Dataset Information

Quantitative evaluation and comparison of two prodrug-activating suicide gene therapies on oral squamous cell carcinoma.

ABSTRACT: Prodrug-activating suicide gene therapy (PA suicide gene therapy for short) for cancer is to introduce cancer cells with suicide genes. The enzyme encoded by suicide gene is not toxic but is able to kill cancer cells by converting a non-toxic prodrug into a toxic compound. This approach is a promising cancer gene therapy that could reduce non-specific toxicity to normal tissue. However, there is no quantitative method to evaluate efficacy of suicide gene therapy in preclinical study. The aim of this study is to develop a new method to quantitatively evaluate and compare prodrug-activating suicide gene therapies. This study was carried out on an oral squamous cell carcinoma (OSCC) cell line CAL-27. Suicide genes were integrated into ROSA26 locus of CAL-27 by CRISPR-Cas9. CAL-27 cell lines stably expressing herpes simplex virus-thymidine kinase (TK) or yeast cytosine deaminase (CD) were used to evaluate and compare PA suicide gene therapies. The efficacies of PA suicide gene therapies were quantitatively evaluated from three aspects: effective prodrug concentration, prodrug treatment time, and bystander effect. This method also could be used for different types of suicide gene therapies and different types of cancer. When the prodrug concentration, treatment time, and rate of suicide gene-positive cells (related to bystander effect) are fixed, anti-cancer effects could be quantitatively measured. This information is important for suicide gene therapy preclinical development.

SUBMITTER: Xu N

PROVIDER: S-EPMC8085848 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundEarly-stage oral squamous cell carcinoma (OSCC) patients have a one-in-four risk of regional metastasis (LN+), which is also the most significant prognostic factor for survival. As there are no validated biomarkers for predicting LN+ in early-stage OSCC, elective neck dissection often leads to over-treatment and under-treatment. We present a machine-learning-based model using the quantitative nuclear phenotype of cancer cells from the primary tumor to predict the risk of nodal disease.Methods and findingsTumor specimens were obtained from 35 patients diagnosed with primary OSCC and received surgery with curative intent. Of the 35 patients, 29 had well (G1) or moderately (G2) differentiated tumors, and six had poorly differentiated tumors. From each, two consecutive sections were stained for hematoxylin & eosin and Feulgen-thionin staining. The slides were scanned, and images were processed to curate nuclear morphometric features for each nucleus, measuring nuclear morphology, DNA amount, and chromatin texture/organization. The nuclei (n = 384,041) from 15 G1 and 14 G2 tumors were randomly split into 80% training and 20% test set to build the predictive model by using Random Forest (RF) analysis which give each tumor cell a score, NRS. The area under ROC curve (AUC) was 99.6% and 90.7% for the training and test sets, respectively. At the cutoff score of 0.5 as the median NRS of each region of interest (n = 481), the AUC was 95.1%. We then developed a patient-level model based on the percentage of cells with an NRS ≥ 0.5. The prediction performance showed AUC of 97.7% among the 80% (n = 23 patient) training set and with the cutoff of 61% positive cells achieved 100% sensitivity and 91.7% specificity. When applying the 61% cutoff to the 20% test set patients, the model achieved 100% accuracy.ConclusionsOur findings may have a clinical impact with an easy, accurate, and objective biomarker from routine pathology tissue, providing an unprecedented opportunity to improve neck management decisions in early-stage OSCC patients.

Dataset Information

Quantitative evaluation and comparison of two prodrug-activating suicide gene therapies on oral squamous cell carcinoma.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets