Faculty Bibliography

KRAS is the most frequently mutated oncogene in lung adenocarcinoma, with G12C and G12V being the most predominant forms. Recent breakthroughs in KRASG12C inhibitors have transformed the clinical management of patients with G12C mutation and advanced our understanding of its function. However, little is known about the targeted disruption of KRASG12V, partly due to a lack of specific inhibitors. Here, we leverage the degradation tag (dTAG) system to develop a KRASG12V transgenic mouse model. We explore the therapeutic potential of KRASG12V degradation and characterize its impact on the tumor microenvironment (TME). Our study reveals that degrading KRASG12V abolishes lung and pancreatic tumors in mice and causes a robust inhibition of KRAS-regulated cancer intrinsic signaling. Importantly, targeted degradation of KRASG12V reprograms the TME towards a stimulatory milieu and drives antitumor immunity, elicited mainly by effector and cytotoxic CD8+ T cells. Our work provides important insights into the impact of degrading KRASG12V on both tumor progression and immune response, highlighting degraders as a powerful strategy for targeting KRAS mutant cancers.

PURPOSE/OBJECTIVE:To commission a beam model in ClearCalc (Radformation Inc.) for use as a secondary dose calculation algorithm and to implement its use into an adaptive workflow for an MR-linear accelerator. METHODS:A beam model was developed using commissioning data for an Elekta Unity MR-linear accelerator and entered into ClearCalc. The beam model consisted of absolute dose calculation settings, output factors, percent depth-dose (PDD) curves, mutli-leaf collimator (MLC) transmission and dose leaf gap error, and cryostat corrections. Beam profiles were hard-coded by the manufacturer into the beam model and were compared with Monaco-derived profiles. The beam model was tested by comparing point doses in a homogenous phantom obtained through measurements using an ionization chamber in water, Monaco, and ClearCalc for various field sizes, source-surface distances (SSDs), and point locations. Additional testing including point dose verification for test plans using a heterogeneous phantom and patient plans. Post clinical implementation, performance of ClearCalc was evaluated for the first 41 patients treated, which included 215 adaptive plans. RESULTS:PDDs generated using ClearCalc fell within 1.2% of measurements. Field profile comparison between ClearCalc and Monaco showed an average pass rate of 98% using a 3%/3 mm gamma criteria. Measured cryostat corrections used in the beam model showed a maximum deviation from unity of 1.4%. Point dose and field monitor units (MUs) comparisons in a homogenous phantom (N = 22), heterogeneous phantoms (N = 22), and patient plans (N = 57) all passed with a threshold of 5%/5MU. Clinically, ClearCalc was implemented as a physics check post adaptive planning completed prior to beam delivery. Point dose and field MUs showed good agreement at a 5%/5MU threshold for prostate stereotactic body radiation therapy (SBRT), pelvic lymph nodes, rectum, and prostate and lymph node plans. DISCUSSION/CONCLUSIONS:This work demonstrated commissioning and clinical implementation of ClearCalc into an adaptive planning workflow. No primary or adaptive plan failures were reported with proper beam model testing.

Tumor-educated platelets (TEPs) are a potential method of liquid biopsy for the diagnosis and monitoring of cancer. However, the mechanism underlying tumor education of platelets is not known, and transcripts associated with TEPs are often not tumor-associated transcripts. We demonstrated that direct tumor transfer of transcripts to circulating platelets is an unlikely source of the TEP signal. We used CDSeq, a latent Dirichlet allocation algorithm, to deconvolute the TEP signal in blood samples from patients with glioblastoma. We demonstrated that a substantial proportion of transcripts in the platelet transcriptome are derived from nonplatelet cells, and the use of this algorithm allows the removal of contaminant transcripts. Furthermore, we used the results of this algorithm to demonstrate that TEPs represent a subset of more activated platelets, which also contain transcripts normally associated with nonplatelet inflammatory cells, suggesting that these inflammatory cells, possibly in the tumor microenvironment, transfer transcripts to platelets that are then found in circulation. Our analysis suggests a useful and efficient method of processing TEP transcriptomic data to enable the isolation of a unique TEP signal associated with specific tumors.

PURPOSE/UNASSIGNED:Cardiac radioablation is an emerging therapy for recurrent ventricular tachycardia. Electrophysiology (EP) data, including electroanatomic maps (EAM) and electrocardiographic imaging (ECGI), provide crucial information for defining the arrhythmogenic target volume. The absence of standardized workflows and software tools to integrate the EP maps into a radiation planning system limits their use. This study developed a comprehensive software tool to enable efficient utilization of the mapping for cardiac radioablation treatment planning. METHODS AND MATERIALS/UNASSIGNED:After the scar area is outlined on the mapping surface, the tool extracts and extends the annotated patch into a closed surface and converts it into a structure set associated with the anatomic images. The tool then exports the structure set and the images as The Digital Imaging and Communications in Medicine Standard in Radiotherapy for a radiation treatment planning system to import. Overlapping the scar structure on simulation CT, a transmural target volume is delineated for treatment planning. RESULTS/UNASSIGNED:The tool has been used to transfer Ensite NavX EAM data into the Varian Eclipse treatment planning system in radioablation on 2 patients with ventricular tachycardia. The ECGI data from CardioInsight was retrospectively evaluated using the tool to derive the target volume for a patient with left ventricular assist device, showing volumetric matching with the clinically used target with a Dice coefficient of 0.71. CONCLUSIONS/UNASSIGNED:HeaRTmap smoothly fuses EP information from different mapping systems with simulation CT for accurate definition of radiation target volume. The efficient integration of EP data into treatment planning potentially facilitates the study and adoption of the technique.

PURPOSE/OBJECTIVE:Stereotactic radiosurgery (SRS) has become an important modality in the treatment of brain metastases. The purpose of this study is to investigate the potential of radiomic features from planning magnetic resonance (MR) images and dose maps to predict local failure after SRS for brain metastases. MATERIALS/METHODS/METHODS:Twenty-eight patients who received Gamma Knife (GK) radiosurgery for brain metastases were retrospectively reviewed in this IRB-approved study. 179 irradiated tumors included 42 that locally failed within one-year follow-up. Using SRS tumor volumes, radiomic features were calculated on T1-weighted contrast-enhanced MR images acquired for treatment planning and planned dose maps. 125 radiomic features regarding tumor shape, dose distribution, MR intensities and textures were extracted for each tumor. Logistic regression with automatic feature selection was built to predict tumor progression from local control after SRS. Feature selection and model evaluation using receiver operating characteristic (ROC) curves were performed in a nested cross validation (CV) scheme. The associations between selected radiomic features and treatment outcomes were statistically assessed by univariate analysis. RESULTS:The logistic model with feature selection achieved ROC AUC of 0.82 ± 0.09 on 5-fold CV, providing 83% sensitivity and 70% specificity for predicting local failure. A total of 10 radiomic features including 1 shape feature, 6 MR images and 3 dose distribution features were selected. These features were significantly associated with treatment outcomes (p < 0.05). The model was validated on independent holdout data with an AUC of 0.78. CONCLUSIONS:Radiomic features from planning MR images and dose maps provided prognostic information in SRS for brain metastases. A model built on the radiomic features shows promise for early prediction of tumor local failure after treatment, potentially aiding in personalized care for brain metastases.

PURPOSE/OBJECTIVE:The treatment couch position of a patient in external beam radiation therapy (EBRT) is usually acquired during initial treatment setup. This procedure has shown potential failure modes leading to near misses and adverse events in radiation treatment. This study aims to develop a method to automatically determine the couch position before setting up a patient for initial treatment. METHODS:The Qfix couch-tops (kVue and DoseMax) have embedded reference marks (BBs) indicating its index levels and couch centerline. With the ESAPI, a C# script was programmed to automatically find the couch-top and embedded BBs in the planning CT and derive the treatment couch position according to treatment isocenter of a plan. Couch positions of EBRT plans with the kVue couch-top and SBRT plans using the DoseMax were calculated using the script. The calculation was evaluated by comparing calculated positions with couch coordinates captured during the initial treatment setup after image guidance. The calculations were further compared with daily treatment couch positions post image-guided adjustment for each treatment fraction. RESULTS:For plans using the kVue couch-top for various treatment sites, the median (5-95 percentiles) differences between calculated and captured couch positions were 0.1 (-0.2 - 0.9), 0.5 (-1.1-2.0), 0.10 (-1.3-1.3) cm in the vertical, longitudinal, and lateral direction respectively. For the DoseMax couch-top, the median differences were 0.1 (-0.2-0.7), 0.2 (-0.3-1.1), and 0.2 (-0.7-0.9) cm in respective direction. The calculated positions were within 1 and 2 cm from the mean fraction positions for 95% patients on DoseMax and kVue couch-top respectively. CONCLUSIONS:A method that automatically and accurately calculates treatment couch position from simulation CT was implemented in Varian Eclipse for Qfix couch-tops. This technique increases the efficiency of patient setup and enhances patient safety by reducing the risks of positioning errors.

PURPOSE/OBJECTIVE:The purpose of this study is to quantify dosimetric changes throughout the delivery of oropharyngeal cancer treatment and to investigate the application of statistical process control (SPC) for the management of significant deviations during the course of radiotherapy. METHODS:Thirteen oropharyngeal cancer patients with daily cone beam computed tomography (CBCT) were retrospectively reviewed. Cone beam computed tomography images of every other fraction were imported to the Velocity software and registered to planning CT using the 6 DOF (degrees of freedom) couch shifts generated during patient setup. Using Velocity "Adaptive Monitoring" module, the setup-corrected CBCT was matched to planning CT using a deformable registration. Volumes and dose metrics at each fraction were calculated and rated with plan values to evaluate interfractional dosimetric variations using a SPC framework. T-tests between plan and fraction volumes were performed to find statistically insignificant fractions. Average upper and lower process capacity limits (UCL, LCL) of each dose metric were derived from these fractions using conventional SPC guidelines. RESULTS:Gross tumor volume (GTV) and organ at risk (OAR) volumes in the first 13 fractions had no significant changes from the pretreatment planning CT. The GTV and the parotid glands subsequently decreased by 10% at the completion of treatment. There were 3-4% increases in parotid mean doses, but no significant differences in dose metrics of GTV and other OARs. The changes were organ and patient dependent. Control charts for various dose metrics were generated to assess the metrics at each fraction for individual patient. CONCLUSIONS:Daily CBCT could be used to monitor dosimetric variations of targets and OARs resulting from volume changes and tissue deformation in oropharyngeal cancer radiotherapy. Treatment review with the guidance of a SPC tool allows for an objective and consistent clinical decision to apply adaptive radiotherapy.