West, C. E. | Search bioRxiv

bioRxiv2022

Topical GZ21T inhibits the growth of actinic keratoses in a UVB induced model of skin carcinogenesis

Actinic keratoses (AKs) are premalignant intraepidermal neoplasms that occur as a result of cumulative sun damage. AKs commonly relapse, and up to 16% undergo malignant transformation into cutaneous squamous cell carcinoma (cSCC). There is a need for novel therapies that reduce the quantity and surface area of AKs as well as prevent malignant transformation to cSCCs. We recently showed that GZ17-6.02, an anti-cancer agent composed of curcumin, haramine, and isovanillin, inhibited the growth of H297.T cells. The present study evaluated the efficacy of a novel topical formulation of GZ17-6.02, known as GZ21T, in a murine model of AK generated by exposing SKH1 mice to ultraviolet irradiation. Treatment of mice with topical GZ21T inhibited the growth of AKs by decreasing both lesion count (p=.028) and surface area occupied by tumor (p=.026). GZ21T also suppressed the progression of AKs to cSCC by decreasing the count (p=.047) and surface area (p=.049) of lesions more likely to represent cSCC. RNA sequencing and proteomic analyses revealed that GZ21T suppressed several pathways, including MAPK (p=.026), Pi3K-Akt (p=.028), HIF-1 (p=.030), Wnt (p=.031), insulin (p=.011), and ErbB (p=.006) signaling. GZ21T also upregulated the autophagy-promoting protein AMPK, while suppressing proteins such as PD-L1, glutaminase, pAkt1 S473, and eEF2K. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/506864v1_ufig1.gif" ALT="Figure 1"> View larger version (44K): org.highwire.dtl.DTLVardef@1254e6corg.highwire.dtl.DTLVardef@3fd190org.highwire.dtl.DTLVardef@1d75eb5org.highwire.dtl.DTLVardef@8afc97_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2019

RFQAmodel: Random Forest Quality Assessment to identify a predicted protein structure in the correct fold

While template-free protein structure prediction protocols now produce good quality models for many targets, modelling failure remains common. For these methods to be useful it is important that users can both choose the best model from the hundreds to thousands of models that are commonly generated for a target, and determine whether this model is likely to be correct. We have developed Random Forest Quality Assessment (RFQAmodel), which assesses whether models produced by a protein structure prediction pipeline have the correct fold. RFQAmodel uses a combination of existing quality assessment scores with two predicted contact map alignment scores. These alignment scores are able to identify correct models for targets that are not otherwise captured. Our classifier was trained on a large set of protein domains that are structurally diverse and evenly balanced in terms of protein features known to have an effect on modelling success, and then tested on a second set of 244 protein domains with a similar spread of properties. When models for each target in this second set were ranked according to the RFQAmodel score, the highest-ranking model had a high-confidence RFQAmodel score for 67 modelling targets, of which 52 had the correct fold. At the other end of the scale RFQAmodel correctly predicted that for 59 targets the highest-ranked model was incorrect. In comparisons to other methods we found that RFQAmodel is better able to identify correct models for targets where only a few of the models are correct. We found that RFQAmodel achieved a similar performance on the model sets for CASP12 and CASP13 free-modelling targets. Finally, by iteratively generating models and running RFQAmodel until a model is produced that is predicted to be correct with high confidence, we demonstrate how such a protocol can be used to focus computational efforts on difficult modelling targets.

bioinformatics↗