Correctly identifying the true driver mutations in a patient’s tumor is a major challenge in precision oncology. Most efforts address frequent mutations, leaving medium- and low-frequency variants mostly unaddressed. For TP53, this identification is crucial for both somatic and germline mutations, with the latter associated with the Li-Fraumeni syndrome (LFS), a multiorgan cancer predisposition. We present TP53_PROF (prediction of functionality), a gene specific machine learning model to predict the functional consequences of every possible missense mutation in TP53, integrating human cell- and yeast-based functional assays scores along with computational scores. Variants were labeled for the training set using well-defined criteria of prevalence in four cancer genomics databases. The model’s predictions provided accuracy of 96.5%. They were validated experimentally, and were compared to population data, LFS datasets, ClinVar annotations and to TCGA survival data. Very high accuracy was shown through all methods of validation. TP53_PROF allows accurate classification of TP53 missense mutations applicable for clinical practice. Our gene specific approach integrated machine learning, highly reliable features and biological knowledge, to create an unprecedented, thoroughly validated and clinically oriented classification model. This approach currently addresses TP53 mutations and will be applied in the future to other important cancer genes.

Antibody-drug conjugates (ADCs) enable targeted delivery of cytotoxic payload to cancer cells. Here, we characterized the mode of action of the ADC patritumab deruxtecan, which is a monoclonal antibody specific for Erb-B2 Receptor Tyrosine Kinase 3 (ERBB3, best known as HER3) coupled to the topoisomerase-I inhibitor DXd. Patritumab deruxtecan decreased viability and induced the relocation of calreticulin fused to green fluorescent protein (CALR-GFP) to the periphery of human osteosarcoma U2OS cells engineered to express HER3 but not in their parental counterparts only expressing the CALR-GFP biosensor. Patritumab deruxtecan as well as its payload DXd induced various traits of immunogenic cell death (ICD) including antibody detectable calreticulin membrane exposure, exodus of high mobility group protein B1 (HMGB1), as well as the release of ATP into cell culture supernatants. Moreover, DXd causes rapid inhibition of DNA-to-RNA transcription, which is a key predictor for ICD. Mouse cancer cells treated with DXd were able to vaccinate syngeneic immunocompetent mice against tumor challenge. Tumor-free mice developed immune memory that led to the rejection of syngeneic tumors after rechallenge. In conclusion, patritumab deruxtecan is equipped with a cytotoxic payload that induces hallmarks of ICD in vitro and elicits antitumor immunity in vivo.

The recent trend in 3D cell modeling has fostered the emergence of a wide range of models, addressing very distinct goals ranging from the fundamental exploration of cell–cell interactions to preclinical assays for personalized medicine. It is clear that no single model will recapitulate the complexity and dynamics of in vivo situations. The key is to define the critical points, achieve a specific goal and design a model where they can be validated. In this report, we focused on cancer progression. We describe our model which is designed to emulate breast carcinoma progression during the invasive phase. We chose to provide topological clues to the target cells by growing them on microsupports, favoring a polarized epithelial organization before they are embedded in a 3D matrix. We then watched for cell organization and differentiation for these models, adding stroma cells then immune cells to follow and quantify cell responses to drug treatment, including quantifying cell death and viability, as well as morphogenic and invasive properties. We used model cell lines including Comma Dβ, MCF7 and MCF10A mammary epithelial cells as well as primary breast cancer cells from patient-derived xenografts (PDX). We found that fibroblasts impacted cell response to Docetaxel and Palbociclib. We also found that NK92 immune cells could target breast cancer cells within the 3D configuration, providing quantitative monitoring of cell cytotoxicity. We also tested several sources for the extracellular matrix and selected a hyaluronan-based matrix as a promising alternative to mouse tumor basement membrane extracts for primary human cancer cells. Overall, we validated a new 3D model designed for breast cancer for preclinical use in personalized medicine.

Aim: Functional screening of new pharmaceutical compounds requires clinically relevant models to monitor essential cellular and immune responses during cancer progression, with or without treatment. Beyond survival, the emergence of resistant tumor cell clones should also be considered, including specific properties related to plasticity, such as invasiveness, stemness, escape from programmed cell death, and immune response. Numerous pathways are involved in these processes. Defining the relevant ones in the context of a specific tumor type will be key to designing an appropriate combination of inhibitors. However, the diversity and potential redundancy of these pathways remain a challenge for therapy. Methods: A new microfluidic device developed by Okomera was dedicated to the screening of drug treatment for breast cancer. This microchip includes 150 droplet-trapping microwells, offering multi-chip settings and multiple treatment choices. Results: After validating the system with established cell lines and a panel of drugs used clinically at Gustave Roussy, preclinical experiments were initiated including patient-derived xenograft (PDX) and primary tumor cells-derived tumoroids with the collaboration of Gustave Roussy clinicians. Tumor-isolated lymphocytes were also added to the tumoroids, using secondary droplets in proof-of-concept experiments. Conclusions: These results show the relevance of the methodology for screening large numbers of drugs, a wide range of doses, and multiple drug combinations. This methodology will be used for two purposes: 1) new drug screening from the compound library, using the high throughput potential of the chip; and 2) pre-clinical assay for a two-weeks response for personalized medicine, allowing evaluation of drug combinations to flag an optimized treatment with potential clinical application.

Implication des facteurs transcriptionnels Slug et Snail dans les carcinomes du sein in-vivo et dans des modèles 3D : Localisation fonctionnelle et lien à la progression tumoral sous traitement: L'émergence de la plasticité et de la résistance parmi les cellules tumorales sous traitement est un problème essentiel pour la thérapie. Parmi les mécanismes impliqués, les voies contrôlant la plasticité cellulaire paraissent incontournables avec l'activation de gènes tels que Slug et Snail. Pour analyser leurs rôles nous avons généré des modèles 3D pertinents et originaux, permettant l'analyse dynamique des cellules tumorales dans un environnement contrôlé. Nous avons d'abord étudié extensivement l'organisation tumorale et le profil d'expression de Slug et Snail dans les divers types de cancers du sein, à partir d'une cohorte de 656 tumeurs organisées en micro tissus sur lames multi-échantillons. Cette analyse topologique nous a permis de mieux comprendre les liens entre gènes, fonctions invasives et proliférations. Basé sur ces résultats, nous avons développé deux types de modèles 3D.Le premier, est analytique. Dans ce modèle, nous pouvons suivre la réponse des cellules tumorales, leur morphogenèse, leur plasticité, leur capacité à migrer et leur éventuelle résistance après une à deux semaines de traitements avec ou sans la présence de cellules stromales et immunitaires compétentes. Le second modèle, en collaboration avec la startup Okomera est destiné au criblage haut débit à partir de micro-puits permettant la croissance de 150 agrégats par puce. Nous l'avons adapté avec un protocole destiné à une utilisation préclinique pour fournir en une semaine un tableau des résistances aux traitements avec des combinaisons utilisés par les cliniciens. Ces deux modèles sont la base de deux publications publiées. L'une pour l'utilisation préclinique, l'autre s'attachant aux mécanismes d'émergence des résistances.