Human leukocyte antigen variants associate with BNT162b2 mRNA vaccine response.

BACKGROUND: Since the beginning of the anti-COVID-19 vaccination campaign, it has become evident that vaccinated subjects exhibit considerable inter-individual variability in the response to the vaccine that could be partly explained by host genetic factors. A recent study reported that the immune response elicited by the Oxford-AstraZeneca vaccine in individuals from the United Kingdom was influenced by a specific allele of the human leukocyte antigen gene HLA-DQB1. METHODS: We carried out a genome-wide association study to investigate the genetic determinants of the antibody response to the Pfizer-BioNTech vaccine in an Italian cohort of 1351 subjects recruited in three centers. Linear regressions between normalized antibody levels and genotypes of more than 7 million variants was performed, using sex, age, centers, days between vaccination boost and serological test, and five principal components as covariates. We also analyzed the association between normalized antibody levels and 204 HLA alleles, with the same covariates as above. RESULTS: Our study confirms the involvement of the HLA locus and shows significant associations with variants in HLA-A, HLA-DQA1, and HLA-DQB1 genes. In particular, the HLA-A*03:01 allele is the most significantly associated with serum levels of anti-SARS-CoV-2 antibodies. Other alleles, from both major histocompatibility complex class I and II are significantly associated with antibody levels. CONCLUSIONS: These results support the hypothesis that HLA genes modulate the response to Pfizer-BioNTech vaccine and highlight the need for genetic studies in diverse populations and for functional studies aimed to elucidate the relationship between HLA-A*03:01 and CD8+ cell response upon Pfizer-BioNTech vaccination.

It is known that people respond differently to vaccines. It has been proposed that differences in their genes might play a role. We studied the individual genetic makeup of 1351 people from Italy to see if there was a link between their genes and how well they responded to the BNT162b2 mRNA COVID-19 vaccine. We discovered certain genetic differences linked to higher levels of protection in those who got the vaccine. Our findings suggest that individual's genetic characteristics play a role in vaccine response. A larger population involving diverse ethnic backgrounds will need to be studied to confirm the generalizability of these findings. Better understanding of this could facilitate improved vaccine designs against new SARS-CoV-2 variants.

"Be sustainable": EOSC-Life recommendations for implementation of FAIR principles in life science data handling.

The main goals and challenges for the life science communities in the Open Science framework are to increase reuse and sustainability of data resources, software tools, and workflows, especially in large-scale data-driven research and computational analyses. Here, we present key findings, procedures, effective measures and recommendations for generating and establishing sustainable life science resources based on the collaborative, cross-disciplinary work done within the EOSC-Life (European Open Science Cloud for Life Sciences) consortium. Bringing together 13 European life science research infrastructures, it has laid the foundation for an open, digital space to support biological and medical research. Using lessons learned from 27 selected projects, we describe the organisational, technical, financial and legal/ethical challenges that represent the main barriers to sustainability in the life sciences. We show how EOSC-Life provides a model for sustainable data management according to FAIR (findability, accessibility, interoperability, and reusability) principles, including solutions for sensitive- and industry-related resources, by means of cross-disciplinary training and best practices sharing. Finally, we illustrate how data harmonisation and collaborative work facilitate interoperability of tools, data, solutions and lead to a better understanding of concepts, semantics and functionalities in the life sciences.

Recommendations for robust and reproducible preclinical research in personalised medicine.

BACKGROUND: Personalised medicine is a medical model that aims to provide tailor-made prevention and treatment strategies for defined groups of individuals. The concept brings new challenges to the translational step, both in clinical relevance and validity of models. We have developed a set of recommendations aimed at improving the robustness of preclinical methods in translational research for personalised medicine. METHODS: These recommendations have been developed following four main steps: (1) a scoping review of the literature with a gap analysis, (2) working sessions with a wide range of experts in the field, (3) a consensus workshop, and (4) preparation of the final set of recommendations. RESULTS: Despite the progress in developing innovative and complex preclinical model systems, to date there are fundamental deficits in translational methods that prevent the further development of personalised medicine. The literature review highlighted five main gaps, relating to the relevance of experimental models, quality assessment practices, reporting, regulation, and a gap between preclinical and clinical research. We identified five points of focus for the recommendations, based on the consensus reached during the consultation meetings: (1) clinically relevant translational research, (2) robust model development, (3) transparency and education, (4) revised regulation, and (5) interaction with clinical research and patient engagement. Here, we present a set of 15 recommendations aimed at improving the robustness of preclinical methods in translational research for personalised medicine. CONCLUSIONS: Appropriate preclinical models should be an integral contributor to interventional clinical trial success rates, and predictive translational models are a fundamental requirement to realise the dream of personalised medicine. The implementation of these guidelines is ambitious, and it is only through the active involvement of all relevant stakeholders in this field that we will be able to make an impact and effectuate a change which will facilitate improved translation of personalised medicine in the future.

Isolation and characterization of two newly established thymoma PDXs from two relapses of the same patient: a new tool to investigate thymic malignancies.

BACKGROUND: Thymic malignancies are a heterogeneous group of rare cancers for which systemic chemotherapy is the standard treatment in the setting of advanced, recurrent or refractory diseases. Both environmental and genetic risk factors have not been fully clarified and few target-specific drugs have been developed for thymic epithelial tumors. A major challenge in studying thymic epithelial tumors is the lack of preclinical models for translational studies. MAIN BODY: Starting from bioptic material of two consecutive recurrences of the same patient, we generated two patient-derived xenografts. The patient-derived xenografts models were characterized for histology by immunohistochemistry and mutations using next-generation sequencing. When compared to the original tumors resected from the patient, the two patient-derived xenografts had preserved morphology after the stain with hematoxylin and eosin, although there was a moderate degree of de-differentiation. From a molecular point of view, the two patient-derived xenografts maintained 74.3 and 61.8% of the mutations present in the human tumor of origin. SHORT CONCLUSION: The newly generated patient-derived xenografts recapitulate both the molecular characteristics and the evolution of the thymoma it derives from well, allowing to address open questions for this rare cancer.

Stable CDK12 Knock-Out Ovarian Cancer Cells Do Not Show Increased Sensitivity to Cisplatin and PARP Inhibitor Treatment.

Cyclin-dependent kinase 12 (CDK12) is a serine/threonine kinase involved in the regulation of RNA polymerase II and in the transcription of a subset of genes involved in the DNA damage response. CDK12 is one of the most mutated genes in ovarian carcinoma. These mutations result in loss-of-function and can predict the responses to PARP1/2 inhibitor and platinum. To investigate the role of CDK12 in ovarian cancer, CRISPR/Cas9 technology was used to generate a stable CDK12 knockout (KO) clone in A2780 ovarian carcinoma cells. This is the first report on a CDK12 null cell line. The clone had slower cell growth and was less clonogenic than parental cells. These data were confirmed in vivo, where CDK12 KO transplanted cells had a much longer time lag and slightly slower growth rate than CDK12-expressing cells. The slower growth was associated with a higher basal level of apoptosis, but there were no differences in the basal level of autophagy and senescence. While cell cycle distribution was similar in parental and knockout cells, there was a doubling in DNA content, with an almost double modal number of chromosomes in the CDK12 KO clone which, however did not display any increase in γH2AX, a marker of DNA damage. We found partial down-regulation of the expression of DNA repair genes at the mRNA level and, among the down-regulated genes, an enrichment in the G2/M checkpoint genes. Although the biological features of CDK12 KO cells are compatible with the function of CDK12, contrary to some reports, we could not find any difference in the sensitivity to cisplatin and olaparib between wild-type and CDK12 KO cells.

Evaluating Translational Methods for Personalized Medicine-A Scoping Review.

The introduction of personalized medicine, through the increasing multi-omics characterization of disease, brings new challenges to disease modeling. The scope of this review was a broad evaluation of the relevance, validity, and predictive value of the current preclinical methodologies applied in stratified medicine approaches. Two case models were chosen: oncology and brain disorders. We conducted a scoping review, following the Joanna Briggs Institute guidelines, and searched PubMed, EMBASE, and relevant databases for reports describing preclinical models applied in personalized medicine approaches. A total of 1292 and 1516 records were identified from the oncology and brain disorders search, respectively. Quantitative and qualitative synthesis was performed on a final total of 63 oncology and 94 brain disorder studies. The complexity of personalized approaches highlights the need for more sophisticated biological systems to assess the integrated mechanisms of response. Despite the progress in developing innovative and complex preclinical model systems, the currently available methods need to be further developed and validated before their potential in personalized medicine endeavors can be realized. More importantly, we identified underlying gaps in preclinical research relating to the relevance of experimental models, quality assessment practices, reporting, regulation, and a gap between preclinical and clinical research. To achieve a broad implementation of predictive translational models in personalized medicine, these fundamental deficits must be addressed.

An Integrated In Silico, In Vitro and Tumor Tissues Study Identified Selenoprotein S (SELENOS) and Valosin-Containing Protein (VCP/p97) as Novel Potential Associated Prognostic Biomarkers in Triple Negative Breast Cancer.

BACKGROUND: Triple negative breast cancer (TNBC) is a heterogeneous group of tumors with early relapse, poor overall survival, and lack of effective treatments. Hence, new prognostic biomarkers and therapeutic targets are needed. METHODS: The expression profile of all twenty-five human selenoproteins was analyzed in TNBC by a systematic approach.In silicoanalysis was performed on publicly available mRNA expression datasets (Cancer Cell Line Encyclopedia, CCLE and Library of Integrated Network-based Cellular Signatures, LINCS). Reverse transcription quantitative PCR analysis evaluated selenoprotein mRNA expression in TNBC versus non-TNBC and normal breast cells, and in TNBC tissues versus normal counterparts. Immunohistochemistry was employed to study selenoproteins in TNBC tissues. STRING and Cytoscape tools were used for functional and network analysis. RESULTS: GPX1, GPX4, SELENOS, TXNRD1 and TXNRD3 were specifically overexpressed in TNBC cells, tissues and CCLE/LINCS datasets. Network analysis demonstrated that SELENOS-binding valosin-containing protein (VCP/p97) played a critical hub role in the TNBCselenoproteins sub-network, being directly associated with SELENOS expression. The combined overexpression of SELENOS and VCP/p97 correlated with advanced stages and poor prognosis in TNBC tissues and the TCGA dataset. CONCLUSION: Combined evaluation of SELENOS and VCP/p97 might represent a novel potential prognostic signature and a therapeutic target to be exploited in TNBC.

Minimal information for chemosensitivity assays (MICHA): a next-generation pipeline to enable the FAIRification of drug screening experiments.

Chemosensitivity assays are commonly used for preclinical drug discovery and clinical trial optimization. However, data from independent assays are often discordant, largely attributed to uncharacterized variation in the experimental materials and protocols. We report here the launching of Minimal Information for Chemosensitivity Assays (MICHA), accessed via https://micha-protocol.org. Distinguished from existing efforts that are often lacking support from data integration tools, MICHA can automatically extract publicly available information to facilitate the assay annotation including: 1) compounds, 2) samples, 3) reagents and 4) data processing methods. For example, MICHA provides an integrative web server and database to obtain compound annotation including chemical structures, targets and disease indications. In addition, the annotation of cell line samples, assay protocols and literature references can be greatly eased by retrieving manually curated catalogues. Once the annotation is complete, MICHA can export a report that conforms to the FAIR principle (Findable, Accessible, Interoperable and Reusable) of drug screening studies. To consolidate the utility of MICHA, we provide FAIRified protocols from five major cancer drug screening studies as well as six recently conducted COVID-19 studies. With the MICHA web server and database, we envisage a wider adoption of a community-driven effort to improve the open access of drug sensitivity assays.

Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity.

The combined impact of common and rare exonic variants in COVID-19 host genetics is currently insufficiently understood. Here, common and rare variants from whole-exome sequencing data of about 4000 SARS-CoV-2-positive individuals were used to define an interpretable machine-learning model for predicting COVID-19 severity. First, variants were converted into separate sets of Boolean features, depending on the absence or the presence of variants in each gene. An ensemble of LASSO logistic regression models was used to identify the most informative Boolean features with respect to the genetic bases of severity. The Boolean features selected by these logistic models were combined into an Integrated PolyGenic Score that offers a synthetic and interpretable index for describing the contribution of host genetics in COVID-19 severity, as demonstrated through testing in several independent cohorts. Selected features belong to ultra-rare, rare, low-frequency, and common variants, including those in linkage disequilibrium with known GWAS loci. Noteworthily, around one quarter of the selected genes are sex-specific. Pathway analysis of the selected genes associated with COVID-19 severity reflected the multi-organ nature of the disease. The proposed model might provide useful information for developing diagnostics and therapeutics, while also being able to guide bedside disease management.

Predictive modeling of gene expression regulation.

BACKGROUND: In-depth analysis of regulation networks of genes aberrantly expressed in cancer is essential for better understanding tumors and identifying key genes that could be therapeutically targeted. RESULTS: We developed a quantitative analysis approach to investigate the main biological relationships among different regulatory elements and target genes; we applied it to Ovarian Serous Cystadenocarcinoma and 177 target genes belonging to three main pathways (DNA REPAIR, STEM CELLS and GLUCOSE METABOLISM) relevant for this tumor. Combining data from ENCODE and TCGA datasets, we built a predictive linear model for the regulation of each target gene, assessing the relationships between its expression, promoter methylation, expression of genes in the same or in the other pathways and of putative transcription factors. We proved the reliability and significance of our approach in a similar tumor type (basal-like Breast cancer) and using a different existing algorithm (ARACNe), and we obtained experimental confirmations on potentially interesting results. CONCLUSIONS: The analysis of the proposed models allowed disclosing the relations between a gene and its related biological processes, the interconnections between the different gene sets, and the evaluation of the relevant regulatory elements at single gene level. This led to the identification of already known regulators and/or gene correlations and to unveil a set of still unknown and potentially interesting biological relationships for their pharmacological and clinical use.

S-Carboxymethyl Cysteine Protects against Oxidative Stress and Mitochondrial Impairment in a Parkinson's Disease In Vitro Model.

The mucolytic agent S-carboxymethylcysteine is widely used as an expectorant for the treatment of numerous respiratory disorders. The metabolic fate of S-carboxymethyl-L-cysteine is complex. Several clinical studies have demonstrated that the metabolism of this agent differs within the same individual, with sulfur oxygenated metabolites generated upon night-time administration. It has been indicated that this drug behaves like a free radical scavenger and that, in this regard, the sulfide is the active species with sulphoxide metabolites (already oxidized) being inactive. Consequently, a night-time consumption of the drug should be more effective upon daytime administration. Still, this diurnal variation in biotransformation (deactivation) is dependent on the genetic polymorphism on which relies the patient population capacities of S-carboxymethyl-L-cysteine sulphoxidation. It has been reported that those cohorts who are efficient sulfur oxidizers will generate inactive oxygenated metabolites. In contrast, those who have a relative deficiency in this mechanism will be subjected to the active sulfide for a more extended period. In this regard, it is noteworthy that 38-39% of Parkinson's disease patients belong to the poor sulphoxide cohort, being exposed to higher levels of active sulfide, the active antioxidant metabolite of S-carboxymethyl-L-cysteine. Parkinson's disease is a neurodegenerative disorder that affects predominately dopaminergic neurons. It has been demonstrated that oxidative stress and mitochondrial dysfunction play a crucial role in the degeneration of dopaminergic neurons. Based on this evidence, in this study, we evaluated the effects of S-carboxymethyl cysteine in an in vitro model of Parkinson's disease in protecting against oxidative stress injury. The data obtained suggested that an S-carboxymethylcysteine-enriched diet could be beneficial during aging to protect neurons from oxidative imbalance and mitochondrial dysfunction, thus preventing the progression of neurodegenerative processes.

L-Methionine Protects against Oxidative Stress and Mitochondrial Dysfunction in an In Vitro Model of Parkinson's Disease.

Methionine is an aliphatic, sulfur-containing, essential amino acid that has been demonstrated to have crucial roles in metabolism, innate immunity, and activation of endogenous antioxidant enzymes, including methionine sulfoxide reductase A/B and the biosynthesis of glutathione to counteract oxidative stress. Still, methionine restriction avoids altered methionine/transmethylation metabolism, thus reducing DNA damage and possibly avoiding neurodegenerative processes. In this study, we wanted to study the preventive effects of methionine in counteracting 6-hydroxydopamine (6-OHDA)-induced injury. In particular, we analyzed the protective effects of the amino acid L-methionine in an in vitro model of Parkinson's disease and dissected the underlying mechanisms compared to the known antioxidant taurine to gain insights into the potential of methionine treatment in slowing the progression of the disease by maintaining mitochondrial functionality. In addition, to ascribe the effects of methionine on mitochondria and oxidative stress, methionine sulfoxide was used in place of methionine. The data obtained suggested that an L-methionine-enriched diet could be beneficial during aging to protect neurons from oxidative imbalance and mitochondrial dysfunction, thus preventing the progression of neurodegenerative processes.

The ER stress response mediator ERO1 triggers cancer metastasis by favoring the angiogenic switch in hypoxic conditions.

Solid tumors are often characterized by a hypoxic microenvironment which contributes, through the hypoxia-inducible factor HIF-1, to the invasion-metastasis cascade. Endoplasmic reticulum (ER) stress also leads tumor cells to thrive and spread by inducing a transcriptional and translational program, the Unfolded Protein Response (UPR), aimed at restoring ER homeostasis. We studied ERO1 alpha (henceforth ERO1), a protein disulfide oxidase with the tumor-relevant characteristic of being positively regulated by both ER stress and hypoxia. Analysis of the redox secretome indicated that pro-angiogenic HIF-1 targets, were blunted in ERO1-devoid breast cancer cells under hypoxic conditions. ERO1 deficiency reduced tumor cell migration and lung metastases by impinging on tumor angiogenesis, negatively regulating the upstream ATF4/CHOP branch of the UPR and selectively impeding oxidative folding of angiogenic factors, among which VEGF-A. Thus, ERO1 deficiency acted synergistically with the otherwise feeble curative effects of anti-angiogenic therapy in aggressive breast cancer murine models and it might be exploited to treat cancers with pathological HIF-1-dependent angiogenesis. Furthermore, ERO1 levels are higher in the more aggressive basal breast tumors and correlate inversely with the disease- and metastasis-free interval of breast cancer patients. Thus, taking advantage of our in vitro data on ERO1-regulated gene products we identified a gene set associated with ERO1 expression in basal tumors and related to UPR, hypoxia, and angiogenesis, whose levels might be investigated in patients as a hallmark of tumor aggressiveness and orient those with lower levels toward an effective anti-angiogenic therapy.

Minimal information for Chemosensitivity assays (MICHA): A next-generation pipeline to enable the FAIRification of drug screening experiments.

Chemosensitivity assays are commonly used for preclinical drug discovery and clinical trial optimization. However, data from independent assays are often discordant, largely attributed to uncharacterized variation in the experimental materials and protocols. We report here the launching of MICHA (Minimal Information for Chemosensitivity Assays), accessed via https://micha-protocol.org. Distinguished from existing efforts that are often lacking support from data integration tools, MICHA can automatically extract publicly available information to facilitate the assay annotation including: 1) compounds, 2) samples, 3) reagents, and 4) data processing methods. For example, MICHA provides an integrative web server and database to obtain compound annotation including chemical structures, targets, and disease indications. In addition, the annotation of cell line samples, assay protocols and literature references can be greatly eased by retrieving manually curated catalogues. Once the annotation is complete, MICHA can export a report that conforms to the FAIR principle (Findable, Accessible, Interoperable and Reusable) of drug screening studies. To consolidate the utility of MICHA, we provide FAIRified protocols from five major cancer drug screening studies, as well as six recently conducted COVID-19 studies. With the MICHA webserver and database, we envisage a wider adoption of a community-driven effort to improve the open access of drug sensitivity assays.

Retinoic Acid Sensitivity of Triple-Negative Breast Cancer Cells Characterized by Constitutive Activation of the notch1 Pathway: The Role of Rarß.

Triple-negative breast cancer (TNBC) is a heterogeneous disease that lacks effective therapeutic options. In this study, we profile eighteen TNBC cell lines for their sensitivity to the anti-proliferative action of all-trans retinoic acid (ATRA). The only three cell lines (HCC-1599, MB-157 and MDA-MB-157) endowed with ATRA-sensitivity are characterized by genetic aberrations of the NOTCH1-gene, causing constitutive activation of the NOTCH1 γ-secretase product, N1ICD. N1ICD renders HCC-1599, MB-157 and MDA-MB-157 cells sensitive not only to ATRA, but also to γ-secretase inhibitors (DAPT; PF-03084014). Combinations of ATRA and γ-secretase inhibitors produce additive/synergistic effects in vitro and in vivo. RNA-sequencing studies of HCC-1599 and MB-157 cells exposed to ATRA and DAPT and ATRA+DAPT demonstrate that the two compounds act on common gene sets, some of which belong to the NOTCH1 pathway. ATRA inhibits the growth of HCC-1599, MB-157 and MDA-MB-157 cells via RARα, which up-regulates several retinoid target-genes, including RARß. RARß is a key determinant of ATRA anti-proliferative activity, as its silencing suppresses the effects exerted by the retinoid. In conclusion, we demonstrate that ATRA exerts a significant anti-tumor action only in TNBC cells showing constitutive NOTCH1 activation. Our results support the design of clinical trials involving combinations between ATRA and γ-secretase inhibitors for the treatment of this TNBC subtype.

ELIXIR-IT HPC@CINECA: high performance computing resources for the bioinformatics community.

BACKGROUND: The advent of Next Generation Sequencing (NGS) technologies and the concomitant reduction in sequencing costs allows unprecedented high throughput profiling of biological systems in a cost-efficient manner. Modern biological experiments are increasingly becoming both data and computationally intensive and the wealth of publicly available biological data is introducing bioinformatics into the "Big Data" era. For these reasons, the effective application of High Performance Computing (HPC) architectures is becoming progressively more recognized also by bioinformaticians. Here we describe HPC resources provisioning pilot programs dedicated to bioinformaticians, run by the Italian Node of ELIXIR (ELIXIR-IT) in collaboration with CINECA, the main Italian supercomputing center. RESULTS: Starting from April 2016, CINECA and ELIXIR-IT launched the pilot Call "ELIXIR-IT HPC@CINECA", offering streamlined access to HPC resources for bioinformatics. Resources are made available either through web front-ends to dedicated workflows developed at CINECA or by providing direct access to the High Performance Computing systems through a standard command-line interface tailored for bioinformatics data analysis. This allows to offer to the biomedical research community a production scale environment, continuously updated with the latest available versions of publicly available reference datasets and bioinformatic tools. Currently, 63 research projects have gained access to the HPC@CINECA program, for a total handout of ~ 8 Millions of CPU/hours and, for data storage, ~ 100 TB of permanent and ~ 300 TB of temporary space. CONCLUSIONS: Three years after the beginning of the ELIXIR-IT HPC@CINECA program, we can appreciate its impact over the Italian bioinformatics community and draw some considerations. Several Italian researchers who applied to the program have gained access to one of the top-ranking public scientific supercomputing facilities in Europe. Those investigators had the opportunity to sensibly reduce computational turnaround times in their research projects and to process massive amounts of data, pursuing research approaches that would have been otherwise difficult or impossible to undertake. Moreover, by taking advantage of the wealth of documentation and training material provided by CINECA, participants had the opportunity to improve their skills in the usage of HPC systems and be better positioned to apply to similar EU programs of greater scale, such as PRACE. To illustrate the effective usage and impact of the resources awarded by the program - in different research applications - we report five successful use cases, which have already published their findings in peer-reviewed journals.

All-Trans Retinoic Acid Stimulates Viral Mimicry, Interferon Responses and Antigen Presentation in Breast-Cancer Cells.

All-trans retinoic acid (ATRA), a recognized differentiating agent, has significant potential in the personalized/stratified treatment of breast cancer. The present study reports on the molecular mechanisms underlying the anti-tumor activity of ATRA in breast cancer. The work is based on transcriptomic experiments performed on ATRA-treated breast cancer cell-lines, short-term tissue cultures of patient-derived mammary-tumors and a xenograft model. ATRA upregulates gene networks involved in interferon-responses, immune-modulation and antigen-presentation in retinoid-sensitive cells and tumors characterized by poor immunogenicity. ATRA-dependent upregulation of these gene networks is caused by a viral mimicry process, involving the activation of endogenous retroviruses. ATRA induces a non-canonical type of viral mimicry, which results in increased expression of the IRF1 (Interferon Responsive Factor 1) transcription factor and the DTX3L (Deltex-E3-Ubiquitin-Ligase-3L) downstream effector. Functional knockdown studies indicate that IRF1 and DTX3L are part of a negative feedback loop controlling ATRA-dependent growth inhibition of breast cancer cells. The study is of relevance from a clinical/therapeutic perspective. In fact, ATRA stimulates processes controlling the sensitivity to immuno-modulatory drugs, such as immune-checkpoint-inhibitors. This suggests that ATRA and immunotherapeutic agents represent rational combinations for the personalized treatment of breast cancer. Remarkably, ATRA-sensitivity seems to be relatively high in immune-cold mammary tumors, which are generally resistant to immunotherapy.

Establishment of patient-derived tumor xenograft models of mucinous ovarian cancer.

Mucinous ovarian carcinoma (mEOC) represents a rare subtype of epithelial ovarian cancer, accounting for 3-4% of all ovarian carcinomas. The rarity of this tumor type renders both the preclinical and clinical research compelling. Very few preclinical in vitro and in vivo models exist. We here report the molecular, metabolic and pharmacological characterization of two patient derived xenografts (PDXs) from mEOC, recently obtained in our laboratory. These PDXs maintain the histological and molecular characteristics of the patient's tumors they derived from, including a wild type TP53. Gene expression analysis and metabolomics profile suggest that they differ from high grade serous/endometrioid ovarian carcinoma PDXs. The pharmacological characterization was undertaken testing the in vivo antitumor activity of both cytotoxic agents (cisplatin, paclitaxel, yondelis, oxaliplatin and 5-fluorouracile) and targeted agents (bevacizumab and lapatinib). These newly established mucinous PDXs do recapitulate mEOC and will be of value in the preclinical development of possible new therapeutic strategies for this tumor type.

Computational deconvolution of transcriptomic data for the study of tumor-infiltrating immune cells.

Cancer is a complex disease characterized by a wide array of mutually interacting components constituting the tumor microenvironment (connective tissue, vascular system, immune cells), many of which are targeted therapeutically. In particular, immune checkpoint inhibitors have recently become an established part of the treatment of cancer. Despite great promise, only a portion of the patients display durable response. Current research efforts are concentrated on the determination of tumor-specific biomarkers predictive of response, such as tumor mutational burden, microsatellite instability, and neo-antigen presentation. However, it is clear that several additional characteristics pertaining to the tumor microenvironment play a critical role in the effectiveness of immunotherapy. Here we comment on the computational methods that are used for the analysis of the tumor microenvironment components from transcriptomic data, discuss the critical needs, and foresee potential evolutions in the field.