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Abstract 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.

Abstract 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.

Abstract 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.

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.

Abstract Alterations to cell polarization or to intercellular junctions are often associated with epithelial cancer progression, including breast cancers (BCa). We show here that the loss of the junctional scaffold protein MAGI1 is associated with bad prognosis in luminal BCa, and promotes tumorigenesis. E-cadherin and the actin binding scaffold AMOTL2 accumulate in MAGI1 deficient cells which are subjected to increased stiffness. These alterations are associated with low YAP activity, the terminal Hippo-pathway effector, but with an elevated ROCK and p38 Stress Activated Protein Kinase activities. Blocking ROCK prevented p38 activation, suggesting that MAGI1 limits p38 activity in part through releasing actin strength. Importantly, the increased tumorigenicity of MAGI1 deficient cells is rescued in the absence of AMOTL2 or after inhibition of p38, demonstrating that MAGI1 acts as a tumor-suppressor in luminal BCa by inhibiting an AMOTL2/p38 stress pathway.

Abstract Modular response analysis (MRA) is a widely used inference technique developed to uncover directions and strengths of connections in molecular networks under a steady-state condition by means of perturbation experiments. We devised several extensions of this methodology to search genomic data for new associations with a biological network inferred by MRA, to improve the predictive accuracy of MRA-inferred networks, and to estimate confidence intervals of MRA parameters from datasets with low numbers of replicates. The classical MRA computations and their extensions were implemented in a freely available R package called aiMeRA ( https://github.com/bioinfo-ircm/aiMeRA/ ). We illustrated the application of our package by assessing the crosstalk between estrogen and retinoic acid receptors, two nuclear receptors implicated in several hormone-driven cancers, such as breast cancer. Based on new data generated for this study, our analysis revealed potential cross-inhibition mediated by the shared corepressors NRIP1 and LCoR. We designed aiMeRA for non-specialists and to allow biologists to perform their own analyses.

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.

Abstract While the utility of circulating cell‐free DNA (cfDNA) in cancer screening and early detection have recently been investigated by testing genetic and epigenetic alterations, here, an original approach by examining cfDNA quantitative and structural features is developed. First, the potential of cfDNA quantitative and structural parameters is independently demonstrated in cell culture, murine, and human plasma models. Subsequently, these variables are evaluated in a large retrospective cohort of 289 healthy individuals and 983 patients with various cancer types; after age resampling, this evaluation is done independently and the variables are combined using a machine learning approach. Implementation of a decision tree prediction model for the detection and classification of healthy and cancer patients shows unprecedented performance for 0, I, and II colorectal cancer stages (specificity, 0.89 and sensitivity, 0.72). Consequently, the methodological proof of concept of using both quantitative and structural biomarkers, and classification with a machine learning method are highlighted, as an efficient strategy for cancer screening. It is foreseen that the classification rate may even be improved by the addition of such biomarkers to fragmentomics, methylation, or the detection of genetic alterations. The optimization of such a multianalyte strategy with this machine learning method is therefore warranted.

Abstract Background Plasmodium falciparum malaria is one of the most widespread parasitic infections in humans and remains a leading global health concern. Malaria elimination efforts are threatened by the emergence and spread of resistance to artemisinin-based combination therapy, the first-line treatment of malaria. Promising molecular markers and pathways associated with artemisinin drug resistance have been identified, but the underlying molecular mechanisms of resistance remains unknown. The genomic data from early period of emergence of artemisinin resistance (2008–2011) was evaluated, with aim to define k13 associated genetic background in Cambodia, the country identified as epicentre of anti-malarial drug resistance, through characterization of 167 parasite isolates using a panel of 21,257 SNPs. Results Eight subpopulations were identified suggesting a process of acquisition of artemisinin resistance consistent with an emergence-selection-diffusion model, supported by the shifting balance theory. Identification of population specific mutations facilitated the characterization of a core set of 57 background genes associated with artemisinin resistance and associated pathways. The analysis indicates that the background of artemisinin resistance was not acquired after drug pressure, rather is the result of fixation followed by selection on the daughter subpopulations derived from the ancestral population. Conclusions Functional analysis of artemisinin resistance subpopulations illustrates the strong interplay between ubiquitination and cell division or differentiation in artemisinin resistant parasites. The relationship of these pathways with the P. falciparum resistant subpopulation and presence of drug resistance markers in addition to k13, highlights the major role of admixed parasite population in the diffusion of artemisinin resistant background. The diffusion of resistant genes in the Cambodian admixed population after selection resulted from mating of gametocytes of sensitive and resistant parasite populations.

The cellular response to replication stress requires the DNA-damage-responsive kinase ATM and its cofactor ATMIN; however, the roles of this signaling pathway following replication stress are unclear. To identify the functions of ATM and ATMIN in response to replication stress, we utilized both transcriptomics and quantitative mass-spectrometry-based phosphoproteomics. We found that replication stress induced by aphidicolin triggered widespread changes in both gene expression and protein phosphorylation patterns. These changes gave rise to distinct early and late replication stress responses. Furthermore, our analysis revealed previously unknown targets of ATM and ATMIN downstream of replication stress. We demonstrate ATMIN-dependent phosphorylation of H2AX and of CRMP2, a protein previously implicated in Alzheimer’s disease but not in the DNA damage response. Overall, our dataset provides a comprehensive resource for discovering the cellular responses to replication stress and, potentially, associated pathologies.

Gel-free liquid chromatography mass spectrometry coupled to chemical proteomics is a powerful approach for characterizing cellular target profiles of small molecules. We have previously described a fast and efficient elution protocol; however, altered target profiles were observed. We hypothesised that elution conditions critically impact the effectiveness of disrupting drug-protein interactions. Thus, a number of elution conditions were systematically assessed with the aim of improving the recovery of all classes of proteins whilst maintaining compatibility with immunoblotting procedures. A double elution with formic acid combined with urea emerged as the most efficient and generically applicable elution method for chemical proteomics Keywords: Chemical proteomics, Mass spectrometry gel-free, Double elution, Dasatinib, Sunitinib

Lipid metabolism and receptor-mediated signaling are highly intertwined processes that cooperate to fulfill cellular functions and safeguard cellular homeostasis. Activation of Toll-like receptors (TLRs) leads to a complex cellular response, orchestrating a diverse range of inflammatory events that need to be tightly controlled. Here, we identified the GPI-anchored Sphingomyelin Phosphodiesterase, Acid-Like 3B (SMPDL3B) in a mass spectrometry screening campaign for membrane proteins co-purifying with TLRs. Deficiency of Smpdl3b in macrophages enhanced responsiveness to TLR stimulation and profoundly changed the cellular lipid composition and membrane fluidity. Increased cellular responses could be reverted by re-introducing affected ceramides, functionally linking membrane lipid composition and innate immune signaling. Finally, Smpdl3b-deficient mice displayed an intensified inflammatory response in TLR-dependent peritonitis models, establishing its negative regulatory role in vivo. Taken together, our results identify the membrane-modulating enzyme SMPDL3B as a negative regulator of TLR signaling that functions at the interface of membrane biology and innate immunity.

Abstract Some mutations in cancer cells can be exploited for therapeutic intervention. However, for many cancer subtypes, including triple‐negative breast cancer (TNBC), no frequently recurring aberrations could be identified to make such an approach clinically feasible. Characterized by a highly heterogeneous mutational landscape with few common features, many TNBCs cluster together based on their ‘basal‐like’ transcriptional profiles. We therefore hypothesized that targeting TNBC cells on a systems level by exploiting the transcriptional cell state might be a viable strategy to find novel therapies for this highly aggressive disease. We performed a large‐scale chemical genetic screen and identified a group of compounds related to the drug PKC412 (midostaurin). PKC412 induced apoptosis in a subset of TNBC cells enriched for the basal‐like subtype and inhibited tumor growth in vivo. We employed a multi‐omics approach and computational modeling to address the mechanism of action and identified spleen tyrosine kinase (SYK) as a novel and unexpected target in TNBC. Quantitative phosphoproteomics revealed that SYK inhibition abrogates signaling to STAT3, explaining the selectivity for basal‐like breast cancer cells. This non‐oncogene addiction suggests that chemical SYK inhibition may be beneficial for a specific subset of TNBC patients and demonstrates that targeting cell states could be a viable strategy to discover novel treatment strategies.

The ability to stratify patients using a set of biomarkers, which predict that toxicity risk would allow for radiotherapy (RT) modulation and serve as a valuable tool for precision medicine and personalized RT. For patients presenting with tumors with a low risk of recurrence, modifying RT schedules to avoid toxicity would be clinically advantageous. Indeed, for the patient at low risk of developing radiation-associated toxicity, use of a hypofractionated protocol could be proposed leading to treatment time reduction and a cost–utility advantage. Conversely, for patients predicted to be at high risk for toxicity, either a more conformal form or a new technique of RT, or a multidisciplinary approach employing surgery could be included in the trial design to avoid or mitigate RT when the potential toxicity risk may be higher than the risk of disease recurrence. In addition, for patients at high risk of recurrence and low risk of toxicity, dose escalation, such as a greater boost dose, or irradiation field extensions could be considered to improve local control without severe toxicities, providing enhanced clinical benefit. In cases of high risk of toxicity, tumor control should be prioritized. In this review, toxicity biomarkers with sufficient evidence for clinical testing are presented. In addition, clinical trial designs and predictive models are described for different clinical situations.

Transport of RNAs to dendrites occurs in neuronal RNA granules, which allows local synthesis of specific proteins at active synapses on demand, thereby contributing to learning and memory. To gain insight into the machinery controlling dendritic mRNA localization and translation, we established a stringent protocol to biochemically purify RNA granules from rat brain. Here, we identified a specific set of interactors for two RNA-binding proteins that are known components of neuronal RNA granules, Barentsz and Staufen2. First, neuronal RNA granules are much more heterogeneous than previously anticipated, sharing only a third of the identified proteins. Second, dendritically localized mRNAs, e.g., Arc and CaMKIIα, associate selectively with distinct RNA granules. Third, our work identifies a series of factors with known roles in RNA localization, translational control, and RNA quality control that are likely to keep localized transcripts in a translationally repressed state, often in distinct types of RNPs.

Abstract We hypothesized that elucidating the interactome of epidermal growth factor receptor (EGFR) forms that are mutated in lung cancer, via global analysis of protein–protein interactions, phosphorylation, and systematically perturbing the ensuing network nodes, should offer a new, more systems‐level perspective of the molecular etiology. Here, we describe an EGFR interactome of 263 proteins and offer a 14‐protein core network critical to the viability of multiple EGFR‐mutated lung cancer cells. Cells with acquired resistance to EGFR tyrosine kinase inhibitors (TKIs) had differential dependence of the core network proteins based on the underlying molecular mechanisms of resistance. Of the 14 proteins, 9 are shown to be specifically associated with survival of EGFR‐mutated lung cancer cell lines. This included EGFR, GRB2, MK12, SHC1, ARAF, CD11B, ARHG5, GLU2B, and CD11A. With the use of a drug network associated with the core network proteins, we identified two compounds, midostaurin and lestaurtinib, that could overcome drug resistance through direct EGFR inhibition when combined with erlotinib. Our results, enabled by interactome mapping, suggest new targets and combination therapies that could circumvent EGFR TKI resistance.

Phagocytosis and inflammation within the lungs is crucial for host defense during bacterial pneumonia. Triggering receptor expressed on myeloid cells (TREM)-2 was proposed to negatively regulate TLR-mediated responses and enhance phagocytosis by macrophages, but the role of TREM-2 in respiratory tract infections is unknown. Here, we established the presence of TREM-2 on alveolar macrophages (AM) and explored the function of TREM-2 in the innate immune response to pneumococcal infection in vivo. Unexpectedly, we found Trem-2(-/-) AM to display augmented bacterial phagocytosis in vitro and in vivo compared to WT AM. Mechanistically, we detected that in the absence of TREM-2, pulmonary macrophages selectively produced elevated complement component 1q (C1q) levels. We found that these increased C1q levels depended on peroxisome proliferator-activated receptor-δ (PPAR-δ) activity and were responsible for the enhanced phagocytosis of bacteria. Upon infection with S. pneumoniae, Trem-2(-/-) mice exhibited an augmented bacterial clearance from lungs, decreased bacteremia and improved survival compared to their WT counterparts. This work is the first to disclose a role for TREM-2 in clinically relevant respiratory tract infections and demonstrates a previously unknown link between TREM-2 and opsonin production within the lungs.

Philadelphia chromosome-positive acute lymphoblastic leukemia (Ph+ ALL) is in part driven by the tyrosine kinase bcr-abl, but imatinib does not produce long-term remission. Therefore, second-generation ABL inhibitors are currently in clinical investigation. Considering different target specificities and the pronounced genetic heterogeneity of Ph+ ALL, which contributes to the aggressiveness of the disease, drug candidates should be evaluated with regard to their effects on the entire Ph+ ALL-specific signaling network. Here, we applied an integrated experimental and computational approach that allowed us to estimate the differential impact of the bcr-abl inhibitors nilotinib, dasatinib, Bosutinib and Bafetinib. First, we determined drug-protein interactions in Ph+ ALL cell lines by chemical proteomics. We then mapped those interactions along with known genetic lesions onto public protein-protein interactions. Computation of global scores through correlation of target affinity, network topology, and distance to disease-relevant nodes assigned the highest impact to dasatinib, which was subsequently confirmed by proliferation assays. In future, combination of patient-specific genomic information with detailed drug target knowledge and network-based computational analysis should allow for an accurate and individualized prediction of therapy.

TANK-binding kinase 1 (TBK1) and inducible IκB-kinase (IKK-i) are central regulators of type-I interferon induction. They are associated with three adaptor proteins called TANK, Sintbad (or TBKBP1) and NAP1 (or TBKBP2, AZI2) whose functional relationship to TBK1 and IKK-i is poorly understood. We performed a systematic affinity purification-mass spectrometry approach to derive a comprehensive TBK1/IKK-i molecular network. The most salient feature of the network is the mutual exclusive interaction of the adaptors with the kinases, suggesting distinct alternative complexes. Immunofluorescence data indicated that the individual adaptors reside in different subcellular locations. TANK, Sintbad and NAP1 competed for binding of TBK1. The binding site for all three adaptors was mapped to the C-terminal coiled-coil 2 region of TBK1. Point mutants that affect binding of individual adaptors were used to reconstitute TBK1/IKK-i-deficient cells and dissect the functional relevance of the individual kinase-adaptor edges within the network. Using a microarray-derived gene expression signature of TBK1 in response virus infection or poly(I∶C) stimulation, we found that TBK1 activation was strictly dependent on the integrity of the TBK1/TANK interaction.

Prediction of drug action in human cells is a major challenge in biomedical research. Additionally, there is strong interest in finding new applications for approved drugs and identifying potential side effects. We present a computational strategy to predict mechanisms, risks and potential new domains of drug treatment on the basis of target profiles acquired through chemical proteomics. Functional protein-protein interaction networks that share one biological function are constructed and their crosstalk with the drug is scored regarding function disruption. We apply this procedure to the target profile of the second-generation BCR-ABL inhibitor bafetinib which is in development for the treatment of imatinib-resistant chronic myeloid leukemia. Beside the well known effect on apoptosis, we propose potential treatment of lung cancer and IGF1R expressing blast crisis.