Off-Protocol Radiation Therapy in Phase 3 Metastatic Solid Tumor Trials.

PURPOSE: Increasing data suggest that radiation therapy, particularly ablative radiation therapy, alters the natural history of metastatic disease. For patients with metastatic disease enrolled in prospective trials testing systemic therapy, the use of off-protocol radiation therapy to improve clinical symptoms or extend the duration of study systemic therapy may influence study endpoints. We sought to evaluate how often off-protocol radiation therapy was permitted among systemic therapy phase 3 trials, how often off-protocol radiation therapy is used, and whether off-protocol radiation therapy correlated with study outcomes. METHODS AND MATERIALS: Two-arm, superiority-design, phase 3 randomized trials testing systemic therapy were screened from ClinicalTrials.gov. Protocol availability was required to assess the trial approach to off-protocol radiation therapy if not described in the manuscript. Adjusted odds ratios with 95% CI were calculated by logistic regression. RESULTS: A total of 112 trials enrolling 80,134 patients were included, with publication dates between 2010 and 2019. Of these, off-protocol radiation therapy was allowed, not discussed, or prohibited during study systemic therapy in 52% (N =58), 25% (N = 28), and 23% (N = 26) of trials, respectively. However, only 2% (2 of 112) of trials reported off-protocol radiation therapy utilization rates, although no data were reported on the use of ablative off-protocol radiation therapy. No trials evaluated or adjusted for the potential influence of off-protocol radiation therapy on study endpoints. Among the subset of open-label studies, trials permissive toward off-protocol radiation therapy were more likely to meet their primary endpoint (adjusted odds ratio, 4.50; 95% CI, 1.23-20.23; P = .04). CONCLUSIONS: Although most trials allowed off-protocol radiation therapy during the receipt of the study systemic therapy, the influence of off-protocol radiation therapy, especially ablative radiation therapy, on study outcomes is underevaluated among phase 3 systemic therapy trials.

Circulating tumor DNA (ctDNA) as a biomarker of response to therapy in advanced Hepatocellular carcinoma treated with Nivolumab.

BACKGROUND: Circulating tumor DNA (ctDNA) is a promising non-invasive marker for detection, diagnosis, treatment selection, and prognosis of hepatocellular carcinoma (HCC). OBJECTIVE: This study aimed to examine the utility of ctDNA as a prognostic and predictive tool in HCC patients treated with nivolumab. METHODS: We analyzed pre-treatment ctDNA from 44 HCC patients using comprehensive genomic testing on a commercially available platform. We utilized log rank test and univariate Cox models to correlate overall survival (OS) and progression-free survival (PFS) with ctDNA expressions. RESULTS: Of 44 patients, 77.3% were men with median age of 67 years. All but 3 patients had at least one alteration identified, and TP53 was the most commonly altered gene (52.3%). Median OS was 17.5 months (95% CI: 12.7, NA). Mutations involving PIK3CA, BRCA1, and CCND1 amplification were associated with shorter OS (P 0.0001, 0.0001 and 0.01, respectively). Median PFS time was 4.01 months (95% CI: 3.06, 9.33). Mutations involving KIT and PIK3CA were associated with shorter PFS (P 0.0001 and 0.0004, respectively), while mutation involving CTNNB1 were associated with longer PFS (p= 0.04). CONCLUSIONS: ctDNA profiling may provide a benefit for prediction of survival and progression of HCC patients treated with nivolumab. Future studies are needed for confirmation.

Measurable imaging-based changes in enhancement of intrahepatic cholangiocarcinoma after radiotherapy reflect physical mechanisms of response.

Background: Although escalated doses of radiation therapy (RT) for intrahepatic cholangiocarcinoma (iCCA) are associated with durable local control (LC) and prolonged survival, uncertainties persist regarding personalized RT based on biological factors. Compounding this knowledge gap, the assessment of RT response using traditional size-based criteria via computed tomography (CT) imaging correlates poorly with outcomes. We hypothesized that quantitative measures of enhancement would more accurately predict clinical outcomes than size-based assessment alone and developed a model to optimize RT. Methods: Pre-RT and post-RT CT scans of 154 patients with iCCA were analyzed retrospectively for measurements of tumor dimensions (for RECIST) and viable tumor volume using quantitative European Association for Study of Liver (qEASL) measurements. Binary classification and survival analyses were performed to evaluate the ability of qEASL to predict treatment outcomes, and mathematical modeling was performed to identify the mechanistic determinants of treatment outcomes and to predict optimal RT protocols. Results: Multivariable analysis accounting for traditional prognostic covariates revealed that percentage change in viable volume following RT was significantly associated with OS, outperforming stratification by RECIST. Binary classification identified ≥33% decrease in viable volume to optimally correspond to response to RT. The model-derived, patient-specific tumor enhancement growth rate emerged as the dominant mechanistic determinant of treatment outcome and yielded high accuracy of patient stratification (80.5%), strongly correlating with the qEASL-based classifier. Conclusion: Following RT for iCCA, changes in viable volume outperformed radiographic size-based assessment using RECIST for OS prediction. CT-derived tumor-specific mathematical parameters may help optimize RT for resistant tumors.

Training robust T1-weighted magnetic resonance imaging liver segmentation models using ensembles of datasets with different contrast protocols and liver disease etiologies.

Image segmentation of the liver is an important step in treatment planning for liver cancer. However, manual segmentation at a large scale is not practical, leading to increasing reliance on deep learning models to automatically segment the liver. This manuscript develops a generalizable deep learning model to segment the liver on T1-weighted MR images. In particular, three distinct deep learning architectures (nnUNet, PocketNet, Swin UNETR) were considered using data gathered from six geographically different institutions. A total of 819 T1-weighted MR images were gathered from both public and internal sources. Our experiments compared each architecture's testing performance when trained both intra-institutionally and inter-institutionally. Models trained using nnUNet and its PocketNet variant achieved mean Dice-Sorensen similarity coefficients>0.9 on both intra- and inter-institutional test set data. The performance of these models suggests that nnUNet and PocketNet liver segmentation models trained on a large and diverse collection of T1-weighted MR images would on average achieve good intra-institutional segmentation performance.

Improving the clinical meaning of surrogate endpoints: An empirical assessment of clinical progression in phase III oncology trials.

Disease progression in clinical trials is commonly defined by radiologic measures. However, clinical progression may be more meaningful to patients, may occur even when radiologic criteria for progression are not met, and often requires a change in therapy in clinical practice. The objective of this study was to determine the utilization of clinical progression criteria within progression-based trial endpoints among phase III trials testing systemic therapies for metastatic solid tumors. The primary manuscripts and protocols of phase III trials were reviewed for whether clinical events, such as refractory pain, tumor bleeding, or neurologic compromise, could constitute a progression event. Univariable logistic regression computed odds ratios (OR) and 95% CI for associations between trial-level covariates and clinical progression. A total of 216 trials enrolling 148,190 patients were included, with publication dates from 2006 through 2020. A major change in clinical status was included in the progression criteria of 13% of trials (n = 27), most commonly as a secondary endpoint (n = 22). Only 59% of trials (n = 16) reported distinct clinical progression outcomes that constituted the composite surrogate endpoint. Compared with other disease sites, genitourinary trials were more likely to include clinical progression definitions (16/33 [48%] vs. 11/183 [6%]; OR, 14.72; 95% CI, 5.99 to 37.84; p < .0001). While major tumor-related clinical events were seldom considered as disease progression events, increased attention to clinical progression may improve the meaningfulness and clinical applicability of surrogate endpoints for patients with metastatic solid tumors.

Addition of Metastasis-Directed Therapy to Systemic Therapy for Oligometastatic Pancreatic Ductal Adenocarcinoma (EXTEND): A Multicenter, Randomized Phase II Trial.

PURPOSE: The EXTEND trial tested the hypothesis that adding comprehensive metastasis-directed therapy (MDT) to chemotherapy would improve progression-free survival (PFS) over chemotherapy alone among patients with oligometastatic pancreatic ductal adenocarcinoma (PDAC). METHODS: EXTEND (ClinicalTrials.gov identifier: NCT03599765) is a multicenter, phase II basket trial randomly assigning patients with ≤five metastases 1:1 to MDT plus systemic therapy versus systemic therapy. Disease progression was defined by radiologic criteria (RECIST v1.1), clinical progression, or death. The primary end point was PFS in the per-protocol population, evaluated after all patients achieved at least 6 months of follow-up. Exploratory end points included systemic immune response measures. RESULTS: Between March 19, 2019, and February 13, 2023, 41 patients were randomly assigned and 40 were eligible for the primary analysis of PFS (19 patients in the MDT arm; 21 patients in the control arm). At a median follow-up time of 17 months, the median PFS time was 10.3 months (95% CI, 4.6 to 14.0) in the MDT arm versus 2.5 months (95% CI, 1.7 to 5.1) in the control arm. PFS was significantly improved by the addition of MDT to systemic therapy (P = .030 for stratified log-rank test) with a hazard ratio of 0.43 (95% CI, 0.20 to 0.94). No grade ≥3 or greater adverse events related to MDT were observed. Systemic immune activation events were associated with MDT and correlated with improved PFS. CONCLUSION: This study supports the addition of MDT to systemic therapy for patients with oligometastatic PDAC. Induction of systemic immunity is a possible mechanism of benefit. These results warrant confirmatory trials to refine treatment strategy and provide external validation.

Translational modeling-based evidence for enhanced efficacy of standard-of-care drugs in combination with anti-microRNA-155 in non-small-cell lung cancer.

BACKGROUND: Elevated microRNA-155 (miR-155) expression in non-small-cell lung cancer (NSCLC) promotes cisplatin resistance and negatively impacts treatment outcomes. However, miR-155 can also boost anti-tumor immunity by suppressing PD-L1 expression. Therapeutic targeting of miR-155 through its antagonist, anti-miR-155, has proven challenging due to its dual molecular effects. METHODS: We developed a multiscale mechanistic model, calibrated with in vivo data and then extrapolated to humans, to investigate the therapeutic effects of nanoparticle-delivered anti-miR-155 in NSCLC, alone or in combination with standard-of-care drugs. RESULTS: Model simulations and analyses of the clinical scenario revealed that monotherapy with anti-miR-155 at a dose of 2.5 mg/kg administered once every three weeks has substantial anti-cancer activity. It led to a median progression-free survival (PFS) of 6.7 months, which compared favorably to cisplatin and immune checkpoint inhibitors. Further, we explored the combinations of anti-miR-155 with standard-of-care drugs, and found strongly synergistic two- and three-drug combinations. A three-drug combination of anti-miR-155, cisplatin, and pembrolizumab resulted in a median PFS of 13.1 months, while a two-drug combination of anti-miR-155 and cisplatin resulted in a median PFS of 11.3 months, which emerged as a more practical option due to its simple design and cost-effectiveness. Our analyses also provided valuable insights into unfavorable dose ratios for drug combinations, highlighting the need for optimizing dose regimens to prevent antagonistic effects. CONCLUSIONS: This work bridges the gap between preclinical development and clinical translation of anti-miR-155 and unravels the potential of anti-miR-155 combination therapies in NSCLC.

Hybridizing mechanistic modeling and deep learning for personalized survival prediction after immune checkpoint inhibitor immunotherapy.

We present a study where predictive mechanistic modeling is combined with deep learning methods to predict individual patient survival probabilities under immune checkpoint inhibitor (ICI) immunotherapy. This hybrid approach enables prediction based on both measures that are calculable from mechanistic models of key mechanisms underlying ICI therapy that may not be directly measurable in the clinic and easily measurable quantities or patient characteristics that are not always readily incorporated into predictive mechanistic models. A deep learning time-to-event predictive model trained on a hybrid mechanistic + clinical data set from 93 patients achieved higher per-patient predictive accuracy based on event-time concordance, Brier score, and negative binomial log-likelihood-based criteria than when trained on only mechanistic model-derived values or only clinical data. Feature importance analysis revealed that both clinical and model-derived parameters play prominent roles in increasing prediction accuracy, further supporting the advantage of our hybrid approach.

EUS-guided hydrogel injection to separate pancreatic head carcinoma from duodenum for enhanced radiotherapy: Multi-site feasibility study.

Background and study aims The proximity of a pancreas head tumor to the duodenum often limits delivery of an ablative dose of radiation therapy. This study evaluated the feasibility and safety of using an injectable polyethylene glycol (PEG) hydrogel between the head of the pancreas and duodenum. Patients and methods In a multi-site feasibility cohort study of patients with localized pancreatic cancer, PEG hydrogel was injected under endoscopic ultrasound guidance to temporarily position the duodenum away from the pancreas. Procedure characteristics were recorded, including hydrogel volume and space created. Patients were monitored for adverse events (AEs) and radiotherapy toxicity. Results In all six intent-to-treat patients (four with borderline resectable, two with locally advanced disease), the ability to place and visualize PEG hydrogel and create space between the duodenum and the head of the pancreas was successful. There were no procedure-related AEs resulting in radiotherapy delay. There were no device-related AEs and no reports of pancreatitis. Conclusions PEG hydrogel was successfully placed, created space between the duodenum and the head of the pancreas, and was not associated with major toxicity. Enhancing radiotherapy for pancreatic cancer by using PEG hydrogel to create peri-duodenal space could have beneficial implications for treatment and warrants more exploration.

Clinical Validation of Mathematically Derived Early Tumor Dynamics for Solid Tumors in Response to Durvalumab.

PURPOSE: Early prediction of response to immunotherapy may help guide patient management by identifying resistance to treatment and allowing adaptation of therapies. This analysis evaluated a mathematical model of response to immunotherapy that provides patient-specific prediction of outcome using the initial change in tumor size/burden from baseline to the first follow-up visit on standard imaging scans. METHODS: We applied the model to 600 patients with advanced solid tumors who received durvalumab in Study 1108, a phase I/II trial, and compared outcome prediction performance versus size-based criteria with RECIST version 1.1 best overall response (BOR), baseline circulating tumor (ct)DNA level, and other clinical/pathologic predictors of immunotherapy response. RESULTS: In multiple solid tumors, the mathematical parameter representing net tumor growth rate at the first on-treatment computed tomography (CT) scan assessed around 6 weeks after starting durvalumab (α1) had a concordance index to predict overall survival (OS) of 0.66-0.77 on multivariate analyses. This measurement of early tumor dynamics significantly improved multivariate OS models that included standard RECIST v1.1 criteria, baseline ctDNA levels, and other clinical/pathologic factors in predicting OS. Furthermore, α1 was assessed consistently at the first on-treatment CT scan, whereas all traditional RECIST BOR groups were confirmed only after this time. CONCLUSION: These results support further exploring α1 as an integral biomarker of response to immunotherapy. This biomarker may be predictive of further benefit and can be assessed before RECIST response groups can be assigned, potentially providing an opportunity to personalize oncologic management.

Clinical and molecular characteristics of patients with brain metastasis secondary to pancreatic ductal adenocarcinoma.

BACKGROUND: The prognosis for patients with pancreatic ductal adenocarcinoma (PDAC) is poor. Secondary brain metastasis (Br-M) occurs in less than 1% of patients. Clinical characteristics and molecular alterations have not been characterized in this rare patients' subset. MATERIALS AND METHODS: The Foundry software platform was used to retrospectively query electronic health records for patients with Br-M secondary to PDAC from 2005 to 2023; clinical, molecular, and overall survival (OS) data were analyzed. RESULTS: Br-M was diagnosed in 44 patients with PDAC. Median follow-up was 78 months; median OS from initial PDAC diagnosis was 47 months. Median duration from PDAC diagnosis to Br-M detection was 24 months; median OS from Br-M diagnosis was 3 months. At Br-M diagnosis, 82% (n = 36) of patients had elevated CA19-9. Lung was the most common preexisting metastatic location (71%) with Br-M, followed by liver (66%). Br-M were most frequently observed in the frontal lobe (34%, n = 15), cerebellar region (23%, n = 10), and leptomeninges (18%, n = 8). KRAS mutations were detected in 94.1% (n = 16) of patients who had molecular data available (n = 17) with KRASG12V being the most frequent subtype 47% (n = 8); KRASG12D in 29% (n = 5); KRASG12R in 18% (n = 3). Patients who underwent Br-M surgical resection (n = 5) had median OS of 8.6 months, while median OS following stereotactic radiosurgery only (n = 11) or whole-brain radiation only (n = 20) was 3.3 and 2.8 months, respectively. CONCLUSION: Br-M is a late PDAC complication, resulting in an extremely poor prognosis especially in leptomeningeal disease. KRAS was mutated in 94.1% of the patients and the KRASG12V subtype was prevalent.

Development and clinical implementation of a digital workflow utilizing 3D-printed oral stents for patients with head and neck cancer receiving radiotherapy.

OBJECTIVES: We describe the development of 3D-printed stents using our digital workflow and their effects on patients enrolled in the lead-in phase of a multi-center, randomized Phase-II trial. MATERIALS AND METHODS: Digital dental models were created for patients using intraoral scanning. Digital processes were implemented to develop the mouth-opening, tongue-depressing, and tongue-lateralizing stents using stereolithography. Time spent and material 3D-printing costs were measured. Physicians assessed mucositis using the Oral Mucositis Assessment Scale (OMAS) and collected MD Anderson Symptom Inventory (MDASI) reports and adverse events (AEs) from patients at various time points (TPs). OMAS and MDASI results were evaluated using paired t-test analysis. RESULTS: 18 patients enrolled into the lead-in phase across 6 independent clinical sites in the USA. 15 patients received stents (average design and fabrication time, 8 h; average material 3D-printing cost, 11 USD). 10 eligible patients with complete OMAS and MDASI reports across all TPs were assessed. OMAS increased significantly from baseline to week 3 of treatment (mean difference = 0.34; 95 % CI, 0.09-0.60; p = 0.01). MDASI increased significantly from baseline to week 3 of treatment (mean difference = 1.02; 95 % CI, 0.40-1.70; p = 0.005), and week 3 of treatment to end of treatment (mean difference = 1.90; 95 % CI, 0.90-2.92; p = 0.002). AEs (grades 1-3) were reported by patients across TPs. Mucositis and radiation dermatitis were primarily attributed to chemoradiation. CONCLUSIONS: 3D-printed stents were successfully fabricated and well tolerated by patients. As patients enroll in the randomized phase of this trial, data herein will establish a baseline for comparative analysis.

Machine learning identifies prognostic subtypes of the tumor microenvironment of NSCLC.

The tumor microenvironment (TME) plays a fundamental role in tumorigenesis, tumor progression, and anti-cancer immunity potential of emerging cancer therapeutics. Understanding inter-patient TME heterogeneity, however, remains a challenge to efficient drug development. This article applies recent advances in machine learning (ML) for survival analysis to a retrospective study of NSCLC patients who received definitive surgical resection and immune pathology following surgery. ML methods are compared for their effectiveness in identifying prognostic subtypes. Six survival models, including Cox regression and five survival machine learning methods, were calibrated and applied to predict survival for NSCLC patients based on PD-L1 expression, CD3 expression, and ten baseline patient characteristics. Prognostic subregions of the biomarker space are delineated for each method using synthetic patient data augmentation and compared between models for overall survival concordance. A total of 423 NSCLC patients (46% female; median age [inter quantile range]: 67 [60-73]) treated with definite surgical resection were included in the study. And 219 (52%) patients experienced events during the observation period consisting of a maximum follow-up of 10 years and median follow up 78 months. The random survival forest (RSF) achieved the highest predictive accuracy, with a C-index of 0.84. The resultant biomarker subtypes demonstrate that patients with high PD-L1 expression combined with low CD3 counts experience higher risk of death within five-years of surgical resection.

Escalated-dose radiotherapy for unresected locally advanced pancreatic cancer: Patterns of care and survival in the United States.

INTRODUCTION: With locally advanced pancreatic cancer (LAPC), uncontrolled local tumor growth frequently leads to mortality. Advancements in radiotherapy (RT) techniques have enabled conformal delivery of escalated-dose RT (EDR), which may have potential local control and overall survival (OS) benefits based on retrospective and early prospective studies. With evidence for EDR emerging, we characterized the adoption of EDR across the United States and its associated outcomes. METHODS: We searched the National Cancer Database for nonsurgically managed LAPC patients diagnosed between 2004 and 2019. Pancreas-directed RT with biologically effective doses (BED10) ≥39 and ≤70 Gy was labeled conventional-dose RT (CDR), and BED10 >70 and ≤132 Gy was labeled EDR. We identified associations of EDR and OS using logistic and Cox regressions, respectively. RESULTS: Among the definitive therapy subset (n = 54,115) of the entire study cohort (n = 91,493), the most common treatments were chemotherapy alone (69%), chemotherapy and radiation (29%), and RT alone (2%). For the radiation therapy subset (n = 16,978), use of pancreas-directed RT remained between 13% and 17% over the study period (ptrend > 0.999). Using multivariable logistic regression, treatment at an academic/research facility (adjusted odds ratio [aOR] 1.46, p < 0.001) and treatment between 2016 and 2019 (aOR 2.54, p < 0.001) were associated with greater receipt of EDR, whereas use of chemotherapy (aOR 0.60, p < 0.001) was associated with less receipt. Median OS estimates for EDR and CDR were 14.5 months and 13.0 months (p < 0.0001), respectively. For radiation therapy subset patients with available survival data (n = 13,579), multivariable Cox regression correlated EDR (adjusted hazard ratio 0.85, 95% confidence interval 0.80-0.91; p < 0.001) with longer OS versus CDR. DISCUSSION AND CONCLUSIONS: Utilization of EDR has increased since 2016, but overall utilization of RT for LAPC has remained at less than one in five patients for almost two decades. These real-world results additionally provide an estimate of effect size of EDR for future prospective trials.

Training Robust T1-Weighted Magnetic Resonance Imaging Liver Segmentation Models Using Ensembles of Datasets with Different Contrast Protocols and Liver Disease Etiologies.

Image segmentation of the liver is an important step in several treatments for liver cancer. However, manual segmentation at a large scale is not practical, leading to increasing reliance on deep learning models to automatically segment the liver. This manuscript develops a deep learning model to segment the liver on T1w MR images. We sought to determine the best architecture by training, validating, and testing three different deep learning architectures using a total of 819 T1w MR images gathered from six different datasets, both publicly and internally available. Our experiments compared each architecture's testing performance when trained on data from the same dataset via 5-fold cross validation to its testing performance when trained on all other datasets. Models trained using nnUNet achieved mean Dice-Sorensen similarity coefficients > 90% when tested on each of the six datasets individually. The performance of these models suggests that an nnUNet liver segmentation model trained on a large and diverse collection of T1w MR images would be robust to potential changes in contrast protocol and disease etiology.

Phase I trial of single-photon emission computed tomography-guided liver-directed radiotherapy for patients with low functional liver volume.

BACKGROUND: Traditional constraints specify that 700 cc of liver should be spared a hepatotoxic dose when delivering liver-directed radiotherapy to reduce the risk of inducing liver failure. We investigated the role of single-photon emission computed tomography (SPECT) to identify and preferentially avoid functional liver during liver-directed radiation treatment planning in patients with preserved liver function but limited functional liver volume after receiving prior hepatotoxic chemotherapy or surgical resection. METHODS: This phase I trial with a 3 + 3 design evaluated the safety of liver-directed radiotherapy using escalating functional liver radiation dose constraints in patients with liver metastases. Dose-limiting toxicities were assessed 6-8 weeks and 6 months after completing radiotherapy. RESULTS: All 12 patients had colorectal liver metastases and received prior hepatotoxic chemotherapy; 8 patients underwent prior liver resection. Median computed tomography anatomical nontumor liver volume was 1584 cc (range = 764-2699 cc). Median SPECT functional liver volume was 1117 cc (range = 570-1928 cc). Median nontarget computed tomography and SPECT liver volumes below the volumetric dose constraint were 997 cc (range = 544-1576 cc) and 684 cc (range = 429-1244 cc), respectively. The prescription dose was 67.5-75 Gy in 15 fractions or 75-100 Gy in 25 fractions. No dose-limiting toxicities were observed during follow-up. One-year in-field control was 57%. One-year overall survival was 73%. CONCLUSION: Liver-directed radiotherapy can be safely delivered to high doses when incorporating functional SPECT into the radiation treatment planning process, which may enable sparing of lower volumes of liver than traditionally accepted in patients with preserved liver function. TRIAL REGISTRATION: NCT02626312.

OSLD nanoDot characterization for carbon radiotherapy dosimetry.

Objective. This study characterized optically-stimulated luminescent dosimeter (OSLD) nanoDots for use in a therapeutic carbon beam using the Imaging and Radiation Oncology Core (IROC) framework for remote output verification.Approach. The absorbed dose correction factors for OSLD (fading, linearity, beam quality, angularity, and depletion), as defined by AAPM TG 191, were characterized for carbon beams. For the various correction factors, the effect of linear energy transfer (LET) was examined by characterizing in both a low and high LET setting.Main results. Fading was not statistically different between reference photons and carbon, nor between low and high LET beams; thus, the standard IROC-defined exponential function could be used to characterize fading. Dose linearity was characterized with a linear fit; while low and high LET carbon linearity was different, these differences were small and could be rolled into the uncertainty budget if using a single linearity correction. A linear fit between beam quality and dose-averaged LET was determined. The OSLD response at various angles of incidence was not statistically different, thus a correction factor need not be applied. There was a difference in depletion between low and high LET irradiations in a primary carbon beam, but this difference was small over the standard five readings. The largest uncertainty associated with the use of OSLDs in carbon was because of thekQcorrection factor, with an uncertainty of 6.0%. The overall uncertainty budget was 6.3% for standard irradiation conditions.Significance. OSLD nanoDot response was characterized in a therapeutic carbon beam. The uncertainty was larger than for traditional photon applications. These findings enable the use of OSLDs for carbon absorbed dose measurements, but with less accuracy than conventional OSLD audit programs.

Translational modeling-based evidence for enhanced efficacy of standard-of-care drugs in combination with anti-microRNA-155 in non-small-cell lung cancer.

Elevated microRNA-155 (miR-155) expression in non-small-cell lung cancer (NSCLC) promotes cisplatin resistance and negatively impacts treatment outcomes. However, miR-155 can also boost anti-tumor immunity by suppressing PD-L1 expression. We developed a multiscale mechanistic model, calibrated with in vivo data and then extrapolated to humans, to investigate the therapeutic effects of nanoparticle-delivered anti-miR-155 in NSCLC, alone or in combination with standard-of-care drugs. Model simulations and analyses of the clinical scenario revealed that monotherapy with anti-miR-155 at a dose of 2.5 mg/kg administered once every three weeks has substantial anti-cancer activity. It led to a median progression-free survival (PFS) of 6.7 months, which compared favorably to cisplatin and immune checkpoint inhibitors. Further, we explored the combinations of anti-miR-155 with standard-of-care drugs, and found strongly synergistic two- and three-drug combinations. A three-drug combination of anti-miR-155, cisplatin, and pembrolizumab resulted in a median PFS of 13.1 months, while a two-drug combination of anti-miR-155 and cisplatin resulted in a median PFS of 11.3 months, which emerged as a more practical option due to its simple design and cost-effectiveness. Our analyses also provided valuable insights into unfavorable dose ratios for drug combinations, highlighting the need for optimizing dose regimen to prevent antagonistic effects. Thus, this work bridges the gap between preclinical development and clinical translation of anti-miR-155 and unravels the potential of anti-miR-155 combination therapies in NSCLC.

Technical note: Radiological clinical equivalence for phantom materials in carbon ion therapy.

PURPOSE: As carbon ion radiotherapy increases in use, there are limited phantom materials for heterogeneous or anthropomorphic phantom measurements. This work characterized the radiological clinical equivalence of several phantom materials in a therapeutic carbon ion beam. METHODS: Eight materials were tested for radiological material-equivalence in a carbon ion beam. The materials were computed tomography (CT)-scanned to obtain Hounsfield unit (HU) values, then irradiated in a monoenergetic carbon ion beam to determine relative linear stopping power (RLSP). The corresponding HU and RLSP for each phantom material were compared to clinical carbon ion calibration curves. For absorbed dose comparison, ion chamber measurements were made in the center of a carbon ion spread-out Bragg peak (SOBP) in water and in the phantom material, evaluating whether the material perturbed the absorbed dose measurement beyond what was predicted by the HU-RLSP relationship. RESULTS: Polyethylene, solid water (Gammex and Sun Nuclear), Blue Water (Standard Imaging), and Techtron HPV had measured RLSP values that agreed within ±4.2% of RLSP values predicted by the clinical calibration curve. Measured RLSP for acrylic was 7.2% different from predicted. The agreement for balsa wood and cork varied between samples. Ion chamber measurements in the phantom materials were within 0.1% of ion chamber measurements in water for most materials (solid water, Blue Water, polyethylene, and acrylic), and within 1.9% for the rest of the materials (balsa wood, cork, and Techtron HPV). CONCLUSIONS: Several phantom materials (Blue Water, polyethylene, solid water [Gammex and Sun Nuclear], and Techtron HPV) are suitable for heterogeneous phantom measurements for carbon ion therapy. Low density materials should be carefully characterized due to inconsistencies between samples.