Peter Rogan | University of Missouri Kansas City (original) (raw)
Papers by Peter Rogan
Int J Rad Biol, 2021
Purpose: Combinations of expressed genes can discriminate radiation-exposed from normal control b... more Purpose: Combinations of expressed genes can discriminate radiation-exposed from normal control blood samples by machine learning (ML) based signatures (with 8-20% misclassification rates). These signatures can quantify therapeutically relevant as well as accidental radiation exposures. The prodromal symptoms of acute radiation syndrome (ARS) overlap those present in influenza and dengue fever infections. Surprisingly, these human radiation signatures misclassified gene expression profiles of virally infected samples as false positive exposures. The present study investigates these and other confounders, and then mitigates their impact on signature accuracy. Methods: This study investigated recall by previous and novel radiation signatures independently derived from multiple Gene Expression Omnibus datasets on common and rare non-neoplastic blood disorders and blood-borne infections (thromboembolism, S. aureus bacteremia, malaria, sickle cell disease, polycythemia vera, and aplastic anemia). Normalized expression levels of signature genes are used as input to ML-based classifiers to predict radiation exposure in other hematological conditions. Results: Except for aplastic anemia, these blood-borne disorders modify the normal baseline expression values of genes present in radiation signatures, leading to false-positive misclassification of radiation exposures in 8-54% of individuals. Shared changes, predominantly in DNA damage response and apoptosis-related gene transcripts in radiation and confounding hematological conditions, compromise the utility of these signatures for radiation assessment. These confounding conditions (sickle cell disease, thrombosis, S. aureus bacteremia, malaria) induce neutrophil extracellular traps, initiated by chromatin decondensation, DNA damage response and fragmentation followed by programmed cell death or extrusion of DNA fragments. Riboviral infections (e.g. influenza or dengue fever) have been proposed to bind and deplete host RNA binding proteins, inducing R-loops in chromatin. R-loops that collide with incoming replication forks can result in incompletely repaired DNA damage, inducing apoptosis and releasing mature virus. To mitigate the effects of confounders, we evaluated predicted radiation-positive samples with novel gene expression signatures derived from radiation-responsive transcripts encoding secreted blood plasma proteins whose expression levels are unperturbed by these conditions. Conclusions: This approach identifies and eliminates misclassified samples with underlying hematological or infectious conditions, leaving only samples with true radiation exposures. Diagnostic accuracy is significantly improved by selecting genes that maximize both sensitivity and specificity in the appropriate tissue using combinations of the best signatures for each of these classes of signatures.
Int J Rad Biol, 2020
Purpose: Inhomogeneous exposures to ionizing radiation can be detected and quantified with the di... more Purpose: Inhomogeneous exposures to ionizing radiation can be detected and quantified with
the dicentric chromosome assay (DCA) of metaphase cells. Complete automation of interpretation
of the DCA for whole-body irradiation has significantly improved throughput without compromising
accuracy, however, low levels of residual false positive dicentric chromosomes (DCs) have confounded
its application for partial-body exposure determination.
Materials and methods: We describe a method of estimating and correcting for false positive
DCs in digitally processed images of metaphase cells. Nearly all DCs detected in unirradiated calibration
samples are introduced by digital image processing. DC frequencies of irradiated calibration
samples and those exposed to unknown radiation levels are corrected subtracting this false
positive fraction from each. In partial-body exposures, the fraction of cells exposed, and radiation
dose can be quantified after applying this modification of the contaminated Poisson method.
Results: Dose estimates of three partially irradiated samples diverged 0.2–2.5 Gy from physical
doses and irradiated cell fractions deviated by 2.3%–15.8% from the known levels. Synthetic partial-
body samples comprised of unirradiated and 3Gy samples from 4 laboratories were correctly
discriminated as inhomogeneous by multiple criteria. Root mean squared errors of these dose estimates
ranged from 0.52 to 1.14Gy2 and from 8.1 to 33.3%2 for the fraction of cells irradiated.
Conclusions: Automated DCA can differentiate whole- from partial-body radiation exposures and
provides timely quantification of estimated whole-body equivalent dose.
MedComm, 2020
Cancer chemotherapy responses have been related to multiple pharmacogenetic biomarkers, often for... more Cancer chemotherapy responses have been related to multiple pharmacogenetic biomarkers, often for the same drug. This study utilizes machine learning to derive multi-gene expression signatures that predict individual patient responses to specific tyrosine kinase inhibitors, including erlotinib, gefitinib, sorafenib, sunitinib, lapatinib and imatinib. Support vector machine (SVM) learning was used to train mathematical models that distinguished sensitivity from resistance to these drugs using a novel systems biology-based approach. This began with expression of genes previously implicated in specific drug responses, then expanded to evaluate genes whose products were related through biochemical pathways and interactions. Optimal pathway-extended SVMs predicted responses in patients at accuracies of 70% (imatinib), 71% (lapatinib), 83% (sunitinib), 83% (erlotinib), 88% (sorafenib) and 91% (gefitinib). These best performing pathway-extended models demonstrated improved balance predicting both sensitive and resistant patient categories, with many of these genes having a known role in cancer aetiology. Ensemble machine learning-based averaging of multiple pathway-extended models derived for an individual drug increased accuracy to >70% for erlotinib, gefitinib, lapatinib and sorafenib. Through incorporation of novel cancer biomarkers, machine learning-based pathway-extended signatures display strong efficacy predicting both sensitive and resistant patient responses to chemotherapy. K E Y W O R D S biochemical pathways, gene signatures, machine learning, systems biology, tyrosine kinase inhibitors This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
The gemcitabine SVM exhibited 62% prediction accuracy for the tumor blocks due to the presence of... more The gemcitabine SVM exhibited 62% prediction accuracy for the tumor blocks due to the presence of samples with poor nucleic acid integrity. Nevertheless, the paclitaxel SVM predicted sensitivity in 84% of patients with no or minimal residual disease.
Rapid sample processing and interpretation of estimated exposures will be critical for triaging e... more Rapid sample processing and interpretation of estimated exposures will be critical for triaging exposed individuals after a major radiation incident. The dicentric chromosome (DC) assay assesses absorbed radiation using metaphase cells from blood. The Automated Dicentric Chromosome Identifier and Dose Estimator System (ADCI) identifies DCs and determines radiation doses. This study aimed to broaden accessibility and speed of this system, while protecting data and software integrity. ADCI Online is a secure web-streaming platform accessible worldwide from local servers. Cloud-based systems containing data and software are separated until they are linked for radiation exposure estimation. Dose estimates are identical to ADCI on dedicated computer hardware. Image processing and selection, calibration curve generation, and dose estimation of 9 test samples completed in <2 days. ADCI Online has the capacity to alleviate analytic bottlenecks in intermediate-to-large radiation incidents. Multiple cloned software instances configured on different cloud environments accelerated dose estimation to within clinically relevant time frames.
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Int J Rad Biol, 2021
Purpose: Combinations of expressed genes can discriminate radiation-exposed from normal control b... more Purpose: Combinations of expressed genes can discriminate radiation-exposed from normal control blood samples by machine learning (ML) based signatures (with 8-20% misclassification rates). These signatures can quantify therapeutically relevant as well as accidental radiation exposures. The prodromal symptoms of acute radiation syndrome (ARS) overlap those present in influenza and dengue fever infections. Surprisingly, these human radiation signatures misclassified gene expression profiles of virally infected samples as false positive exposures. The present study investigates these and other confounders, and then mitigates their impact on signature accuracy. Methods: This study investigated recall by previous and novel radiation signatures independently derived from multiple Gene Expression Omnibus datasets on common and rare non-neoplastic blood disorders and blood-borne infections (thromboembolism, S. aureus bacteremia, malaria, sickle cell disease, polycythemia vera, and aplastic anemia). Normalized expression levels of signature genes are used as input to ML-based classifiers to predict radiation exposure in other hematological conditions. Results: Except for aplastic anemia, these blood-borne disorders modify the normal baseline expression values of genes present in radiation signatures, leading to false-positive misclassification of radiation exposures in 8-54% of individuals. Shared changes, predominantly in DNA damage response and apoptosis-related gene transcripts in radiation and confounding hematological conditions, compromise the utility of these signatures for radiation assessment. These confounding conditions (sickle cell disease, thrombosis, S. aureus bacteremia, malaria) induce neutrophil extracellular traps, initiated by chromatin decondensation, DNA damage response and fragmentation followed by programmed cell death or extrusion of DNA fragments. Riboviral infections (e.g. influenza or dengue fever) have been proposed to bind and deplete host RNA binding proteins, inducing R-loops in chromatin. R-loops that collide with incoming replication forks can result in incompletely repaired DNA damage, inducing apoptosis and releasing mature virus. To mitigate the effects of confounders, we evaluated predicted radiation-positive samples with novel gene expression signatures derived from radiation-responsive transcripts encoding secreted blood plasma proteins whose expression levels are unperturbed by these conditions. Conclusions: This approach identifies and eliminates misclassified samples with underlying hematological or infectious conditions, leaving only samples with true radiation exposures. Diagnostic accuracy is significantly improved by selecting genes that maximize both sensitivity and specificity in the appropriate tissue using combinations of the best signatures for each of these classes of signatures.
Int J Rad Biol, 2020
Purpose: Inhomogeneous exposures to ionizing radiation can be detected and quantified with the di... more Purpose: Inhomogeneous exposures to ionizing radiation can be detected and quantified with
the dicentric chromosome assay (DCA) of metaphase cells. Complete automation of interpretation
of the DCA for whole-body irradiation has significantly improved throughput without compromising
accuracy, however, low levels of residual false positive dicentric chromosomes (DCs) have confounded
its application for partial-body exposure determination.
Materials and methods: We describe a method of estimating and correcting for false positive
DCs in digitally processed images of metaphase cells. Nearly all DCs detected in unirradiated calibration
samples are introduced by digital image processing. DC frequencies of irradiated calibration
samples and those exposed to unknown radiation levels are corrected subtracting this false
positive fraction from each. In partial-body exposures, the fraction of cells exposed, and radiation
dose can be quantified after applying this modification of the contaminated Poisson method.
Results: Dose estimates of three partially irradiated samples diverged 0.2–2.5 Gy from physical
doses and irradiated cell fractions deviated by 2.3%–15.8% from the known levels. Synthetic partial-
body samples comprised of unirradiated and 3Gy samples from 4 laboratories were correctly
discriminated as inhomogeneous by multiple criteria. Root mean squared errors of these dose estimates
ranged from 0.52 to 1.14Gy2 and from 8.1 to 33.3%2 for the fraction of cells irradiated.
Conclusions: Automated DCA can differentiate whole- from partial-body radiation exposures and
provides timely quantification of estimated whole-body equivalent dose.
MedComm, 2020
Cancer chemotherapy responses have been related to multiple pharmacogenetic biomarkers, often for... more Cancer chemotherapy responses have been related to multiple pharmacogenetic biomarkers, often for the same drug. This study utilizes machine learning to derive multi-gene expression signatures that predict individual patient responses to specific tyrosine kinase inhibitors, including erlotinib, gefitinib, sorafenib, sunitinib, lapatinib and imatinib. Support vector machine (SVM) learning was used to train mathematical models that distinguished sensitivity from resistance to these drugs using a novel systems biology-based approach. This began with expression of genes previously implicated in specific drug responses, then expanded to evaluate genes whose products were related through biochemical pathways and interactions. Optimal pathway-extended SVMs predicted responses in patients at accuracies of 70% (imatinib), 71% (lapatinib), 83% (sunitinib), 83% (erlotinib), 88% (sorafenib) and 91% (gefitinib). These best performing pathway-extended models demonstrated improved balance predicting both sensitive and resistant patient categories, with many of these genes having a known role in cancer aetiology. Ensemble machine learning-based averaging of multiple pathway-extended models derived for an individual drug increased accuracy to >70% for erlotinib, gefitinib, lapatinib and sorafenib. Through incorporation of novel cancer biomarkers, machine learning-based pathway-extended signatures display strong efficacy predicting both sensitive and resistant patient responses to chemotherapy. K E Y W O R D S biochemical pathways, gene signatures, machine learning, systems biology, tyrosine kinase inhibitors This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
The gemcitabine SVM exhibited 62% prediction accuracy for the tumor blocks due to the presence of... more The gemcitabine SVM exhibited 62% prediction accuracy for the tumor blocks due to the presence of samples with poor nucleic acid integrity. Nevertheless, the paclitaxel SVM predicted sensitivity in 84% of patients with no or minimal residual disease.
Rapid sample processing and interpretation of estimated exposures will be critical for triaging e... more Rapid sample processing and interpretation of estimated exposures will be critical for triaging exposed individuals after a major radiation incident. The dicentric chromosome (DC) assay assesses absorbed radiation using metaphase cells from blood. The Automated Dicentric Chromosome Identifier and Dose Estimator System (ADCI) identifies DCs and determines radiation doses. This study aimed to broaden accessibility and speed of this system, while protecting data and software integrity. ADCI Online is a secure web-streaming platform accessible worldwide from local servers. Cloud-based systems containing data and software are separated until they are linked for radiation exposure estimation. Dose estimates are identical to ADCI on dedicated computer hardware. Image processing and selection, calibration curve generation, and dose estimation of 9 test samples completed in <2 days. ADCI Online has the capacity to alleviate analytic bottlenecks in intermediate-to-large radiation incidents. Multiple cloned software instances configured on different cloud environments accelerated dose estimation to within clinically relevant time frames.
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