Quantifying intra-tumoral genetic heterogeneity of glioblastoma toward precision medicine using MRI and a data-inclusive machine learning algorithm

Glioblastoma (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 outcomes. We 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. The classification accuracy of each gene was 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. This 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.Comment: 36 pages, 8 figures, 3 table

Structure, mutagenesis and QM:MM modelling of 3-ketosteroid Δ1-dehydrogenase from Sterolibacterium denitrificans – the role of new putative membrane-associated domain and proton-relay system in catalysis

3-Ketosteroid Δ1-dehydrogenases (KstD) are important microbial flavin enzymes that initiate the metabolism of steroid ring A and find application in the synthesis of steroid drugs. We present a structure of the KstD from Sterolibacterium denitrificans (AcmB), which contains a previously uncharacterized putative membrane-associated domain and extended proton-relay system. The experimental and theoretical studies show that the steroid 1-dehydrogenation proceeds according to the Ping-Pong bi-bi kinetics and a two-step base-assisted elimination (E2cB) mechanism. The mechanism is validated by evaluating the experimental and theoretical kinetic isotope effect for deuterium substituted substrates. The role of the active site residues is quantitatively assessed by point mutations, experimental activity assays, and QM/MM MD modelling of the reductive half-reaction (RHR). The pre-steady-state kinetics also reveals that the low pH (6.5) optimum of AcmB is dictated by the oxidative half-reaction (OHR), while the RHR exhibits a slight optimum at the pH usual for the KstD family of 8.5. The modelling confirms the origin of the enantioselectivity of C2-H activation and substrate specificity for Δ4-3-ketosteroids. Finally, the cholest-4-en-3-one turns out to be the best substrate of AcmB in terms of ΔG of binding and predicted rate of dehydrogenation

Structure, mutagenesis, and QM:MM modeling of 3-ketosteroid Δ1\Delta^{1}-dehydrogenase from Sterolibacterium denitrificans : the role of a new putative membrane-associated domain and proton-relay system in catalysis

3-Ketosteroid Δ1-dehydrogenases (KstD) are important microbial flavin enzymes that initiate the metabolism of steroid ring A and find application in the synthesis of steroid drugs. We present a structure of the KstD from Sterolibacterium denitrificans (AcmB), which contains a previously uncharacterized putative membrane-associated domain and extended proton-relay system. The experimental and theoretical studies show that the steroid Δ1-dehydrogenation proceeds according to the Ping–Pong bi–bi kinetics and a two-step base-assisted elimination (E2cB) mechanism. The mechanism is validated by evaluating the experimental and theoretical kinetic isotope effect for deuterium-substituted substrates. The role of the active-site residues is quantitatively assessed by point mutations, experimental activity assays, and QM/MM MD modeling of the reductive half-reaction (RHR). The pre-steady-state kinetics also reveals that the low pH (6.5) optimum of AcmB is dictated by the oxidative half-reaction (OHR), while the RHR exhibits a slight optimum at the pH usual for the KstD family of 8.5. The modeling confirms the origin of the enantioselectivity of C2-H activation and substrate specificity for Δ4-3-ketosteroids. Finally, the cholest-4-en-3-one turns out to be the best substrate of AcmB in terms of ΔG of binding and predicted rate of dehydrogenation

Quantifying intra-tumoral genetic heterogeneity of glioblastoma toward precision medicine using MRI and a data-inclusive machine learning algorithm.

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

Classification performance of PDGFRA using CV based on biopsy samples.

Classification performance of PDGFRA using CV based on biopsy samples.</p

Fig 6 -

Contributions of MRI contrast images to the classification of (a) EGFR, (b) PDGFRA, and (c) PTEN, by WSO-SVM.</p

Classification performance of PTEN using CV based on biopsy samples.

Classification performance of PTEN using CV based on biopsy samples.</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