Personalized cancer vaccines have emerged as a promising avenue for cancer treatment or prevention strategies. This approach targets the specific genetic alterations in individual patient’s tumors, offering a more personalized and effective treatment option. Previous studies have shown that generalized peptide vaccines targeting a limited scope of gene mutations are ineffective, emphasizing the need for personalized approaches. While studies have explored personalized mRNA vaccines, personalized peptide vaccines have not yet been studied in this context. Pancreatic ductal adenocarcinoma (PDAC) remains challenging in oncology, necessitating innovative therapeutic strategies. In this study, we developed a personalized peptide vaccine design methodology, employing RNA sequencing (RNAseq) to identify prevalent gene mutations underlying PDAC development in a specific patient solid tumor tissue. We performed RNAseq analysis for trimming adapters, read alignment, and somatic variant calling. We also developed a Python program, called GeneFinder, which validates the alignment of the RNAseq analysis. The Python program is freely available to download. Using chromosome number and locus data, GeneFinder identifies the target gene along the UCSC hg38 reference set. Based on the gene mutation data, we developed a personalized PDAC cancer vaccine that targets 100 highly prevalent gene mutations in the individual patient. We predicted peptide-MHC binding affinity, immunogenicity, antigenicity, allergenicity, and toxicity for each epitope. Then, we selected the top 50 and 100 epitopes based on our previously published vaccine design methodology. Finally, we generated pMHC-TCR 3D molecular model complex structures which are freely available fo download. The designed personalized cancer vaccine contains epitopes commonly found in PDAC solid tumor tissue. Our personalized vaccine was composed of neoantigens, allowing for a more precise and targeted immune response against cancer cells. Additionally, we identified mutated genes which were also found in the reference study where we obtained the sequencing data, thus validating our vaccine design methodology. This is the first study designing a personalized peptide vaccine using human patient data to identify gene mutations associated with the specific tumor of interest.

This study addresses the challenge of assessing the carcinogenic potential of hazardous chemical mixtures, such as PFAS, which are known to contribute significantly to cancer development. Here, we propose a novel framework called HNNMixCancer that utilizes a Hybrid Neural Network (HNN) integrated into a machine-learning framework. This framework incorporates a mathematical model to simulate chemical mixtures and enables the creation of classification models for binary and multiclass classification and regression. Through extensive experimentation, we demonstrate that our HNN model outperforms other methodologies, including Random Forest, Bootstrap Aggregating, Adaptive Boosting, Support Vector Regressor, Gradient Boosting, Kernel Ridge, Decision Tree with AdaBoost, and KNeighbors, achieving a superior accuracy of 92.7% in binary classification. To address the limited availability of experimental data and enrich the training data, we generate an assumption-based virtual library of chemical mixtures using a known carcinogenic and non-carcinogenic single chemical for all the classification models. Remarkably, in this case, all methods achieve accuracies exceeding 98% for binary classification. In external validation tests, our HNN method achieves the highest accuracy of 80.5%. Furthermore, in multiclass classification, the HNN demonstrates an overall accuracy of 96.3%, outperforming RF, Bagging, and AdaBoost, which achieved 91.4%, 91.7%, and 80.2%, respectively. In regression models, HNN, RF, SVR, GB, KR, DT with AdaBoost, and KN achieve average R2 values of 0.96, 0.90, 0.77, 0.94, 0.96, 0.96, and 0.97, respectively, showcasing their effectiveness in predicting the concentration at which a chemical mixture becomes carcinogenic. Our method exhibits exceptional predictive power in prioritizing carcinogenic chemical mixtures, even when relying on assumption-based mixtures. This capability is particularly valuable for toxicology studies that lack experimental data on the carcinogenicity and toxicity of chemical mixtures. To our knowledge, this study introduces the first method for predicting the carcinogenic potential of chemical mixtures. The HNNMixCancer framework offers a novel alternative for dose-dependent carcinogen prediction. Ongoing efforts involve implementing the HNN method to predict mixture toxicity and expanding the application of HNNMixCancer to include multi-mixtures.

Perfluoroalkyl and polyfluoroalkyl substances (PFAS) are persistent environmental contaminants of emerging concern, recognized for their toxicity and potential carcinogenic properties. PFAS chemicals are reported to act as endocrine disruptors through their interactions with various nuclear receptors (NRs). Despite their significant impact, comprehensive screenings and detailed analyses of PFAS binding strengths at both the orthosteric and allosteric sites of NRs are currently lacking in the literature. Our study addresses this gap by focusing on the binding interaction analysis of both common and uncommon PFAS with key nuclear receptors such as the vitamin D receptor (VDR), peroxisome proliferator-activated receptor gamma (PPARγ), pregnane X receptor (PXR), and estrogen receptor alpha (ERα). We used advanced docking simulations to screen 9,507 PFAS chemicals at the orthosteric and allosteric sites of the PPARγ, PXR, VDR, and ERα. We verified the accuracy of our docking protocol through multiple docking procedures and validations. The python code used for PFAS chemical screening is deposited in the supplementary file. Among the PFAS assessed at the orthosteric site of the VDR, 21 demonstrated similar binding affinities compared to the calcitriol, while 130 PFAS, exhibited stronger binding affinities than calcitriol. At the VDR allosteric site, where lithocholic acid (LCA) natively binds, 412 PFAS matched the binding energies of LCA, and 2,229 showed even greater binding energies. Similar testing on the PXR, PPARγ, and ERα revealed that the orthosteric site of PPARγ had 1,863 PFAS binding with a higher affinity than native ligand ET1 and 693 PFAS binding at the allosteric site with greater affinity than T35 native ligand. At the PXR orthosteric site, 650 PFAS bound with a greater affinity than native ligand 4WH, while at the allosteric site, 9,148 PFAS demonstrated a greater binding affinity than the known allosetric ligand glycerol. At the orthosteric site of ERα, 40 PFAS displayed binding with a greater affinity than the native ligand estradiol and at the allosteric site, only 8 PFAS exhibited greater binding affinity than coactivator ligand SRC-1. Further, our mixture modeling analysis indicates that PFAS can bind in various combinations with themselves and with endogenous or native ligands simultaneously, to disrupt the endocrine system and cause carcinogenic responses. These findings reveal that PFAS can interfere with nuclear receptor activity by displacing endogenous or native ligands by binding to the orthosteric and allosteric sites. This study elucidates the mechanisms through which PFAS exert their endocrine-disrupting effects, potentially leading to more targeted environmental regulations and therapeutic strategies. Importantly, this study is the first to explore the binding of PFAS at allosteric sites and to model PFAS mixtures at nuclear receptors. Given the high concentration and persistence of PFAS in humans, this study called for the urgent need for further research into the carcinogenic mechanisms of PFAS and the development of intervention strategies that target nuclear receptors. Our ongoing studies extend these findings by further investigating the interactions of PFAS and co-exposed chemicals with nuclear receptors. These investigations are supported by experimental verification, which will enhance our understanding of PFAS broad effects and aid in the design of effective mitigation strategies.

Host ribosome-associated scaffold protein Receptor for Activated C Kinase 1 (RACK1) is utilized by a diverse group of human viruses for Internal Ribosomal Entry Sites (IRES) – mediated translation of viral mRNAs. We recently reported inhibition of herpes virus by small molecules targeting the RACK1 functional site. Here, we tested these molecules against HIV-1 and HCV, as HIV-1 contains two potential IRES sites and HCV translation occurs exclusively through IRES. Compounds significantly downregulated activities of HIV-1- and HCV-related dicistronic reporter constructs in transfected HEK293T cells. The compounds also strongly downregulated production of the HIV-1 capsid protein p24 in HIV-infected cells, as well as production of HIV-1 Gag precursor p55 and p55-derived proteins p24 and p17 in cells infected with the HIV-1 virus. Hepatitis C virus (HCV) IRES activities were also significantly inhibited by RACK1 inhibitor compounds. Since a number of human and plant pathogenic viruses are reported to use IRES, the RACK1 compounds can be established as broad host-targeted antivirals.