Transcript inhibition in long-term BPA-exposed FRTL-5 cells.

<p>(<b>A</b>) Proliferation rate analysis of FRTL-5 cells exposed for 28 days to 10⁻⁹ M BPA. Cells were counted every 7 days and the population doubling calculated as described in Material and Methods. Data are reported as mean ± standard deviation of three independent experiments. (<b>B</b>) qRT-PCR analysis of <i>Atf4</i>, <i>Ddit3</i>, <i>Tp53</i> and (<b>C</b>) of <i>Cops4</i>, <i>Cops5</i>, <i>Cops6</i>, <i>Cops8</i> and <i>Ddb1</i> in FRTL-5 cells treated for 28 days. Data are reported as the ratio between mRNA levels in 10⁻⁹ M BPA-treated and control samples. The mean ± standard deviation of three independent experiments is reported. *<i>p</i>-value <0.05; **<i>p</i>-value <0.01.</p

Summary of BPA mechanisms of toxicity.

<p>(<b>A</b>) Cells exposure to high-dose of BPA can strongly deregulates the expression of a single (a) or few genes in the same pathway (b), impairing a cellular function. (<b>B</b>) Low-dose BPA exposure can induce a slight deregulation of many genes in the same pathway without compromising a specific phenotype. If a second injury is applied, the damage is highlighted by phenotypic changes potentially representing a hazard for health.</p

Time-dependent transcriptome perturba4tion induced by low-dose BPA in FRTL-5 cells.

<p>Volcano plots of microarray data after 1-day (<b>A</b>), 3-day (<b>B</b>) and 7-day (<b>C</b>) treatment with 10⁻⁹ M BPA compared to untreated cells. The <i>y</i>-axis value is the negative logarithm (base 10) of the corrected <i>p</i>-value. A green horizontal line on the plot represents the significant threshold for <i>p</i>-value. The <i>x</i>-axis is shown as the logarithm (base 2) of the FC in expression level between treated and control cells. The vertical green lines on the plot represent the thresholds for FC. Red dots are up-regulated probes; green dots are down-regulated probes. The number of down/up-regulated genes for each Volcano plot is reported in the underlying table. (<b>D</b>) Venn Diagram showing the gene set overlap between 3- and 7-day treatments.</p

Time-dependent inhibition of genes in FRTL-5 cells treated with BPA verified by qRT-PCR.

<p>qRT-PCR analysis of some microarray down-regulated genes in FRTL-5 cells after 1-, 3-, and 7-day 10⁻⁹ M BPA treatment. In (<b>A</b>) and (<b>B</b>), genes enriched in the “DNA replication, recombination, and repair, developmental disorder, hereditary disorder” network, predicted in the 7-day gene set, are reported. In (<b>C</b>) and (<b>D</b>), transcripts leading to the prediction of p53 inhibition are shown. Data are reported as the ratio between mRNA content in BPA-treated and control samples. The mean ± standard deviation of three independent experiments is reported. *<i>p-value</i> <0.05; **<i>p-value</i> <0.01.</p

Genes enriched in the “DNA replication, recombination and repair” network modulated after 7-days BPA treatment.

<p>Genes enriched in the “DNA replication, recombination and repair” network modulated after 7-days BPA treatment.</p

Analysis of FRTL-5 cell response to UV-C irradiation following long-term BPA treatment.

<p>(<b>A</b>) Proliferation rate analysis of FRTL-5 cells treated for 28 days with 10⁻⁹ M BPA and then subjected to UV-C irradiation. Cells were counted every 24 hrs until 144 hrs post-irradiation. Population doubling was calculated as described in Materials and Methods. Data are reported as mean ± standard deviation of three independent experiments. (<b>B</b>) Quantification of DNA damage by comet assay. Data are reported as mean ± standard deviation of the tail intensity of around 100 cells analyzed for each point. (<b>C</b>) qRT-PCR analysis of the pattern of <i>p21</i> transcript levels following UV-C irradiation. Data are reported as the ratio between <i>p21</i> transcript levels in irradiated and control cells. The mean ± standard deviation of three independent experiments is reported. *<i>p</i>-value <0.05; **<i>p</i>-value <0.01. (<b>D</b>) Quantification of apoptotic cells by TUNEL staining. Data are reported as percentage of TUNEL positive cells per total cell number identified by DAPI staining. The results are expressed as mean ± standard deviation of several fields analyzed in three independent experiments.</p

Tp53 and TF regulation following BPA treatment in FRTL5 cells.

<p>(<b>A</b>) qRT-PCR analysis of Tp53 transcript in FRTL-5 cells after 1-, 3-, and 7-day treatment with 10⁻⁹ M BPA. Data are reported as the ratio between transcript levels in BPA-treated and control samples. The mean ± standard deviation of three independent experiments is reported. *<i>p-value</i> <0.05; **<i>p-value</i> <0.01. (<b>B</b>) Western blot analysis of Tp53 nuclear protein levels in FRTL-5 cells after 1-, 3-, and 7-day treatment with 10⁻⁹ M BPA. (<b>C</b>) Schematic representation of <i>Tp53</i> promoter (-300/+130bp), depicting binding sites for TF predicted modulated by IPA. (<b>D</b>) Western blot analysis of p65 and c-Myc nuclear protein levels in FRTL-5 cells after 1-, 3-, and 7-day treatment with 10⁻⁹ M BPA. Topoisomerase I was used as loading control. Data are representative of three independent experiments (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0151618#pone.0151618.s004" target="_blank">S4 Fig</a>). (<b>E</b>) Western blot analysis of p-Akt (Ser 473) and Akt in FRTL-5 cells after 7-day treatment with 10⁻⁹ M BPA. β-actin was used as loading control. Data are representative of three independent experiments (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0151618#pone.0151618.s004" target="_blank">S4 Fig</a>).</p

Classification performance of WSO-SVM in comparison with the best competing algorithm in each category.

The overall best competing algorithm is highlighted by **.</p

Pipeline of the proposed method.

Background and objectiveGlioblastoma (GBM) is one of the most aggressive and lethal human cancers. Intra-tumoral genetic heterogeneity poses a significant challenge for treatment. Biopsy is invasive, which motivates the development of non-invasive, MRI-based machine learning (ML) models to quantify intra-tumoral genetic heterogeneity for each patient. This capability holds great promise for enabling better therapeutic selection to improve patient outcome.MethodsWe proposed a novel Weakly Supervised Ordinal Support Vector Machine (WSO-SVM) to predict regional genetic alteration status within each GBM tumor using MRI. WSO-SVM was applied to a unique dataset of 318 image-localized biopsies with spatially matched multiparametric MRI from 74 GBM patients. The model was trained to predict the regional genetic alteration of three GBM driver genes (EGFR, PDGFRA and PTEN) based on features extracted from the corresponding region of five MRI contrast images. For comparison, a variety of existing ML algorithms were also applied. Classification accuracy of each gene were compared between the different algorithms. The SHapley Additive exPlanations (SHAP) method was further applied to compute contribution scores of different contrast images. Finally, the trained WSO-SVM was used to generate prediction maps within the tumoral area of each patient to help visualize the intra-tumoral genetic heterogeneity.ResultsWSO-SVM achieved 0.80 accuracy, 0.79 sensitivity, and 0.81 specificity for classifying EGFR; 0.71 accuracy, 0.70 sensitivity, and 0.72 specificity for classifying PDGFRA; 0.80 accuracy, 0.78 sensitivity, and 0.83 specificity for classifying PTEN; these results significantly outperformed the existing ML algorithms. Using SHAP, we found that the relative contributions of the five contrast images differ between genes, which are consistent with findings in the literature. The prediction maps revealed extensive intra-tumoral region-to-region heterogeneity within each individual tumor in terms of the alteration status of the three genes.ConclusionsThis study demonstrated the feasibility of using MRI and WSO-SVM to enable non-invasive prediction of intra-tumoral regional genetic alteration for each GBM patient, which can inform future adaptive therapies for individualized oncology.</div

Different data sources that can be leveraged by WSO-SVM and existing ML algorithms.

Different data sources that can be leveraged by WSO-SVM and existing ML algorithms.</p