Tumours in 177 pet hamsters

Background: Even though tumours are considered to occur frequently in pet hamsters, there is only a small number of scientific reports in current literature. Methods: Pathological reports from 177 hamsters were evaluated. Results: Of these, 78 were male and 75 were female. Median age of affected hamsters was 12 months (range 2–34). Integumental tumours were the most common neoplasms (62%, 109/177). As far as species was known, the number of Syrian hamsters (52%, 30/58) affected by tumours seemed to be lower than the number of affected dwarf hamsters (85%, 47/55). Tumours of the hematopoietic system were the second most frequently neoplasms (17%, 30/177). Relative number of neoplasms of the endocrine system, tumours of the digestive system (1.7%, 3/177) and other tumours (4%, 7/177 each) was low. The majority of integumental tumours were epithelial (66%; 91/126). Conclusion: This study aimed to analyze data from veterinary surgeries and pathological institutes about the occurrence of spontaneous tumours in Syrian hamsters and dwarf hamsters to give information about the frequency of tumours, prognosis and survival time. This is the first study about tumours in pet hamsters in Germany so far

Prominent features of the amino acid mutation landscape in cancer.

Cancer can be viewed as a set of different diseases with distinctions based on tissue origin, driver mutations, and genetic signatures. Accordingly, each of these distinctions have been used to classify cancer subtypes and to reveal common features. Here, we present a different analysis of cancer based on amino acid mutation signatures. Non-negative Matrix Factorization and principal component analysis of 29 cancers revealed six amino acid mutation signatures, including four signatures that were dominated by either arginine to histidine (Arg>His) or glutamate to lysine (Glu>Lys) mutations. Sample-level analyses reveal that while some cancers are heterogeneous, others are largely dominated by one type of mutation. Using a non-overlapping set of samples from the COSMIC somatic mutation database, we validate five of six mutation signatures, including signatures with prominent arginine to histidine (Arg>His) or glutamate to lysine (Glu>Lys) mutations. This suggests that our classification of cancers based on amino acid mutation patterns may provide avenues of inquiry pertaining to specific protein mutations that may generate novel insights into cancer biology

Cancer-associated arginine-to-histidine mutations confer a gain in pH sensing to mutant proteins.

The intracellular pH (pHi) of most cancers is constitutively higher than that of normal cells and enhances proliferation and cell survival. We found that increased pHi enabled the tumorigenic behaviors caused by somatic arginine-to-histidine mutations, which are frequent in cancer and confer pH sensing not seen with wild-type proteins. Experimentally raising the pHi increased the activity of R776H mutant epidermal growth factor receptor (EGFR-R776H), thereby increasing proliferation and causing transformation in fibroblasts. An Arg-to-Gly mutation did not confer these effects. Molecular dynamics simulations of EGFR suggested that decreased protonation of His776 at high pH causes conformational changes in the αC helix that may stabilize the active form of the kinase. An Arg-to-His, but not Arg-to-Lys, mutation in the transcription factor p53 (p53-R273H) decreased its transcriptional activity and attenuated the DNA damage response in fibroblasts and breast cancer cells with high pHi. Lowering pHi attenuated the tumorigenic effects of both EGFR-R776H and p53-R273H. Our data suggest that some somatic mutations may confer a fitness advantage to the higher pHi of cancer cells

Prominent features of the amino acid mutation landscape in cancer

<div><p>Cancer can be viewed as a set of different diseases with distinctions based on tissue origin, driver mutations, and genetic signatures. Accordingly, each of these distinctions have been used to classify cancer subtypes and to reveal common features. Here, we present a different analysis of cancer based on amino acid mutation signatures. Non-negative Matrix Factorization and principal component analysis of 29 cancers revealed six amino acid mutation signatures, including four signatures that were dominated by either arginine to histidine (Arg>His) or glutamate to lysine (Glu>Lys) mutations. Sample-level analyses reveal that while some cancers are heterogeneous, others are largely dominated by one type of mutation. Using a non-overlapping set of samples from the COSMIC somatic mutation database, we validate five of six mutation signatures, including signatures with prominent arginine to histidine (Arg>His) or glutamate to lysine (Glu>Lys) mutations. This suggests that our classification of cancers based on amino acid mutation patterns may provide avenues of inquiry pertaining to specific protein mutations that may generate novel insights into cancer biology.</p></div

Normalized NMF mixture coefficients for individual samples.

<p>Plot of the normalized mixture coefficients across the three mutation signatures with high R>H or E>K components for every individual sample. Colors represent the greatest contributing mutation signature for each sample based on the full individual-level NMF analysis. Here we see a dramatic separation of samples in the E>K component to the near exclusion of other signatures.</p

Amino acid mutation signatures for individual samples.

<p>(A) A heatmap representation of the six-component NMF clustering results for individual cancer samples (only those with >10 total nonsynonymous mutations). Samples with the same maximum signature component were grouped and sorted. Four amino acid mutation signatures identified (R>H, E>K, E>K/E>Q, Complex 2) overlap with signatures in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0183273#pone.0183273.g001" target="_blank">Fig 1A</a>. Color scale represents scaled contribution of each signature for a given sample. Signature and NMF fit details can be found in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0183273#pone.0183273.s003" target="_blank">S3 Fig</a>. (B) Bars show the total fraction of individual samples with a majority of a particular signature within each cancer. Within cancers, a large fraction of individual samples tend to have similar signature components.</p