SEPepQuant enhances the detection of possible isoform regulations in shotgun proteomics.

Shotgun proteomics is essential for protein identification and quantification in biomedical research, but protein isoform characterization is challenging due to the extensive number of peptides shared across proteins, hindering our understanding of protein isoform regulation and their roles in normal and disease biology. We systematically assess the challenge and opportunities of shotgun proteomics-based protein isoform characterization using in silico and experimental data, and then present SEPepQuant, a graph theory-based approach to maximize isoform characterization. Using published data from one induced pluripotent stem cell study and two human hepatocellular carcinoma studies, we demonstrate the ability of SEPepQuant in addressing the key limitations of existing methods, providing more comprehensive isoform-level characterization, identifying hundreds of isoform-level regulation events, and facilitating streamlined cross-study comparisons. Our analysis provides solid evidence to support a widespread role of protein isoform regulation in normal and disease processes, and SEPepQuant has broad applications to biological and translational research.

Proteogenomic and metabolomic characterization of human glioblastoma.

Glioblastoma (GBM) is the most aggressive nervous system cancer. Understanding its molecular pathogenesis is crucial to improving diagnosis and treatment. Integrated analysis of genomic, proteomic, post-translational modification and metabolomic data on 99 treatment-naive GBMs provides insights to GBM biology. We identify key phosphorylation events (e.g., phosphorylated PTPN11 and PLCG1) as potential switches mediating oncogenic pathway activation, as well as potential targets for EGFR-, TP53-, and RB1-altered tumors. Immune subtypes with distinct immune cell types are discovered using bulk omics methodologies, validated by snRNA-seq, and correlated with specific expression and histone acetylation patterns. Histone H2B acetylation in classical-like and immune-low GBM is driven largely by BRDs, CREBBP, and EP300. Integrated metabolomic and proteomic data identify specific lipid distributions across subtypes and distinct global metabolic changes in IDH-mutated tumors. This work highlights biological relationships that could contribute to stratification of GBM patients for more effective treatment.

Draft Genome Assemblies and Annotations of Agrypnia vestita Walker, and Hesperophylax magnus Banks Reveal Substantial Repetitive Element Expansion in Tube Case-Making Caddisflies (Insecta: Trichoptera).

Trichoptera (caddisflies) play an essential role in freshwater ecosystems; for instance, larvae process organic material from the water and are food for a variety of predators. Knowledge on the genomic diversity of caddisflies can facilitate comparative and phylogenetic studies thereby allowing scientists to better understand the evolutionary history of caddisflies. Although Trichoptera are the most diverse aquatic insect order, they remain poorly represented in terms of genomic resources. To date, all long-read based genomes have been sequenced from individuals in the retreat-making suborder, Annulipalpia, leaving ∼275 Ma of evolution without high-quality genomic resources. Here, we report the first long-read based de novo genome assemblies of two tube case-making Trichoptera from the suborder Integripalpia, Agrypnia vestita Walker and Hesperophylax magnus Banks. We find that these tube case-making caddisflies have genome sizes that are at least 3-fold larger than those of currently sequenced annulipalpian genomes and that this pattern is at least partly driven by major expansion of repetitive elements. In H. magnus, long interspersed nuclear elements alone exceed the entire genome size of some annulipalpian counterparts suggesting that caddisflies have high potential as a model for understanding genome size evolution in diverse insect lineages.

Multi-Institutional Validation of a Knowledge-Based Planning Model for Patients Enrolled in RTOG 0617: Implications for Plan Quality Controls in Cooperative Group Trials.

PURPOSE: This study aimed to evaluate the feasibility of using a single-institution, knowledge-based planning (KBP) model as a dosimetric plan quality control (QC) for multi-institutional clinical trials. The efficacy of this QC tool was retrospectively evaluated using a subset of plans submitted to Radiation Therapy Oncology Group (RTOG) study 0617. METHODS AND MATERIALS: A single KBP model was created using commercially available software (RapidPlan; Varian Medical Systems, Palo Alto, CA) and data from 106 patients with non-small cell lung cancer who were treated at a single institution. All plans had prescriptions that ranged from 60 Gy in 30 fractions to 74 Gy in 37 fractions and followed the planning guidelines from RTOG 0617. Two sets of optimization objectives were created to produce different trade-offs using the single KBP model predictions: one prioritizing target coverage and a second prioritizing lung sparing (LS) while allowing an acceptable variation in target coverage. Three institutions submitted a high volume of clinical plans to RTOG 0617 and provided data on 25 patients, which were replanned using both sets of optimization objectives. Model-generated, dose-volume histogram predictions were used to identify patients who exceeded the lung clinical target volume (CTV) V20Gy >37% and would benefit from the LS objectives. Overall plan quality differences between KBP-generated plans and clinical plans were evaluated at RTOG 0617-defined dosimetric endpoints. RESULTS: Target coverage and organ at risk sparing was significantly improved for most KBP-generated plans compared with those from clinical trial data. The KBP model using prioritized target coverage objectives reduced heart Dmean and V40Gy by 2.1 Gy and 5.2%, respectively. Similarly, using LS objectives reduced the lung CTV Dmean and V20Gy by 2.0 Gy and 2.9%, respectively. The KBP predictions correctly identified all patients with lung CTV V20Gy > 37% (5 of 25 patients) and significantly reduced the dose to the lung CTV by applying the LS optimization objectives. CONCLUSIONS: A single-institution KBP model can be applied as a QC tool for multi-institutional clinical trials to improve overall plan quality and provide decision-support to determine the need for anatomy-based dosimetric trade-offs.

Functional-guided radiotherapy using knowledge-based planning.

BACKGROUND AND PURPOSE: There are two significant challenges when implementing functional-guided radiotherapy using 4DCT-ventilation imaging: (1) lack of knowledge of realistic patient specific dosimetric goals for functional lung and (2) ensuring consistent plan quality across multiple planners. Knowledge-based planning (KBP) is positioned to address both concerns. MATERIAL AND METHODS: A KBP model was created from 30 previously planned functional-guided lung patients. Standard organs at risk (OAR) in lung radiotherapy and a ventilation contour delineating areas of high ventilation were included. Model validation compared dose-metrics to standard OARs and functional dose-metrics from 20 independent cases that were planned with and without KBP. RESULTS: A significant improvement was observed for KBP optimized plans in V20Gy and mean dose to functional lung (p = 0.005 and 0.001, respectively), V20Gy and mean dose to total lung minus GTV (p = 0.002 and 0.01, respectively), and mean doses to esophagus (p = 0.005). CONCLUSION: The current work developed a KBP model for functional-guided radiotherapy. Modest, but statistically significant, improvements were observed in functional lung and total lung doses.

Neural network dose models for knowledge-based planning in pancreatic SBRT.

PURPOSE: Stereotactic body radiation therapy (SBRT) for pancreatic cancer requires a skillful approach to deliver ablative doses to the tumor while limiting dose to the highly sensitive duodenum, stomach, and small bowel. Here, we develop knowledge-based artificial neural network dose models (ANN-DMs) to predict dose distributions that would be approved by experienced physicians. METHODS: Arc-based SBRT treatment plans for 43 pancreatic cancer patients were planned, delivering 30-33 Gy in five fractions. Treatments were overseen by one of two physicians with individual treatment approaches, with variations in prescribed dose, target volume delineation, and primary organs at risk. Using dose distributions calculated by a commercial treatment planning system (TPS), physician-approved treatment plans were used to train ANN-DMs that could predict physician-approved dose distributions based on a set of geometric parameters (vary from voxel to voxel) and plan parameters (constant across all voxels for a given patient). Patient datasets were randomly allocated, with two-thirds used for training, and one-third used for validation. Differences between TPS and ANN-DM dose distributions were used to evaluate model performance. ANN-DM design, including neural network structure and parameter choices, was evaluated to optimize dose model performance. RESULTS: Remarkable improvements in ANN-DM accuracy (i.e., from > 30% to < 5% mean absolute dose error, relative to the prescribed dose) were achieved by training separate dose models for the treatment style of each physician. Increased neural network complexity (i.e., more layers, more neurons per layer) did not improve dose model accuracy. Mean dose errors were less than 5% at all distances from the PTV, and mean absolute dose errors were on the order of 5%, but no more than 10%. Dose-volume histogram errors (in cm3 ) demonstrated good model performance above 25 Gy, but much larger errors were seen at lower doses. CONCLUSIONS: ANN-DM dose distributions showed excellent overall agreement with TPS dose distributions, and accuracy was substantially improved when each physician's treatment approach was taken into account by training their own dedicated models. In this manner, one could feasibly train ANN-DMs that could predict the dose distribution desired by a given physician for a given treatment site.

FMEA of manual and automated methods for commissioning a radiotherapy treatment planning system.

PURPOSE: To evaluate the level of risk involved in treatment planning system (TPS) commissioning using a manual test procedure, and to compare the associated process-based risk to that of an automated commissioning process (ACP) by performing an in-depth failure modes and effects analysis (FMEA). METHODS: The authors collaborated to determine the potential failure modes of the TPS commissioning process using (a) approaches involving manual data measurement, modeling, and validation tests and (b) an automated process utilizing application programming interface (API) scripting, preloaded, and premodeled standard radiation beam data, digital heterogeneous phantom, and an automated commissioning test suite (ACTS). The severity (S), occurrence (O), and detectability (D) were scored for each failure mode and the risk priority numbers (RPN) were derived based on TG-100 scale. Failure modes were then analyzed and ranked based on RPN. The total number of failure modes, RPN scores and the top 10 failure modes with highest risk were described and cross-compared between the two approaches. RPN reduction analysis is also presented and used as another quantifiable metric to evaluate the proposed approach. RESULTS: The FMEA of a MTP resulted in 47 failure modes with an RPNave of 161 and Save of 6.7. The highest risk process of "Measurement Equipment Selection" resulted in an RPNmax of 640. The FMEA of an ACP resulted in 36 failure modes with an RPNave of 73 and Save of 6.7. The highest risk process of "EPID Calibration" resulted in an RPNmax of 576. CONCLUSIONS: An FMEA of treatment planning commissioning tests using automation and standardization via API scripting, preloaded, and pre-modeled standard beam data, and digital phantoms suggests that errors and risks may be reduced through the use of an ACP.

Simulated Online Adaptive Magnetic Resonance-Guided Stereotactic Body Radiation Therapy for the Treatment of Oligometastatic Disease of the Abdomen and Central Thorax: Characterization of Potential Advantages.

PURPOSE: To characterize potential advantages of online-adaptive magnetic resonance (MR)-guided stereotactic body radiation therapy (SBRT) to treat oligometastatic disease of the non-liver abdomen and central thorax. METHODS AND MATERIALS: Ten patients treated with RT for unresectable primary or oligometastatic disease of the non-liver abdomen (n=5) or central thorax (n=5) underwent imaging throughout treatment on a clinical MR image guided RT system. The SBRT plans were created on the basis of tumor/organ at risk (OAR) anatomy at initial computed tomography simulation (PI), and simulated adaptive plans were created on the basis of observed MR image set tumor/OAR "anatomy of the day" (PA). Each PA was planned under workflow constraints to simulate online-adaptive RT. Prescribed dose was 50 Gy/5 fractions, with goal coverage of 95% planning target volume (PTV) by 95% of the prescription, subject to hard OAR constraints. The PI was applied to each MR dataset and compared with PA to evaluate changes in dose delivered to tumor/OARs, with dose escalation when possible. RESULTS: Hard OAR constraints were met for all PIs based on anatomy from initial computed tomography simulation, and all PAs based on anatomy from each daily MR image set. Application of the PI to anatomy of the day caused OAR constraint violation in 19 of 30 cases. Adaptive planning increased PTV coverage in 21 of 30 cases, including 14 cases in which hard OAR constraints were violated by the nonadaptive plan. For 9 PA cases, decreased PTV coverage was required to meet hard OAR constraints that would have been violated in a nonadaptive setting. CONCLUSIONS: Online-adaptive MRI-guided SBRT may allow PTV dose escalation and/or simultaneous OAR sparing compared with nonadaptive SBRT. A prospective clinical trial is underway at our institution to evaluate clinical outcomes of this technique.

Online Magnetic Resonance Image Guided Adaptive Radiation Therapy: First Clinical Applications.

PURPOSE: To demonstrate the feasibility of online adaptive magnetic resonance (MR) image guided radiation therapy (MR-IGRT) through reporting of our initial clinical experience and workflow considerations. METHODS AND MATERIALS: The first clinically deployed online adaptive MR-IGRT system consisted of a split 0.35T MR scanner straddling a ring gantry with 3 multileaf collimator-equipped (60)Co heads. The unit is supported by a Monte Carlo-based treatment planning system that allows real-time adaptive planning with the patient on the table. All patients undergo computed tomography and MR imaging (MRI) simulation for initial treatment planning. A volumetric MRI scan is acquired for each patient at the daily treatment setup. Deformable registration is performed using the planning computed tomography data set, which allows for the transfer of the initial contours and the electron density map to the daily MRI scan. The deformed electron density map is then used to recalculate the original plan on the daily MRI scan for physician evaluation. Recontouring and plan reoptimization are performed when required, and patient-specific quality assurance (QA) is performed using an independent in-house software system. RESULTS: The first online adaptive MR-IGRT treatments consisted of 5 patients with abdominopelvic malignancies. The clinical setting included neoadjuvant colorectal (n=3), unresectable gastric (n=1), and unresectable pheochromocytoma (n=1). Recontouring and reoptimization were deemed necessary for 3 of 5 patients, and the initial plan was deemed sufficient for 2 of the 5 patients. The reasons for plan adaptation included tumor progression or regression and a change in small bowel anatomy. In a subsequently expanded cohort of 170 fractions (20 patients), 52 fractions (30.6%) were reoptimized online, and 92 fractions (54.1%) were treated with an online-adapted or previously adapted plan. The median time for recontouring, reoptimization, and QA was 26 minutes. CONCLUSION: Online adaptive MR-IGRT has been successfully implemented with planning and QA workflow suitable for routine clinical application. Clinical trials are in development to formally evaluate adaptive treatments for a variety of disease sites.

Quantifying Unnecessary Normal Tissue Complication Risks due to Suboptimal Planning: A Secondary Study of RTOG 0126.

PURPOSE: The purpose of this study was to quantify the frequency and clinical severity of quality deficiencies in intensity modulated radiation therapy (IMRT) planning in the Radiation Therapy Oncology Group 0126 protocol. METHODS AND MATERIALS: A total of 219 IMRT patients from the high-dose arm (79.2 Gy) of RTOG 0126 were analyzed. To quantify plan quality, we used established knowledge-based methods for patient-specific dose-volume histogram (DVH) prediction of organs at risk and a Lyman-Kutcher-Burman (LKB) model for grade ≥2 rectal complications to convert DVHs into normal tissue complication probabilities (NTCPs). The LKB model was validated by fitting dose-response parameters relative to observed toxicities. The 90th percentile (22 of 219) of plans with the lowest excess risk (difference between clinical and model-predicted NTCP) were used to create a model for the presumed best practices in the protocol (pDVH0126,top10%). Applying the resultant model to the entire sample enabled comparisons between DVHs that patients could have received to DVHs they actually received. Excess risk quantified the clinical impact of suboptimal planning. Accuracy of pDVH predictions was validated by replanning 30 of 219 patients (13.7%), including equal numbers of presumed "high-quality," "low-quality," and randomly sampled plans. NTCP-predicted toxicities were compared to adverse events on protocol. RESULTS: Existing models showed that bladder-sparing variations were less prevalent than rectum quality variations and that increased rectal sparing was not correlated with target metrics (dose received by 98% and 2% of the PTV, respectively). Observed toxicities were consistent with current LKB parameters. Converting DVH and pDVH0126,top10% to rectal NTCPs, we observed 94 of 219 patients (42.9%) with ≥5% excess risk, 20 of 219 patients (9.1%) with ≥10% excess risk, and 2 of 219 patients (0.9%) with ≥15% excess risk. Replanning demonstrated the predicted NTCP reductions while maintaining the volume of the PTV receiving prescription dose. An equivalent sample of high-quality plans showed fewer toxicities than low-quality plans, 6 of 73 versus 10 of 73 respectively, although these differences were not significant (P=.21) due to insufficient statistical power in this retrospective study. CONCLUSIONS: Plan quality deficiencies in RTOG 0126 exposed patients to substantial excess risk for rectal complications.

Knowledge-based prediction of plan quality metrics in intracranial stereotactic radiosurgery.

PURPOSE: The objective of this work was to develop a comprehensive knowledge-based methodology for predicting achievable dose-volume histograms (DVHs) and highly precise DVH-based quality metrics (QMs) in stereotactic radiosurgery/radiotherapy (SRS/SRT) plans. Accurate QM estimation can identify suboptimal treatment plans and provide target optimization objectives to standardize and improve treatment planning. METHODS: Correlating observed dose as it relates to the geometric relationship of organs-at-risk (OARs) to planning target volumes (PTVs) yields mathematical models to predict achievable DVHs. In SRS, DVH-based QMs such as brain V10Gy (volume receiving 10 Gy or more), gradient measure (GM), and conformity index (CI) are used to evaluate plan quality. This study encompasses 223 linear accelerator-based SRS/SRT treatment plans (SRS plans) using volumetric-modulated arc therapy (VMAT), representing 95% of the institution's VMAT radiosurgery load from the past four and a half years. Unfiltered models that use all available plans for the model training were built for each category with a stratification scheme based on target and OAR characteristics determined emergently through initial modeling process. Model predictive accuracy is measured by the mean and standard deviation of the difference between clinical and predicted QMs, δQM = QMclin - QMpred, and a coefficient of determination, R(2). For categories with a large number of plans, refined models are constructed by automatic elimination of suspected suboptimal plans from the training set. Using the refined model as a presumed achievable standard, potentially suboptimal plans are identified. Predictions of QM improvement are validated via standardized replanning of 20 suspected suboptimal plans based on dosimetric predictions. The significance of the QM improvement is evaluated using the Wilcoxon signed rank test. RESULTS: The most accurate predictions are obtained when plans are stratified based on proximity to OARs and their PTV volume sizes. Volumes are categorized into small (VPTV < 2 cm(3)), medium (2 cm(3) < VPTV < 25 cm(3)), and large (25 cm(3) < VPTV). The unfiltered models demonstrate the ability to predict GMs to ∼1 mm and fractional brain V10Gy to ∼25% for plans with large VPTV and critical OAR involvements. Increased accuracy and precision of QM predictions are obtained when high quality plans are selected for the model training. For the small and medium VPTV plans without critical OAR involvement, predictive ability was evaluated using the refined model. For training plans, the model predicted GM to an accuracy of 0.2 ± 0.3 mm and fractional brain V10Gy to 0.04 ± 0.12, suggesting highly accurate predictive ability. For excluded plans, the average δGM was 1.1 mm and fractional brain V10Gy was 0.20. These δQM are significantly greater than those of the model training plans (p < 0.001). For CI, predictions are close to clinical values and no significant difference was observed between the training and excluded plans (p = 0.19). Twenty outliers with δGM > 1.35 mm were identified as potentially suboptimal, and replanning these cases using predicted target objectives demonstrates significant improvements on QMs: on average, 1.1 mm reduction in GM (p < 0.001) and 23% reduction in brain V10Gy (p < 0.001). After replanning, the difference of δGM distribution between the 20 replans and the refined model training plans was marginal. CONCLUSIONS: The results demonstrate the ability to predict SRS QMs precisely and to identify suboptimal plans. Furthermore, the knowledge-based DVH predictions were directly used as target optimization objectives and allowed a standardized planning process that bettered the clinically approved plans. Full clinical application of this methodology can improve consistency of SRS plan quality in a wide range of PTV volume and proximity to OARs and facilitate automated treatment planning for this critical treatment site.

Comparison of implanted fiducial markers and self-expandable metallic stents for pancreatic image guided radiation therapy localization.

PURPOSE: The delivery of high-quality radiation therapy to pancreatic adenocarcinoma requires accurate localization. Radiopaque implanted fiducial markers (IFM) and self-expandable metallic stents (SEMS) have both been proposed as means of achieving accurate localization in image guided radiation therapy (IGRT). The suitability of IFM and SEMS for localization were evaluated in this study based on geometric and dosimetric surrogates. METHODS AND MATERIALS: In a retrospective study of 54 patients with pancreatic cancer who underwent tumor-directed IGRT, 9 were identified as having both IFM and SEMS. For each patient, cone beam computed tomography (CT) scans from each of 6 weeks of treatment were selected for review and comparison with the simulation CT. The centroids of both the IFM and SEMS on each cone beam CT were aligned with those on the planning CT to quantify geometric differences between IFM- and SEMS-based localization. This difference was used to mark the isocenter displacement from the original IFM-localized treatment plan to evaluate the dosimetric implications of SEMS localization. IFM were used as the localization standard given their intratumoral location, and the stability of IFM was evaluated by variability of intrafiducial distance. The original treatment plan was computed on the planning CT at the isocenter shifted by the determined displacement, and dose-volume histograms were calculated for the target volume and organs at risk. RESULTS: The average displacement for SEMS localization over all fractions in all patients was 7.7 mm. Planning target volume coverage by 90% of prescription dose was significantly reduced (mean, 11.1%; range, 0.5% to -46.6%) for SEMS compared with IFM localization (P < .05). Dose tolerances were exceeded for stomach, duodenum, and small bowel for 3, 3, and 5 of 9 patients, respectively, when SEMS localization was used. CONCLUSIONS: SEMS-based compared with IFM-based localization results in significant variability of radiation therapy localization for pancreatic cancer. IFM-based localization should be considered the standard of care for tumor-directed pancreatic cancer IGRT.

Contouring variations and the role of atlas in non-small cell lung cancer radiation therapy: Analysis of a multi-institutional preclinical trial planning study.

PURPOSE: To quantify variations in target and normal structure contouring and evaluate dosimetric impact of these variations in non-small cell lung cancer (NSCLC) cases. To study whether providing an atlas can reduce potential variation. METHODS AND MATERIALS: Three NSCLC cases were distributed sequentially to multiple institutions for contouring and radiation therapy planning. No segmentation atlas was provided for the first 2 cases (Case 1 and Case 2). Contours were collected from submitted plans and consensus contour sets were generated. The volume variation among institution contours and the deviation of them from consensus contours were analyzed. The dose-volume histograms for individual institution plans were recalculated using consensus contours to quantify the dosimetric changes. An atlas containing targets and critical structures was constructed and was made available when the third case (Case 3) was distributed for planning. The contouring variability in the submitted plans of Case 3 was compared with that in first 2 cases. RESULTS: Planning target volume (PTV) showed large variation among institutions. The PTV coverage in institutions' plans decreased dramatically when reevaluated using the consensus PTV contour. The PTV contouring consistency did not show improvement with atlas use in Case 3. For normal structures, lung contours presented very good agreement, while the brachial plexus showed the largest variation. The consistency of esophagus and heart contouring improved significantly (t test; P < .05) in Case 3. Major factors contributing to the contouring variation were identified through a survey questionnaire. CONCLUSIONS: The amount of contouring variations in NSCLC cases was presented. Its impact on dosimetric parameters can be significant. The segmentation atlas improved the contour agreement for esophagus and heart, but not for the PTV in this study. Quality assurance of contouring is essential for a successful multi-institutional clinical trial.

Automated radiation therapy treatment plan workflow using a commercial application programming interface.

PURPOSE: The objective of this study was to create a workflow for the automation and standardization of treatment plan generation and evaluation using an application programming interface (API) to access data from a commercial treatment planning system (Varian Medical Systems, Inc, Palo Alto, CA). METHODS AND MATERIALS: The automation workflow begins with converting electronic patient-specific physician treatment planning orders that specify demographics, simulation instructions, and dosimetric objectives for targets and organs at risk into XML files. These XML files are used to generate standard contour names, beam, and patient-specific intensity modulated radiation therapy (IMRT) optimization templates to be executed in a commercial treatment planning system (TPS) by the user. A set of computer programs have been developed to provide quality control (QC) reports that verify demographic information in the TPS against the treatment planning orders, ensure the existence and proper naming of organs at risk, and generate patient-specific plan evaluation reports that provide real-time feedback on the concordance of an active treatment plan to the physician-specified treatment planning goals. RESULTS: A workflow for lung IMRT was chosen as a test scenario. Contour, beam, and patient-specific IMRT optimization templates were automatically generated from the physician treatment planning orders and loaded into the planning system. The QC reports were developed for lung IMRT, including the option of patient-specific modifications to the standard templates. The API QC reporting includes a dynamic program that runs in parallel to the TPS during the planning process, providing real-time feedback as to whether physician-specified treatment plan parameters have improved or worsened from previous iterations. CONCLUSIONS: User-created computer programs to access information in the TPS database by means of a commercial TPS API enable automation and standardization of treatment plan generation and evaluation.