RosettaDDGPrediction for high-throughput mutational scans: From stability to binding.

Reliable prediction of free energy changes upon amino acid substitutions (ΔΔGs) is crucial to investigate their impact on protein stability and protein-protein interaction. Advances in experimental mutational scans allow high-throughput studies thanks to multiplex techniques. On the other hand, genomics initiatives provide a large amount of data on disease-related variants that can benefit from analyses with structure-based methods. Therefore, the computational field should keep the same pace and provide new tools for fast and accurate high-throughput ΔΔG calculations. In this context, the Rosetta modeling suite implements effective approaches to predict folding/unfolding ΔΔGs in a protein monomer upon amino acid substitutions and calculate the changes in binding free energy in protein complexes. However, their application can be challenging to users without extensive experience with Rosetta. Furthermore, Rosetta protocols for ΔΔG prediction are designed considering one variant at a time, making the setup of high-throughput screenings cumbersome. For these reasons, we devised RosettaDDGPrediction, a customizable Python wrapper designed to run free energy calculations on a set of amino acid substitutions using Rosetta protocols with little intervention from the user. Moreover, RosettaDDGPrediction assists with checking completed runs and aggregates raw data for multiple variants, as well as generates publication-ready graphics. We showed the potential of the tool in four case studies, including variants of uncertain significance in childhood cancer, proteins with known experimental unfolding ΔΔGs values, interactions between target proteins and disordered motifs, and phosphomimetics. RosettaDDGPrediction is available, free of charge and under GNU General Public License v3.0, at https://github.com/ELELAB/RosettaDDGPrediction.

Impaired activation of plasmacytoid dendritic cells via toll-like receptor 7/9 and STING is mediated by melanoma-derived immunosuppressive cytokines and metabolic drift.

Introduction: Plasmacytoid dendritic cells (pDCs) infiltrate a large set of human cancers. Interferon alpha (IFN-α) produced by pDCs induces growth arrest and apoptosis in tumor cells and modulates innate and adaptive immune cells involved in anti-cancer immunity. Moreover, effector molecules exert tumor cell killing. However, the activation state and clinical relevance of pDCs infiltration in cancer is still largely controversial. In Primary Cutaneous Melanoma (PCM), pDCs density decreases over disease progression and collapses in metastatic melanoma (MM). Moreover, the residual circulating pDC compartment is defective in IFN-α production. Methods: The activation of tumor-associated pDCs was evaluated by in silico and microscopic analysis. The expression of human myxovirus resistant protein 1 (MxA), as surrogate of IFN-α production, and proximity ligation assay (PLA) to test dsDNA-cGAS activation were performed on human melanoma biopsies. Moreover, IFN-α and CXCL10 production by in vitro stimulated (i.e. with R848, CpG-A, ADU-S100) pDCs exposed to melanoma cell lines supernatants (SN-mel) was tested by intracellular flow cytometry and ELISA. We also performed a bulk RNA-sequencing on SN-mel-exposed pDCs, resting or stimulated with R848. Glycolytic rate assay was performed on SN-mel-exposed pDCs using the Seahorse XFe24 Extracellular Flux Analyzer. Results: Based on a set of microscopic, functional and in silico analyses, we demonstrated that the melanoma milieu directly impairs IFN-α and CXCL10 production by pDCs via TLR-7/9 and cGAS-STING signaling pathways. Melanoma-derived immunosuppressive cytokines and a metabolic drift represent relevant mechanisms enforcing pDC-mediated melanoma escape. Discussion: These findings propose a new window of intervention for novel immunotherapy approaches to amplify the antitumor innate immune response in cutaneous melanoma (CM).

Safety of Inhomogeneous Dose Distribution IMRT for High-Grade Glioma Reirradiation: A Prospective Phase I/II Trial (GLIORAD TRIAL).

Glioblastoma multiforme (GBM) is the most aggressive astrocytic primary brain tumor, and concurrent temozolomide (TMZ) and radiotherapy (RT) followed by maintenance of adjuvant TMZ is the current standard of care. Despite advances in imaging techniques and multi-modal treatment options, the median overall survival (OS) remains poor. As an alternative to surgery, re-irradiation (re-RT) can be a therapeutic option in recurrent GBM. Re-irradiation for brain tumors is increasingly used today, and several studies have demonstrated its feasibility. Besides differing techniques, the published data include a wide range of doses, emphasizing that no standard approach exists. The current study aimed to investigate the safety of moderate-high-voxel-based dose escalation in recurrent GBM. From 2016 to 2019, 12 patients met the inclusion criteria and were enrolled in this prospective single-center study. Retreatment consisted of re-irradiation with a total dose of 30 Gy (up to 50 Gy) over 5 days using the IMRT (arc VMAT) technique. A dose painting by numbers (DPBN)/dose escalation plan were performed, and a continuous relation between the voxel intensity of the functional image set and the risk of recurrence in that voxel were used to define target and dose distribution. Re-irradiation was well tolerated in all treated patients. No toxicities greater than G3 were recorded; only one patient had severe G3 acute toxicity, characterized by muscle weakness and fatigue. Median overall survival (OS2) and progression-free survival (PFS2) from the time of re-irradiation were 10.4 months and 5.7 months, respectively; 3-, 6-, and 12-month OS2 were 92%, 75%, and 42%, respectively; and 3-, 6-, and 12-month PFS2 were 83%, 42%, and 8%, respectively. Our work demonstrated a tolerable tolerance profile of this approach, and the future prospective phase II study will analyze the efficacy in terms of PFS and OS.

Artificial Intelligence Applied to Pancreatic Imaging: A Narrative Review.

The diagnosis, evaluation, and treatment planning of pancreatic pathologies usually require the combined use of different imaging modalities, mainly, computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET). Artificial intelligence (AI) has the potential to transform the clinical practice of medical imaging and has been applied to various radiological techniques for different purposes, such as segmentation, lesion detection, characterization, risk stratification, or prediction of response to treatments. The aim of the present narrative review is to assess the available literature on the role of AI applied to pancreatic imaging. Up to now, the use of computer-aided diagnosis (CAD) and radiomics in pancreatic imaging has proven to be useful for both non-oncological and oncological purposes and represents a promising tool for personalized approaches to patients. Although great developments have occurred in recent years, it is important to address the obstacles that still need to be overcome before these technologies can be implemented into our clinical routine, mainly considering the heterogeneity among studies.

How a very large sarcomatoid lung cancer was efficiently managed with lattice radiation therapy: a case report.

BACKGROUND: The management of large tumors represent a concerning issue in the palliative setting. Since a surgical approach is excluded and systemic therapy has reported limited efficacy, the patients are commonly referred for radiation therapy as last resort. However, to improve quality of life and to avoid excessive toxicity, low doses of palliative radiotherapy (RT) are delivered. In these cases, with limited and short response. Lattice radiation therapy (LRT) represents an innovative technique aiming to increase tumor response without enhancing adjacent organs at risk (OAR) toxicity, by administering inhomogeneous doses with ablative high dose areas inside the tumor and low doses near the OAR. CASE DESCRIPTION: A 69-year-old male patient was admitted to our hospital complaining of sacral pain and mild dyspnea. After a suspicious opacity on X-ray, the chest computed tomography (CT), the positron emission tomography/CT (PET/CT) and the endobronchial ultra sound-guided transbronchial needle aspiration confirmed the diagnosis of a bulky sarcomatoid lung cancer (stage IV: cT4N3M1c). After an effective antalgic RT on the sacral metastasis and three lines of systemic therapy without response, the patient started to have a disabling dyspnea. Thus, we administered LRT on the bulky lesion. The patients experienced no significant toxicity, with a marked lesion response on the 3 month-follow CT and a significant improvement in symptoms and in his daily life. CONCLUSIONS: This is the first LRT treatment done in our Center and it provides another evidence in the efficacy of LRT planning. It shows how LRT could represent an innovative technique to provide durable response in large tumors, without increasing treatment-related toxicity.

EPID-based 3D dosimetry for pre-treatment FFF VMAT stereotactic body radiotherapy plan verification using dosimetry CheckTM.

PURPOSE: The software Dosimetry Check (DC) reconstructs the 3D dose distribution on CT images data set by using EPID measured signal. This study aimed to evaluate DC for stereotactic body radiotherapy (SBRT) with unflattened photon beams (FFF) for dosimetric independent plan verification in pre-treatment modality. METHODS: DC v.4.1 was configured for Varian TrueBeam STx FFF beams equipped with EPID aS-1200. The DC FFF models were tested using arc open fields (from 1×1 cm2 to 15×15 cm2) and VMAT (Volumetric Modulated Arc Therapy) SBRT plans on phantom and patient CTs. DC dose distributions (DDC) were compared with that calculated by Eclipse with Acuros XB algorithm (DAXB) and one measured by Octavius 1000 SRS detector (DOCT). All differences were quantified in terms of the local 3D gamma passing rate (%GP), DVH and point dose differences. RESULTS: DC was configured for FFF VMAT using an appropriate correction procedure. %GP2%2mm (mean±standard deviation) of DOCT-DDC was 96.3±2.7% for open fields whereas it was 90.1±5.9% for plans on homogeneous phantom CT. However, average %GP3%3mm of DAXB-DDC was 95.0±4.1 for treatments on patient CT. The fraction of plans passing the %GP3%3mm DQA tolerance level [10% (50%) of maximum dose threshold] were 20/20 (14/20) and 18/20 (16/20) for OCT on phantom CT and DC on patient CT, respectively. CONCLUSIONS: DC characterization for FFF beams was performed. For stereotactic VMAT plan verifications DC showed good agreement with TPS whereas underlined discrepancies with Octavius in the high dose regions. A customized tolerance level is required for EPID-based VMAT FFF pre-treatment verification when DC system is applied.

Knowledge-based treatment planning: An inter-technique and inter-system feasibility study for prostate cancer.

PURPOSE: Helical Tomotherapy (HT) plans were used to create two RapidPlan knowledge-based (KB) models to generate plans with different techniques and to guide the optimization in a different treatment planning system for prostate plans. Feasibility and performance of these models were evaluated. MATERIAL AND METHODS: two sets of 35 low risk (LR) and 30 intermediate risk (IR) prostate cancer cases who underwent HT treatments were selected to train RapidPlan models. The KB predicted constraints were used to perform new 20KB plans using RapidArc technique (KB-RA) (inter-technique validation), and to optimise 20 new HT (KB-HT) plans in the Tomoplan (inter-system validation). For each validation modality, KB plans were benchmarked with the manual plans created by an expert planner (EP). RESULTS: RapidPlan was successfully configured using HT plans. The KB-RA plans fulfilled the clinical dose-volume requirements in 100% and 92% of cases for planning target volumes (PTVs) and organs at risk (OARs), respectively. For KB-HT plans these percentages were found to be a bit lower: 90% for PTVs and 86% for OARs. In comparison to EP plans, the KB-RA plans produced higher bladder doses for both LR and IR, and higher rectum doses for LR. KB-HT and EP plans produced similar results. CONCLUSION: RapidPlan can be trained to create models by using plans of a different treatment modality. These models were suitable for generating clinically acceptable plans for inter-technique and inter-system applications. The use of KB models based on plans of consolidated technique could be useful with a new treatment modality.