Biological optimization for hybrid proton-photon radiotherapy.

Objective.Hybrid proton-photon radiotherapy (RT) is a cancer treatment option to broaden access to proton RT. Additionally, with a refined treatment planning method, hybrid RT has the potential to offer superior plan quality compared to proton-only or photon-only RT, particularly in terms of target coverage and sparing organs-at-risk (OARs), when considering robustness to setup and range uncertainties. However, there is a concern regarding the underestimation of the biological effect of protons on OARs, especially those in close proximity to targets. This study seeks to develop a hybrid treatment planning method with biological dose optimization, suitable for clinical implementation on existing proton and photon machines, with each photon or proton treatment fraction delivering a uniform target dose.Approach.The proposed hybrid biological dose optimization method optimized proton and photon plan variables, along with the number of fractions for each modality, minimizing biological dose to the OARs and surrounding normal tissues. To mitigate underestimation of hot biological dose spots, proton biological dose was minimized within a ring structure surrounding the target. Hybrid plans were designed to be deliverable separately and robustly on existing proton and photon machines, with enforced uniform target dose constraints for the proton and photon fraction doses. A probabilistic formulation was utilized for robust optimization of setup and range uncertainties for protons and photons. The nonconvex optimization problem, arising from minimum monitor unit constraint and dose-volume histogram constraints, was solved using an iterative convex relaxation method.Main results.Hybrid planning with biological dose optimization effectively eliminated hot spots of biological dose, particularly in normal tissues surrounding the target, outperforming proton-only planning. It also provided superior overall plan quality and OAR sparing compared to proton-only or photon-only planning strategies.Significance.This study presents a novel hybrid biological treatment planning method capable of generating plans with reduced biological hot spots, superior plan quality to proton-only or photon-only plans, and clinical deliverability on existing proton and photon machines, separately and robustly.

Development and characterization of the first proton minibeam system for single-gantry proton facility.

BACKGROUND: Minibeam represents a preclinical spatially fractionated radiotherapy modality with great translational potential. The advantage lies in its high therapeutic index (compared to GRID and LATTICE) and ability to treat at greater depth (compared to microbeam). Proton minibeam radiotherapy (pMBRT) is a synergy of proton and minibeam. While the single-gantry proton facility has gained popularity due to its affordability and compact design, it often has limited beam time available for research purposes. Conversely, given the current requirement of pMBRT on specific minibeam hardware collimators, necessitates a reproducible and fast setup to minimize pMBRT treatment time and streamline the switching time between pMBRT and conventional treatment for clinically translation. PURPOSE: The contribution of this work is the development and characterization of the first pMBRT system tailored for single-gantry proton facility. The system allows for efficient and reproducible plug-and-play setup, achievable within minutes. METHODS: The single room pMBRT system is constructed based on IBA ProteusONE proton machine. The end of nozzle is attached with beam modifying accessories though an accessory drawer. A small snout is attached to the accessory drawer and used to hold apertures and range shifters. The minibeam aperture consists of two components: a fitting ring and an aperture body. Three minibeam apertures were manufactured. The first-generation apertures underwent qualitatively analysis with film, and the second generation aperture underwent more comprehensive quantitative measurement. The reproducibility of the setup is accessed, and the film measurements are performed to characterize the pMBRT system in cross validation with Monte Carlo (MC) simulations. RESULTS: We presented initial results of large field pMBRT aperture and the film measurements indicates the effect of source-to-isocenter distance = 930 cm in Y proton scanning direction. Consistent with TOPAS MC simulation, the dose uniformity of pMBRT field <2 cm is demonstrated to be better than 2%, rendering its suitability for pre-clinical studies. Subsequently, we developed the second generation of aperture with five slits and characterized the aperture with film dosimetry studies and compared the results to the benchmark MC. Comprehensive film measurements were also performed to evaluate the effect of divergence, air gap and gantry-angle dependency and repeatability and revealing a consistent performance within 5%. Furthermore, the 2D gamma analysis indicated a passing rate exceeding 99% using 3% dose difference and 0.2 mm distance agreement criteria. We also establish the peak valley dose ratio and the depth dose profile measurements, and the results are within 10% from MC simulation. CONCLUSIONS: We have developed the first pMBRT system tailored for a single-gantry proton facility, which has demonstrated accuracy in benchmark with MC simulations, and allows for efficient plug-and-play setup, emphasizing efficiency.

ATRA sensitized the response of hepatocellular carcinoma to Sorafenib by downregulation of p21-activated kinase 1.

BACKGROUND: Sorafenib resistance greatly reduces the efficacy of treatments in advanced hepatocellular carcinoma (HCC) patients, but the underlying mechanisms are not thoroughly understood. All-trans retinoic acid (ATRA), an anti-leukaemia agent, has attracted considerable attention due to its role in sensitizing cells to other anticancer treatments. We aimed to investigate the combined effect of ATRA and Sorafenib on HCC and the underlying mechanisms. METHODS: CCK-8, cell sphere formation, trans-well migration, and wound-healing assays were used to analyse the biological behaviours of HCC cells in vitro. Western blotting and qRT-PCR analysis were conducted to measure the expression of p21 activated kinase 1 (PAK1) and phospho-p21 activated kinase 1 (pPAK1). Xenograft models were established to confirm the synergistic effects of ATRA and Sorafenib in vivo. TUNEL assays and immunohistochemistry were utilized to determine apoptosis, proliferation, PAK1 and pPAK1 levels in tumour tissues. RESULTS: We observed that PAK1 was overexpressed in HCC, and its expression was negatively correlated with the survival of patients. PAK1 promoted the proliferation, self-renewal and epithelial-mesenchymal transition of HCC cells. Correlation analysis indicated that the IC50 of Sorafenib was positively correlated with the level of pPAK1 in HCC cell lines. ATRA inhibited the progression of HCC and sensitized HCC response to Sorafenib by downregulation of PAK1, as shown by the calculated coefficient of drug interaction and the data obtained from xenograft models. CONCLUSIONS: Our findings indicated that instead of treatment with Sorafenib alone, the combination of ATRA and Sorafenib provides a more effective treatment for HCC patients. Video Abstract.

BMI1-induced CD127+KLRG1+ memory T cells enhance the efficacy of liver cancer immunotherapy.

The efficacy of HCC (hepatocellular carcinoma) immunotherapy is hindered by the limited reactivity and short duration of tumor-infiltrating T cells. These deficiencies may be ascribed to the proliferative ability of T cells. The primary objective of this study was to identify the key factor regulating tumor-infiltrating lymphocytes (TIL) proliferation within the HCC microenvironment. Through the utilization of tissue-infiltrated T cell proteomics and fraction proteomics, we analyzed the differential proteins in T cells among HCC, liver fibrosis, and hemangioma (serving as controls) groups. Additionally, we examined the differential regulatory TFs of T cells between the HCC and VH (volunteer healthy, as a control) groups. Using cyTOF and flow cytometry technologies, as well as generating CD8+ T-specific BMI1 knockout mice, we confirmed that BMI1 controls CD127+KLRG1+ memory cell differentiation. Through RNA-seq and MeRIP-seq, we verified that BMI1 regulates TCF1 expression independently of its classical function. Furthermore, by conducting Tyramide signal amplification (TSA) IHC analysis, employing a hydrodynamic mouse HCC model, and utilizing liver-specific nanoparticle targeting therapy, we demonstrated that BMI1 in HCC influences the proliferation of infiltrating CD8+T. BMI1 inhibition promotes effector T cell differentiation while suppressing memory T cell differentiation. Moreover, liver-specific BMI1 knockdown proves beneficial in ameliorating T cell dysfunction and decelerating HCC progression. Our research group has pioneered the exploration of the proteomics of HCC-infiltrated T cells, shedding light on the pivotal role of BMI1 in controlling CD127+KLRG1+ memory CD8+ T cell differentiation, which serves as the cornerstone for achieving immunotherapy efficacy in HCC.

Fraction optimization for hybrid proton-photon treatment planning.

BACKGROUND: Hybrid proton-photon radiotherapy (RT) can provide better plan quality than proton or photon only RT, in terms of robustness of target coverage and sparing of organs-at-risk (OAR). PURPOSE: This work develops a hybrid treatment planning method that can optimize the number of proton and photon fractions as well as proton and photon plan variables, so that the hybrid plans can be clinically delivered day-to-day using either proton or photon machine. METHODS: In the new hybrid treatment planning method, the total dose distribution (sum of proton dose and photon dose) is optimized for robust target coverage and optimal OAR sparing, by jointly optimizing proton spots and photon fluences, while the target dose uniformity is also enforced individually on both proton dose and photon dose, so that the hybrid plans can be separately and robustly delivered on proton or photon machine. To ensure the target dose uniformity for proton and photon plans, the number of proton and photon fractions is explicitly modeled and optimized, so that the target dose for proton and photon dose components is constrained to be a constant fraction of the total prescription dose while the plan quality based on total dose is optimized. The feasibility of hybrid planning using the proposed method is validated with representative clinical cases including abdomen, lung, head-and-neck (HN), and brain. RESULTS: For all cases, hybrid plans provided better overall plan quality and OAR sparing than proton-only or photon-only plans, better target dose uniformity and robustness than proton-only plans, quantified by treatment planning objectives and dosimetric parameters. Moreover, for HN and brain cases, hybrid plans also had better target coverage than photon-only plans. CONCLUSIONS: We have developed a new hybrid treatment planning method that optimizes number of proton and photon fractions as well as proton spots and photon fluences, for generating hybrid plans that can be separately and robustly delivered on proton or photon machines. Preliminary results have demonstrated that hybrid plans generated by the new method have better plan quality than proton-only or photon-only plans.

An iterative convex relaxation method for proton LET optimization.

Objective:A constant relative biological effectiveness of 1.1 in current clinical practice of proton radiotherapy (RT) is a crude approximation and may severely underestimate the biological dose from proton RT to normal tissues, especially near the treatment target at the end of Bragg peaks that exhibits high linear energy transfer (LET). LET optimization can account for biological effectiveness of protons during treatment planning, for minimizing biological proton dose and hot spots to normal tissues. However, the LET optimization is usually nonlinear and nonconvex to solve, for which this work will develop an effective optimization method based on iterative convex relaxation (ICR).Approach: In contrast to the generic nonlinear optimization method, such as Quasi-Newton (QN) method, that does not account for specific characteristics of LET optimization, ICR is tailored to LET modeling and optimization in order to effectively and efficiently solve the LET problem. Specifically, nonlinear dose-averaged LET term is iteratively linearized and becomes convex during ICR, while nonconvex dose-volume constraint and minimum-monitor-unit constraint are also handled by ICR, so that the solution for LET optimization is obtained by solving a sequence of convex and linearized convex subproblems. Since the high LET mostly occurs near the target, a 1 cm normal-tissue expansion of clinical target volume (CTV) (excluding CTV), i.e. CTV1cm, is defined to as an auxiliary structure during treatment planning, where LET is minimized.Main results: ICR was validated in comparison with QN for abdomen, lung, and head-and-neck cases. ICR was effective for LET optimization, as ICR substantially reduced the LET and biological dose in CTV1cm the ring, with preserved dose conformality to CTV. Compared to QN, ICR had smaller LET, physical and biological dose in CTV1cm, and higher conformity index values; ICR was also computationally more efficient, which was about 3 times faster than QN.Significance: A LET-specific optimization method based on ICR has been developed for solving proton LET optimization, which has been shown to be more computationally efficient than generic nonlinear optimizer via QN, with better plan quality in terms of LET, biological and physical dose conformality.

SULT2B1-CS-DOCK2 axis regulates effector T-cell exhaustion in HCC microenvironment.

BACKGROUND AND AIMS: HCC is a malignant disease. Compared with tyrosine kinase inhibitors (the classical therapy), immune checkpoint inhibitors are more effective in the treatment of HCC, despite their limited efficacy. Among these restricted factors, exhaustion of tumor-infiltrated lymphocytes, especially CD8 + T cells, is a core event. We aimed to determine the key factors contributing to CD8 + T-cell infiltration in HCC and investigate the underlying mechanisms. APPROACH AND RESULTS: Using machine learning and multiplex immunohistochemistry analysis, we showed that dedicator of cytokinesis protein 2 (DOCK2) was a potential indicator of infiltrated CD8 + T cells in HCC. Using RNA sequencing, flow cytometry analysis, and mouse HCC models, we demonstrated that DOCK2 inactivation accounted for infiltrated CD8 + T-cell exhaustion in tumors. Using quasi-targeted metabolomics, mass spectrum, and mass cytometry by time of flight analysis, we found that cholesterol sulfate synthesized by sulfotransferase 2B1 in tumor cells suppressed DOCK2 enzymatic activity of T cells. Through virtual screening, molecular docking simulation, and experiments validation, we demonstrated that tolazamide reversed DOCK2 inactivation-mediated CD8 + T-cell exhaustion and enhanced anti-programmed death-ligand 1 antibody+apatinib immunotherapeutic effects on HCC. CONCLUSIONS: This study indicates that DOCK2 controls CD8 + T-cell infiltration in HCC, and cholesterol sulfate synthesized by sulfotransferase 2B1 in tumor cells promotes effector T-cell exhaustion. The findings suggest that the usage of conventional drugs affects immunotherapy efficacy in HCC patients.

TVL1-IMPT: Optimization of Peak-to-Valley Dose Ratio Via Joint Total-Variation and L1 Dose Regularization for Spatially Fractionated Pencil-Beam-Scanning Proton Therapy.

PURPOSE: Proton minibeam radiation therapy (pMBRT) is a novel proton modality of spatially fractionated RT. pMBRT can reduce the radiation damage to normal tissues via biological dose sparing of high peak-to-valley dose ratio (PVDR). This work will develop a new spatially fractionated IMPT treatment planning method for pMBRT that jointly optimizes the plan quality and maximizes the PVDR. METHODS: The new optimization method simultaneously maximizes the normal-tissue PVDR and optimizes the dose distribution at tumor targets and organs at risk. The PVDR maximization is through the joint total variation (TV) and L1 regularization with respect to the normal-tissue dose. That is, the beam-eye view projects dose slices of several depths for each beam angle; the TV of dose is maximized, corresponding to the PVDR maximization; and the L1 of dose is minimized, corresponding to the minimization of the organs-at-risk dose and maximization of survival fraction (SF). RESULTS: The new IMPT method with TV and L1 regularization was validated in comparison with the conventional IMPT method for pMBRT in several clinical cases. The results show that TVL1 provided larger PVDR and SF than the conventional IMPT method for biological sparing of normal tissues, with preserved plan quality in terms of physical dose distribution. CONCLUSIONS: A new spatially fractionated IMPT treatment planning method was developed for pMBRT that can optimize and improve normal-tissue PVDR and SF by incorporating TV and L1 dose regularization with properly chosen regularization parameters into IMPT.

SEA-Net: Structure-Enhanced Attention Network for Limited-Angle CBCT Reconstruction of Clinical Projection Data.

This work aims to improve limited-angle (LA) cone beam computed tomography (CBCT) by developing deep learning (DL) methods for real clinical CBCT projection data, which is the first feasibility study of clinical-projection-data-based LA-CBCT, to the best of our knowledge. In radiation therapy (RT), CBCT is routinely used as the on-board imaging modality for patient setup. Compared to diagnostic CT, CBCT has a long acquisition time, e.g., 60 seconds for a full 360° rotation, which is subject to the motion artifact. Therefore, the LA-CBCT, if achievable, is of the great interest for the purpose of RT, for its proportionally reduced scanning time in addition to the radiation dose. However, LA-CBCT suffers from severe wedge artifacts and image distortions. Targeting at real clinical projection data, we have explored various DL methods such as image/data/hybrid-domain methods and finally developed a so-called Structure-Enhanced Attention Network (SEA-Net) method that has the best image quality from clinical projection data among the DL methods we have implemented. Specifically, the proposed SEA-Net employs a specialized structure enhancement sub-network to promote texture preservation. Based on the observation that the distribution of wedge artifacts in reconstruction images is non-uniform, the spatial attention module is utilized to emphasize the relevant regions while ignores the irrelevant ones, which leads to more accurate texture restoration.

Proteomics-based identification of the role of osteosarcoma amplified-9 in hepatocellular carcinoma recurrence.

Hepatocellular carcinoma (HCC) is one of the most prevalent malignancies; its recurrence is associated with high mortality and poor recurrence-free survival and is affected by multisystem and multilevel pathological changes. To identify the key proteins associated with tumor recurrence and the underlying mechanisms, proteomic profiling of tumor specimens from early recurrence and nonrecurrence patients was performed in this study. Proteomics was applied to identify differentially expressed proteins during the early recurrence of HCC after surgery. Osteosarcoma amplified-9 (OS-9) was discovered, and the correlation between OS-9 expression and the clinicopathological characteristics of patients was analyzed. Invasion and migration were examined in SMMC-7721 cells with and without OS-9 overexpression. Proteomics was performed once again using SMMC-7721 cells with OS-9 overexpression to further analyze the proteins with altered expression. OS-9 was overexpressed in the early recurrence group, and OS-9 overexpression was associated with high serum alpha-fetoprotein levels and poor recurrence-free survival in 196 patients with HCC. The invasion and migration abilities of SMMC-7721 cells were enhanced in the OS-9 overexpression group. Bioinformatic functional enrichment methods, including Gene Ontology annotation and Kyoto Encyclopedia of Genes and Genomes pathway analysis, revealed that the hypoxia-inducible factor 1 (HIF-1) and tumor necrosis factor (TNF) signaling pathways were activated in the OS-9 overexpression group. The migration and invasion capacities of OS-9 overexpressed HCC cell line were weakened while treated with HIF-1α or TNF-α inhibitors. Conclusion: Our results suggest that the overexpression of OS-9 is related to HCC recurrence, thereby contributing to the migration and invasion capacities of HCC cell line by regulating the HIF-1 and TNF pathways.

Interleukin-2 inducible T-cell kinase: a potential prognostic biomarker and tumor microenvironment remodeling indicator for hepatocellular carcinoma.

BACKGROUND: The heterogeneous tumor microenvironment (TME) contributes to poor prognosis of hepatocellular carcinoma (HCC). However, determining the modulation of TME during HCC progression remains a challenge. METHODS: Herein, the stromal score and immune score of HCC samples from The Cancer Genome Atlas database were calculated using the ESTIMATE algorithm and differentially expressed genes (DEGs) were obtained. Key DEGs were identified based on a protein-protein interaction network and survival analysis. Immunohistochemistry was carried out using primary samples to evaluate key DEGs expression. The CIBERSORT algorithm was applied to evaluate immune components. Gene Set Enrichment Analysis (GSEA) and correlation analysis were carried out to determine the relationship between key DEGs and tumor-infiltrating immune cells (TICs). RESULTS: The stromal score, immune score and estimate score correlated significantly with 1-year recurrence-free survival of patients with HCC. Interleukin-2 inducible T-cell kinase (ITK) was identified as the most prognostic DEG for patients with HCC. GSEA revealed that genes in the high ITK subgroup were enriched in inflammatory-immunological terms. CIBERSORT analysis identified nine TIC subsets that correlated with ITK expression. CONCLUSION: We identified ITK as a novel indicator for early post-surgery tumor recurrence and microenvironment remodeling in HCC, providing a potential therapeutic target to treat HCC.

Severity of early allograft dysfunction following donation after circulatory death liver transplantation: a multicentre study.

BACKGROUND: Early allograft dysfunction (EAD) is associated with decreased graft and patient survival rates. This study aimed to identify the severity of EAD and develop a predictive model for EAD after donation after circulatory death (DCD) liver transplantation (LT). Furthermore, the influence of operative time on EAD incidence was also evaluated. METHODS: In this retrospective, multicentre cohort study, nomograms were established based on a single-centre training cohort (n=321) and validated in a 3-center validation cohort (n=501). RESULTS: The incidence rate of EAD was 46.4% (149/321) in the training cohort and 40.5% (203/501) in the validation cohort. Of the 149 EAD patients in the training cohort, 77 patients with either elevated alanine aminotransferase (ALT) or aspartate aminotransferase (AST) were classified as having EAD type A, and the rest of the EAD patients were classified as having EAD type B. Recipients with EAD type B had lower graft and patient survival rates than recipients with EAD type A (P=0.043 and 0.044, respectively). We further developed a nomogram to predict EAD (graft weight, cold ischemia time, donor age, model for end-stage liver disease (MELD) score) and another nomogram to predict EAD type B (graft weight, cold ischemia time, MELD score). The nomograms for the prediction of EAD and EAD type B had good discrimination [concordance index (C-index) =0.712 (0.666-0.758), 0.707 (0.641-0.773)] and calibration [Hosmer-Lemeshow (HL) P=0.384, P=0.425] in the validation cohort. An increased operative time (>6 h) was associated with increased EAD and EAD type B incidence in the high-risk group (P=0.005, P=0.020, respectively). CONCLUSIONS: EAD type B was associated with decreased graft and patient survival rates. The novel nomograms effectively predicted the incidence of EAD and EAD type B in DCD LT patients.

A two-circular RNA signature of donor circFOXN2 and circNECTIN3 predicts early allograft dysfunction after liver transplantation.

BACKGROUND: Early allograft dysfunction (EAD) following liver transplantation is associated with poor recipient and graft survival. In recent years, circular RNAs (circRNAs) have emerged as important components of endogenous RNAs. This study aims to explore the expression profile and predictive value of graft circular RNAs for EAD after liver transplantation. METHODS: RNA sequencing was conducted to identify the circRNA profile in donor liver tissues. Furthermore, quantitative real-time polymerase chain reaction (qRT-PCR) was used to identify candidate circRNAs. A novel model combining circular RNA signature was established to predict EAD based on the multivariate analysis. RESULTS: A total of 442 circRNAs were differentially expressed between the EAD and non-EAD groups, of which, 223 were significantly upregulated and 219 were downregulated in the EAD group (Fold change >2, P<0.05). qRT-PCR validation indicated that circFOXN2 and circNECTIN3 levels in the EAD group were significantly lower than those in the non-EAD group (P=0.038, 0.024, respectively; n=115). Among the 115 recipients, 32 recipients with high circFOXN2 expression were classified as circular RNA signature A and the rest recipients with low circFOXN2 expression were categorized into circular RNA signature B (n=33, high circNECTIN3 expression) and C (n=50, low circNECTIN3 expression). The incidence rates of EAD in signature A, B and C were significantly different (3.1%, 21.2% and 42.0%, respectively; P=0.000). According to the multivariate analysis, a novel predictive model for EAD was developed based on CIT (P=0.000) and circular RNA signature (P=0.013). The novel model displayed a high predictive value for EAD with areas under the curve (AUC) of 0.870 (95% CI: 0.797-0.942). CONCLUSIONS: Donor circFOXN2 and circNEXTIN3 were associated with the incidence of EAD. The novel model combing the two-circular RNA signature had a high predictive value for EAD.