Personalized Composite Dosimetric Score-Based Machine Learning Model of Severe Radiation-Induced Lymphopenia Among Patients With Esophageal Cancer.

PURPOSE: Radiation-induced lymphopenia (RIL) is common among patients undergoing radiation therapy (RT)' Severe RIL has been linked to adverse outcomes. The severity and risk of RIL can be predicted from baseline clinical characteristics and dosimetric parameters. However, dosimetric parameters, e.g. dose-volume (DV) indices, are highly correlated with one another and are only weakly associated with RIL. Here we introduce the novel concept of "composite dosimetric score" (CDS) as the index that condenses the dose distribution in immune tissues of interest to study the dosimetric dependence of RIL. We derived an improved multivariate classification scheme for risk of grade 4 RIL (G4RIL), based on this novel RT dosimetric feature, for patients receiving chemo RT for esophageal cancer. METHODS AND MATERIALS: DV indices were extracted for 734 patients who received chemo RT for biopsy-proven esophageal cancer. Nonnegative matrix factorization was used to project the DV indices of lung, heart, and spleen into a single CDS; XGBoost was employed to explore significant interactions among predictors; and logistic regression was applied to combine the resultant CDS with baseline clinical factors and interaction terms to facilitate individualized prediction of immunotoxicity. Five-fold cross-validation was applied to evaluate the model performance. RESULTS: The CDS for selected immune organs at risk (ie, heart, lung, and spleen) (OR 1.791; 95 CI [1.350, 2.377]) was a statistically significant risk determinant for G4RIL. Pearson correlation coefficients for CDS versus G4RIL risk for individual immune organs at risk were greater than any single DV indicx. Personalized prediction of G4RIL based on CDS and 4 clinical risk factors yielded an area under the curve value of 0.78. Interaction between age and CDS revealed that G4RIL risk increased more sharply with increasing CDS for patients aged ≥65 years. CONCLUSIONS: Risk of immunotoxicity for patients undergoing chemo RT for esophageal cancer can be predicted by CDS. The CDS concept can be extended to immunotoxicity in other cancer types and in dose-response models currently based on DV indices. Personalized treatment planning should leverage composite dosimetric scoring methods rather than using individual or subsets of DV indices.

Fluorescence tracking demonstrates T cell recirculation is transiently impaired by radiation therapy to the tumor.

T cells recirculate through tissues and lymphatic organs to scan for their cognate antigen. Radiation therapy provides site-specific cytotoxicity to kill cancer cells but also has the potential to eliminate the tumor-specific T cells in field. To dynamically study the effect of radiation on CD8 T cell recirculation, we used the Kaede mouse model to photoconvert tumor-infiltrating cells and monitor their movement out of the field of radiation. We demonstrate that radiation results in loss of CD8 T cell recirculation from the tumor to the lymph node and to distant sites. Using scRNASeq, we see decreased proliferating CD8 T cells in the tumor following radiation therapy resulting in a proportional enrichment in exhausted phenotypes. By contrast, 5 days following radiation increased recirculation of T cells from the tumor to the tumor draining lymph node corresponds with increased immunosurveillance of the treated tumor. These data demonstrate that tumor radiation therapy transiently impairs systemic T cell recirculation from the treatment site to the draining lymph node and distant untreated tumors. This may inform timing therapies to improve systemic T cell-mediated tumor immunity.

Hypofractionated Radiotherapy-Related Lymphopenia Is Associated With Worse Survival in Unresectable Intrahepatic Cholangiocarcinoma.

OBJECTIVE: The aim of this study was to evaluate the incidence of radiotherapy (RT)-related lymphopenia, its predictors, and association with survival in unresectable intrahepatic cholangiocarcinoma (ICC) treated with hypofractionated-RT (HF-RT). METHODS: Retrospective analysis of 96 patients with unresectable ICC who underwent HF-RT (median 58.05 Gy in 15 fractions) between 2009 and 2022 was performed. Absolute lymphocyte count (ALC) nadir within 12 weeks of RT was analyzed. Primary variable of interest was severe lymphopenia, defined as Grade 3+ (ALC <0.5 k/µL) per CTCAE v5.0. Primary outcome of interest was overall survival (OS) from RT. RESULTS: Median follow-up was 16 months. Fifty-two percent of patients had chemotherapy pre-RT, 23% during RT, and 40% post-RT. Pre-RT, median ALC was 1.1 k/µL and 5% had severe lymphopenia. Post-RT, 68% developed RT-related severe lymphopenia. Patients who developed severe lymphopenia had a significantly lower pre-RT ALC (median 1.1 vs. 1.5 k/µL, P=0.01) and larger target tumor volume (median 125 vs. 62 cm3, P=0.02). In our multivariable Cox model, severe lymphopenia was associated with a 1.7-fold increased risk of death (P=0.04); 1-year OS rates were 63% vs 77% (P=0.03). Receipt of photon versus proton-based RT (OR=3.50, P=0.02), higher mean liver dose (OR=1.19, P<0.01), and longer RT duration (OR=1.49, P=0.02) predicted severe lymphopenia. CONCLUSIONS: HF-RT-related lymphopenia is an independent prognostic factor for survival in patients with unresectable ICC. Patients with lower baseline ALC and larger tumor volume may be at increased risk, and use of proton therapy, minimizing mean liver dose, and avoiding treatment breaks may reduce RT-related lymphopenia.

Radiation Therapy for Stage IIA/B Seminoma: Modeling Secondary Cancer Risk for Protons and VMAT versus 3D Photons.

Radiation therapy (RT) is an effective treatment for stage IIA and select stage IIB seminomas. However, given the long life expectancy of seminoma patients, there are concerns about the risk of secondary cancers from RT. This study assessed differences in secondary cancer risk for stage II seminoma patients following proton pencil-beam scanning (PBS) and photon VMAT, compared to 3D conformal photon RT. Ten seminoma patients, five with a IIA staging who received 30 GyRBE and five with a IIB staging who received 36 GyRBE, had three RT plans generated. Doses to organs at risk (OAR) were evaluated, and secondary cancer risks were calculated as the Excess Absolute Risk (EAR) and Lifetime Attributable Risk (LAR). PBS reduced the mean OAR dose by 60% on average compared to 3D, and reduced the EAR and LAR for all OAR, with the greatest reductions seen for the bowel, liver, and stomach. VMAT reduced high doses but increased the low-dose bath, leading to an increased EAR and LAR for some OAR. PBS provided superior dosimetric sparing of OAR compared to 3D and VMAT in stage II seminoma cases, with models demonstrating that this may reduce secondary cancer risk. Therefore, proton therapy shows the potential to reduce acute and late side effects of RT for this population.

The Next Chapter in Immunotherapy and Radiation Combination Therapy: Cancer-Specific Perspectives.

Immunotherapeutic agents have revolutionized cancer treatment over the past decade. However, most patients fail to respond to immunotherapy alone. A growing body of preclinical studies highlights the potential for synergy between radiation therapy and immunotherapy, but the outcomes of clinical studies have been mixed. This review summarizes the current state of immunotherapy and radiation combination therapy across cancers, highlighting existing challenges and promising areas for future investigation.

Severe Lymphopenia During Chemoradiation Therapy for Esophageal Cancer: Comprehensive Analysis of Randomized Phase 2B Trial of Proton Beam Therapy Versus Intensity Modulated Radiation Therapy.

PURPOSE: Lymphocytes play an important role in antitumor immunity; however, they are also especially vulnerable to depletion during chemoradiation therapy (CRT). The purpose of this study was to compare the incidence of grade 4 lymphopenia (G4L) between proton beam therapy (PBT) and intensity modulated photon radiation therapy (IMRT) in patients with esophageal cancer treated with CRT in a completed randomized trial and to ascertain patient heterogeneity to G4L risk based on treatment and established prognostic factors. METHODS AND MATERIALS: Between April 2012 and March 2019, a single-institution, open-label, nonblinded, phase 2 randomized trial (NCT01512589) was conducted at the University of Texas MD Anderson Cancer Center. Patients were randomly assigned to IMRT or PBT, either definitively or preoperatively. This secondary analysis of the randomized trial was G4L during concurrent CRT according to Common Terminology Criteria for Adverse Events version 5.0. RESULTS: Among 105 patients evaluable for analysis, 44 patients (42%) experienced G4L at a median of 28 days after the start date of concurrent CRT. Induction chemotherapy (P = .003), baseline absolute lymphocyte count (P < .001), radiation therapy modality (P = .002), and planning treatment volume (P = .033) were found to be significantly associated with G4L. Multivariate classification analysis partitioned patients into 5 subgroups for whom the incidence of G4L was observed in 0%, 14%, 35%, 70%, and 100% of patients. The benefit of PBT over IMRT was most pronounced in patients with an intermediate baseline absolute lymphocyte count and large planning treatment volume (P = .011). CONCLUSIONS: This is the first prospective evidence that limiting dose scatter by PBT significantly reduced the incidence of G4L, especially in the intermediate-risk patients. The implication of this immune-sparing effect of PBT, especially in the context of standard adjuvant immunotherapy, needs further examination in the current phase 3 randomized trials.

A stochastic model of blood flow to calculate blood dose during radiotherapy.

Purpose. Lymphopenia is a common side effect in patients treated with radiotherapy, potentially caused by direct cell killing of circulating lymphocytes in the blood. To investigate this hypothesis, a method to assess dose to circulating lymphocytes is needed.Methods. A stochastic model to simulate systemic blood flow in the human body was developed based on a previously designed compartment model. Blood dose was obtained by superimposing the spatiotemporal distribution of blood particles with a time-varying dose rate field, and used as a surrogate for dose to circulating lymphocytes. We discuss relevant theory on compartmental modeling and how to combine it with models of explicit organ vasculature.Results. A general workflow was established which can be used for any anatomical site. Stochastic compartments can be replaced by explicit models of organ vasculatures for improved spatial resolution, and tumor compartments can be dynamically assigned. Generating a patient-specific blood flow distribution takes about one minute, fast enough to investigate the effect of varying treatment parameters such as the dose rate. Furthermore, the anatomical structures contributing most to the overall blood dose can be identified, which could potentially be used for lymphocyte-sparing treatment planning.Conclusion. The ability to report the blood dose distribution during radiotherapy is imperative to test and act upon the current paradigm that radiation-induced lymphopenia is caused by direct cell killing of lymphocytes in the blood. We have built a general model that can do so for various treatment sites. The presented framework is publicly available athttp://github.com/mghro/hedos.

Mitigating Radiotoxicity in the Central Nervous System: Role of Proton Therapy.

OPINION STATEMENT: Central nervous system (CNS) radiotoxicity remains a challenge in neuro-oncology. Dose distribution advantages of protons over photons have prompted increased use of brain-directed proton therapy. While well-recognized among pediatric populations, the benefit of proton therapy among adults with CNS malignancies remains controversial. We herein discuss the role of protons in mitigating late CNS radiotoxicities in adult patients. Despite limited clinical trials, evidence suggests toxicity profile advantages of protons over conventional radiotherapy, including retention of neurocognitive function and brain volume. Modelling studies predict superior dose conformality of protons versus state-of-the-art photon techniques reduces late radiogenic vasculopathies, endocrinopathies, and malignancies. Conversely, potentially higher brain tissue necrosis rates following proton therapy highlight a need to resolve uncertainties surrounding the impact of variable biological effectiveness of protons on dose distribution. Clinical trials comparing best photon and particle-based therapy are underway to establish whether protons substantially improve long-term treatment-related outcomes in adults with CNS malignancies.

Declarations of Independence: How Embedded Multicollinearity Errors Affect Dosimetric and Other Complex Analyses in Radiation Oncology.

The statistical technique of multiple regression, commonly referred to as "multivariable regression," is often used in clinical research to quantify the relationships between multiple predictor variables and a single outcome variable of interest. The foundational theory underpinning multivariable regression assumes that all predictor variables are independent of one another. In other words, the effect of each independent variable is measured by its contribution to the regression equation while all other variables remain unchanged. In the presence of correlations between two or more variables, however, it is impossible to change one variable without a consequent change in the variable(s) it is linked to. This condition, known as "multicollinearity," can introduce errors into multivariable regression models by affecting estimates of the regression coefficients that quantify the relationship between individual predictor variables and the outcome variable. Errors that arise due to violations of the multicollinearity assumption are of special interest to radiation oncology researchers. Because of high levels of correlation among variables derived from points along individual organ dose-volume histogram (DVH) curves, as well as strong intercorrelations among dose-volume parameters in neighboring organs, dosimetric analyses are particularly subject to multicollinearity errors. For example, dose-volume parameters for the heart are strongly correlated not only with other points along the heart DVH curve but are likely also correlated with dose-volume parameters in neighboring organs such as the lung. In this paper, we describe the problem of multicollinearity in accessible terms and discuss examples of violations of the multicollinearity assumption within the radiation oncology literature. Finally, we provide recommendations regarding best practices for identifying and managing multicollinearity in complex data sets.

Central Endocrine Complications Among Childhood Cancer Survivors Treated With Radiation Therapy: A PENTEC Comprehensive Review.

PURPOSE: Children who receive cranial radiation therapy (RT) as a component of treatment for malignancy are often at risk of long-term central endocrine toxicity secondary to radiation to the hypothalamic-pituitary axis (HPA). A comprehensive analysis was performed of central endocrine late effects in survivors of childhood cancer treated with RT as part of the Pediatric Normal Tissue Effects in the Clinic (PENTEC) consortium. METHODS AND MATERIALS: A systematic review of the risk of RT-related central endocrine effects was performed in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). A total of 4629 publications were identified, of which 16 met criteria for inclusion in dose modeling analysis, with a total of 570 patients in 19 cohorts. Eighteen cohorts reported outcomes for growth hormone deficiency (GHD), 7 reported outcomes for central hypothyroidism (HT), and 6 reported outcomes for adrenocorticotropic hormone (ACTH) deficiency. RESULTS: Normal tissue complication probability modeling for GHD (18 cohorts, 545 patients) yielded D50 = 24.9 Gy (95% CI, 20.9-28.0) and γ50 = 0.5 (95% CI, 0.27-0.78). The normal tissue complication probability model fit for whole brain irradiation in children with a median age of >5 years indicated a 20% risk of GHD for patients who receive a mean dose of 21 Gy in 2-Gy fractions to the HPA. For HT, among 7 cohorts (250 patients), D50 = 39 Gy (95% CI, 34.1-53.2) and γ50 = 0.81 (95% CI, 0.46-1.35), with a 20% risk of HT in children who receive a mean dose of 22 Gy in 2-Gy fractions to the HPA. For ACTH deficiency (6 cohorts, 230 patients), D50 = 61 Gy (95% CI, 44.7-119.4) and γ50 = 0.76 (95% CI, 0.5-1.19); there is a 20% risk of ACTH deficiency in children who receive a mean dose of 34 Gy in 2-Gy fractions to the HPA. CONCLUSIONS: RT dose to the HPA increases the risk of central endocrine toxicity, including GHD, HT, and ACTH deficiency. In some clinical situations, these toxicities may be difficult to avoid, and counseling of patients and families with respect to anticipated outcomes is important.

Neural network based ensemble model to predict radiation induced lymphopenia after concurrent chemo-radiotherapy for non-small cell lung cancer from two institutions.

The use of adjuvant Immune Checkpoint Inhibitors (ICI) after concurrent chemo-radiation therapy (CCRT) has become the standard of care for locally advanced non-small cell lung cancer (LA-NSCLC). However, prolonged radiotherapy regimens are known to cause radiation-induced lymphopenia (RIL), a long-neglected toxicity that has been shown to correlate with response to ICIs and survival of patients treated with adjuvant ICI after CCRT. In this study, we aim to develop a novel neural network (NN) approach that integrates patient characteristics, treatment related variables, and differential dose volume histograms (dDVH) of lung and heart to predict the incidence of RIL at the end of treatment. Multi-institutional data of 139 LA-NSCLC patients from two hospitals were collected for training and validation of our suggested model. Ensemble learning was combined with a bootstrap strategy to stabilize the model, which was evaluated internally using repeated cross validation. The performance of our proposed model was compared to conventional models using the same input features, such as Logistic Regression (LR) and Random Forests (RF), using the Area Under the Curve (AUC) of Receiver Operating Characteristics (ROC) curves. Our suggested model (AUC=0.77) outperformed the comparison models (AUC=0.72, 0.74) in terms of absolute performance, indicating that the convolutional structure of the network successfully abstracts additional information from the differential DVHs, which we studied using Gradient-weighted Class Activation Map. This study shows that clinical factors combined with dDVHs can be used to predict the risk of RIL for an individual patient and shows a path toward preventing lymphopenia using patient-specific modifications of the radiotherapy plan.

Intra-brain vascular models within the ICRP mesh-type adult reference phantoms for applications to internal dosimetry.

Objective. Phantoms of the International Commission on Radiological Protection provide a framework for standardized dosimetry. The modeling of internal blood vessels-essential to tracking circulating blood cells exposed during external beam radiotherapy and to account for radiopharmaceutical decays while still in blood circulation-is, however, limited to the major inter-organ arteries and veins. Intra-organ blood is accounted for only through the assignment of a homogeneous mixture of parenchyma and blood [single-region (SR) organs]. Our goal was to develop explicit dual-region (DR) models of intra-organ blood vasculature of the adult male brain (AMB) and adult female brain (AFB).Approach. A total of 4000 vessels were created amongst 26 vascular trees. The AMB and AFB models were then tetrahedralized for coupling to the PHITS radiation transport code. Absorbed fractions were computed for monoenergetic alpha particles, electrons, positrons, and photons for both decay sites within the blood vessels and for tissues outside these vessels. RadionuclideS-values were computed for 22 and 10 radionuclides commonly employed in radiopharmaceutical therapy and nuclear medicine diagnostic imaging, respectively.Main results. For radionuclide decays, values ofS(brain tissue ← brain blood) assessed in the traditional manner (SR) were higher than those computed using our DR models by factors of 1.92, 1.49, and 1.57 for therapeutic alpha-emitters, beta-emitters, and Auger electron-emitters, respectively in the AFB and by factors of 1.65, 1.37, and 1.42 for these same radionuclide categories in the AMB. Corresponding ratios of SR and DR values ofS(brain tissue ← brain blood) were 1.34 (AFB) and 1.26 (AMB) for four SPECT radionuclides, and were 1.32 (AFB) and 1.24 (AMB) for six common PET radionuclides.Significance. The methodology employed in this study can be explored in other organs of the body for proper accounting of blood self-dose for that fraction of the radiopharmaceutical still in general circulation.

Predicting Severity of Radiation Induced Lymphopenia in Individual Proton Therapy Patients for Varying Dose Rate and Fractionation Using Dynamic 4-Dimensional Blood Flow Simulations.

PURPOSE: Radiation-induced lymphopenia has gained attention recently as the result of its correlation with survival in a range of indications, particularly when combining radiation therapy (RT) with immunotherapy. The purpose of this study is to use a dynamic blood circulation model combined with observed lymphocyte depletion in patients to derive the in vivo radiosensitivity of circulating lymphocytes and study the effect of RT delivery parameters. METHODS AND MATERIALS: We assembled a cohort of 17 patients with hepatocellular carcinoma treated with proton RT alone in 15 fractions (fx) using conventional dose rates (beam-on time [BOT], 120 seconds) for whom weekly absolute lymphocyte counts (ALCs) during RT and follow-up were available. We used HEDOS, a time-dependent, whole-body, blood flow computational framework, in combination with explicit liver blood flow modeling, to calculate the dose volume histograms for circulating lymphocytes for changing BOTs (1 second-300 seconds) and fractionations (5 fx, 15 fx). From this, we used the linear cell survival model and an exponential model to determine patient-specific lymphocyte radiation sensitivity, α, and recovery, σ, respectively. RESULTS: The in vivo-derived patient-specific α had a median of 0.65 Gy-1 (range, 0.30-1.38). Decreasing BOT to 1 second led to an increased average end-of-treatment ALC of 27.5%, increasing to 60.3% when combined with the 5-fx regimen. Decreasing to 5 fx at the conventional dose rate led to an increase of 17.0% on average. The benefit of both increasing dose rate and reducing the number of fractions was patient specificࣧpatients with highly sensitive lymphocytes benefited most from decreasing BOT, whereas patients with slow lymphocyte recovery benefited most from the shorter fractionation regimen. CONCLUSIONS: We observed that increasing dose rate at the same fractionation reduced ALC depletion more significantly than reducing the number of fractions. High-dose-rates led to an increased sparing of lymphocytes when shortening the fractionation regimen, particularly for patients with radiosensitive lymphocytes at elevated risk.

Predictive Model of Liver Toxicity to Aid the Personalized Selection of Proton Versus Photon Therapy in Hepatocellular Carcinoma.

PURPOSE: Our objective was to develop an externally validated model for predicting liver toxicity after radiation therapy in patients with hepatocellular carcinoma (HCC) that can integrate both photon and proton dose distributions with patient-specific characteristics. METHODS AND MATERIALS: Training data consisted of all patients with HCC treated between 2008 and 2019 at our institution (n = 117, 60%/40% photon/proton). We developed a shallow convolutional neural network (CNN) to predict posttreatment liver dysfunction from the differential dose-volume histogram (DVH) and baseline liver metrics. To reduce bias and improve robustness, we used ensemble learning (CNNE). After a preregistered study analysis plan, we evaluated stability using internal bootstrap resampling and generalizability using a data set from a different institution (n = 88). Finally, we implemented a class activation map method to characterize the critical DVH subregions and benchmarked the model against logistic regression and XGBoost. The models were evaluated using the area under the receiver operating characteristic curve and area under the precision-recall curve. RESULTS: The CNNE model showed similar internal performance and robustness compared with the benchmarks. CNNE exceeded the benchmark models in external validation, with an area under the receiver operating characteristic curve of 0.78 versus 0.55 to 0.70, and an area under the precision-recall curve of 0.6 versus 0.43 to 0.52. The model showed improved predictive power in the photon group, excellent specificity in both modalities, and high sensitivity in the photon high-risk group. Models built solely on DVHs confirm outperformance of the CNNE and indicate that the proposed structure efficiently abstracts features from both proton and photon dose distributions. The activation map method demonstrates the importance of the low-dose bath and its interaction with low liver function at baseline. CONCLUSIONS: We developed and externally validated a patient-specific prediction model for hepatic toxicity based on the entire DVH and clinical factors that can integrate both photon and proton therapy cohorts. This model complements the new American Society for Radiation Oncology clinical practice guidelines and could support value-driven integration of proton therapy into the management of HCC.

Severe Radiation-Induced Lymphopenia Attenuates the Benefit of Durvalumab After Concurrent Chemoradiotherapy for NSCLC.

Introduction: Durvalumab after concurrent chemoradiation (CCRT) for NSCLC improves survival, but only in a subset of patients. We investigated the effect of severe radiation-induced lymphopenia (sRIL) on survival in these patients. Methods: Outcomes after CCRT (2010-2019) or CCRT followed by durvalumab (2018-2019) were reviewed. RIL was defined by absolute lymphocyte count (ALC) nadir in samples collected at end of CCRT; sRIL was defined as nadir ALC less than 0.23 × 109/L (the lowest tertile). Progression-free survival (PFS) and overall survival (OS) were calculated by the Kaplan-Meier method. Cox proportional hazard modeling evaluated associations between clinical variables and survival. Results: Of 309 patients, 192 (62%) received CCRT only and 117 (38%) CCRT plus durvalumab. Multivariable logistic regression analysis indicated that sRIL was associated with planning target volume (OR = 1.002, p = 0.001), stage IIIB disease (OR = 2.77, p = 0.04), and baseline ALC (OR = 0.36, p < 0.01). Durvalumab extended median PFS (23.3 versus 14.1 mo, p = 0.003) and OS (not reached versus 30.8 mo, p < 0.01). sRIL predicted poorer PFS and OS in both treatment groups. Among patients with sRIL, durvalumab did not improve survival (median = 24.6 mo versus 18.1 mo CCRT only, p = 0.079). On multivariable analyses, sRIL (OR = 1.81, p < 0.01) independently predicted poor survival. Conclusions: Severe RIL compromises survival benefits from durvalumab after CCRT for NSCLC. Measures to mitigate RIL after CCRT may be warranted to enhance the benefit of consolidation durvalumab.

Lymphocyte Depletion Rate as a Biomarker of Radiation Dose to Circulating Lymphocytes During Fractionated Partial-Body Radiation Therapy.

Purpose: Radiation causes exponential depletion of circulating lymphocyte populations; in turn, radiation-induced lymphopenia is associated with worse survival for many solid tumors, possibly owing to attenuated antitumor immune responses. Identifying reliable and reproducible methods of calculating the radiation dose to circulating immune cells may facilitate development of techniques to reduce the risk and severity of radiation-induced toxic effects to circulating lymphocytes. Methods and Materials: Patient-specific lymphocyte loss rates were derived from a clinical data set including 684 adult patients with solid tumors. Multivariable linear regression was used to model the relationship between the lymphocyte loss rate and physical parameters of the radiation plan that determine circulating blood dose. Results: During partial-body radiation, lymphocyte loss rates are determined by physical parameters of the radiation plan that reflect radiation exposure to circulating cells, including target volume size, dose per fraction squared, and anatomic site treated. Differences in observed versus predicted lymphocyte loss rates may be partly explained by variations in concurrent chemotherapy regimens. Conclusions: We describe a novel method of using patient-specific lymphocyte loss kinetics to approximate the effective radiation dose to circulating lymphocytes during focal fractionated photon radiation therapy. Clinical applications of these findings include the early identification of patients at particularly high risk of severe radiation-induced lymphopenia based on physical parameters of the radiation therapy plan.

Regulatory Programs of B-cell Activation and Germinal Center Reaction Allow B-ALL Escape from CD19 CAR T-cell Therapy.

Chimeric antigen receptor (CAR) T-cell therapy has led to tremendous successes in the treatment of B-cell malignancies. However, a large fraction of treated patients relapse, often with disease expressing reduced levels of the target antigen. Here, we report that exposing CD19+ B-cell acute lymphoblastic leukemia (B-ALL) cells to CD19 CAR T cells reduced CD19 expression within hours. Initially, CD19 CAR T cells caused clustering of CD19 at the T cell-leukemia cell interface followed by CD19 internalization and decreased CD19 surface expression on the B-ALL cells. CD19 expression was then repressed by transcriptional rewiring. Using single-cell RNA sequencing and single-cell assay for transposase-accessible chromatin using sequencing, we demonstrated that a subset of refractory CD19low cells sustained decreased CD19 expression through transcriptional programs of physiologic B-cell activation and germinal center reaction. Inhibiting B-cell activation programs with the Bruton's tyrosine kinase inhibitor ibrutinib increased the cytotoxicity of CD19 CAR T cells without affecting CAR T-cell viability. These results demonstrate transcriptional plasticity as an underlying mechanism of escape from CAR T cells and highlight the importance of combining CAR T-cell therapy with targeted therapies that aim to overcome this plasticity. See related Spotlight by Zhao and Melenhorst, p. 1040.

A mesh-based model of liver vasculature: implications for improved radiation dosimetry to liver parenchyma for radiopharmaceuticals.

PURPOSE: To develop a model of the internal vasculature of the adult liver and demonstrate its application to the differentiation of radiopharmaceutical decay sites within liver parenchyma from those within organ blood. METHOD: Computer-generated models of hepatic arterial (HA), hepatic venous (HV), and hepatic portal venous (HPV) vascular trees were algorithmically created within individual lobes of the ICRP adult female and male livers (AFL/AML). For each iteration of the algorithm, pressure, blood flow, and vessel radii within each tree were updated as each new vessel was created and connected to a viable bifurcation site. The vascular networks created inside the AFL/AML were then tetrahedralized for coupling to the PHITS radiation transport code. Specific absorbed fractions (SAF) were computed for monoenergetic alpha particles, electrons, positrons, and photons. Dual-region liver models of the AFL/AML were proposed, and particle-specific SAF values were computed assuming radionuclide decays in blood within two locations: (1) sites within explicitly modeled hepatic vessels, and (2) sites within the hepatic blood pool residing outside these vessels to include the capillaries and blood sinuses. S values for 22 and 10 radionuclides commonly used in radiopharmaceutical therapy and imaging, respectively, were computed using the dual-region liver models and compared to those obtained in the existing single-region liver model. RESULTS: Liver models with virtual vasculatures of ~ 6000 non-intersecting straight cylinders representing the HA, HPV, and HV circulations were created for the ICRP reference. For alpha emitters and for beta and auger-electron emitters, S values using the single-region models were approximately 11% (AML) to 14% (AFL) and 11% (AML) to 13% (AFL) higher than the S values obtained using the dual-region models, respectively. CONCLUSIONS: The methodology employed in this study has shown improvements in organ parenchymal dosimetry through explicit consideration of blood self-dose for alpha particles (all energies) and for electrons at energies below ~ 100 keV.

Predictive Modeling of Survival and Toxicity in Patients With Hepatocellular Carcinoma After Radiotherapy.

PURPOSE: To stratify patients and aid clinical decision making, we developed machine learning models to predict treatment failure and radiation-induced toxicities after radiotherapy (RT) in patients with hepatocellular carcinoma across institutions. MATERIALS AND METHODS: The models were developed using linear and nonlinear algorithms, predicting survival, nonlocal failure, radiation-induced liver disease, and lymphopenia from baseline patient and treatment parameters. The models were trained on 207 patients from Massachusetts General Hospital. Performance was quantified using Harrell's c-index, area under the curve (AUC), and accuracy in high-risk populations. Models' structures were optimized in a nested cross-validation approach to prevent overfitting. A study analysis plan was registered before external validation using 143 patients from MD Anderson Cancer Center. Clinical utility was assessed using net-benefit analysis. RESULTS: The survival model stratified high-risk versus low-risk patients well in the external validation cohort (c-index = 0.75), better than existing risk scores. Predictions of 1-year survival and nonlocal failure were excellent (external AUC = 0.74 and 0.80, respectively), especially in the high-risk group (accuracy > 90%). Cause-of-death analysis showed differential modes of treatment failure in these cohorts and indicated that these models could be used to stratify RT patients for liver-sparing treatment regimen or combination approaches with systemic agents. Predictions of liver disease and lymphopenia were good but less robust (external AUC = 0.68 and 0.7, respectively), suggesting the need for more comprehensive consideration of dosimetry and better predictive biomarkers. The liver disease model showed excellent accuracy in the high-risk group (92%) and revealed possible interactions of platelet count with initial liver function. CONCLUSION: Machine learning approaches can provide reliable outcome predictions in patients with hepatocellular carcinoma after RT in diverse cohorts across institutions. The excellent performance, particularly in high-risk patients, suggests novel strategies for patient stratification and treatment selection.