Funding Sources / Paganetti team

NIH grant support

  • P01   CA261669   (co-PI: Paganetti)
  • R01   CA187003   (PI: Schuemann)
  • R01   CA229178   (PI: Paganetti)
  • R01   CA248901   (PI: Paganetti)
  • R01   EB031102    (PI: Paganetti)
  • R01  CA266419    (PI: Schuemann)
  • R21   CA252562   (PI: Schuemann)
  • R21  CA 279068   (PI: Bertolet)
  • Contr.  2020A003480  (PI: Paganetti)
  • K99   CA267560   (PI: Bertolet)

Foundation support

  • Dubai Harvard Foundation (PI: Chamseddine)

Internal support

  • Federal Share Income grant

Details about each funded project are given below

National Institute of Health (NIH)

NCI P01 CA261669           Investigating Patient,Tumor,and Treatment Factors Underlying Sensitivity of Cancers and Normal Tissues to Proton vs. Photon Radiation

The overall goals are (a) understanding relative clinical, biological and immuno-suppressive determinants of response to proton vs. photon therapy; (b) developing personalized response predictive models based on such determinants; and (c) applying individualized (as opposed to population-based) approaches for the selection of the optimum radiation modality for each patient and to enhance the therapeutic potential with IMRT and IMPT dose distributions tailored to an individual patient’s baseline and tumor characteristics. These goals will be achieved in three synergistic projects. Project 1: Understanding Normal Tissue Toxicity to Identify Patients Most Likely to Benefit from Proton vs. Photon Therapy; Project 2: Radiation-Induced Lymphopenia: Understanding, Predictive Modeling and Developing Photon and Proton-Based Mitigation Strategies; and Project 3: Investigating Enhanced Sensitivity of Tumors to Proton Beam Therapy: Mechanisms and Biomarkers.  The projects are highly integrated in that treatment modality selection and dose distribution optimization to maximally enhance the therapeutic ratio must consider and balance normal tissue complications (Project 1), radiation-induced immuno-suppression (Project 2)  and tumor response based on genotypic factors (Project 3), which cannot be accomplished by any one project alone.

NCI R01 CA229178          Fast Individualized Delivery Adaptation in Proton Therapy

While adaptive therapy has been studied before, none of the previous studies deals with the unique challenges (range uncertainties) and unique capabilities (beamlet optimization and prompt gamma imaging) of proton therapy. This proposal will for the first time address these aspects by developing innovative hardware and software methodologies. We envision treatment planning and delivery to be fully adaptive in terms of intra- fractional changes in patient geometry. This proposal aims at predicting the dose distribution (or a surrogate thereof) in the patient immediately prior to treatment delivery and correct for any discrepancies between the measured and intended dose in less than 2 minutes. This will enable us to deliver (proton) radiation therapy in an adaptive setting and much reduced target volume margins (2mm isotropic plus 2mm range margin in proton therapy in the beam direction) daily while the patient is positioned on the treatment table. We propose to achieve this goal by simultaneously developing fast hardware and software tools that take advantage of in-room prompt gamma and cone-beam CT imaging in combination with fast dose calculation. We will combine this technology with a novel framework on beamlet adaptation. While some of our methods will improve photon therapy as well, we will focus on proton therapy because it offers unique opportunities to dose verification in vivo as well as unique challenges due to range uncertainties. Our methodology will be made available to the entire proton therapy community.

NCI R01 CA248901          Developing whole-body computational phantoms for blood dosimetry to model the impact of radiation on the immune system
This proposal will develop methods to quantify the interaction of radiation with the immune system, particularly the blood (i.e., circulating lymphocytes). We envision that future treatment planning in radiation oncology will treat lymphatic nodes and the blood as organs at risk and include them in the treatment optimization process so as to influence the level at which the radiation treatment impacts the immune system of the patient. During radiation therapy, depletion of circulating lymphocytes originates mainly from (1) immediate cell killing during irradiation of blood vessels, and thus circulating lymphocytes, within the treatment field and (2) to a lesser extent, the radiation dose to lymphocytes residing within lymphoid organs that can mobilize their lymphocyte population upon systemic depletion. There are currently no whole-body computational phantoms available that can facilitate the calculation of blood or lymphocyte dose-volume histograms. However, this tool is a prerequisite to the use of bio-mathematical models for clinical trial design. The phantoms to be developed in this study will be the first to fill this urgent need for the radiation therapy and research communities. In addition to the overall innovative nature of this project, several of our methods are novel and have never been employed in our field: • The first use of tetrahedral mesh structures to model blood vessels (SA1) • The first implementation of a whole-body compartment model for blood flow (SA2) • The first four-dimensional modeling of blood flow using vasculature structures (SA3) • The first model of the mouse vasculature for pre-clinical studies (SA4)

NIBIB R01 EB031102        Constrained Disentanglement (CODE) Network for CT Metal Artifact Reduction in Radiation Therapy

A majority of cancer patients receive radiation therapy as a critical part of their treatment, and many of these patients have metal objects leading to metal-induced CT artifacts. Metal artifacts compromise or preclude radiation therapy in an estimated 15% of all radiation therapy patients. Our overall goal is to eliminate CT metal artifacts in general and improve RT in particular using an integrated deep learning solution.

NCI R01 CA226419         Using experimentally-guided multi-scale modeling to determining the mechanism of FLASH tissue sparing
The underlying mechanism of FLASH induced sparing of healthy tissue is still unknown. As corollary, the constraints imposed on the clinical parameters (e.g. dose, dose rate and time within and between treatment fields) to induce the FLASH tissue sparing effect are still not determined.We propose an interplay between experiments and modeling to determine the underlying mechanism of FLASH-RT tissue sparing by employing TOPAS-nBio to determine the involved chemical reactions based on their intrinsic time features. We propose to test the hypothesis and validate the model with the following aims: SA 1: Investigate the mechanisms of proton FLASH-RT 1. Conduct multi-scale experiments to guide the modeling: 2. Model the mechanism and chemical processes at relevant time scales in TOPAS-nBio: SA 2: Validate the model and determine clinical parameters for FLASH tissue sparing.
NCI R01 CA187003          TOPAS – nBio, a Monte Carlo tool for radiation biology research
The goal of this proposal is to continue our successful development of TOPAS-nBio, a Monte Carlo simulation toolkit specifically designed to connect research disciplines. TOPAS-nBio simulates the initial energy deposition events (physics), then follows the diffusion and reaction of chemical species (chemistry) to infer biological observables at the cell and organelle scale (biology). The developed application already lays the foundation to investigate biological effects of radiation in cell organelles using a mechanistic systems biology modeling approach. However, with constant advances in our understanding of cellular repair processes, the ques- tions asked by the radiation biology community are increasing in complexity. These advances, including more detailed information of various cells lines/types, deficiencies in DNA repair pathways and potential contributions of non-nuclear cell components, need to be considered to correctly describe cell response to radiation damages. Accordingly, in this renewal application, we focus on improving the accuracy of the simulations by including more representative chem- ical reactions and transitioning towards a predictive model that can be applied to specific cell types. To further extend the reach of TOPAS-nBio, we will include changes in the microenvi- ronment across tumor volumes and move towards mechanistic modeling of radiation effects in vivo. Thus, the new developments of TOPAS-nBio will offer predictions of biological outcome from the initial radiation track structure for various cell types for in vitro and in vivo experiments, and thereby drive hypothesis generation at the forefront of bio-physical research. TOPAS-nBio provides an ideal framework to include and test new effect models, cell lines or microenviron- mental conditions. Overall, TOPAS-nBio will continue the mission to advance our under- standing of the fundamental response of tissue to radiation.

NCI R21 CA252562           Understanding the importance of dose-rate variations for patient treatments in FLASH and conventional radiation therapy
Radiation therapy has been an important component in the treatment of cancer for many decades. While the parameters of tumor control have been relatively well understood, normal tissue complication probabilities (NTCPs) are often only thought of as constraining factors in the design of a treatment regimen. However, with continuously improving treatments of primary lesions, NTCP and their impact on the quality of life have moved into the focus of research. One of the most prominent methods reported to spare healthy tissue is FLASH radiation therapy, i.e. ultrafast (>40 Gy/s) irradiations, which reportedly results in incredible tissue sparing effects without compromising efficacy for tumor cure. Conversely, very low dose-rate irradiations have also been reported to offer protective features. Together, these experiments suggest that the current clinical routine, treating patients with a medium dose-rate of ~2 Gy/min, could in fact be the least favorable when considering healthy tissue side effects. In clinical practice, patients are treated with a variety of radiation treatment modalities. While the resulting target doses are similar across modalities, the dose delivered to the normal tissue and the time structure of the delivery can vary significantly. Despite the range of dose-rates across treatment plans, the biological effect is estimated only based on the total dose received. We hypothesize that dose-rate plays an important role in the outcome of radiation therapy that can be exploited for specific treatment scenarios. For extreme cases such as FLASH therapy, averaged dose-rates do not capture the relevant time structures adequately. We will apply Monte Carlo simulations to determine time structures from the clinical scale of minutes (spot scanning / gantry rotation time) to nanoseconds (intra-spot delivery time) and assess potential effects on healthy tissue sparing that would improve the quality of life for radiation therapy patients.

NCI R21 CA279068           GPU-based SPECT Reconstruction Using Reverse Monte Carlo Simulations
Interest in applications of radiopharmaceutical conjugates has notably increased in the last few years for the treatment of a variety of cancers. These conjugates are composed of chelators to target cancer cells and radionuclides to employ the cytotoxicity of ionizing radiation. Radiation dosimetry is required to determine the dosages, efficacy, and safety of these treatments, and 3D quantitative imaging of the biodistribution of activity represents the best tool to perform dosimetry. For most radionuclides employed (non-positron-emitters), SPECT imaging is needed for patient-specific dosimetry. However, multiple physical factors affect SPECT image quality, such as attenuation, scattering, or the response collimator-detector system in SPECT scans. To account for them, Monte Carlo techniques can be used due to their remarkable accuracy in representing physical processes relevant to the transport of ionizing radiation. In particular, 3D SPECT reconstruction from the acquired bidimensional projections may be obtained by transporting backward the photons detected in the gamma camera projections, although many photons to be reversely transported require specially optimized architecture and simulations. This project will develop a new reverse Monte Carlo software for SPECT reconstruction, built from scratch in the GPU to speed up simulations. First, only the relevant reverse physical processes will be selected and modeled using inverse processes of the well-characterized TOPAS Monte Carlo code for radiation transport. Then, specific properties of collimator-detector systems will be integrated into our code to determine the angular distributions for the photons detected. Finally, these developments will be integrated into a GPU-based platform and shared with the Informatics Technology for Cancer Research program at NCI for further results of specific commercial SPECT scans from the research community.

NCI K99 CA267560                     Radiation dosimetry for alpha-particle radiopharmaceutical therapy and application to pediatric neuroblastoma
Radiopharmaceutical treatments with α-particles represent a promising approach to treat some tumors and metastases. This modality leverages the short range of α-particles, up to tens of microns, to deliver radiation only to cancer cells while sparing the surrounding healthy tissue. To do so, an α-emitting radionuclide is bounded to an affinitive ligand which is used to target biomolecules expressed in tumoral cells. Currently, here are several clinical applications either approved, such as 223Ra for the treatment of bony metastases, or under investigation. Particularly, α-RPT could be used for the treatment of high-risk pediatric neuroblastoma, whose prognosis keeps poor. As the rationale behind radiopharmaceutical treatments is to exploit the differential amount of radiation imparted to tumors and healthy tissue, a rigorous determination of radiation dosimetry and effects is requested to develop this technique to their full extent. Starting with the study of α-particles in general, this research will be oriented to the treatment of pediatric neuroblastoma using the radiopharmaceutical [211At]MM4, which targets the overexpression of PARP-1 proteins in these tumors.  First, microdosimetric calculations will be connected with actual damage to the DNA using the Monte Carlo toolkit TOPAS and its extension for subcellular structures, TOPAS-nBio. Second, initial damage to neuroblastoma cell lines will be studied using the affinity of [211At]MM4 for PARP-1 in these cell lines to create realistic sub-cellular models of α-particle irradiation. Permanent damage after the occurrence of repair mechanisms will be also modelled assessed through experimental data published by Dr. Makvandi’s group from the University of Pennsylvania. Finally, biodistribution of radiopharmaceutical across organs and blood in animal models and phantoms will be assessed and used to predict treatment outcomes. The principal investigator will use the experience and expertise of his mentoring team (Dr. Harald Paganetti and Dr. Jan Schuemann) to learn the skills and abilities necessary to accomplish the proposed research. He will also attend seminars, coursework and conferences on radiobiology, Monte Carlo simulations and grant writing and leadership skills, which will ensure a strong foundation for running an independent laboratory after this project.

NCI contract 2020A003480          Monte Carlo for Pediatric Proton Epidemiology

Epidemiological studies on the long-term effect of radiation therapy (such as the development of a second radiation-induced cancer) require reconstruction of the primary and secondary (scattered) dose delivered to the patient at the time of treatment.The long-term goal of this project is to 
a) develop a generic framework to accomplish this
b) to apply the framework to pediatric proton therapy patients at the MGH

Dubai Harvard Foundation for Medical Research

Artificial Intelligence‐Enabled Treatment Personalization in Radiotherapy
The goal of this project is to explore machine learning methods to understand outcome in radiation therapy in order to allow treatment stratification.