Sopa: a technology-invariant pipeline for analyses of image-based spatial omics.

Spatial omics data allow in-depth analysis of tissue architectures, opening new opportunities for biological discovery. In particular, imaging techniques offer single-cell resolutions, providing essential insights into cellular organizations and dynamics. Yet, the complexity of such data presents analytical challenges and demands substantial computing resources. Moreover, the proliferation of diverse spatial omics technologies, such as Xenium, MERSCOPE, CosMX in spatial-transcriptomics, and MACSima and PhenoCycler in multiplex imaging, hinders the generality of existing tools. We introduce Sopa ( https://github.com/gustaveroussy/sopa ), a technology-invariant, memory-efficient pipeline with a unified visualizer for all image-based spatial omics. Built upon the universal SpatialData framework, Sopa optimizes tasks like segmentation, transcript/channel aggregation, annotation, and geometric/spatial analysis. Its output includes user-friendly web reports and visualizer files, as well as comprehensive data files for in-depth analysis. Overall, Sopa represents a significant step toward unifying spatial data analysis, enabling a more comprehensive understanding of cellular interactions and tissue organization in biological systems.

Mathematical modelling, selection and hierarchical inference to determine the minimal dose in IFNα therapy against myeloproliferative neoplasms.

Myeloproliferative neoplasms (MPN) are blood cancers that appear after acquiring a driver mutation in a hematopoietic stem cell. These hematological malignancies result in the overproduction of mature blood cells and, if not treated, induce a risk of cardiovascular events and thrombosis. Pegylated IFN$\alpha $ is commonly used to treat MPN, but no clear guidelines exist concerning the dose prescribed to patients. We applied a model selection procedure and ran a hierarchical Bayesian inference method to decipher how dose variations impact the response to the therapy. We inferred that IFN$\alpha $ acts on mutated stem cells by inducing their differentiation into progenitor cells; the higher the dose, the higher the effect. We found that the treatment can induce long-term remission when a sufficient (patient-dependent) dose is reached. We determined this minimal dose for individuals in a cohort of patients and estimated the most suitable starting dose to give to a new patient to increase the chances of being cured.

Robustness analysis of dynamic trajectory radiotherapy and volumetric modulated arc therapy plans for head and neck cancer.

Background and purpose: Dynamic trajectory radiotherapy (DTRT) has been shown to improve healthy tissue sparing compared to volumetric arc therapy (VMAT). This study aimed to assess and compare the robustness of DTRT and VMAT treatment-plans for head and neck (H&N) cancer to patient-setup (PS) and machine-positioning uncertainties. Materials and methods: The robustness of DTRT and VMAT plans previously created for 46 H&N cases, prescribed 50-70 Gy to 95 % of the planning-target-volume, was assessed. For this purpose, dose distributions were recalculated using Monte Carlo, including uncertainties in PS (translation and rotation) and machine-positioning (gantry-, table-, collimator-rotation and multi-leaf collimator (MLC)). Plan robustness was evaluated by the uncertainties' impact on normal tissue complication probabilities (NTCP) for xerostomia and dysphagia and on dose-volume endpoints. Differences between DTRT and VMAT plan robustness were compared using Wilcoxon matched-pair signed-rank test (α = 5 %). Results: Average NTCP for moderate-to-severe xerostomia and grade ≥ II dysphagia was lower for DTRT than VMAT in the nominal scenario (0.5 %, p = 0.01; 2.1 %, p < 0.01) and for all investigated uncertainties, except MLC positioning, where the difference was not significant. Average differences compared to the nominal scenario were ≤ 3.5 Gy for rotational PS (≤ 3°) and machine-positioning (≤ 2°) uncertainties, <7 Gy for translational PS uncertainties (≤ 5 mm) and < 20 Gy for MLC-positioning uncertainties (≤ 5 mm). Conclusions: DTRT and VMAT plan robustness to the investigated uncertainties depended on uncertainty direction and location of the structure-of-interest to the target. NTCP remained on average lower for DTRT than VMAT even when considering uncertainties.

Improving HLA typing imputation accuracy and eplet identification with local next-generation sequencing training data.

Assessing donor/recipient HLA compatibility at the eplet level requires second field DNA typings but these are not always available. These can be estimated from lower-resolution data either manually or with computational tools currently relying, at best, on data containing typing ambiguities. We gathered NGS typing data from 61,393 individuals in 17 French laboratories, for loci A, B, and C (100% of typings), DRB1 and DQB1 (95.5%), DQA1 (39.6%), DRB3/4/5, DPB1, and DPA1 (10.5%). We developed HaploSFHI, a modified iterative maximum likelihood algorithm, to impute second field HLA typings from low- or intermediate-resolution ones. Compared with the reference tools HaploStats, HLA-EMMA, and HLA-Upgrade, HaploSFHI provided more accurate predictions across all loci on two French test sets and four European-independent test sets. Only HaploSFHI could impute DQA1, and solely HaploSFHI and HaploStats provided DRB3/4/5 imputations. The improved performance of HaploSFHI was due to our local and nonambiguous data. We provided explanations for the most common imputation errors and pinpointed the variability of a low number of low-resolution haplotypes. We thus provided guidance to select individuals for whom sequencing would optimize incompatibility assessment and cost-effectiveness of HLA typing, considering not only well-imputed second field typing(s) but also well-imputed eplets.

Comorbidities, mortality and metabolic profile in individuals with primary biliary cholangitis-A Phenome-Wide-Association-Study.

BACKGROUND AND AIMS: Primary biliary cholangitis (PBC) is a chronic, immune-mediated liver disease that can lead to fibrosis and cirrhosis. In this cohort study, we aimed to investigate morbidity and mortality in conjunction with metabolomic changes of PBC in a UK population-based cohort. METHODS: 454 participants with PBC and 908 propensity score (age, sex, BMI, ethnicity) matched controls without liver disease were included in the study. A subset of participants with PBC and controls were analysed for their metabolomic profile. Further, PBC-associated comorbidities were investigated by PheWAS analysis. Lastly, we assessed causes of death in individuals with PBC using a Fine and Grey competing-risks regression model. RESULTS: Compared to the control group, various pathways associated with the metabolism of amino acids, lipids, and liver biochemistry were significantly enriched in individuals with PBC. We found reduced levels of S-HDL-cholesterol and Glycoprotein Acetyls in individuals with PBC as well as an association with diseases of the circulatory system. Notably, PBC individuals had a higher prevalence of digestive diseases, autoimmune diseases, cardiovascular diseases, anaemias, mental disorders, and urinary tract infections compared to the control group. Strikingly, the overall mortality was almost three times higher in the PBC group compared to the control group, with diseases of the digestive system accounting for a significant elevation of the death rate. A subsequent analysis, enhanced by propensity score matching that included the APRI score, demonstrated that the observed morbidity could not be exclusively attributed to advanced hepatic disease. CONCLUSIONS: Our study provides a detailed perspective on the morbidity of individuals with PBC. The exploration of potential effects of disease state on morbidity suggest that early detection and early treatment of PBC could enhance patient prognosis and prevent the onset of comorbid diseases. Finally, the metabolomic alterations could represent a link between the pathophysiological processes underlying PBC development, progression, and associated morbidity.

Consideration of image guidance in patterns of failure analyses of intensity-modulated radiotherapy for head and neck cancer: a systematic review.

BACKGROUND: Intensity-modulated radiation therapy (IMRT) is considered standard of care for head and neck squamous cell carcinoma (HNSCC). Improved conformity of IMRT and smaller margins, however, have led to concerns of increased rates of marginal failures. We hypothesize that while patterns of failure (PoF) after IMRT for HNSCC have been published before, the quality of patient positioning and image guided radiotherapy (IGRT) have rarely been taken into account, and their importance remains unclear. This work provides a systematic review of the consideration of IGRT in PoF studies after IMRT for HNSCC. MATERIALS AND METHODS: A systematic literature search according to PRISMA guidelines was performed on PubMed for HNSCC, IMRT and PoF terms and conference abstracts from ESTRO and ASTRO 2020 and 2021 were screened. Studies were included if they related PoF of HNSCC after IMRT to the treated volumes. Data on patient and treatment characteristics, IGRT, treatment adaptation, PoF and correlation of PoF to IGRT was extracted, categorized and analyzed. RESULTS: One-hundred ten studies were included. The majority (70) did not report any information on IGRT. The remainder reported daily IGRT (18), daily on day 1-3 or 1-5, then weekly (7), at least weekly (12), or other schemes (3). Immobilization was performed with masks (78), non-invasive frames (4), or not reported (28). The most common PoF classification was "in-field/marginal/out-of-field", reported by 76 studies. Only one study correlated PoF in nasopharyngeal cancer patients to IGRT. CONCLUSION: The impact of IGRT on PoF in HNSCC is severely underreported in existing literature. Only one study correlated PoF to IGRT measures and setup uncertainty. Further, most PoF studies relied on outdated terminology ("in/out-of-field"). A clearly defined and up-to-date PoF terminology is necessary to evaluate PoFs properly, as is systematic and preferably prospective data generation. PoF studies should consistently and comprehensively consider and report on IGRT.

Imaging-guided prognostic score-based approach to assess the benefits of combotherapy versus monotherapy with immune checkpoint inhibitors in metastatic MSI-H colorectal cancer patients.

BACKGROUND: This retrospective study determined survival responses to immune checkpoint inhibitors (ICIs), comparing mono- (mono) and combo-immunotherapy (combo) in patients with microsatellite instability-high (MSI-H) metastatic colorectal cancer (mCRC) by analyzing quantitative imaging data and clinical factors. METHODS: One hundred fifty patients were included from two centers and divided into training (n = 105) and validation (n = 45) cohorts. Radiologists manually annotated chest-abdomen-pelvis computed tomography and calculated tumor burden. Progression-free survival (PFS) was assessed, and variables were selected through Recursive Feature Elimination. Cutoff values were determined using maximally selected rank statistics to binarize features, forming a risk score with hazard ratio-derived weights. RESULTS: In total, 2258 lesions were annotated with excellent reproducibility. Key variables in the training cohort included: total tumor volume (cutoff: 73 cm3), lesion count (cutoff: 20), age (cutoff: 60) and the presence of peritoneal carcinomatosis. Their respective weights were 1.13, 0.96, 0.91, and 0.38, resulting in a risk score cutoff of 1.36. Low-score patients showed similar overall survival and PFS regardless of treatment, while those with a high-score had significantly worse survivals with mono vs combo (P = 0.004 and P = 0.0001). In the validation set, low-score patients exhibited no significant difference in overall survival and PFS with mono or combo. However, patients with a high-score had worse PFS with mono (P = 0.046). CONCLUSIONS: A score based on total tumor volume, lesion count, the presence of peritoneal carcinomatosis, and age can guide MSI-H mCRC treatment decisions, allowing oncologists to identify suitable candidates for mono and combo ICI therapies.

Organs-at-risk dose and normal tissue complication probability with dynamic trajectory radiotherapy (DTRT) for head and neck cancer.

We compared dynamic trajectory radiotherapy (DTRT) to state-of-the-art volumetric modulated arc therapy (VMAT) for 46 head and neck cancer cases. DTRT had lower dose to salivary glands and swallowing structure, resulting in lower predicted xerostomia and dysphagia compared to VMAT. DTRT is deliverable on C-arm linacs with high dosimetric accuracy.

A novel batch-effect correction method for scRNA-seq data based on Adversarial Information Factorization.

Single-cell RNA sequencing (scRNA-seq) technology produces an unprecedented resolution at the level of a unique cell, raising great hopes in medicine. Nevertheless, scRNA-seq data suffer from high variations due to the experimental conditions, called batch effects, preventing any aggregated downstream analysis. Adversarial Information Factorization provides a robust batch-effect correction method that does not rely on prior knowledge of the cell types nor a specific normalization strategy while being adapted to any downstream analysis task. It compares to and even outperforms state-of-the-art methods in several scenarios: low signal-to-noise ratio, batch-specific cell types with few cells, and a multi-batches dataset with imbalanced batches and batch-specific cell types. Moreover, it best preserves the relative gene expression between cell types, yielding superior differential expression analysis results. Finally, in a more complex setting of a Leukemia cohort, our method preserved most of the underlying biological information for each patient while aligning the batches, improving the clustering metrics in the aggregated dataset.

Diethylnitrosamine Induction of Hepatocarcinogenesis in Mice.

Diethylnitrosamine (DEN) is a chemical hepatocarcinogenic agent that triggers a large array of oncogenic mutations after a single injection. Initiated hepatocytes subsequently undergo clonal expansion within a proliferative environment, rendering the DEN model a comprehensive carcinogen. In rodent studies, DEN finds extensive utility in experimental liver cancer research, mimicking several aspects of human hepatocellular carcinoma (HCC), including angiogenesis, metabolic reprogramming, immune exhaustion, and the ability to metastasize. Beyond the wealth of scientific insights gleaned from this model, the objective of this chapter is to review morphological, genomic, and immunological characteristics associated to DEN-induced HCC. Furthermore, this chapter provides a detailed procedural guide to effectively induce hepatocarcinogenesis in mice through a single intraperitoneal injection of DEN.

Dosimetrically motivated beam-angle optimization for non-coplanar arc radiotherapy with and without dynamic collimator rotation.

BACKGROUND: Non-coplanar techniques have shown to improve the achievable dose distribution compared to standard coplanar techniques for multiple treatment sites but finding optimal beam directions is challenging. Dynamic collimator trajectory radiotherapy (colli-DTRT) is a new intensity modulated radiotherapy technique that uses non-coplanar partial arcs and dynamic collimator rotation. PURPOSE: To solve the beam angle optimization (BAO) problem for colli-DTRT and non-coplanar VMAT (NC-VMAT) by determining the table-angle and the gantry-angle ranges of the partial arcs through iterative 4π fluence map optimization (FMO) and beam direction elimination. METHODS: BAO considers all available beam directions sampled on a gantry-table map with the collimator angle aligned to the superior-inferior axis (colli-DTRT) or static (NC-VMAT). First, FMO is performed, and beam directions are scored based on their contributions to the objective function. The map is thresholded to remove the least contributing beam directions, and arc candidates are formed by adjacent beam directions with the same table angle. Next, FMO and arc candidate trimming, based on objective function penalty score, is performed iteratively until a desired total gantry angle range is reached. Direct aperture optimization on the final set of colli-DTRT or NC-VMAT arcs generates deliverable plans. colli-DTRT and NC-VMAT plans were created for seven clinically-motivated cases with targets in the head and neck (two cases), brain, esophagus, lung, breast, and prostate. colli-DTRT and NC-VMAT were compared to coplanar VMAT plans as well as to class-solution non-coplanar VMAT plans for the brain and head and neck cases. Dosimetric validation was performed for one colli-DTRT (head and neck) and one NC-VMAT (breast) plan using film measurements. RESULTS: Target coverage and conformity was similar for all techniques. colli-DTRT and NC-VMAT plans had improved dosimetric performance compared to coplanar VMAT for all treatment sites except prostate where all techniques were equivalent. For the head and neck and brain cases, mean dose reduction-in percentage of the prescription dose-to parallel organs was on average 0.7% (colli-DTRT), 0.8% (NC-VMAT) and 0.4% (class-solution) compared to VMAT. The reduction in D2% for the serial organs was on average 1.7% (colli-DTRT), 2.0% (NC-VMAT) and 0.9% (class-solution). For the esophagus, lung, and breast cases, mean dose reduction to parallel organs was on average 0.2% (colli-DTRT) and 0.3% (NC-VMAT) compared to VMAT. The reduction in D2% for the serial organs was on average 1.3% (colli-DTRT) and 0.9% (NC-VMAT). Estimated delivery times for colli-DTRT and NC-VMAT were below 4 min for a full gantry angle range of 720°, including transitions between arcs, except for the brain case where multiple arcs covered the whole table angle range. These times are in the same order as the class-solution for the head and neck and brain cases. Total optimization times were 25%-107% longer for colli-DTRT, including BAO, compared to VMAT. CONCLUSIONS: We successfully developed dosimetrically motivated BAO for colli-DTRT and NC-VMAT treatment planning. colli-DTRT and NC-VMAT are applicable to multiple treatment sites, including body sites, with beneficial or equivalent dosimetric performances compared to coplanar VMAT and reasonable delivery times.

BR55 Ultrasound Molecular Imaging of Clear Cell Renal Cell Carcinoma Reflects Tumor Vascular Expression of VEGFR-2 in a Patient-Derived Xenograft Model.

Standard imaging cannot reliably predict the nature of renal tumors. Among malignant renal tumors, clear cell renal cell carcinoma (ccRCC) is the most common histological subtype, in which the vascular endothelial growth factor 2 (VEGFR-2) is highly expressed in the vascular endothelium. BR55, a contrast agent for ultrasound imaging, consists of gas-core lipid microbubbles that specifically target and bind to the extracellular portion of the VEGFR-2. The specific information provided by ultrasound molecular imaging (USMI) using BR55 was compared with the vascular tumor expression of the VEGFR-2 by immunohistochemical (IHC) staining in a preclinical model of ccRCC. Patients' ccRCCs were orthotopically grafted onto Nod-Scid-Gamma (NSG) mice to generate patient-derived xenografts (PdX). Mice were divided into four groups to receive either vehicle or axitinib an amount of 2, 7.5 or 15 mg/kg twice daily. Perfusion parameters and the BR55 ultrasound contrast signal on PdX renal tumors were analyzed at D0, D1, D3, D7 and D11, and compared with IHC staining for the VEGFR-2 and CD34. Significant Pearson correlation coefficients were observed between the area under the curve (AUC) and the CD34 (0.84, p < 10-4), and between the VEGFR-2-specific signal obtained by USMI and IHC (0.72, p < 10-4). USMI with BR55 could provide instant, quantitative information on tumor VEGFR-2 expression to characterize renal masses non-invasively.

Delivery time reduction for mixed photon-electron radiotherapy by using photon MLC collimated electron arcs.

Objective. Electron arcs in mixed-beam radiotherapy (Arc-MBRT) consisting of intensity-modulated electron arcs with dynamic gantry rotation potentially reduce the delivery time compared to mixed-beam radiotherapy containing electron beams with static gantry angle (Static-MBRT). This study aims to develop and investigate a treatment planning process (TPP) for photon multileaf collimator (pMLC) based Arc-MBRT.Approach. An existing TPP for Static-MBRT plans is extended to integrate electron arcs with a dynamic gantry rotation and intensity modulation using a sliding window technique. The TPP consists of a manual setup of electron arcs, and either static photon beams or photon arcs, shortening of the source-to-surface distance for the electron arcs, initial intensity modulation optimization, selection of a user-defined number of electron beam energies based on dose contribution to the target volume and finally, simultaneous photon and electron intensity modulation optimization followed by full Monte Carlo dose calculation. Arc-MBRT plans, Static-MBRT plans, and photon-only plans were created and compared for four breast cases. Dosimetric validation of two Arc-MBRT plans was performed using film measurements.Main results. The generated Arc-MBRT plans are dosimetrically similar to the Static-MBRT plans while outperforming the photon-only plans. The mean heart dose is reduced by 32% on average in the MBRT plans compared to the photon-only plans. The estimated delivery times of the Arc-MBRT plans are similar to the photon-only plans but less than half the time of the Static-MBRT plans. Measured and calculated dose distributions agree with a gamma passing rate of over 98% (3% global, 2 mm) for both delivered Arc-MBRT plans.Significance. A TPP for Arc-MBRT is successfully developed and Arc-MBRT plans showed the potential to improve the dosimetric plan quality similar as Static-MBRT while maintaining short delivery times of photon-only treatments. This further facilitates integration of pMLC-based MBRT into clinical practice.

Synergic prognostic value of 3D CT scan subcutaneous fat and muscle masses for immunotherapy-treated cancer.

BACKGROUND: Our aim was to explore the prognostic value of anthropometric parameters in a large population of patients treated with immunotherapy. METHODS: We retrospectively included 623 patients with advanced non-small cell lung cancer (NSCLC) (n=318) or melanoma (n=305) treated by an immune-checkpoint-inhibitor having a pretreatment (thorax-)abdomen-pelvis CT scan. An external validation cohort of 55 patients with NSCLC was used. Anthropometric parameters were measured three-dimensionally (3D) by a deep learning software (Anthropometer3DNet) allowing an automatic multislice measurement of lean body mass, fat body mass (FBM), muscle body mass (MBM), visceral fat mass (VFM) and sub-cutaneous fat mass (SFM). Body mass index (BMI) and weight loss (WL) were also retrieved. Receiver operator characteristic (ROC) curve analysis was performed and overall survival was calculated using Kaplan-Meier (KM) curve and Cox regression analysis. RESULTS: In the overall cohort, 1-year mortality rate was 0.496 (95% CI: 0.457 to 0.537) for 309 events and 5-year mortality rate was 0.196 (95% CI: 0.165 to 0.233) for 477 events. In the univariate Kaplan-Meier analysis, prognosis was worse (p<0.001) for patients with low SFM (<3.95 kg/m2), low FBM (<3.26 kg/m2), low VFM (<0.91 kg/m2), low MBM (<5.85 kg/m2) and low BMI (<24.97 kg/m2). The same parameters were significant in the Cox univariate analysis (p<0.001) and, in the multivariate stepwise Cox analysis, the significant parameters were MBM (p<0.0001), SFM (0.013) and WL (0.0003). In subanalyses according to the type of cancer, all body composition parameters were statistically significant for NSCLC in ROC, KM and Cox univariate analysis while, for melanoma, none of them, except MBM, was statistically significant. In multivariate Cox analysis, the significant parameters for NSCLC were MBM (HR=0.81, p=0.0002), SFM (HR=0.94, p=0.02) and WL (HR=1.06, p=0.004). For NSCLC, a KM analysis combining SFM and MBM was able to separate the population in three categories with the worse prognostic for the patients with both low SFM (<5.22 kg/m2) and MBM (<6.86 kg/m2) (p<0001). On the external validation cohort, combination of low SFM and low MBM was pejorative with 63% of mortality at 1 year versus 25% (p=0.0029). CONCLUSIONS: 3D measured low SFM and MBM are significant prognosis factors of NSCLC treated by immune checkpoint inhibitors and can be combined to improve the prognostic value.

A biology-driven deep generative model for cell-type annotation in cytometry.

Cytometry enables precise single-cell phenotyping within heterogeneous populations. These cell types are traditionally annotated via manual gating, but this method lacks reproducibility and sensitivity to batch effect. Also, the most recent cytometers-spectral flow or mass cytometers-create rich and high-dimensional data whose analysis via manual gating becomes challenging and time-consuming. To tackle these limitations, we introduce Scyan https://github.com/MICS-Lab/scyan, a Single-cell Cytometry Annotation Network that automatically annotates cell types using only prior expert knowledge about the cytometry panel. For this, it uses a normalizing flow-a type of deep generative model-that maps protein expressions into a biologically relevant latent space. We demonstrate that Scyan significantly outperforms the related state-of-the-art models on multiple public datasets while being faster and interpretable. In addition, Scyan overcomes several complementary tasks, such as batch-effect correction, debarcoding and population discovery. Overall, this model accelerates and eases cell population characterization, quantification and discovery in cytometry.

Optimizing IFN Alpha Therapy against Myeloproliferative Neoplasms.

Myeloproliferative neoplasms (MPNs) are hematologic malignancies that result from acquired driver mutations in hematopoietic stem cells (HSCs), causing overproduction of blood cells and an increased risk of thrombohemorrhagic events. The most common MPN driver mutation affects the JAK2 gene (JAK2V617F ). Interferon alpha (IFNα) is a promising treatment against MPNs by inducing a hematologic response and molecular remission for some patients. Mathematical models have been proposed to describe how IFNα targets mutated HSCs, indicating that a minimal dose is necessary for long-term remission. This study aims to determine a personalized treatment strategy. First, we show the capacity of an existing model to predict cell dynamics for new patients from data that can be easily obtained in clinic. Then, we study different treatment scenarios in silico for three patients, considering potential IFNα dose-toxicity relations. We assess when the treatment should be interrupted depending on the response, the patient's age, and the inferred development of the malignant clone without IFNα We find that an optimal strategy would be to treat patients with a constant dose so that treatment could be interrupted as quickly as possible. Higher doses result in earlier discontinuation but also higher toxicity. Without knowledge of the dose-toxicity relationship, trade-off strategies can be found for each patient. A compromise strategy is to treat patients with medium doses (60-120 µg/week) for 10-15 years. Altogether, this work demonstrates how a mathematical model calibrated from real data can help build a clinical decision-support tool to optimize long-term IFNα therapy for MPN patients. SIGNIFICANCE STATEMENT: Myeloproliferative neoplasms (MPNs) are chronic blood cancers. Interferon alpha (IFNα) is a promising treatment with the potential to induce a molecular response by targeting mutated hematopoietic stem cells. MPN patients are treated over several years, and there is a lack of knowledge concerning the posology strategy and the best timing for interrupting therapy. The study opens avenues for rationalizing how to treat MPN patients with IFNα over several years, promoting a more personalized approach to treatment.

Technical note: Feasibility of gating for dynamic trajectory radiotherapy - Mechanical accuracy and dosimetric performance.

BACKGROUND: Dynamic trajectory radiotherapy (DTRT) extends state-of-the-art volumetric modulated arc therapy (VMAT) by dynamic table and collimator rotations during beam-on. The effects of intrafraction motion during DTRT delivery are unknown, especially regarding the possible interplay between patient and machine motion with additional dynamic axes. PURPOSE: To experimentally assess the technical feasibility and quantify the mechanical and dosimetric accuracy of respiratory gating during DTRT delivery. METHODS: A DTRT and VMAT plan are created for a clinically motivated lung cancer case and delivered to a dosimetric motion phantom (MP) placed on the table of a TrueBeam system using Developer Mode. The MP reproduces four different 3D motion traces. Gating is triggered using an external marker block, placed on the MP. Mechanical accuracy and delivery time of the VMAT and DTRT deliveries with and without gating are extracted from the logfiles. Dosimetric performance is assessed by means of gamma evaluation (3% global/2 mm, 10% threshold). RESULTS: The DTRT and VMAT plans are successfully delivered with and without gating for all motion traces. Mechanical accuracy is similar for all experiments with deviations <0.14° (gantry angle), <0.15° (table angle), <0.09° (collimator angle) and <0.08 mm (MLC leaf positions). For DTRT (VMAT), delivery times are 1.6-2.3 (1.6- 2.5) times longer with than without gating for all motion traces except one, where DTRT (VMAT) delivery is 5.0 (3.6) times longer due to a substantial uncorrected baseline drift affecting only DTRT delivery. Gamma passing rates with (without) gating for DTRT/VMAT were ≥96.7%/98.5% (≤88.3%/84.8%). For one VMAT arc without gating it was 99.6%. CONCLUSION: Gating is successfully applied during DTRT delivery on a TrueBeam system for the first time. Mechanical accuracy is similar for VMAT and DTRT deliveries with and without gating. Gating substantially improved dosimetric performance for DTRT and VMAT.

Association of Helicobacter pylori Positivity With Risk of Disease and Mortality.

INTRODUCTION: Helicobacter pylori colonizes the human stomach. Infection causes chronic gastritis and increases the risk of gastroduodenal ulcer and gastric cancer. Its chronic colonization in the stomach triggers aberrant epithelial and inflammatory signals that are also associated with systemic alterations. METHODS: Using a PheWAS analysis in more than 8,000 participants in the community-based UK Biobank, we explored the association of H. pylori positivity with gastric and extragastric disease and mortality in a European country. RESULTS: Along with well-established gastric diseases, we dominantly found overrepresented cardiovascular, respiratory, and metabolic disorders. Using multivariate analysis, the overall mortality of H. pylori -positive participants was not altered, while the respiratory and Coronovirus 2019-associated mortality increased. Lipidomic analysis for H. pylori -positive participants revealed a dyslipidemic profile with reduced high-density lipoprotein cholesterol and omega-3 fatty acids, which may represent a causative link between infection, systemic inflammation, and disease. DISCUSSION: Our study of H. pylori positivity demonstrates that it plays an organ- and disease entity-specific role in the development of human disease and highlights the importance of further research into the systemic effects of H. pylori infection.

CustOmics: A versatile deep-learning based strategy for multi-omics integration.

The availability of patient cohorts with several types of omics data opens new perspectives for exploring the disease's underlying biological processes and developing predictive models. It also comes with new challenges in computational biology in terms of integrating high-dimensional and heterogeneous data in a fashion that captures the interrelationships between multiple genes and their functions. Deep learning methods offer promising perspectives for integrating multi-omics data. In this paper, we review the existing integration strategies based on autoencoders and propose a new customizable one whose principle relies on a two-phase approach. In the first phase, we adapt the training to each data source independently before learning cross-modality interactions in the second phase. By taking into account each source's singularity, we show that this approach succeeds at taking advantage of all the sources more efficiently than other strategies. Moreover, by adapting our architecture to the computation of Shapley additive explanations, our model can provide interpretable results in a multi-source setting. Using multiple omics sources from different TCGA cohorts, we demonstrate the performance of the proposed method for cancer on test cases for several tasks, such as the classification of tumor types and breast cancer subtypes, as well as survival outcome prediction. We show through our experiments the great performances of our architecture on seven different datasets with various sizes and provide some interpretations of the results obtained. Our code is available on (https://github.com/HakimBenkirane/CustOmics).