Pan-cancer analysis of the interplay between mutational signatures and cellular signaling.

Cancer is a multi-faceted disease with intricate relationships between mutagenic processes, alterations in cellular signaling, and the tissue microenvironment. To date, these processes have been largely studied in isolation. A systematic understanding of how they interact and influence each other is lacking. Here, we present a framework for systematically characterizing the interaction between pairs of mutational signatures and between signatures and signaling pathway alterations. We applied this framework to large-scale data from TCGA and PCAWG and identified multiple positive and negative interactions, both cross֊tissue and tissue֊specific, that provide new insights into the molecular routes observed in tumorigenesis and their respective drivers. This framework allows for a more fine-grained dissection of common and distinct etiology of mutational signatures. We further identified several interactions with both positive and negative impacts on patient survival, demonstrating their clinical relevance and potential for improving personalized cancer care.

Relating mutational signature exposures to clinical data in cancers via signeR 2.0.

BACKGROUND: Cancer is a collection of diseases caused by the deregulation of cell processes, which is triggered by somatic mutations. The search for patterns in somatic mutations, known as mutational signatures, is a growing field of study that has already become a useful tool in oncology. Several algorithms have been proposed to perform one or both the following two tasks: (1) de novo estimation of signatures and their exposures, (2) estimation of the exposures of each one of a set of pre-defined signatures. RESULTS: Our group developed signeR, a Bayesian approach to both of these tasks. Here we present a new version of the software, signeR 2.0, which extends the possibilities of previous analyses to explore the relation of signature exposures to other data of clinical relevance. signeR 2.0 includes a user-friendly interface developed using the R-Shiny framework and improvements in performance. This version allows the analysis of submitted data or public TCGA data, which is embedded in the package for easy access. CONCLUSION: signeR 2.0 is a valuable tool to generate and explore exposure data, both from de novo or fitting analyses and is an open-source R package available through the Bioconductor project at ( https://doi.org/10.18129/B9.bioc.signeR ).

Mutational landscape of intestinal crypt cells after long-term in vivo exposure to high fat diet.

Obesity is a modifiable risk factor in cancer development, especially for gastrointestinal cancer. While the etiology of colorectal cancer is well characterized by the adenoma-carcinoma sequence, it remains unclear how obesity influences colorectal cancer development. Dietary components of a high fat diet along with obesity have been shown to modulate the cancer risk by perturbing the homeostasis of intestinal stem cells, yet how adiposity impacts the development of genomic instability has not been studied. Mutational signatures are a powerful way to understand how a complex biological response impacts genomic stability. We utilized a mouse model of diet-induced obesity to study the mutational landscape of intestinal crypt cells after a 48-week exposure to an experimental high fat diet in vivo. By clonally enriching single crypt derived cells in organoid culture and obtaining whole genome sequences, we analyzed and compared the mutational landscape of intestinal epithelial cells from normal diet and high fat diet mice. Single nucleotide substitution signatures and indel signatures present in our cohort are found equally active in both diet groups and reflect biological processes of normal aging, cellular replication, and oxidative stress induced during organoid culturing. Thus, we demonstrate that in the absence of activating mutations or chemical exposure, high fat diet alone is not sufficient to increase genomic instability.

Clickable Cisplatin Derivatives as Versatile Tools to Probe the DNA Damage Response to Chemotherapy.

Cisplatin induces DNA crosslinks that are highly cytotoxic. Hence, platinum complexes are frequently used in the treatment of a broad range of cancers. Efficiency of cisplatin treatment is limited by the tumor-specific DNA damage response to the generated lesions. We reasoned that better tools to investigate the repair of DNA crosslinks induced by cisplatin would therefore be highly useful in addressing drug limitations. Here, we synthesized a series of cisplatin derivatives that are compatible with click chemistry, thus allowing visualization and isolation of DNA-platinum crosslinks from cells to study cellular responses. We prioritized one alkyne and one azide Pt(II) derivative, Pt-alkyne-53 and Pt-azide-64, for further biological characterization. We demonstrate that both compounds bind DNA and generate DNA lesions and that the viability of treated cells depends on the active DNA repair machinery. We also show that the compounds are clickable with both a fluorescent probe as well as biotin, thus they can be visualized in cells, and their ability to induce crosslinks in genomic DNA can be quantified. Finally, we show that Pt-alkyne-53 can be used to identify DNA repair proteins that bind within its proximity to facilitate its removal from DNA. The compounds we report here can be used as valuable experimental tools to investigate the DNA damage response to platinum complexes and hence might shed light on mechanisms of chemoresistance.

Decomposing the mutational landscape of cancer genomes with RepairSig.

Mutational signatures are the outcomes of mutagenic processes that occur prior to, and during, tumorigenesis as a result of DNA damage, DNA repair, and DNA replication. In this issue of Cell Systems, Wojtowicz et al. introduce a new computational model aimed at deconstructing the mutational processes that shape cancer genomes.