Baseline DSB repair prediction of chronic rare Grade ≥ 3 toxicities induced by radiotherapy using classification algorithms.

Small fractions of patients suffer from radiotherapy late severe adverse events (AEs Grade ≥ 3), which are usually irreversible and badly affect their quality of life. A novel functional DNA repair assay characterizing several steps of double-strand break (DSB) repair mechanisms was used. DNA repair activities of peripheral blood mononuclear cells were monitored for 1 week using NEXT-SPOT assay in 177 breast and prostate cancer patients. Only seven patients had Grade ≥ 3 AEs, 6 months after radiotherapy initiation. The machine learning method established the importance of variables among demographic, clinical and DNA repair data. The most relevant ones, all related to DNA repair, were employed to build a predictor. Predictors constructed with random forest and minimum bounding sphere predicted late Grade ≥ 3 AEs with a sensitivity of 100% and specificity of 77.17 and 86.22%, respectively. This multiplex functional approach strongly supports a dominant role for DSB repair in the development of chronic AEs. It also showed that affected patients share specific features related to functional aspects of DSB repair. This strategy may be suitable for routine clinical analysis and paves the way for modelling DSB repair associated with severe AEs induced by radiotherapy.

Custom scoring based on ecological topology of gut microbiota associated with cancer immunotherapy outcome.

The gut microbiota influences the clinical responses of cancer patients to immunecheckpoint inhibitors (ICIs). However, there is no consensus definition of detrimental dysbiosis. Based on metagenomics (MG) sequencing of 245 non-small cell lung cancer (NSCLC) patient feces, we constructed species-level co-abundance networks that were clustered into species-interacting groups (SIGs) correlating with overall survival. Thirty-seven and forty-five MG species (MGSs) were associated with resistance (SIG1) and response (SIG2) to ICIs, respectively. When combined with the quantification of Akkermansia species, this procedure allowed a person-based calculation of a topological score (TOPOSCORE) that was validated in an additional 254 NSCLC patients and in 216 genitourinary cancer patients. Finally, this TOPOSCORE was translated into a 21-bacterial probe set-based qPCR scoring that was validated in a prospective cohort of NSCLC patients as well as in colorectal and melanoma patients. This approach could represent a dynamic diagnosis tool for intestinal dysbiosis to guide personalized microbiota-centered interventions.

Theragnostic Gadolinium-Based Nanoparticles Safely Augment X-ray Radiation Effects in Patients with Cervical Cancer.

Activated guided irradiation by X-ray (AGuIX) nanoparticles are gadolinium-based agents that have the dual benefit of mimicking the effects of a magnetic resonance imaging (MRI) contrast agent used in a clinical routine and enhancing the radiotherapeutic activity of conventional X-rays (for cancer treatment). This "theragnostic" action is explained on the one hand by the paramagnetic properties of gadolinium and on the other hand by the generation of high densities of secondary radiation following the interaction of ionizing radiation and high-Z atoms, which leads to enhanced radiation dose deposits within the tumors where the nanoparticles accumulate. Here, we report the results of a phase I trial that aimed to assess the safety and determine the optimal dose of AGuIX nanoparticles in combination with chemoradiation and brachytherapy in patients with locally advanced cervical cancer. AGuIX nanoparticles were administered intravenously and appropriately accumulated within tumors on a dose-dependent manner, as assessed by T1-weighted MRI, with a rapid urinary clearance of uncaught nanoparticles. We show that the observed tumor accumulation of the compounds can support precise delineation of functional target volumes at the time of brachytherapy based on gadolinium enhancement. AGuIX nanoparticles combined with chemoradiation appeared well tolerated among the 12 patients treated, with no dose-limiting toxicity observed. Treatment yielded excellent local control, with all patients achieving complete remission of the primary tumor. One patient had a distant tumor recurrence. These results demonstrate the clinical feasibility of using theranostic nanoparticles to augment the accuracy of MRI-based treatments while focally enhancing the radiation activity in tumors.

Automatic gross tumor volume segmentation with failure detection for safe implementation in locally advanced cervical cancer.

Background and Purpose: Automatic segmentation methods have greatly changed the RadioTherapy (RT) workflow, but still need to be extended to target volumes. In this paper, Deep Learning (DL) models were compared for Gross Tumor Volume (GTV) segmentation in locally advanced cervical cancer, and a novel investigation into failure detection was introduced by utilizing radiomic features. Methods and materials: We trained eight DL models (UNet, VNet, SegResNet, SegResNetVAE) for 2D and 3D segmentation. Ensembling individually trained models during cross-validation generated the final segmentation. To detect failures, binary classifiers were trained using radiomic features extracted from segmented GTVs as inputs, aiming to classify contours based on whether their Dice Similarity Coefficient ( DSC ) < T and DSC ⩾ T . Two distinct cohorts of T2-Weighted (T2W) pre-RT MR images captured in 2D sequences were used: one retrospective cohort consisting of 115 LACC patients from 30 scanners, and the other prospective cohort, comprising 51 patients from 7 scanners, used for testing. Results: Segmentation by 2D-SegResNet achieved the best DSC, Surface DSC ( SDSC 3 mm ), and 95th Hausdorff Distance (95HD): DSC = 0.72 ± 0.16, SDSC 3 mm =0.66 ± 0.17, and 95HD = 14.6 ± 9.0 mm without missing segmentation ( M =0) on the test cohort. Failure detection could generate precision ( P = 0.88 ), recall ( R = 0.75 ), F1-score ( F = 0.81 ), and accuracy ( A = 0.86 ) using Logistic Regression (LR) classifier on the test cohort with a threshold T = 0.67 on DSC values. Conclusions: Our study revealed that segmentation accuracy varies slightly among different DL methods, with 2D networks outperforming 3D networks in 2D MRI sequences. Doctors found the time-saving aspect advantageous. The proposed failure detection could guide doctors in sensitive cases.

Nonpersistent Nanoarchitectures Enhance Concurrent Chemoradiotherapy in an Immunocompetent Orthotopic Model of HPV+ Head/Neck Carcinoma.

Cisplatin chemoradiotherapy (CRT) is the established standard of care for managing locally advanced human papillomavirus-positive head/neck carcinoma. The typically young patients may suffer serious and long-time side effects caused by the treatment, such as dysphagia, and hearing loss. Thus, ensuring a satisfactory post-treatment quality of life is paramount. One potential replacing approach to the classical CRT involves the combination of standard-dose radiotherapy and radiosensitizers such as noble metal nanoparticles (NPs). However, several concerns about size, shape, and biocompatibility limit the translation of metal nanomaterials to the clinical practice. Here, it is demonstrated that a new model of nonpersistent gold nanoarchitectures containing cisplatin (NAs-Cluster-CisPt) generates, in combination with radiotherapy, a significant in vivo tumor-reducing effect compared to the standard CRT, achieving a complete tumor clearance in 25% of the immunocompetent models that persist for 60 days. These findings, together with the negligible amount of metals recognized in the excretory organs, highlight that the concurrent administration of NAs-Cluster-CisPt and radiotherapy has the potential to overcome some clinical limitations associated to NP-based approaches while enhancing the treatment outcome with respect to standard CRT. Overall, despite further mechanistic investigations being essential, these data support the exploiting of nonpersistent metal-nanomaterial-mediated approaches for oral cancer management.

Meta-Analysis of Rice Phosphoproteomics Data to Understand Variation in Cell Signaling Across the Rice Pan-Genome.

Phosphorylation is the most studied post-translational modification, and has multiple biological functions. In this study, we have reanalyzed publicly available mass spectrometry proteomics data sets enriched for phosphopeptides from Asian rice (Oryza sativa). In total we identified 15,565 phosphosites on serine, threonine, and tyrosine residues on rice proteins. We identified sequence motifs for phosphosites, and link motifs to enrichment of different biological processes, indicating different downstream regulation likely caused by different kinase groups. We cross-referenced phosphosites against the rice 3,000 genomes, to identify single amino acid variations (SAAVs) within or proximal to phosphosites that could cause loss of a site in a given rice variety and clustered the data to identify groups of sites with similar patterns across rice family groups. The data has been loaded into UniProt Knowledge-Base─enabling researchers to visualize sites alongside other data on rice proteins, e.g., structural models from AlphaFold2, PeptideAtlas, and the PRIDE database─enabling visualization of source evidence, including scores and supporting mass spectra.

LymphoDose: a lymphocyte dose estimation framework-application to brain radiotherapy.

Objective. Severe radiation-induced lymphopenia occurs in 40% of patients treated for primary brain tumors and is an independent risk factor of poor survival outcomes. We developed anin-silicoframework that estimates the radiation doses received by lymphocytes during volumetric modulated arc therapy brain irradiation.Approach. We implemented a simulation consisting of two interconnected compartmental models describing the slow recirculation of lymphocytes between lymphoid organs (M1) and the bloodstream (M2). We used dosimetry data from 33 patients treated with chemo-radiation for glioblastoma to compare three cases of the model, corresponding to different physical and biological scenarios: (H1) lymphocytes circulation only in the bloodstream i.e. circulation inM2only; (H2) lymphocytes recirculation between lymphoid organs i.e. circulation inM1andM2interconnected; (H3) lymphocytes recirculation between lymphoid organs and deep-learning computed out-of-field (OOF) dose to head and neck (H&N) lymphoid structures. A sensitivity analysis of the model's parameters was also performed.Main results. For H1, H2 and H3 cases respectively, the irradiated fraction of lymphocytes was 99.8 ± 0.7%, 40.4 ± 10.2% et 97.6 ± 2.5%, and the average dose to irradiated pool was 309.9 ± 74.7 mGy, 52.6 ± 21.1 mGy and 265.6 ± 48.5 mGy. The recirculation process considered in the H2 case implied that irradiated lymphocytes were irradiated in the field only 1.58 ± 0.91 times on average after treatment. The OOF irradiation of H&N lymphoid structures considered in H3 was an important contribution to lymphocytes dose. In all cases, the estimated doses are low compared with lymphocytes radiosensitivity, and other mechanisms could explain high prevalence of RIL in patients with brain tumors.Significance. Our framework is the first to take into account OOF doses and recirculation in lymphocyte dose assessment during brain irradiation. Our results demonstrate the need to clarify the indirect effects of irradiation on lymphopenia, in order to potentiate the combination of radio-immunotherapy or the abscopal effect.

Enhancing radioprotection: A chitosan-based chelating polymer is a versatile radioprotective agent for prophylactic and therapeutic interventions against radionuclide contamination.

To mitigate the risk of radioactive isotope dissemination, the development of preventative and curative measures is of particular interest. For mass treatment, the developed solution must be easily administered, preferably orally, with effective, nontoxic decorporating properties against a wide range of radioactive isotopes. Currently, most orally administered chelation therapy products are quickly absorbed into the blood circulation, where chelation of the radioactive isotope is a race against time due to the short circulation half-life of the therapeutic. This report presents an alternative therapeutic approach by using a functionalized chitosan (chitosan@DOTAGA) with chelating properties that remains within the gastrointestinal tract and is eliminated in feces, that can protect against ingested radioactive isotopes. The polymer shows important in vitro chelation properties towards different metallic cations of importance, including (Cs(I), Ir(III), Th(IV), Tl(I), Sr(II), U(VI) and Co(II)), at different pH (from 1 to 7) representing the different environments in the gastrointestinal tract. An in vivo proof of concept is presented on a rodent model of uranium contamination following an oral administration of Chitosan@DOTAGA. The polymer partially prevents the accumulation of uranium within the kidneys (providing a protective effect) and completely prevents its uptake by the spleen.

Deep-Learning for Rapid Estimation of the Out-of-Field Dose in External Beam Photon Radiation Therapy - A Proof of Concept.

PURPOSE: The dose deposited outside of the treatment field during external photon beam radiation therapy treatment, also known as out-of-field dose, is the subject of extensive study as it may be associated with a higher risk of developing a second cancer and could have deleterious effects on the immune system that compromise the efficiency of combined radio-immunotherapy treatments. Out-of-field dose estimation tools developed today in research, including Monte Carlo simulations and analytical methods, are not suited to the requirements of clinical implementation because of their lack of versatility and their cumbersome application. We propose a proof of concept based on deep learning for out-of-field dose map estimation that addresses these limitations. METHODS AND MATERIALS: For this purpose, a 3D U-Net, considering as inputs the in-field dose, as computed by the treatment planning system, and the patient's anatomy, was trained to predict out-of-field dose maps. The cohort used for learning and performance evaluation included 3151 pediatric patients from the FCCSS database, treated in 5 clinical centers, whose whole-body dose maps were previously estimated with an empirical analytical method. The test set, composed of 433 patients, was split into 5 subdata sets, each containing patients treated with devices unseen during the training phase. Root mean square deviation evaluated only on nonzero voxels located in the out-of-field areas was computed as performance metric. RESULTS: Root mean square deviations of 0.28 and 0.41 cGy/Gy were obtained for the training and validation data sets, respectively. Values of 0.27, 0.26, 0.28, 0.30, and 0.45 cGy/Gy were achieved for the 6 MV linear accelerator, 16 MV linear accelerator, Alcyon cobalt irradiator, Mobiletron cobalt irradiator, and betatron device test sets, respectively. CONCLUSIONS: This proof-of-concept approach using a convolutional neural network has demonstrated unprecedented generalizability for this task, although it remains limited, and brings us closer to an implementation compatible with clinical routine.

Eradicating gross tumor disease: a prerequisite for efficient radioimmunotherapy?

Radiation therapy may induce off-target antitumor "abscopal" immunostimulatory and immunosuppressive effects. Several preclinical and early clinical studies revealed promising results when combining radiation therapy with immunostimulatory agents. Most radioimmunotherapy randomized trials showed disappointing results in patients with advanced tumors. In contrast, outcomes were encouraging when immunotherapy was delivered on top of gross disease elimination with curative-intent radiation therapy. In this review, we highlight available results from randomized trials and discuss the potential impact of overall tumor burden on the observed efficacy of radioimmunotherapy.

SpectiCal: m/z Calibration of MS2 Peptide Spectra Using Known Low Mass Ions.

Most tandem mass spectrometry fragmentation spectra have small calibration errors that can lead to suboptimal interpretation and annotation. We developed SpectiCal, a software tool that can read mzML files from data-dependent acquisition proteomics experiments in parallel, compute m/z calibrations for each file prior to identification analysis based on known low-mass ions, and produce information about frequently observed peaks and their explanations. Using calibration coefficients, the data can be corrected to generate new calibrated mzML files. SpectiCal was tested using five public data sets, creating a table of commonly observed low-mass ions and their identifications. Information about the calibration and individual peaks is written in PDF and TSV files. This includes information for each peak, such as the number of runs in which it appears, the percentage of spectra in which it appears, and a plot of the aggregated region surrounding each peak. SpectiCal can be used to compute MS run calibrations, examine MS runs for artifacts that might hinder downstream analysis, and generate tables of detected low-mass ions for further analysis. SpectiCal is freely available at https://github.com/PlantProteomes/SpectiCal.

An international phase II trial and immune profiling of SBRT and atezolizumab in advanced pretreated colorectal cancer.

BACKGROUND: Immuno-radiotherapy may improve outcomes for patients with advanced solid tumors, although optimized combination modalities remain unclear. Here, we report the colorectal (CRC) cohort analysis from the SABR-PDL1 trial that evaluated the PD-L1 inhibitor atezolizumab in combination with stereotactic body radiation therapy (SBRT) in advanced cancer patients. METHODS: Eligible patients received atezolizumab 1200 mg every 3 weeks until progression or unmanageable toxicity, together with ablative SBRT delivered concurrently with the 2nd cycle (recommended dose of 45 Gy in 3 fractions, adapted upon normal tissue tolerance constraint). SBRT was delivered to at least one tumor site, with at least one additional measurable lesion being kept from the radiation field. The primary efficacy endpoint was one-year progression-free survival (PFS) rate from the start of atezolizumab. Sequential tumor biopsies were collected for deep multi-feature immune profiling. RESULTS: Sixty pretreated (median of 2 prior lines) advanced CRC patients (38 men [63%]; median age, 59 years [range, 20-81 years]; 77% with liver metastases) were enrolled in five centers (France: n = 4, Spain: n = 1) from 11/2016 to 04/2019. All but one (98%) received atezolizumab and 54/60 (90%) received SBRT. The most frequently irradiated site was lung (n = 30/54; 56.3%). Treatment-related G3 (no G4-5) toxicity was observed in 3 (5%) patients. Median OS and PFS were respectively 8.4 [95%CI:5.9-11.6] and 1.4 months [95%CI:1.2-2.6], including five (9%) patients with PFS > 1 year (median time to progression: 19.2 months, including 2/5 MMR-proficient). Best overall responses consisted of stable disease (n = 38; 64%), partial (n = 3; 5%) and complete response (n = 1; 2%). Immune-centric multiplex IHC and RNAseq showed that SBRT redirected immune cells towards tumor lesions, even in the case of radio-induced lymphopenia. Baseline tumor PD-L1 and IRF1 nuclear expression (both in CD3 + T cells and in CD68 + cells) were higher in responding patients. Upregulation of genes that encode for proteins known to increase T and B cell trafficking to tumors (CCL19, CXCL9), migration (MACF1) and tumor cell killing (GZMB) correlated with responses. CONCLUSIONS: This study provides new data on the feasibility, efficacy, and immune context of tumors that may help identifying advanced CRC patients most likely to respond to immuno-radiotherapy. TRIAL REGISTRATION: EudraCT N°: 2015-005464-42; Clinicaltrial.gov number: NCT02992912.

Translational Frontiers and Clinical Opportunities of Immunologically Fitted Radiotherapy.

Ionizing radiation can have a wide range of impacts on tumor-immune interactions, which are being studied with the greatest interest and at an accelerating pace by the medical community. Despite its undeniable immunostimulatory potential, it clearly appears that radiotherapy as it is prescribed and delivered nowadays often alters the host's immunity toward a suboptimal state. This may impair the full recovery of a sustained and efficient antitumor immunosurveillance posttreatment. An emerging concept is arising from this awareness and consists of reconsidering the way of designing radiation treatment planning, notably by taking into account the individualized risks of deleterious radio-induced immune alteration that can be deciphered from the planned beam trajectory through lymphocyte-rich organs. In this review, we critically appraise key aspects to consider while planning immunologically fitted radiotherapy, including the challenges linked to the identification of new dose constraints to immune-rich structures. We also discuss how pharmacologic immunomodulation could be advantageously used in combination with radiotherapy to compensate for the radio-induced loss, for example, with (i) agonists of interleukin (IL)2, IL4, IL7, IL9, IL15, or IL21, similarly to G-CSF being used for the prophylaxis of severe chemo-induced neutropenia, or with (ii) myeloid-derived suppressive cell blockers.

The 2023 Report on the Proteome from the HUPO Human Proteome Project.

Since 2010, the Human Proteome Project (HPP), the flagship initiative of the Human Proteome Organization (HUPO), has pursued two goals: (1) to credibly identify the protein parts list and (2) to make proteomics an integral part of multiomics studies of human health and disease. The HPP relies on international collaboration, data sharing, standardized reanalysis of MS data sets by PeptideAtlas and MassIVE-KB using HPP Guidelines for quality assurance, integration and curation of MS and non-MS protein data by neXtProt, plus extensive use of antibody profiling carried out by the Human Protein Atlas. According to the neXtProt release 2023-04-18, protein expression has now been credibly detected (PE1) for 18,397 of the 19,778 neXtProt predicted proteins coded in the human genome (93%). Of these PE1 proteins, 17,453 were detected with mass spectrometry (MS) in accordance with HPP Guidelines and 944 by a variety of non-MS methods. The number of neXtProt PE2, PE3, and PE4 missing proteins now stands at 1381. Achieving the unambiguous identification of 93% of predicted proteins encoded from across all chromosomes represents remarkable experimental progress on the Human Proteome parts list. Meanwhile, there are several categories of predicted proteins that have proved resistant to detection regardless of protein-based methods used. Additionally there are some PE1-4 proteins that probably should be reclassified to PE5, specifically 21 LINC entries and ∼30 HERV entries; these are being addressed in the present year. Applying proteomics in a wide array of biological and clinical studies ensures integration with other omics platforms as reported by the Biology and Disease-driven HPP teams and the antibody and pathology resource pillars. Current progress has positioned the HPP to transition to its Grand Challenge Project focused on determining the primary function(s) of every protein itself and in networks and pathways within the context of human health and disease.

Proteomic changes induced by longevity-promoting interventions in mice.

Using mouse models and high-throughput proteomics, we conducted an in-depth analysis of the proteome changes induced in response to seven interventions known to increase mouse lifespan. This included two genetic mutations, a growth hormone receptor knockout (GHRKO mice) and a mutation in the Pit-1 locus (Snell dwarf mice), four drug treatments (rapamycin, acarbose, canagliflozin, and 17α-estradiol), and caloric restriction. Each of the interventions studied induced variable changes in the concentrations of proteins across liver, kidney, and gastrocnemius muscle tissue samples, with the strongest responses in the liver and limited concordance in protein responses across tissues. To the extent that these interventions promote longevity through common biological mechanisms, we anticipated that proteins associated with longevity could be identified by characterizing shared responses across all or multiple interventions. Many of the proteome alterations induced by each intervention were distinct, potentially implicating a variety of biological pathways as being related to lifespan extension. While we found no protein that was affected similarly by every intervention, we identified a set of proteins that responded to multiple interventions. These proteins were functionally diverse but tended to be involved in peroxisomal oxidation and metabolism of fatty acids. These results provide candidate proteins and biological mechanisms related to enhancing longevity that can inform research on therapeutic approaches to promote healthy aging.

Detection and editing of the updated Arabidopsis plastid- and mitochondrial-encoded proteomes through PeptideAtlas.

Arabidopsis (Arabidopsis thaliana) ecotype Col-0 has plastid and mitochondrial genomes encoding over 100 proteins. Public databases (e.g. Araport11) have redundancy and discrepancies in gene identifiers for these organelle-encoded proteins. RNA editing results in changes to specific amino acid residues or creation of start and stop codons for many of these proteins, but the impact of RNA editing at the protein level is largely unexplored due to the complexities of detection. Here, we assembled the nonredundant set of identifiers, their correct protein sequences, and 452 predicted nonsynonymous editing sites of which 56 are edited at lower frequency. We then determined accumulation of edited and/or unedited proteoforms by searching ∼259 million raw tandem MS spectra from ProteomeXchange, which is part of PeptideAtlas (www.peptideatlas.org/builds/arabidopsis/). We identified all mitochondrial proteins and all except 3 plastid-encoded proteins (NdhG/Ndh6, PsbM, and Rps16), but no proteins predicted from the 4 ORFs were identified. We suggest that Rps16 and 3 of the ORFs are pseudogenes. Detection frequencies for each edit site and type of edit (e.g. S to L/F) were determined at the protein level, cross-referenced against the metadata (e.g. tissue), and evaluated for technical detection challenges. We detected 167 predicted edit sites at the proteome level. Minor frequency sites were edited at low frequency at the protein level except for cytochrome C biogenesis 382 at residue 124 (Ccb382-124). Major frequency sites (>50% editing of RNA) only accumulated in edited form (>98% to 100% edited) at the protein level, with the exception of Rpl5-22. We conclude that RNA editing for major editing sites is required for stable protein accumulation.

Detection of the Arabidopsis Proteome and Its Post-translational Modifications and the Nature of the Unobserved (Dark) Proteome in PeptideAtlas.

This study describes a new release of the Arabidopsis thaliana PeptideAtlas proteomics resource (build 2023-10) providing protein sequence coverage, matched mass spectrometry (MS) spectra, selected post-translational modifications (PTMs), and metadata. 70 million MS/MS spectra were matched to the Araport11 annotation, identifying ∼0.6 million unique peptides and 18,267 proteins at the highest confidence level and 3396 lower confidence proteins, together representing 78.6% of the predicted proteome. Additional identified proteins not predicted in Araport11 should be considered for the next Arabidopsis genome annotation. This release identified 5198 phosphorylated proteins, 668 ubiquitinated proteins, 3050 N-terminally acetylated proteins, and 864 lysine-acetylated proteins and mapped their PTM sites. MS support was lacking for 21.4% (5896 proteins) of the predicted Araport11 proteome: the "dark" proteome. This dark proteome is highly enriched for E3 ligases, transcription factors, and for certain (e.g., CLE, IDA, PSY) but not other (e.g., THIONIN, CAP) signaling peptides families. A machine learning model trained on RNA expression data and protein properties predicts the probability that proteins will be detected. The model aids in discovery of proteins with short half-life (e.g., SIG1,3 and ERF-VII TFs) and for developing strategies to identify the missing proteins. PeptideAtlas is linked to TAIR, tracks in JBrowse, and several other community proteomics resources.

Integrating [18F]-Fluorodeoxyglucose Positron Emission Tomography with Computed Tomography with Radiation Therapy and Immunomodulation in Precision Therapy for Solid Tumors.

[18F]-FDG positron emission tomography with computed tomography (PET/CT) imaging is widely used to enhance the quality of care in patients diagnosed with cancer. Furthermore, it holds the potential to offer insight into the synergic effect of combining radiation therapy (RT) with immuno-oncological (IO) agents. This is achieved by evaluating treatment responses both at the RT and distant tumor sites, thereby encompassing the phenomenon known as the abscopal effect. In this context, PET/CT can play an important role in establishing timelines for RT/IO administration and monitoring responses, including novel patterns such as hyperprogression, oligoprogression, and pseudoprogression, as well as immune-related adverse events. In this commentary, we explore the incremental value of PET/CT to enhance the combination of RT with IO in precision therapy for solid tumors, by offering supplementary insights to recently released joint guidelines.