Reconstructing kinase network topologies from phosphoproteomics data reveals cancer-associated rewiring

Understanding how oncogenic mutations rewire regulatory-protein networks is important for rationalizing the mechanisms of oncogenesis and for individualizing anticancer treatments. We report a chemical phosphoproteomics method to elucidate the topology of kinase-signaling networks in mammalian cells. We identified >6,000 protein phosphorylation sites that can be used to infer >1,500 kinase–kinase interactions and devised algorithms that can reconstruct kinase network topologies from these phosphoproteomics data. Application of our methods to primary acute myeloid leukemia and breast cancer tumors quantified the relationship between kinase expression and activity, and enabled the identification of hitherto unknown kinase network topologies associated with drug-resistant phenotypes or specific genetic mutations. Using orthogonal methods we validated that PIK3CA wild-type cells adopt MAPK-dependent circuitries in breast cancer cells and that the kinase TTK is important in acute myeloid leukemia. Our phosphoproteomic signatures of network circuitry can identify kinase topologies associated with both phenotypes and genotypes of cancer cells

eEF2K Activity Determines Synergy to Cotreatment of Cancer Cells With PI3K and MEK Inhibitors

PI3K-mammalian target of rapamycin and MAPK/ERK kinase (MEK)/mitogen-activated protein kinase (MAPK) are the most frequently dysregulated signaling pathways in cancer. A problem that limits the success of therapies that target individual PI3K-MAPK members is that these pathways converge to regulate downstream functions and often compensate each other, leading to drug resistance and transient responses to therapy. In order to overcome resistance, therapies based on cotreatments with PI3K/AKT and MEK/MAPK inhibitors are now being investigated in clinical trials, but the mechanisms of sensitivity to cotreatment are not fully understood. Using LC-MS/MS-based phosphoproteomics, we found that eukaryotic elongation factor 2 kinase (eEF2K), a key convergence point downstream of MAPK and PI3K pathways, mediates synergism to cotreatment with trametinib plus pictilisib (which target MEK1/2 and PI3Kα/δ, respectively). Inhibition of eEF2K by siRNA or with a small molecule inhibitor reversed the antiproliferative effects of the cotreatment with PI3K plus MEK inhibitors in a cell model–specific manner. Systematic analysis in 12 acute myeloid leukemia cell lines revealed that eEF2K activity was increased in cells for which PI3K plus MEKi cotreatment is synergistic, while PKC potentially mediated resistance to such cotreatment. Together, our study uncovers eEF2K activity as a key mediator of responses to PI3Ki plus MEKi and as a potential biomarker to predict synergy to cotreatment in cancer cells

Computational Analysis of Cholangiocarcinoma Phosphoproteomes Identifies Patient-Specific Drug Targets

Cholangiocarcinoma is a form of hepatobiliary cancer with an abysmal prognosis. Despite advances in our understanding of cholangiocarcinoma pathophysiology and its genomic landscape, targeted therapies have not yet made a significant impact on its clinical management. The low response rates of targeted therapies in cholangiocarcinoma suggest that patient heterogeneity contributes to poor clinical outcome. Here we used mass spectrometry–based phosphoproteomics and computational methods to identify patient-specific drug targets in patient tumors and cholangiocarcinoma-derived cell lines. We analyzed 13 primary tumors of patients with cholangiocarcinoma with matched nonmalignant tissue and 7 different cholangiocarcinoma cell lines, leading to the identification and quantification of more than 13,000 phosphorylation sites. The phosphoproteomes of cholangiocarcinoma cell lines and patient tumors were significantly correlated. MEK1, KIT, ERK1/2, and several cyclin-dependent kinases were among the protein kinases most frequently showing increased activity in cholangiocarcinoma relative to nonmalignant tissue. Application of the Drug Ranking Using Machine Learning (DRUML) algorithm selected inhibitors of histone deacetylase (HDAC; belinostat and CAY10603) and PI3K pathway members as high-ranking therapies to use in primary cholangiocarcinoma. The accuracy of the computational drug rankings based on predicted responses was confirmed in cell-line models of cholangiocarcinoma. Together, this study uncovers frequently activated biochemical pathways in cholangiocarcinoma and provides a proof of concept for the application of computational methodology to rank drugs based on efficacy in individual patients. SIGNIFICANCE: Phosphoproteomic and computational analyses identify patient-specific drug targets in cholangiocarcinoma, supporting the potential of a machine learning method to predict personalized therapies

Identificat el receptor de la relaxació del còlon

Un grup d'investigadors del Departament de Biologia Cel·lular, Fisiologia e Immunologia i de l'Institut de Neurociències de la UAB han confirmat que el receptor P2Y1 és responsable de la relaxació del còlon. Una recerca prèviament realitzada a la UAB en mostres de colon humà, ha estat confirmada en aquest estudi amb un ratolí genèticament modificat que no té el receptor i per tant el colon no es relaxa. Futurs estudis permetran valorar la funció del receptor en diferents patologies.Un grupo de investigadores del Departamento de Biología Celular, Fisiología e Inmunología y del Instituto de Neurociencias de la UAB han confirmado que el receptor P2Y1 es responsable de la relajación del colon. Una investigación previamente realizada en la UAB en muestras de colon humano, ha sido confirmada en este estudio con ratón genéticamente modificado que no tiene el receptor y por tanto el colon del cual no se relaja. Futuros estudios permitirán valorar la función del receptor en diferentes patologías

Report of the Workshop on Age estimation of European anchovy (Engraulis encrasicolus)

Based on the results of a full-scale otolith exchange held in 2014, the Working Group on Biological Parameters (WGBIOP 2015) identified the need for an age reading workshop on European Anchovy otoliths (WKARA2). This workshop (chaired by Andres Uriarte, Spain, Begoña Villamor, Spain and Gualtiero Basilone, Italy), was held in Pasaia, Gui-puzcoa (Spain) from the 28 November to 2 December 2016. Five countries took part in this workshop (Spain, Italy, Croatia, Greece and Tunisia), with a total of 16 participants from 9 laboratories. In total 17 areas/stocks were analysed (4 from the Atlantic area and 13 from Mediterranean Sea) The aim of this workshop was to review the information on age determination, discuss the results of the previous exchange (2014), review the validation methods existing on these species, clarify the interpretation of annual rings and update the age reading pro-tocol and a reference collection of well-defined otoliths. Age validation studies, in the Bay of Biscay and preliminary validation studies in Divi-sion 9a, Alboran Sea and Strait of Sicily areas were presented, including a compilation of age validation studies of this species as well in the literature. There are several ar-eas/stocks in which validations of the anchovy annual age determination have not been done yet. Due to the poor percentage of agreement achieved in the 2014 Exchange (mean agree-ment of 66%; mean CV of 58%), the workshop proceeded with a detailed and joint dis-cussion on the growth patterns shown by otoliths from the different areas to find out the major reasons for discrepancies in age determination among readers. At the same time, the joint discussion allowed a better understanding of the pattern of otolith growth in-crements by areas to improve the guidelines for their interpretation. The discussions on examples among otoliths which generated discrepancies in the age determination led to conclude that there were two major sources of disagreements: a) Divergent otolith inter-pretation: different interpretations of the marks, growth bands and edges in terms of their conformity with the expected growth pattern of the anchovies, seasonal formation of the otolith by ages and most common checks. and b) wrong application of the age allocation Rules: it was evidenced during the workshop that for the birthdate first July (or first June) in some cases the age determination rule was not being correctly applied during the first half of the year (from January to June). Following the workshop discussions there has been a progressive change in the percep-tion of the growth pattern applicable to these anchovy otoliths in many areas which led to some revisions of the otolith interpretation and assigned ages, by which growth at ages 0 and 1 are far prominent than at older ages and the occurrence of checks became more frequently admitted. Furthermore, there have been evidences that the age determination rules have in some instances been inconsistently applied. All these evidences led to con-clude on the need to review past age determinations. Although this task should be de-layed until running an exchange in 2018 to be sure that all the readers apply the protocol and the current criteria of this workshop coherently, since current criteria would change the otoliths interpretation and the age determination in many areas. In addition, for the Mediterranean regions the convenience of midyear birthdates was put in question in comparison with the simplicity of the conventional birthdates at first of January (as these anchovies are in the northern hemisphere). As a corollary of the former statements, intercalibration exercises by areas, for the differ-ent countries taking part in the age reading of the same exploited stock, are still required. Finally, this Workshop adopted a common protocol for all areas in order to standardize the anchovy age assignments and to improve the coherence of the age estimates. An agreed collection of otoliths by areas were produced and upload to the Age Readers Fo-rum

Drug ranking using machine learning systematically predicts the efficacy of anti-cancer drugs

Artificial intelligence and machine learning (ML) promise to transform cancer therapies by accurately predicting the most appropriate therapies to treat individual patients. Here, we present an approach, named Drug Ranking Using ML (DRUML), which uses omics data to produce ordered lists of >400 drugs based on their anti-proliferative efficacy in cancer cells. To reduce noise and increase predictive robustness, instead of individual features, DRUML uses internally normalized distance metrics of drug response as features for ML model generation. DRUML is trained using in-house proteomics and phosphoproteomics data derived from 48 cell lines, and it is verified with data comprised of 53 cellular models from 12 independent laboratories. We show that DRUML predicts drug responses in independent verification datasets with low error (mean squared error < 0.1 and mean Spearman’s rank 0.7). In addition, we demonstrate that DRUML predictions of cytarabine sensitivity in clinical leukemia samples are prognostic of patient survival (Log rank p < 0.005). Our results indicate that DRUML accurately ranks anti-cancer drugs by their efficacy across a wide range of pathologies

Current professional standing of young medical oncologists in Spain: a nationwide survey by the Spanish Society of Medical Oncology + MIR section

Job performance; Oncology professionals; Professional standingRendiment laboral; Professionals d'oncologia; Situació professionalDesempeño laboral; Profesionales de oncología; Situación profesionalBackground There is a lack of knowledge about the career paths and employment situation of young medical oncologists. The aim of our study was to evaluate the current professional standing of these professionals in Spain. Methods The Spanish Society of Medical Oncology + MIR section conducted a national online survey in May 2021 of young medical oncology consultants (< 6 years of expertise) and final year medical oncology residents. Results A total of 162 responses were eligible for analysis and included participants from 16 autonomous communities; 64% were women, 80% were consultants, and 20% were residents. More than half of the participants performed routine healthcare activity and only 7% research activity. Almost three quarters (73%) were subspecialized in a main area of interest and almost half of these chose this area because it was the only option available after residency. Half of the respondents (51%) considered working abroad and 81% believed the professional standing in Spain was worse than in other countries. After finishing their residency, only 22 were offered a job at their training hospital. Just 16% of participants had a permanent employment contract and 87% were concerned (score of ≥ 5 on a scale of 1–10) about their job stability. In addition, one quarter of the participants in our study showed an interest in increasing their research activity. Conclusions The choice of subspecialty in medical oncology may depend on job opportunities after residency rather than personal interest. The abundance of temporary contracts may have influenced the job stability concerns observed. Future mentoring strategies should engage in building a long-term career path for young medical oncologists.This project received funding from the Spanish Society of Medical Oncology (SEOM)

Extracellular matrix educates an immunoregulatory tumor macrophage phenotype found in ovarian cancer metastasis

: Recent studies have shown that the tumor extracellular matrix (ECM) associates with immunosuppression, and that targeting the ECM can improve immune infiltration and responsiveness to immunotherapy. A question that remains unresolved is whether the ECM directly educates the immune phenotypes seen in tumors. Here, we identify a tumor-associated macrophage (TAM) population associated with poor prognosis, interruption of the cancer immunity cycle, and tumor ECM composition. To investigate whether the ECM was capable of generating this TAM phenotype, we developed a decellularized tissue model that retains the native ECM architecture and composition. Macrophages cultured on decellularized ovarian metastasis shared transcriptional profiles with the TAMs found in human tissue. ECM-educated macrophages have a tissue-remodeling and immunoregulatory phenotype, inducing altered T cell marker expression and proliferation. We conclude that the tumor ECM directly educates this macrophage population found in cancer tissues. Therefore, current and emerging cancer therapies that target the tumor ECM may be tailored to improve macrophage phenotype and their downstream regulation of immunity

Characterization of four subtypes in morphologically normal tissue excised proximal and distal to breast cancer

Widespread mammographic screening programs and improved self-monitoring allow for breast cancer to be detected earlier than ever before. Breast-conserving surgery is a successful treatment for select women. However, up to 40% of women develop local recurrence after surgery despite apparently tumor-free margins. This suggests that morphologically normal breast may harbor early alterations that contribute to increased risk of cancer recurrence. We conducted a comprehensive transcriptomic and proteomic analysis to characterize 57 fresh-frozen tissues from breast cancers and matched histologically normal tissues resected proximal to (<2 cm) and distant from (5–10 cm) the primary tumor, using tissues from cosmetic reduction mammoplasties as baseline. Four distinct transcriptomic subtypes are identified within matched normal tissues: metabolic; immune; matrisome/epithelial–mesenchymal transition, and non-coding enriched. Key components of the subtypes are supported by proteomic and tissue composition analyses. We find that the metabolic subtype is associated with poor prognosis (p < 0.001, HR6.1). Examination of genes representing the metabolic signature identifies several genes able to prognosticate outcome from histologically normal tissues. A subset of these have been reported for their predictive ability in cancer but, to the best of our knowledge, these have not been reported altered in matched normal tissues. This study takes an important first step toward characterizing matched normal tissues resected at pre-defined margins from the primary tumor. Unlocking the predictive potential of unexcised tissue could prove key to driving the realization of personalized medicine for breast cancer patients, allowing for more biologically-driven analyses of tissue margins than morphology alone