Call

Proton and carbon ions are characterized by physical and biological features that allow a high radiation dose to tumors, minimizing irradiation to adjacent normal tissues. For this reason, radioresistant tumors and tumors located near highly radiosensitive critical organs, such as brain tumors, represent the best target for this kind of therapy [1]. The potential side effects of proton therapy can include acute and late effects. Among the late events, the most severe is radiation-induced cerebral necrosis (RICN). In the literature, the definition of RICN varies across studies, and in addition, wide variation in the reporting may be also attributable to improved quality and frequency of diagnostic imaging, increased awareness within the oncology community and length of follow-up [2]. The incidence of RICN is poorly known [3] Some reports indicate that depending on the treatment protocol the actual incidence could be anywhere from 3% to 24% [4] and has a latency of 3 month up to 13 year after treatment [5]. Although the expected incidence of RICN may be low, it is a severe and potentially lethal late effect that can extremely impact these patients quality of life. In particular for proton therapy, a few retrospective clinical studies from adult populations have demonstrated its occurrence with a 2-year rate of 4.6% (for temporal lobe necrosis) [6]. Importantly, in the pediatric population, it has been described that radiation necrosis induced by proton beam radiotherapy tends to occurs early after treatment and away from the main lesion, which is concerning for recurrence [7]. Little is known about the mechanism by which RICN occurs. Recent retrospective studies showed that some of potential risk factors for RICN, includes: total dose given, the total volume irradiated, fraction size, the linear energy transfer (LET), treatment modality, among others [2, 5, 8, 9, 10]. Bearing this, the main aim of this thesis is to understand how RICN is affected by some of the described physical factors related to proton therapy, in a multidisciplinary study taking advantage of advanced brain cellular models. The student will have the opportunity to work at Centro de Ciências e Tecnologias Nucleares (C2TN/IST) and at the Cyclotron Centre Bronowice (CCB)/Institute of Nuclear Physics Polish Academy of Sciences (IFJ PAN) in Krakow, Poland. The student will also profit from collaboration with researchers from the iBB-Institute for Bioengineering and Biosciences from Instituto Superior Técnico, Universidade de Lisboa, which will contribute with their expertise in development of innovative advanced models of healthy and diseased human tissues from induced pluripotent stem cells (hiPSCs) and their applications for personalized medicine. REFERENCES [1] Paganetti et al Roadmap: proton therapy physics and biology Published 26 February 2021 •Physics in Medicine & Biology, Volume 66, Number 5 [2] Vellayappan, Balamurugan et al. “Diagnosis and Management of Radiation Necrosis in Patients With Brain Metastases.” Frontiers in oncology vol. 8 395. 28 Sep. 2018, doi:10.3389/fonc.2018.00395. [3] Chao, S.T et al. “Challenges with the diagnosis and treatment of cerebral radiation necrosis”. Int. J. Radiat. Oncol. Biol. Phys. 2013, 87, 449–457. [4] Ruben, J.D. et al “Cerebral radiation necrosis: Incidence, outcomes, and risk factors with emphasis on radiation parameters and chemotherapy”. Int. J. Radiat. Oncol. Biol. Phys. 2006, 65, 499–508. [5] Fink, J. et al “Radiation necrosis: Relevance with respect to treatment of primary and secondary brain tumors". Curr. Neurol. Neurosci. Rep. 2012, 12, 276–285. [6] Kitpanit S, et al “Temporal Lobe Necrosis in Head and Neck Cancer Patients after Proton Therapy to the Skull Base”. Int J Part Ther. 2020 Spring;6(4):17-28. doi: 10.14338/IJPT-20-00014.1. [7] Davanzo, J. et al Radiation Necrosis Following Proton Beam Therapy in the Pediatric Population: a Case Series. Cureus, 2017 9(10), e1785. https://doi.org/10.7759/cureus.1785. [8] Lee, D.-S et al. Radiation-induced brain injury: Retrospective analysis of twelve pathologically proven cases. Radiat. Oncol. J. 2011, 29, 147–155. [9] Lawrence, Y.R. et al “Radiation dose-volume effects in the brain”. Int. J. Radiat. Oncol. Biol. Phys. 2010, 76, S20–S27. [10] Haas-Kogan D et al National Cancer Institute Workshop on Proton Therapy for Children: Considerations Regarding Brainstem Injury. Int J Radiat Oncol Biol Phys. 2018 May 1;101(1):152-168. doi: 10.1016/j.ijrobp.2018.01.013. PMID: 29619963; PMCID: PMC5903576.

Unexpected late effects of proton therapy (PT) have been observed over the last few years as the number of patients treated with PT has increased. These side effects could be related to the uncertainties in the relative high biological effectiveness (RBE) of protons stopping in organs at risk (OAR).RBE values across the spread-out Bragg peak (SOBP) of protons range can generally vary from 0.9 – 1.7 (Paganetti). The use of a constant RBE value of 1.1, which is still the accepted clinical practice, disregards experimental evidence that the RBE depends on many factors, including - dose per fraction (higher for smaller dose fractions) - Tissue type (higher for late-response tissue, in particular in the brain); - LET and depth (LET increases with depth reaching a maximum on the distal edge of the Bragg peak); - biological endpoint (higher for neurological late-response endpoints, including brainstem necrosis and blindness). DNA damage generated by particle tracks with higher LET are thought to be more complex and more difficult to repair. This effect will increase the magnitude of RBE in critical healthy tissues when protons are stopped in them. Moreover, the effect of fractionation certainly plays a role. There is evidence that RBE is higher with fractionation, further amplifying the high-LET effect of stopping protons.

The targeting accuracy of proton therapy (PT) for moving soft-tissue tumours is expected to greatly improve by real-time magnetic resonance imaging (MRI) guidance. The integration of MRI and PT at the treatment isocenter would offer the opportunity of combining the unparalleled soft-tissue contrast and real-time imaging capabilities of MRI with the most conformal dose distribution and best dose steering capability provided by modern PT. However, hybrid systems for MR-integrated PT (MRiPT) have not been realized so far due to a number of hitherto open technological challenges. In recent years, various research groups have started addressing these challenges and exploring the technical feasibility and clinical potential of MRiPT. In addition, there has been a significant increase in the use of machine learning methods for use with Magnetic Resonance Imaging. The thesis will focus on assessing and developing machine learning techniques for MRiPT. The work will investigate the use of machine learning methods for organ segmentation, MRI to CT conversions, dose calculation and motion modelling. In addition, MR images always require magnetic field corrections, which can affect the quality of the MR image. The machine learning methods will be used to reduce the impact of magnetic field corrections on the final MR image used from the patient. There four aspects currently under study in the field of MR-guided proton therapy and which are (1) modelling and experimental investigations of electromagnetic interactions between the MRI and PT systems, (2) integration of MRiPT workflows in clinical facilities, (3) proton dose calculation algorithms in magnetic fields, and (4) MRI-only based proton treatment planning approaches, which include auto-segmentation and magnetic field corrections. The PhD project will focus developing Machine Learning methods for use with MRiPT. Machine learning methods will be investigated for integrating MRI into the clinical workflow PT center.

Proton radiotherapy is recognized as an effective method to treat cancers, allowing to better cover the tumor compared to conventional photon therapy while better sparing the surrounding healthy tissues. Further improvements in proton therapy can be achieved with advanced treatment planning methods offering fast and precise dose optimization and calculation in clinical routine. A better quantification, understanding, and modelling of the physical and biological uncertainties in patients will probably reduce the risk of complications resulting from proton therapy, such as development of secondary tumors or necrosis in about 10% of patients. The state-of-the-art of advanced treatment planning techniques are based on Monte Carlo methods. They allow to precisely calculate dose distributions in a patient. Monte Carlo methods exploiting graphic cards (GPU) are nowadays being explored since they significantly improve the time of calculations compared to processor-based (CPU) Monte Carlo engines. The GPU-accelerated Monte Carlo code FRED, developed by our research partners from the Sapienza University of Rome, has been recently used at CCB Krakow and Maastricht proton therapy facilities for treatment planning studies and patient quality assurance, as well as detector research. In the framework of this project, new treatment plan optimization methods and planning strategies will be developed, including robust optimization of treatment plans taking into account range, delineation, setup, organ motion uncertainties as well as biological uncertainties. For this purpose FRED MC code and clinical data from the two involved proton facilities will be exploited. The student will have the opportunity to work at Centro de Ciências e Tecnologias Nucleares (C2TN/IST), at Cyclotron Centre Bronowice (CCB)/Institute of Nuclear Physics Polish Academy of Sciences (IFJ PAN) in Krakow, Poland and at the Maastro Clinic in Maastricht, Netherlands.

The rationale of Radiotherapy has always been a trade-off between tumour control and healthy organ toxicity, where healthy organs are in many cases very sensitive to radiation. However, after recent developments in FLASH radiotherapy, this paradigm has been challenged due to a significant reduction in the toxicity of healthy organs. “FLASH” is based on very high dose-rate irradiation (dose rate ≥50 Gy/s), short beam-on times (≤100 ms) and large single doses (≥10 Gy) per beam [1]. Recent clinical implementation of FLASH has been mostly focused on delivering a single beam with FLASH to treat cancers on the skin or in the extremities (hands and legs) with electron or proton beams. However, at the moment the clinical application of FLASH with proton beams is still being studied. In particular it is still unknown how to perform FLASH with multi-beams, since single beam doses above 10Gy to 12Gy are not common in standard radiotherapy, because of the very high risk of radiation toxicity. In this project the FLASH effect in protecting healthy tissues will thus be studied and treatment planning strategies with proton multi-beam irradiation will be investigated and proposed, including the combination of FLASH proton radiotherapy with other treatment delivery techniques, towards the maximization of treatment benefit.

Particle therapy using protons and ions promises high dose conformity due to its characteristic localized dose deposition. The theoretically achievable conformity is often compromised due to the concurrent sensitivity to uncertainties in patient setup, daily anatomy, and dose calculation. Current approaches to monitor particle ranges during treatment hope to mitigate the influence of these uncertainties, effectively minimizing the robustness requirements for the underlying treatment plans. While range verification methods show promising results and start to find their way into the clinic, it is not fully clear how to maximize the benefit of range information. In addition, it’s also not clear what is the needed precision for range measurements done in patients, using a variety of measuring devices such as dual energy CT, prompt gamma or proton imaging (such as proton tomography etc). The underlying issue is the required explicit decision if a detected range difference is acceptable or not. Some violations might be in-line with previous assumptions when designing the treatment plan, while others might be compensated for in later treatment sessions. Making these quantitative range-information based decisions mainly boils down to inferring the changes to the underlying dose distributions from the range measurements. Yet so far there is no methodology to accurately quantify these changes to the dose.

Radiotherapy (RT) is commonly associated with the treatment of cancer, where it kills or slows down the growth of tumor cells. In addition, it has also been used to successfully treat amyloidosis [1,2], a superfamily of chronic degenerative disorders caused by deposits of toxic protein aggregates in cells and tissues. Recently, low-dose RT (LDRT) has shown positive results on widespread incurable neurodegenerative disorders such as Alzheimer's disease (AD) or Parkinson's disease (PD) [3,4]. The RT modality has been tested with photons, while proton therapy (PT) has so far not been evaluated. The overall aim of this proposal is to analyze the destructive potential of low-dose proton therapy (LDPT) on the accumulation of toxic protein aggregates associated with these neurodegeneration disorders. Monte Carlo (MC) simulations will be confronted with experiments on purified amyloidogenic protein solutions and live-cell models of neurodegenerative disorders. This is to study the effect protons and other types of radiation have on toxic protein aggregates. The results of this multidisciplinary project will lay the groundwork for possible applications of PT on a wide spectrum of neurodegenerative disorders.

Treatment of some tumors close to sensitive structures, such as the central nervous system in pediatric cancers is still compromised by the radiation tolerance of normal tissue. On the other hand, the treatment of radioresistant tumors is one of the major challenges in radiation therapy (RT). New approaches are being developed that combine spatial fractionation of the dose and narrow (0.5-1mm) beam sizes. Very high doses (> 50 Gy) are delivered in one fraction using parallel beams with inter-beam separations between 2 and 4 mm. This delivery mode is known as minibeam radiation therapy (MBRT) [1] and has been applied for both photons and protons, resulting in negligible neurotoxicity in small animal experiments even in whole-brain irradiations with very high doses (25-30 Gy) [2,3]. It has also been shown that MBRT leads to better tumor control in gliomas than standard RT. The high linear energy transfer of heavier ions like carbon, neon, silicon, and argon makes them new potential candidates for MBRT due to their superior relative biological effectiveness and reduced oxygen effect as compared to X-rays and protons [4]. MBRT requires high-precision dose measurements, which is currently challenging due to the very narrow beams used. The volume averaging effect or the lack of secondary electron equilibrium starts to play a non-negligible role and the approximations of classical radiation physics tend to be less valid when compared to larger fields. The precise measurement of dosimetric quantities is relevant to understand the intriguing biological effects observed in MBRT and how they depend on the beam structure. On the other hand, to understand the relationship between the measured physical parameters and the observed biological effects, one needs powerful computational tools that allow simulating the physical and chemical processes at very different scales (from organs to cells). In this proposal, the student will contribute to the advancement of MBRT using heavy ions. The project comprises two main goals: 1) evaluation and intercomparison of several instruments developed for high-resolution dosimetry applied in the context of charged particle MBRT [5] and 2) development of fast Monte Carlo simulations to guide pre-clinical experiments and to perform the first evaluation of treatment plans for charged particle MBRT. The work will be developed at Laboratório de Instrumentação e Física Experimental de Partículas (LIP), Lisbon, Portugal and at the Institut Curie-Orsay Research Center (IC-ORC), Orsay, France. The proposal also involves collaboration with other international groups, namely at the Helmholtz Centre for Heavy Ion Research (GSI), Darmstadt, Germany, and at the National Institute for Radiological Sciences (NIRS), Japan. References: [1] Prezado Y, Fois GR. “Proton-minibeam radiation therapy: a proof of concept.” Med Phys. 2013 Mar;40(3):031712. [2] Prezado Y, Jouvion G, Hardy D, Patriarca A, Nauraye C, Bergs J, et al. “Proton minibeam radiation therapy spares normal rat brain: Long-Term Clinical, Radiological and Histopathological Analysis.” Sci Rep. 2017 31;7(1):14403. [3] Lamirault C, Doyère V, Juchaux M, Pouzoulet F, Labiod D, Dendale R, et al. « Short and long-term evaluation of the impact of proton minibeam radiation therapy on motor, emotional and cognitive functions.” Sci Rep. 2020 Aug 11;10(1):13511. [4] Prezado Y, Hirayama R, Matsufuji N, Inaniwa T, Martínez-Rovira I, Seksek O, Bertho A, Koike S, Labiod D, Pouzoulet F, Polledo L, Warfving N, Liens A, Bergs J, Shimokawa T. “A Potential Renewed Use of Very Heavy Ions for Therapy: Neon Minibeam Radiation Therapy.” Cancers 2021, 13: 1356. [5] Guardiola C, De Marzi L, Prezado Y. "Verification of a Monte Carlo dose calculation engine in proton minibeam radiotherapy in a passive scattering beamline for preclinical trials." Br J Radiol. 2020, 93(1107):20190578.

Owing to the favorable physical properties of interaction of protons with matter, application of proton beams in radiation therapy for highly selective cancer treatment is rapidly spreading worldwide. To date, over 90 ion therapy facilities are operational, predominantly with proton beams, and about the same amount is under construction or planning. Over the last decades, considerable improvements have been achieved in accelerator technology, beam delivery and medical physics to enhance conformation of the dose delivery to complex shaped tumor volumes, with excellent sparing of surrounding healthy tissue and critical organs. Nevertheless, full clinical exploitation of the proton beam advantages is still challenged, especially by uncertainties in the knowledge of the beam range in the actual patient anatomy during the fractionated course of treatment, thus calling for continued multidisciplinary research in this rapidly emerging field. To date, there are two main physical channels that may be explored for in-vivo monitoring of the proton therapy treatments. The first one is imaging beta+ decays via dedicated or commercial positron emission tomographs (PET), and the second is prompt gamma (PG) imaging. Both beta+ decaying nuclei or PG emission are produced by nuclear reactions between the penetrating protons and the nuclei of the patient being irradiated. In order to provide valuable feedback to the fractionated treatment, both methods need to compare the measured PET and/or PG distribution with the computed predictions. Studies that our teams have performed show that using off-the-shell Monte Carlo simulation codes such as Geant4 is too time consuming for daily clinical applications. Timing is still unacceptable even when typical high performance computing clusters available at some clinical centers are utilized. Hence, in this thesis we propose to develop an approach (and realize it in a custom software) that accelerates the simulation of beta+ creation and decay during clinical proton irradiation. The software must take into account the computed tomogram (CT) of the patient and the beam fluency on a voxel-by-voxel basis and estimate the production of beta+ activity based on the stored look-up values determined previously by validated, detailed Monte Carlo simulations. Development of such fast simulation approaches is crucial to fully realize the potential of the PET-based range monitoring systems, including those to be eventually operated at the planned proton facility at C2TN in Sacavém, Portugal. In addition, the same code will be utilized in the task of monitoring proton treatments at the MDACC (MD Anderson Cancer Center) in Houston, Texas, USA, under the ongoing TPPT (time-of-flight PET for monitoring proton therapy) consortium (between LIP, ICNAS-UC, PETsys electronics, The University of Texas at Austin, and the MDACC).

The combined use of immunotherapy (IT) and radiotherapy (RT) has the potential to significantly improve the treatment of breast cancer. In particular, the use of particle therapies such as proton therapy has emerged as being highly promising due to the predictably different immunologic effects resulting from irradiation with photons or protons. Notably, particle therapy is foreseen to result in reduced off-target tissue irradiation, sparing healthy tissues and leading to a reduced local depletion of infiltrating lymphocytes, which should contribute to augmented antitumor immune responses, the elimination of disseminated metastases, and a better patient prognosis. With this in mind, the aim of this study is to do a systematic comparison between the potential synergic effects between IT and RT, namely comparing standard photon therapy with the emerging proton therapy. The evaluation of the effects of both IT/RT regimens on biological systems will start with studies on relevant breast cancer cellular models, which, if successful, will be extended to relevant animal models. The selected candidate will have the possibility to work at two academic institutions of research excellence: the Centro de Ciências e Tecnologias Nucleares (C2TN), a research unit of Instituto Superior Técnico, Universidade de Lisboa; and the German Cancer Research Center (DKFZ), namely on the Division of Biomedical Physics in Radiation Oncology group. This work will also benefit from the ongoing collaboration and joint ventures between the C2TN group and the Translational Oncobiology group from Instituto de Medicina Molecular João lobo Antunes (iMM-JLB), Faculdade de Medicina, ULisboa and their complementary expertise in the fields of oncobiology and radiobiology, considering the potential clinical applications of new therapeutic approaches for metastatic breast cancer. The research proposal will profit from the ongoing partnership with the Institute of Nuclear Sciences Applied to Health (ICNAS, U. Coimbra) as well.