Zhang, Hailei

Abstract Background Standardizing beam modeling in the treatment planning system is challenging due to machine‐specific configuration variations. Because of the proton therapy system (PTS) complexity and continuous development, there is no vendor's representative (“golden”) beam data like in LINACs at this stage. Purpose The primary goal of this study is to create a PTS‐specific repository of source model parameters and beam commissioning data for scanning proton therapy treatment planning systems and establish a set of reference values with associated bands of variations for this data. Methods We conducted a survey of 31 PTSs at 27 proton therapy centers to gather beam commissioning source model parameters used in their treatment planning system, ensuring a diverse selection of PTSs from major vendors: Hitachi, IBA, Varian, and Mevion. For each proton therapy center, we collected (i) beam meterset calibration in protons per MU, (ii) in‐air spot size at isocenter in terms of standard deviation in x‐ (crossline, σ x ) and y‐ (inline, σ y ) directions, (iii) in‐air angular spread at isocenter in x‐direction (θ x ) and y‐direction (θ y ) and (iv) distal falloff. For each PTS, we plotted each parameter as a function of nominal energy and performed polynomial regression analysis with optimal model selection based on Akaike Information Criterion. We calculated intraclass correlation coefficients (ICC) to quantify inter‐PTS variability and generated uncertainty bands representing standard deviations across PTSs for each parameter. Additionally, we computed percentage deviations of each individual PTS from the overall multi‐vendor mean to identify systematic vendor‐specific patterns and outliers across the 31 total PTSs analyzed. Results Multi‐vendor beam parameter analysis across 31 PTSs revealed systematic vendor‐specific patterns in inter‐PTS consistency and energy dependencies. Beam meterset calibration demonstrated excellent inter‐PTS consistency across most vendors (ICC: Varian ProBeam ≈ 0, IBA Proteus ONE = 0.006, Mevion S250i with HYPERSCAN = 0.015), with IBA Proteus PLUS systems showing moderate variation (ICC = 0.156 for the dedicated nozzle, ICC = 0.248, for the universal nozzle). Spot size and angular spread variability were highly vendor‐dependent, ranging from excellent consistency in IBA Proteus ONE to substantial variation in IBA Proteus PLUS with the universal nozzle (angular spread ICC = 0.566). Distal falloff maintained excellent consistency across all vendors (ICC < 0.023), indicating reliable energy spread characteristics. Uncertainty band analysis revealed characteristic energy‐dependent patterns, with most systems showing larger uncertainties at lower energies for spot size and angular spread, while IBA Proteus PLUS with the universal nozzle exhibited the largest uncertainty bands overall. Percentage deviation analysis demonstrated vendor‐specific clustering patterns, with some systems showing systematic positive or negative deviations from the multi‐vendor mean. Conclusions The reference data provided in this work will help to cross‐check the beam model during treatment planning system commissioning. Furthermore, this information is useful for Monte Carlo applications and research, where beam characteristics are required to set up phase space parameters, thereby avoiding the need to model the entire PTS. Finally, the results of this study will have indispensable educational value by providing valuable insights into the characteristics and capabilities of different PTSs.

Abstract Background: Survival advantages with neoadjuvant chemotherapy (NACT) have been observed in patients (pts) with locally advanced breast cancer (BC) who achieve pathological complete response (pCR). Predicting risk of early recurrence in pts without pCR remains difficult. Circulating tumor DNA (ctDNA) may be a biomarker of neoadjuvant therapy response and recurrence. This study applied tumor genotyping of peripheral blood in pts with HER2− tumor BRCA-mutated (tBRCAm) BC treated with neoadjuvant niraparib. The relationship between ctDNA and tumor response was also characterized, with a focus on circulating TP53 mutant allelic frequency (MAF). Methods: Blood was collected from 21 pts enrolled in the study (NCT03329937) evaluating neoadjuvant niraparib in pts with localized HER2− tBRCAm BC. Pts received niraparib 200 mg once daily for two 28-day cycles. After 2 cycles, pts underwent surgery or received up to 6 cycles of niraparib and/or NACT prior to surgery. Blood samples were collected at screening, Cycle 1 Day 28 (C1D28), Cycle 2 Day 28 (C2D28), and pre-surgery. Ultrasound (USG) was done after each cycle and MRI after Cycle 2. Next-generation sequencing of cell-free plasma DNA was carried out using a proprietary target capture and analysis (Paweletz, Clin Cancer Res 2016). Results: ctDNA and TP53 mutations (TP53m) were detected at screening in 10/21 pts (47.6%). Across all 21 pts there was a correlation between TP53 MAF and baseline tumor stage (T) (mean [standard deviation], T1 0 [0], T2 0.05 [0.09], and T3 0.21 [0.12]; P=0.005). Linear regression modelling showed a positive correlation between tumor volume and baseline TP53 MAF (R2=0.206, P=0.0386). Baseline TP53 MAF also appeared to be associated with advanced disease stage (Table 1). Depletion of detectable TP53m from baseline was evident by C1D28 and persisted to pre-surgery (Table 2). This corresponded with decreases in tumor volume at C1D28 and further decreases at C2D28. Of 11 pts analyzed, 6 pts with &gt;90% tumor volume decreases by MRI or USG at C2D28 had sustained TP53m depletion. Three pts had pCR; of these, 2 had sustained TP53m depletion (data unavailable for third pCR pt). Two pts with less robust tumor responses had increased pre-surgery TP53 MAF. Conclusions: We report the potential utility of ctDNA as a predictive biomarker for neoadjuvant niraparib response in pts with HER2− tBRCAm BC. Although sample size limited analysis of genes other than TP53, baseline ctDNA strongly correlated with tumor volume, and TP53 MAF was associated with tumor volume and trended towards association with disease stage. Longitudinal analysis of TP53 MAF suggested a correlation with niraparib response. Further study of ctDNA dynamics during NACT and correlation with pCR and long-term clinical outcomes is warranted. Funding: GlaxoSmithKline (GSK) study 213355; NCT03329937. Medical writing support was provided by Fishawack Indicia, part of Fishawack Health, funded by GSK. Table 1.Baseline TP53 MAF association with disease stage and subtypeTP53 MAFDisease Stage (I–III)Disease SubtypeStage IStage IIStage IIIHR+TNBC(n=8)(n=10)(n=3)(n=6)(n=15)Mean (SD)0.01 (0.02)0.07 (0.12)0.10 (0.05)0.02 (0.02)0.06 (0.10)HR, hormone receptor; MAF, mutant allelic frequency; SD, standard deviation; TNBC, triple-negative breast cancer. Table 2.TP53 MAF correlation with tumor volume decrease by ultrasound and MRIOutcomeScreeningCycle 1 Day 28 Cycle 2 Day 28 Pre-Surgeryn11997TP53 MAF, mean, (SD)0.09 (0.11)000.01 (0.02)n11112Percent change in tumor volume from baseline by MRI, mean (SD)-N/A*−72.6 (22.5)N/A*Percent change in tumor volume from baseline by ultrasound, mean (SD)-−60.8 (24.0)−80.6 (21.8)−95.9 (4.3)*MRI was only performed at Cycle 2 Day 28. MAF, mutant allelic frequency; MRI, magnetic resonance imaging; N/A, not applicable; SD, standard deviation. Citation Format: Ming Shan, Laura Spring, Minetta C Liu, Erika Hamilton, Hanna Irie, Cesar A Santa-Maria, Steven J Isakoff, James Reeves, Leif W Ellisen, Lee P Lim, Kavita Garg, Caterina Bertucci, Bin Feng, Hailei Zhang, Kaiming Sun, Julie R Graham, Erin Hofstatter, Hyo Han. Relationship between circulating tumor DNA and response to neoadjuvant niraparib in HER2-negative, BRCA-mutated breast cancer [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P5-13-21.