CHAIMELEON Project: Creation of a Pan-European Repository of Health Imaging Data for the Development of AI-Powered Cancer Management Tools.

The CHAIMELEON project aims to set up a pan-European repository of health imaging data, tools and methodologies, with the ambition to set a standard and provide resources for future AI experimentation for cancer management. The project is a 4 year long, EU-funded project tackling some of the most ambitious research in the fields of biomedical imaging, artificial intelligence and cancer treatment, addressing the four types of cancer that currently have the highest prevalence worldwide: lung, breast, prostate and colorectal. To allow this, clinical partners and external collaborators will populate the repository with multimodality (MR, CT, PET/CT) imaging and related clinical data. Subsequently, AI developers will enable a multimodal analytical data engine facilitating the interpretation, extraction and exploitation of the information stored at the repository. The development and implementation of AI-powered pipelines will enable advancement towards automating data deidentification, curation, annotation, integrity securing and image harmonization. By the end of the project, the usability and performance of the repository as a tool fostering AI experimentation will be technically validated, including a validation subphase by world-class European AI developers, participating in Open Challenges to the AI Community. Upon successful validation of the repository, a set of selected AI tools will undergo early in-silico validation in observational clinical studies coordinated by leading experts in the partner hospitals. Tool performance will be assessed, including external independent validation on hallmark clinical decisions in response to some of the currently most important clinical end points in cancer. The project brings together a consortium of 18 European partners including hospitals, universities, R&D centers and private research companies, constituting an ecosystem of infrastructures, biobanks, AI/in-silico experimentation and cloud computing technologies in oncology.

Spores of Clostridium engineered for clinical efficacy and safety cause regression and cure of tumors in vivo.

Spores of some species of the strictly anaerobic bacteria Clostridium naturally target and partially lyse the hypoxic cores of tumors, which tend to be refractory to conventional therapies. The anti-tumor effect can be augmented by engineering strains to convert a non-toxic prodrug into a cytotoxic drug specifically at the tumor site by expressing a prodrug-converting enzyme (PCE). Safe doses of the favored prodrug CB1954 lead to peak concentrations of 6.3 µM in patient sera, but at these concentration(s) known nitroreductase (NTR) PCEs for this prodrug show low activity. Furthermore, efficacious and safe Clostridium strains that stably express a PCE have not been reported. Here we identify a novel nitroreductase from Neisseria meningitidis, NmeNTR, which is able to activate CB1954 at clinically-achievable serum concentrations. An NmeNTR expression cassette, which does not contain an antibiotic resistance marker, was stably localized to the chromosome of Clostridium sporogenes using a new integration method, and the strain was disabled for safety and containment by making it a uracil auxotroph. The efficacy of Clostridium-Directed Enzyme Prodrug Therapy (CDEPT) using this system was demonstrated in a mouse xenograft model of human colon carcinoma. Substantial tumor suppression was achieved, and several animals were cured. These encouraging data suggest that the novel enzyme and strain engineering approach represent a promising platform for the clinical development of CDEPT.

Development of a multicomponent prediction model for acute esophagitis in lung cancer patients receiving chemoradiotherapy.

PURPOSE: To construct a model for the prediction of acute esophagitis in lung cancer patients receiving chemoradiotherapy by combining clinical data, treatment parameters, and genotyping profile. PATIENTS AND METHODS: Data were available for 273 lung cancer patients treated with curative chemoradiotherapy. Clinical data included gender, age, World Health Organization performance score, nicotine use, diabetes, chronic disease, tumor type, tumor stage, lymph node stage, tumor location, and medical center. Treatment parameters included chemotherapy, surgery, radiotherapy technique, tumor dose, mean fractionation size, mean and maximal esophageal dose, and overall treatment time. A total of 332 genetic polymorphisms were considered in 112 candidate genes. The predicting model was achieved by lasso logistic regression for predictor selection, followed by classic logistic regression for unbiased estimation of the coefficients. Performance of the model was expressed as the area under the curve of the receiver operating characteristic and as the false-negative rate in the optimal point on the receiver operating characteristic curve. RESULTS: A total of 110 patients (40%) developed acute esophagitis Grade ≥2 (Common Terminology Criteria for Adverse Events v3.0). The final model contained chemotherapy treatment, lymph node stage, mean esophageal dose, gender, overall treatment time, radiotherapy technique, rs2302535 (EGFR), rs16930129 (ENG), rs1131877 (TRAF3), and rs2230528 (ITGB2). The area under the curve was 0.87, and the false-negative rate was 16%. CONCLUSION: Prediction of acute esophagitis can be improved by combining clinical, treatment, and genetic factors. A multicomponent prediction model for acute esophagitis with a sensitivity of 84% was constructed with two clinical parameters, four treatment parameters, and four genetic polymorphisms.