Mutations, Bottlenecks, and Clonal Sweeps: How Environmental Carcinogens and Genomic Changes Shape Clonal Evolution during Tumor Progression

Genetic evolution of keratinocytes to cutaneous squamous cell carcinoma

We performed multi-omic profiling of epidermal keratinocytes, precancerous actinic keratoses, and squamous cell carcinomas to understand the molecular transitions during skin carcinogenesis. Single-cell mutational analyses showed that most keratinocytes in normal skin had lower mutation burdens than melanocytes and fibroblasts, however keratinocytes with TP53 or NOTCH1 mutations had substantially higher mutation burdens, suggesting that these mutations prime keratinocytes for transformation by increasing their mutation rate. Mutational profiling and spatial transcriptomics on squamous cell carcinomas adjacent to actinic keratoses revealed TERT promoter and CDKN2A mutations emerging in actinic keratoses, whereas additional mutations inactivating ARID2 and activating the MAPK-pathway delineated the transition to squamous cell carcinomas. Spatial variation in gene expression patterns was common in both tumor and immune cells, with high expression of checkpoint molecules at the invasive front of tumors. In conclusion, this study documents key events during the evolution of cutaneous squamous cell carcinoma.

Osteosarcoma cells exhibit functional interactions with stromal cells, fostering a lung microenvironment conducive to the establishment of metastatic tumor cells

BACKGROUND

Osteosarcoma (OS) stands out as the most common bone tumor, with approximately 20% of the patients receiving a diagnosis of metastatic OS at their initial assessment. A significant challenge lies in the frequent existence of undetected metastases during the initial diagnosis. Mesenchymal stem cells (MSCs) possess unique abilities that facilitate tumor growth, and their interaction with OS cells is crucial for metastatic spread.

METHODS AND RESULTS

We demonstrated that, in vitro, MSCs exhibited a heightened migration response toward the secretome of non-metastatic OS cells. When challenged to a secretome derived from lungs preloaded with OS cells, MSCs exhibited greater migration toward lungs colonized with metastatic OS cells. Moreover, in vivo, MSCs displayed preferential migratory and homing behavior toward lungs colonized by metastatic OS cells. Metastatic OS cells, in turn, demonstrated an increased migratory response to the MSCs' secretome. This behavior was associated with heightened cathepsin D (CTSD) expression and the release of active metalloproteinase 2 (MMP2) by metastatic OS cells.

CONCLUSIONS

Our assessment focused on two complementary tumor capabilities crucial to metastatic spread, emphasizing the significance of inherent cell features. The findings underscore the pivotal role of signaling integration within the niche, with a complex interplay of migratory responses among established OS cells in the lungs, prometastatic OS cells in the primary tumor, and circulating MSCs. Pulmonary metastases continue to be a significant factor contributing to OS mortality. Understanding these mechanisms and identifying differentially expressed genes is essential for pinpointing markers and targets to manage metastatic spread and improve outcomes for patients with OS.

A Treg-related riskscore model may improve the prognosis evaluation of colorectal cancer

BACKGROUND

Colorectal cancer (CRC) poses a significant health challenge. This study aims to investigate the prognostic value of a regulatory T cell (Treg)-related gene signature in CRC.

METHODS

We extracted the gene expression and clinical data on CRC from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The gene module related to Treg was identified by weighted gene co-expression network analysis (WGCNA). The genes in the significant module were filtered by univariate Cox, least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analysis. A riskscore model was established in terms of the key Treg-related genes. The reliability of this riskscore model was validated using the external GEO dataset. The association of riskscore with clinical features, mutation patterns and signaling pathways was explored.

RESULTS

Genes in the blue module showed the strongest association with Tregs. After a series of filtering cycles, seven Treg-related key genes, GDE1, GSR, HSPB1, AOC2, TBX19, TAMM41 and TIGD6, were selected to construct a riskscore model. This model performed well in evaluating the patients' survival in TCGA cohort, and was further affirmed by the GSE17536 validation cohort. For precise evaluation of the patients' survival, we established a nomogram in light of riskscore and clinical factors. Patients in different risk groups had distinct clinical features, mutation patterns and signaling pathway activities. The expression of five key genes was significantly associated with Treg infiltration in the CRC samples.

CONCLUSION

We established a useful riskscore model in light of seven Treg-related genes. This model may contribute to the prognosis evaluation, direct tailored treatment, and hopefully improve clinical outcomes of the CRC patients.