Modular response analysis (MRA) is an effective method to infer biological networks from perturbation data. However, it has several limitations such as strong sensitivity to noise, need of performing independent perturbations that hit a single node at a time, and linear approximation of dependencies within the network. Previously, we addressed the sensitivity of MRA to noise by reinterpreting MRA as a multilinear regression problem. We demonstrated the advantages of this approach over the conventional MRA and other known inference methods, particularly in handling noise measurements and nonlinear networks. Here, we provide new contributions to complement this theory. First, we overcome the need of perturbations to be independent, thereby augmenting MRA applicability. Second, using analysis of variance and lack-of-fit tests, we can now assess MRA compatibility with the data and identify the primary source of errors. In cases where nonlinearity prevails, we propose extending the model to a second-order polynomial. Third, we demonstrate how to effectively use prior knowledge about a network. We validated these results using 4 networks with known dynamics (3, 4, and 6 nodes) and 40 simulated networks, ranging from 10 to 200 nodes. Finally, we incorporated these innovations into our R software package MRARegress to offer a comprehensive, extended theory for MRA and to facilitate its use by the community. Mathematical aspects, tests details, and scripts are provided as Supplementary Information (see 'Data Availability Statement').

The tumor microenvironment promotes cancer progression in part by supporting cancer stem cells (CSC). In colorectal cancer (CRC), progastrin (PG), an orphan growth factor secreted by tumor cells within the tumor and its microenvironment, maintains CSCs by unidentified mechanisms. Here, the orphan receptor Protein Zero-Related protein (PZR) is identified as an essential component of PG activity and demonstrated its utility as a therapeutic target. PZR is essential for growth of PG-expressing tumors, while genetic inactivation in mice of Mpzl1, which encodes PZR, disrupted chemically-induced colon transformation. Mechanistically, PG binds cellular glycosylated and dimeric PZR and promotes SHP2/SRC/β-catenin-dependent CSC-like signaling. Blocking PZR by monoclonal antibodies inhibited PG-dependent expansion of tumoroids derived from murine intestinal tumors and patient-derived CRC cell lines, while in mice, it reduced adenoma formation triggered by Apc loss in stem cells and disrupted the tumor-initiating capacity of PG-expressing CRC cells. High GAST (which encodes PG) and MPZL1 transcript levels in primary colon cancer patients is predictive of worse prognosis. Collectively, these findings support the inhibition of PZR as a potential targeted treatment of PG-expressing CRC.

In pancreatic ductal adenocarcinoma (PDAC), the dense stroma rich in cancer-associated fibroblasts (CAFs) and the immunosuppressive microenvironment confer resistance to treatments. To overcome such resistance, we tested the combination of FOLFIRINOX (DNA damage-inducing chemotherapy drugs) with VE-822 (an ataxia-telangiectasia and RAD3-related inhibitor that targets DNA damage repair). PDAC spheroid models and organoids were used to assess the combination effects. Tumour growth and the immune and fibrotic microenvironment were evaluated by immunohistochemistry, single-cell analysis and spatial proteomics in patient-derived xenograft (PDX) and orthotopic immunocompetent KPC mouse models. The FOLFIRINOX and VE-822 combination had a strong synergistic effect in several PDAC cell lines, whatever their BRCA1, BRCA2 and ATM mutation status and resistance to standard chemotherapy agents. This was associated with high DNA damage and inhibition of DNA repair signalling pathways, leading to increased apoptosis. In immunocompetent and PDX mouse models of PDAC, the combination inhibited tumour growth more effectively than FOLFIRINOX alone. This was associated with tumour microenvironment remodelling, particularly decreased proportion of fibroblast activated protein-positive CAFs and increased anti-tumorigenic immune cell infiltration and interaction. The FOLFIRINOX and VE-822 combination is a promising strategy to improve FOLFIRINOX efficacy and overcome drug resistance in PDAC.

The analysis of protein dynamics or turnover in patients has the potential to reveal altered protein recycling, such as in Alzheimer's disease, and to provide informative data regarding drug efficacy or certain biological processes. The observed protein dynamics in a solid tissue or a fluid is the net result of not only protein synthesis and degradation but also transport across biological compartments. We report an accurate 3-biological compartment model able to simultaneously account for the protein dynamics observed in blood plasma and the cerebrospinal fluid (CSF) including a hidden central nervous system (CNS) compartment. We successfully applied this model to 69 proteins of a single individual displaying similar or very different dynamics in plasma and CSF. This study puts a strong emphasis on the methods and tools needed to develop this type of model. We believe that it will be useful to any researcher dealing with protein dynamics data modeling.

Drug-tolerance has emerged as one of the major non-genetic adaptive processes driving resistance to targeted therapy (TT) in non-small cell lung cancer (NSCLC). However, the kinetics and sequence of molecular events governing this adaptive response remain poorly understood. Here, we combine real-time monitoring of the cell-cycle dynamics and single-cell RNA sequencing in a broad panel of oncogenic addiction such as EGFR-, ALK-, BRAF- and KRAS-mutant NSCLC, treated with their corresponding TT. We identify a common path of drug adaptation, which invariably involves alveolar type 1 (AT1) differentiation and Rho-associated protein kinase (ROCK)-mediated cytoskeletal remodeling. We also isolate and characterize a rare population of early escapers, which represent the earliest resistance-initiating cells that emerge in the first hours of treatment from the AT1-like population. A phenotypic drug screen identify farnesyltransferase inhibitors (FTI) such as tipifarnib as the most effective drugs in preventing relapse to TT in vitro and in vivo in several models of oncogenic addiction, which is confirmed by genetic depletion of the farnesyltransferase. These findings pave the way for the development of treatments combining TT and FTI to effectively prevent tumor relapse in oncogene-addicted NSCLC patients. Emergence of drug tolerant cells drives adaptive targeted therapy resistance in non-small cell lung cancer (NSCLC). Here, the authors identify a common molecular event underpinning resistance to multiple targeted therapies in a panel of mutant NSCLC models that can be targeted with farnesyltransferase inhibition.

Motivation The knowledge of protein dynamics, or turnover, in patients provides invaluable information related to certain diseases, drug efficacy, or biological processes. A great corpus of experimental and computational methods has been developed, including by us, in the case of human patients followed in vivo. Moving one step further, we propose a novel modeling approach to capture population protein dynamics using Bayesian methods.Results Using two datasets, we demonstrate that models inspired by population pharmacokinetics can accurately capture protein turnover within a cohort and account for inter-individual variability. Such models pave the way for comparative studies searching for altered dynamics or biomarkers in diseases.Availability and implementation R code and preprocessed data are available from zenodo.org. Raw data are available from panoramaweb.org.

Background: Blastic Plasmacytoid Dendritic Cell Neoplasm (BPDCN) is a rare haematological malignancy characterized by NF-κB activation (Sapienza et al., 2014), but also BCL2 overexpression (Montero et al., 2017), UV exposure damages (Griffin et al., 2023), neural signatures (Sapienza et al., 2021) and fatty acid metabolism (Ceroi et al., 2016). However, BPDCN is heterogenous: recent studies showed two subgroups, based on immune response involving CD11b/CD177 (Summerer et al., 2021) or on pure pDC/conventional DC-enriched signatures (Künster et al., 2022). Künster et al. also showed NF-κB signalling pathway versus EZH2 dependence (Künster et al., 2024). Methods: We conducted a retrospective study on bone marrow (BM) aspirates and peripheral blood (PB) samples obtained at diagnosis (French pDC network ROMI DC-2008-713/DC-2016-27-91, Besançon, France). Our previously published transcriptomic data of 13 BPDCN (GSE89565) were used as a training cohort and a second cohort (n=37) was used as a validation to determine two subgroups of BPDCN, using bulk RNAsequencing (NEBNext Ultra II, New England Biolabs, Ipswich, MA, on NextSeq500, Illumina, San Diego, CA) on sorted populations (lymphocytes and blastic pDCs). Data were compared to 6 pDC and 4 lymphocyte populations obtained from healthy donors (HD) from French Blood Establishment. Paired MethylSequencing (MethylSeq) was performed on 30 blastic pDCs with SureSelectXT (Agilent, Santa Clara, CA) on NextSeq500 (Illumina). In-house pipelines on R were developed for transcription factor activities (DoRothEA/VIPER package), fusion transcript (Arriba/FusionCatcher/Starfusion), mutations (GATK), alternative splicing (Whippet), deconvolution (BisqueRNA), T-cell clone diversity (MIXCR), inference between blastic pDCs and lymphocytes (BulkSignalR), methylation status (Bismark/Methylkit/DMRichR and clusterProfiler). Probability of overall survival (OS) was determined using the Kaplan-Meyer method and log-rank test. Results: Two clusters were identified in the training cohort (median age= 61y[15-82], 11 males/13), one cluster with only men exhibiting MYB/MYC rearrangements (n=6) and another with apparent shorter OS, however without significance (n=7). These two clusters were not entirely transposed in the validation cohort (median age=67y[8-93], 26 males/37). As expected, epigenetic/splicing factor genes were highly mutated and MYB fusions were detected. Two clusters may be defined on blast fraction, but they were not strongly distinguished by unsupervised clustering. Interestingly, the lymphocyte fraction signature was shown to reflect the blasts. Combining z-scores of the 2 fractions, we note a correlation with two extreme cluster E1 and E2 without two distinct groups, but a continuity. E2 highly expressed pDC markers, HLA-II, IFN-I signalling, DNA replication, UV damages and fatty acid β-oxidation pathways. Deconvolution showed a various microenvironment, with higher T-cell clone diversity (unique TCRA/B). In contrast, E1 showed cell-cell signalling, NF-kB pathway, neural, hematopoiesis and TLR4 signatures in blasts, associated with T-cell activation, Th2 profile, IL4/IL13 signalling and ILC2 signatures,. Moreover, exon skipping was enhanced in contrast to E2 and HD pDCs. The inference study between blasts and lymphocytes showed interactions involving IL18, EGFR, IL13 in E2 and CD14, FGF in E1. Methylseq confirmed an involvement of neural and Wnt pathways in E1, while E2 showed hypomethylation of cellular response and immunological synapses targets. At last, despite short survival, our group 1 exhibited an adverse prognosis (OS 4.3 months vs 9.4 months, p=0.0431). Conclusions: even if BPDCN can be divided in two subgroups, this strategy splits intermediate patients who are very similar. We prefer to define a continuity between 2 extreme clusters. Despite the lack of a clear-cut distinction, the clusters harbour specific biological features and clinical impact. E1 confirm the C2 cluster of Künster et al., 2024 on an independent cohort. Our E2 cluster would be associated with UV damage, replication activation and fatty acid metabolism while E1 exhibit less differentiated signature, with NF-κB activation, neural signatures and different ability to chat with lymphocytes compared to E2. Prognosis and therapeutic impacts are hypothesized, NF-κB being targetable by proteasome inhibitors for instance.

Rationale: The tumor microenvironment (TME) and its multifaceted interactions with cancer cells are major targets for cancer treatment. Single-cell technologies have brought major insights into the TME, but the resulting complexity often precludes conclusions on function. Methods: We combined single-cell RNA sequencing and spatial transcriptomic data to explore the relationship between different cancer-associated fibroblast (CAF) populations and immune cell exclusion in breast tumors. The significance of the findings was then evaluated in a cohort of tumors (N=75) from breast cancer patients using immunohistochemistry analysis. Results: Our data show for the first time the degree of spatial organization of different CAF populations in breast cancer. We found that IL-iCAFs, Detox-iCAFs, and IFNγ-iCAFs tended to cluster together, while Wound-myCAFs, TGFβ-myCAFs, and ECM-myCAFs formed another group that overlapped with elevated TGF-β signaling. Differential gene expression analysis of areas with CD8+ T-cell infiltration/exclusion within the TGF-β signaling-rich zones identified elastin microfibrillar interface protein 1 (EMILIN1) as a top modulated gene. EMILIN1, a TGF-β inhibitor, was upregulated in IFNγ-iCAFs directly modulating TGFβ immunosuppressive function. Histological analysis of 75 breast cancer samples confirmed that high EMILIN1 expression in the tumor margins was related to high CD8+ T-cell infiltration, consistent with our spatial gene expression analysis. High EMILIN1 expression was also associated with better prognosis of patients with breast cancer, underscoring its functional significance for the recruitment of cytotoxic T cells into the tumor area. Conclusion: Our data show that correlating TGF-β signaling to a CAF subpopulation is not enough because proteins with TGF-β-modulating activity originating from other CAF subpopulations can alter its activity. Therefore, therapeutic targeting should remain focused on biological processes rather than on specific CAF subtypes.

Rationale: The tumor microenvironment (TME) and its multifaceted interactions with cancer cells are major targets for cancer treatment. Single -cell technologies have brought major insights into the TME, but the resulting complexity often precludes conclusions on function. Methods: We combined single -cell RNA sequencing and spatial transcriptomic data to explore the relationship between different cancer -associated fibroblast (CAF) populations and immune cell exclusion in breast tumors. The significance of the findings was then evaluated in a cohort of tumors (N=75) from breast cancer patients using immunohistochemistry analysis. Results: Our data show for the first time the degree of spatial organization of different CAF populations in breast cancer. We found that IL-iCAFs, Detox-iCAFs, and IFN gamma-iCAFs tended to cluster together, while Wound-myCAFs, TGF8-myCAFs, and ECM-myCAFs formed another group that overlapped with elevated TGF-8 signaling. Differential gene expression analysis of areas with CD8+ T -cell infiltration/exclusion within the TGF-8 signaling -rich zones identified elastin microfibrillar interface protein 1 (EMILIN1) as a top modulated gene. EMILIN1, a TGF-8 inhibitor, was upregulated in IFN gamma-iCAFs directly modulating TGF8 immunosuppressive function. Histological analysis of 75 breast cancer samples confirmed that high EMILIN1 expression in the tumor margins was related to high CD8+ T -cell infiltration, consistent with our spatial gene expression analysis. High EMILIN1 expression was also associated with better prognosis of patients with breast cancer, underscoring its functional significance for the recruitment of cytotoxic T cells into the tumor area. Conclusion: Our data show that correlating TGF-8 signaling to a CAF subpopulation is not enough because proteins with TGF-8-modulating activity originating from other CAF subpopulations can alter its activity. Therefore, therapeutic targeting should remain focused on biological processes rather than on specific CAF subtypes.

The study of cellular networks mediated by ligand-receptor interactions has attracted much attention recently owing to single-cell omics. However, rich collections of bulk data accompanied with clinical information exists and continue to be generated with no equivalent in single-cell so far. In parallel, spatial transcriptomic (ST) analyses represent a revolutionary tool in biology. A large number of ST projects rely on multicellular resolution, for instance the Visium™ platform, where several cells are analyzed at each location, thus producing localized bulk data. Here, we describe BulkSignalR, a R package to infer ligand-receptor networks from bulk data. BulkSignalR integrates ligand-receptor interactions with downstream pathways to estimate statistical significance. A range of visualization methods complement the statistics, including functions dedicated to spatial data. We demonstrate BulkSignalR relevance using different datasets, including new Visium liver metastasis ST data, with experimental validation of protein colocalization. A comparison with other ST packages shows the significantly higher quality of BulkSignalR inferences. BulkSignalR can be applied to any species thanks to its built-in generic ortholog mapping functionality.

Chromosome stability is a key point in genome evolution, particularly that of the Y chromosome. The Y chromosome loss in blood and tumor cells is well established. Through processes that are common to other chromosomes too, the Y chromosome undergoes degradation and fragmentation in the blood stream before elimination. This process gives rise to circulating DNA (cirDNA) fragments, whose examination may provide potential insight into the role of DNA fragmentation in blood for the Y chromosome elimination. In this study, we employed shallow whole genome sequencing (sWGS) to comprehensively assess the total cirDNA and the individual chromosome fragment size profiles in the plasma of healthy male individuals. Here, we show that (i) the fragment size profiles of total circulating DNA (cirDNA) and DNA fragments originating from autosomes and the X chromosome in blood plasma are homogeneous, and have a remarkably low variability (mean CV = 7

Background Protozoan parasites are known to attach specific and diverse group of proteins to their plasma membrane via a GPI anchor. In malaria parasites, GPI-anchored proteins (GPI-APs) have been shown to play an important role in host–pathogen interactions and a key function in host cell invasion and immune evasion. Because of their immunogenic properties, some of these proteins have been considered as malaria vaccine candidates. However, identification of all possible GPI-APs encoded by these parasites remains challenging due to their sequence diversity and limitations of the tools used for their characterization. Methods The FT-GPI software was developed to detect GPI-APs based on the presence of a hydrophobic helix at both ends of the premature peptide. FT-GPI was implemented in C ++and applied to study the GPI-proteome of 46 isolates of the order Haemosporida. Using the GPI proteome of Plasmodium falciparum strain 3D7 and Plasmodium vivax strain Sal-1, a heuristic method was defined to select the most sensitive and specific FT-GPI software parameters. Results FT-GPI enabled revision of the GPI-proteome of P. falciparum and P. vivax, including the identification of novel GPI-APs. Orthology- and synteny-based analyses showed that 19 of the 37 GPI-APs found in the order Haemosporida are conserved among Plasmodium species. Our analyses suggest that gene duplication and deletion events may have contributed significantly to the evolution of the GPI proteome, and its composition correlates with speciation. Conclusion FT-GPI-based prediction is a useful tool for mining GPI-APs and gaining further insights into their evolution and sequence diversity. This resource may also help identify new protein candidates for the development of vaccines for malaria and other parasitic diseases.

Motivation Modular response analysis (MRA) is a well-established method to infer biological networks from perturbation data. Classically, MRA requires the solution of a linear system and results are sensitive to noise in the data and perturbation intensities. Applications to networks of 10 nodes or more are difficult due to noise propagation. Results We propose a new formulation of MRA as a multilinear regression problem. This enables to integrate all the replicates and potential, additional perturbations in a larger, over determined and more stable system of equations. More relevant confidence intervals on network parameters can be obtained and we show competitive performance for networks of size up to 100. Prior knowledge integration in the form of known null edges further improves these results. Availability and implementation The R code used to obtain the presented results is available from GitHub: https://github.com/J-P-Borg/BioInformatics Contact Patrice.ravel@umontpellier.fr

Figure S1. Cross-docking of sunitinib and dasatinib in NQO2 and PDXK.

The ErbB family of receptor tyrosine kinases is a primary target for small molecules and antibodies for pancreatic cancer treatment. Nonetheless, the current treatments for this tumor are not optimal due to lack of efficacy, resistance, or toxicity. Here, using the novel BiXAb™ tetravalent format platform, we generated bispecific antibodies against EGFR, HER2, or HER3 by considering rational epitope combinations. We then screened these bispecific antibodies and compared them with the parental single antibodies and antibody pair combinations. The screen readouts included measuring binding to the cognate receptors (mono and bispecificity), intracellular phosphorylation signaling, cell proliferation, apoptosis and receptor expression, and also immune system engagement assays (antibody-dependent cell-mediated cytotoxicity and complement-dependent cytotoxicity). Among the 30 BiXAbs™ tested, we selected 3Patri-1Cetu-Fc, 3Patri-1Matu-Fc and 3Patri-2Trastu-Fc as lead candidates. The in vivo testing of these three highly efficient bispecific antibodies against EGFR and HER2 or HER3 in pre-clinical mouse models of pancreatic cancer showed deep antibody penetration in these dense tumors and robust tumor growth reduction. Application of such semi-rational/semi-empirical approach, which includes various immunological assays to compare pre-selected antibodies and their combinations with bispecific antibodies, represents the first attempt to identify potent bispecific antibodies against ErbB family members in pancreatic cancer.

The development of high-throughput genomic technologies associated with recent genetic perturbation techniques such as short hairpin RNA (shRNA), gene trapping, or gene editing (CRISPR/Cas9) has made it possible to obtain large perturbation data sets. These data sets are invaluable sources of information regarding the function of genes, and they offer unique opportunities to reverse engineer gene regulatory networks in specific cell types. Modular response analysis (MRA) is a well-accepted mathematical modeling method that is precisely aimed at such network inference tasks, but its use has been limited to rather small biological systems so far. In this study, we show that MRA can be employed on large systems with almost 1,000 network components. In particular, we show that MRA performance surpasses general-purpose mutual information-based algorithms. Part of these competitive results was obtained by the application of a novel heuristic that pruned MRA-inferred interactions a posteriori . We also exploited a block structure in MRA linear algebra to parallelize large system resolutions.

Rationale: Patients with colorectal cancer die mainly due to liver metastases (CRC-LM). Although the tumor microenvironment (TME) plays an important role in tumor development and therapeutic response, our understanding of the individual TME components, especially cancer-associated fibroblasts (CAFs), remains limited. Methods: We analyzed CRC-LM CAFs and cancer cells by single-cell transcriptomics and used bioinformatics for data analysis and integration with related available single-cell and bulk transcriptomic datasets. We validated key findings by RT-qPCR, western blotting, and immunofluorescence. Results: By single-cell transcriptomic analysis of 4,397 CAFs from six CRC-LM samples, we identified two main CAF populations, contractile CAFs and extracellular matrix (ECM)-remodeling/pro-angiogenic CAFs, and four subpopulations with distinct phenotypes. We found that ECM-remodeling/pro-angiogenic CAFs derive from portal resident fibroblasts. They associate with areas of strong desmoplastic reaction and Wnt signaling in low-proliferating tumor cells engulfed in a stiff extracellular matrix. By integrating public single-cell primary liver tumor data, we propose a model to explain how different liver malignancies recruit CAFs of different origins to this organ. Lastly, we found that LTBP2 plays an important role in modulating collagen biosynthesis, ECM organization, and adhesion pathways. We developed fully human antibodies against LTBP2 that depleted LTBP2+ CAFs in vitro. Conclusion: This study complements recent reports on CRC-LM CAF heterogeneity at the single-cell resolution. The number of sequenced CAFs was more than one order of magnitude larger compared to existing data. LTBP2 targeting by antibodies might create opportunities to deplete ECM-remodeling CAFs in CRC-LMs. This might be combined with other therapies, e.g., anti-angiogenic compounds as already done in CRC. Moreover, we showed that in intrahepatic cholangiocarcinoma, in which ECM-remodeling CAF proportion is similar to that of CRC-LM, several genes expressed by ECM-remodeling CAFs, such as LTBP2, were associated with survival.

Purpose: Drug-tolerant “dormant” cells (DTC) have emerged as one of the major non-genetic mechanisms driving resistance to targeted therapy in lung cancer, although the sequence of events leading to entry and exit from dormancy remain poorly described. Here, we provide a step-by-step phenotypic and molecular characterization of the different processes involved during the adaptive response to osimertinib using several EGFR-mutated lung cancer models. This strategy led to the identification of a common vulnerability of drug-tolerant cells which could be efficiently and safely targeted by a clinical stage drug. Experimental design: We used the FUCCI (fluorescence ubiquitination cell cycle indicator) system to determine the cell cycle dynamics in real time during the adaptive response to osimertinib in a panel of EGFR-mutated lung cancer cell lines. We performed scRNAseq on untreated and osimertinib-treated G1 and S/G2 sorted cells during early relapse to determine the molecular mechanisms underlying entry and exit from dormancy. We validated our observations in several in vitro and in vivo models as well as in publicly available patient data. Results: FUCCI labelling allowed the identification of a rare population of S/G2 cycling cells (referred to as early escapers) that emerged in the first hours of treatment amongst a majority of stably arrested and progressively dying G1 cells. scRNAseq data revealed that early escapers emerged from a differentiated alveolar type 1 (AT1) phenotype which was invariably associated with an increase in contractile-related gene signatures, F-actin polymerization and Rho/ROCK pathway activation. Using a screen of Rho-pathway inhibitors, we found that tipifarnib, a farnesyltransferase inhibitor (FTi), induced a complete clearance of DTC in vitro. Co-treatment with tipifarnib, a clinically active FTi, safely and durably prevented relapse to osimertinib in a PC9-xenograft model as well as in a PDX model of EGFRL858R/T790M lung cancer for up to 6 months with no evidence of toxicity. Several farnesylated targets were identified in both G1 and S/G2 treated cells, which could explain the high efficiency of tipifarnib in preventing the adaptive response to osimertinib. Finally, we observed that osimertinib + tipifarnib co-treatment completely suppressed the emergence of the AT1 phenotype, prevented mitosis of S/G2-treated cells and increased the apoptotic response through activation of the unfolded protein response (UPR) pathway. Conclusion: Our data strongly support the use of tipifarnib in combination with osimertinib in the clinic to effectively, durably and safely prevent relapse. Citation Format: Sarah Figarol, Célia Delahaye, Rémi Gence, Raghda Asslan, Sandra Pagano, Jacques Colinge, Jean-Philippe Villemin, Antonio Maraver, Isabelle Lajoie-Mazenc, Estelle Clermont, Anne Casanova, Anne Pradines, Julien Mazières, Olivier Calvayrac, Gilles Favre. Tipifarnib prevents emergence of resistance to osimertinib in EGFR-mutant NSCLC [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr LB080.

Besides the standard parameters used for colorectal cancer (CRC) management, new features are needed in clinical practice to improve progression-free and overall survival. In some cancers, the microenvironment mechanical properties can contribute to cancer progression and metastasis formation, or constitute a physical barrier for drug penetration or immune cell infiltration. These mechanical properties remain poorly known for colon tissues. Using a multidisciplinary approach including clinical data, physics and geostatistics, we characterized the stiffness of healthy and malignant colon specimens. For this purpose, we analyzed a prospective cohort of 18 patients with untreated colon adenocarcinoma using atomic force microscopy to generate micrometer-scale mechanical maps. We characterized the stiffness of normal epithelium samples taken far away or close to the tumor area and selected tumor tissue areas. These data showed that normal epithelium was softer than tumors. In tumors, stroma areas were stiffer than malignant epithelial cell areas. Among the clinical parameters, tumor left location, higher stage, and RAS mutations were associated with increased tissue stiffness. Thus, in patients with CRC, measuring tumor tissue rigidity may have a translational value and an impact on patient care.

SUMMARYAggressive neoplastic growth can be initiated by a limited number of genetic alterations, such as the well-established cooperation between loss of cell architecture and hyperactive signaling pathways. However, our understanding of how these different alterations interact and influence each other remains very incomplete. Using Drosophila paradigms of imaginal wing disc epithelial growth, we have monitored the changes in Notch pathway activity according to the polarity status of cells (scrib mutant). We show that the scrib mutation impacts the direct transcriptional output of the Notch pathway, without altering the global distribution of Su(H), the Notch dedicated transcription factor. The Notch-dependent neoplasms require however, the action of a group of transcription factors, similar to those previously identified for Ras/scrib neoplasm (namely AP-1, Stat92E, Ftz-F1, and bZIP factors), further suggesting the importance of this transcription factor network during neoplastic growth. Finally our work highlights some Notch/scrib specificities, in particular the role of the PAR domain containing bZIP transcription factor and Notch direct target Pdp1 for neoplastic growth.