups based on Breslow thickness (≤1 mm, 1–2 mm and >2 mm) with a sensitivity of 78.00% and specificity of 89.00% and a substantial agreement (κ = 0.67; 95% CI, 0.58–0.76). We compared our results to the performance of dermatologists and dermatology residents who assessed dermoscopic and clinical images of these melanomas, and reached a sensitivity of 60.38% and specificity of 80.86% with a moderate agreement (κ = 0.41; 95% CI, 0.39–0.43). Based on our findings, this novel method may help predict the appropriate safety margins for curative melanoma excision.

Background: After the outbreak of the corona virus disease-19 (COVID-19) pandemic, teledermatology was implemented in the Hungarian public healthcare system for the first time. Our objective was to assess aggregated diagnostic agreements and to determine the effectiveness of an asynchronous teledermatology system for skin cancer screening. Methods: This retrospective single-center study included cases submitted for teledermatology consultation during the first wave of the COVID-19 pandemic. Follow-up of the patients was performed to collect the results of any subsequent personal examination. Results: 749 patients with 779 lesions were involved. 15 malignant melanomas (9.9%), 78 basal cell carcinomas (51.3%), 21 squamous cell carcinomas (13.8%), 7 other malignancies (4.6%) and 31 actinic keratoses (20.4%) were confirmed. 87 malignancies were diagnosed in the high-urgency group (42.2%), 49 malignancies in the moderate-urgency group (21.6%) and 16 malignancies in the low-urgency group (4.6%) (p < 0.0001). Agreement of malignancies was substantial for primary (86.3%; κ = 0.647) and aggregated diagnoses (85.3%; κ = 0.644). Agreement of total lesions was also substantial for primary (81.2%; κ = 0.769) and aggregated diagnoses (87.9%; κ = 0.754). Conclusions: Our findings showed that asynchronous teledermatology using a mobile phone application served as an accurate skin cancer screening system during the first wave of the COVID-19 pandemic.

Genes showing higher expression in either tumor or metastatic tissues can help in better understanding tumor formation, and can serve as biomarkers of progression or as therapy targets with minimal off-target effects. Our goal was to establish an integrated database using available transcriptome-level datasets and to create a web-platform enabling mining of this database by comparing normal, tumor and metastatic data across all genes in real time.We utilized data generated by either gene arrays or RNA-seq. Gene array data were manually selected from NCBI-GEO. RNA sequencing data was downloaded from the TCGA, TARGET, and GTEx repositories. TCGA and TARGET contain predominantly tumor and metastatic samples from adult and pediatric patients, while GTEx samples are from healthy tissues. Statistical significance was computed using Mann-Whitney or Kruskall-Wallis tests.The entire database contains 56,938 samples including 33,520 samples from 3,180 gene chip-based studies (453 metastatic, 29,376 tumorous and 3,691 normal samples), 11,010 samples from TCGA (394 metastatic, 9,886 tumorous and 730 normal), 1,193 samples from TARGET (1 metastatic, 1,180 tumor, 12 normal) and 11,215 normal samples from GTEx. The most consistently up-regulated genes across multiple tumor types were TOP2A (mean FC=7.8), SPP1 (FC=7.0) and CENPA (FC=6.03) and the most consistently down-regulated gene was ADH1B (mean FC=0.15). Validation of differential expression using equally sized training and test sets confirmed reliability of the database in breast, colon, and lung cancer (pwww.tnmplot.com.

Whole exome sequencing (WES) enables the analysis of all protein coding sequences in the human genome. This technology enables the investigation of cancer-related genetic aberrations that are predominantly located in the exonic regions. WES delivers high-throughput results at a reasonable price. Here, we review analysis tools enabling utilization of WES data in clinical and research settings. Technically, WES initially allows the detection of single nucleotide variants (SNVs) and copy number variations (CNVs), and data obtained through these methods can be combined and further utilized. Variant calling algorithms for SNVs range from standalone tools to machine learning-based combined pipelines. Tools for CNV detection compare the number of reads aligned to a dedicated segment. Both SNVs and CNVs help to identify mutations resulting in pharmacologically druggable alterations. The identification of homologous recombination deficiency enables the use of PARP inhibitors. Determining microsatellite instability and tumor mutation burden helps to select patients eligible for immunotherapy. To pave the way for clinical applications, we have to recognize some limitations of WES, including its restricted ability to detect CNVs, low coverage compared to targeted sequencing, and the missing consensus regarding references and minimal application requirements. Recently, Galaxy became the leading platform in non-command line-based WES data processing. The maturation of next-generation sequencing is reinforced by Food and Drug Administration (FDA)-approved methods for cancer screening, detection, and follow-up. WES is on the verge of becoming an affordable and sufficiently evolved technology for everyday clinical use.

Clear cell renal carcinoma is the most frequent type of kidney cancer, with an increasing incidence rate worldwide. In this research, we used a proteotranscriptomic approach to differentiate normal and tumor tissues in clear cell renal cell carcinoma (ccRCC). Using transcriptomic data of patients with malignant and paired normal tissue samples from gene array cohorts, we identified the top genes over-expressed in ccRCC. We collected surgically resected ccRCC specimens to further investigate the transcriptomic results on the proteome level. The differential protein abundance was evaluated using targeted mass spectrometry (MS). We assembled a database of 558 renal tissue samples from NCBI GEO and used these to uncover the top genes with higher expression in ccRCC. For protein level analysis 162 malignant and normal kidney tissue samples were acquired. The most consistently upregulated genes were IGFBP3, PLIN2, PLOD2, PFKP, VEGFA, and CCND1 (p < 10−5 for each gene). Mass spectrometry further validated the differential protein abundance of these genes (IGFBP3, p = 7.53 × 10−18; PLIN2, p = 3.9 × 10−39; PLOD2, p = 6.51 × 10−36; PFKP, p = 1.01 × 10−47; VEGFA, p = 1.40 × 10−22; CCND1, p = 1.04 × 10−24). We also identified those proteins which correlate with overall survival. Finally, a support vector machine-based classification algorithm using the protein-level data was set up. We used transcriptomic and proteomic data to identify a minimal panel of proteins highly specific for clear cell renal carcinoma tissues. The introduced gene panel could be used as a promising tool in the clinical setting.