Functional proteomics of colon cancer Consensus Molecular Subtypes.

BACKGROUND: The Colorectal Cancer Subtyping Consortium established four Consensus Molecular Subtypes (CMS) in colorectal cancer: CMS1 (microsatellite-instability [MSI], Immune), CMS2 (Canonical, epithelial), CMS3 (Metabolic), and CMS4 (Mesenchymal). However, only MSI tumour patients have seen a change in their disease management in clinical practice. This study aims to characterise the proteome of colon cancer CMS and broaden CMS's clinical utility. METHODS: One-hundred fifty-eight paraffin samples from stage II-III colon cancer patients treated with adjuvant chemotherapy were analysed through DIA-based mass-spectrometry proteomics. RESULTS: CMS1 exhibited overexpression of immune-related proteins, specifically related to neutrophils, phagocytosis, antimicrobial response, and a glycolytic profile. These findings suggested potential therapeutic strategies involving immunotherapy and glycolytic inhibitors. CMS3 showed overexpression of metabolic proteins. CMS2 displayed a heterogeneous protein profile. Notably, two proteomics subtypes within CMS2, with different protein characteristics and prognoses, were identified. CMS4 emerged as the most distinct group, featuring overexpression of proteins related to angiogenesis, extracellular matrix, focal adhesion, and complement activation. CMS4 showed a high metastatic profile and suggested possible chemoresistance that may explain its worse prognosis. CONCLUSIONS: DIA proteomics revealed new features for each colon cancer CMS subtype. These findings provide valuable insights into potential therapeutic targets for colorectal cancer subtypes in the future.

Utility of CYP2D6 copy number variants as prognostic biomarker in localized anal squamous cell carcinoma.

BACKGROUND: Anal squamous cell carcinoma (ASCC) is an infrequent tumor whose treatment has not changed since the 1970s. The aim of this study is the identification of biomarkers allowing personalized treatments and improvement of therapeutic outcomes. METHODS: Forty-six paraffin tumor samples from ASCC patients were analyzed by whole-exome sequencing. Copy number variants (CNVs) were identified and their relation to disease-free survival (DFS) was studied and validated in an independent retrospective cohort of 101 ASCC patients from the Multidisciplinary Spanish Digestive Cancer Group (GEMCAD). GEMCAD cohort proteomics allowed assessing the biological features of these tumors. RESULTS: On the discovery cohort, the median age was 61 years old, 50% were males, stages I/II/III: 3 (7%)/16 (35%)/27 (58%), respectively, median DFS was 33 months, and overall survival was 45 months. Twenty-nine genes whose duplication was related to DFS were identified. The most representative was duplications of the CYP2D locus, including CYP2D6, CYP2D7P, and CYP2D8P genes. Patients with CYP2D6 CNV had worse DFS at 5 years than those with two CYP2D6 copies (21% vs. 84%; p < .0002, hazard ratio [HR], 5.8; 95% confidence interval [CI], 2.7-24.9). In the GEMCAD validation cohort, patients with CYP2D6 CNV also had worse DFS at 5 years (56% vs. 87%; p = .02, HR = 3.6; 95% CI, 1.1-5.7). Mitochondria and mitochondrial cell-cycle proteins were overexpressed in patients with CYP2D6 CNV. CONCLUSIONS: Tumor CYP2D6 CNV identified patients with a significantly worse DFS at 5 years among localized ASCC patients treated with 5-fluorouracil, mitomycin C, and radiotherapy. Proteomics pointed out mitochondria and mitochondrial cell-cycle genes as possible therapeutic targets for these high-risk patients. PLAIN LANGUAGE SUMMARY: Anal squamous cell carcinoma is an infrequent tumor whose treatment has not been changed since the 1970s. However, disease-free survival in late staged tumors is between 40% and 70%. The presence of an alteration in the number of copies of CYP2D6 gene is a biomarker of worse disease-free survival. The analysis of the proteins in these high-risk patients pointed out mitochondria and mitochondrial cell-cycle genes as possible therapeutic targets. Therefore, the determination of the number of copies of CYP2D6 allows the identification of anal squamous carcinoma patients with a high-risk of relapse that could be redirected to a clinical trial. Additionally, this study may be useful to suggest new treatment strategies to increase current therapy efficacy.

Metabolomics unravels subtype-specific characteristics related to neoadjuvant therapy response in breast cancer patients.

INTRODUCTION: Breast cancer is the most diagnosed tumor and the leading cause of cancer death in women worldwide. Metabolomics allows the quantification of the entire set of metabolites in blood samples, making it possible to study differential metabolomics patterns related to neoadjuvant treatment in the breast cancer neoadjuvant setting. OBJECTIVES: Characterizing metabolic differences in breast cancer blood samples according to their response to neoadjuvant treatment. METHODS: One hundred and three plasma samples of breast cancer patients, before receiving neoadjuvant treatment, were analyzed through UPLC-MS/MS metabolomics. Then, metabolomics data were analyzed using probabilistic graphical models and biostatistics methods. RESULTS: Metabolomics data allowed the identification of differences between groups according to response to neoadjuvant treatment. These differences were specific to each breast cancer subtype. Patients with HER2+ tumors showed differences in metabolites related to amino acids and carbohydrates pathways between the two pathological response groups. However, patients with triple-negative tumors showed differences in metabolites related to the long-chain fatty acids pathway. Patients with Luminal B tumors showed differences in metabolites related to acylcarnitine pathways. CONCLUSIONS: It is possible to identify differential metabolomics patterns between complete and partial responses to neoadjuvant therapy, being this metabolomic profile specific for each breast cancer subtype.

Sorting Transcriptomics Immune Information from Tumor Molecular Features Allows Prediction of Response to Anti-PD1 Therapy in Patients with Advanced Melanoma.

Immunotherapy based on anti-PD1 antibodies has improved the outcome of advanced melanoma. However, prediction of response to immunotherapy remains an unmet need in the field. Tumor PD-L1 expression, mutational burden, gene profiles and microbiome profiles have been proposed as potential markers but are not used in clinical practice. Probabilistic graphical models and classificatory algorithms were used to classify melanoma tumor samples from a TCGA cohort. A cohort of patients with advanced melanoma treated with PD-1 inhibitors was also analyzed. We established that gene expression data can be grouped in two different layers of information: immune and molecular. In the TCGA, the molecular classification provided information on processes such as epidermis development and keratinization, melanogenesis, and extracellular space and membrane. The immune layer classification was able to distinguish between responders and non-responders to immunotherapy in an independent series of patients with advanced melanoma treated with PD-1 inhibitors. We established that the immune information is independent than molecular features of the tumors in melanoma TCGA cohort, and an immune classification of these tumors was established. This immune classification was capable to determine what patients are going to respond to immunotherapy in a new cohort of patients with advanced melanoma treated with PD-1 inhibitors Therefore, this immune signature could be useful to the clinicians to identify those patients who will respond to immunotherapy.

Molecular characterization of triple negative breast cancer formaldehyde-fixed paraffin-embedded samples by data-independent acquisition proteomics.

Triple negative breast cancer accounts for 15%-20% of all breast carcinomas and is clinically characterized by an aggressive phenotype and poor prognosis. Triple negative tumors do not benefit from targeted therapies, so further characterization is needed to define subgroups with potential therapeutic value. In this work, the proteomes of 125 formalin-fixed paraffin-embedded samples from patients diagnosed with non-metastatic triple negative breast cancer were analyzed using data-independent acquisition + in a LTQ-Orbitrap Fusion Lumos mass spectrometer coupled to an EASY-nLC 1000. 1206 proteins were identified in at least 66% of the samples. Hierarchical clustering, probabilistic graphical models and Significance Analysis of Microarrays were combined to characterize proteomics-based molecular groups. Two molecular groups were defined with differences in biological processes such as glycolysis, translation and immune response. These two molecular groups showed also several differentially expressed proteins. This clinically homogenous dataset may serve to design new therapeutic strategies in the future.

Genetic Profile and Functional Proteomics of Anal Squamous Cell Carcinoma: Proposal for a Molecular Classification.

Anal squamous cell carcinoma is a rare tumor. Chemo-radiotherapy yields a 50% 3-year relapse-free survival rate in advanced anal cancer, so improved predictive markers and therapeutic options are needed. High-throughput proteomics and whole-exome sequencing were performed in 46 paraffin samples from anal squamous cell carcinoma patients. Hierarchical clustering was used to establish groups de novo Then, probabilistic graphical models were used to study the differences between groups of patients at the biological process level. A molecular classification into two groups of patients was established, one group with increased expression of proteins related to adhesion, T lymphocytes and glycolysis; and the other group with increased expression of proteins related to translation and ribosomes. The functional analysis by the probabilistic graphical model showed that these two groups presented differences in metabolism, mitochondria, translation, splicing and adhesion processes. Additionally, these groups showed different frequencies of genetic variants in some genes, such as ATM, SLFN11 and DST Finally, genetic and proteomic characteristics of these groups suggested the use of some possible targeted therapies, such as PARP inhibitors or immunotherapy.

Immune expression profile identification in a group of proliferative verrucous leukoplakia patients: a pre-cancer niche for oral squamous cell carcinoma development.

OBJECTIVES: To explore the pathophysiology of proliferative verrucous leukoplakia, a rare oral disorder that exhibits high rates of recurrence and malignant transformation, through a RNAseq case-control study. MATERIAL AND METHODS: We obtained oral biopsies from 10 patients with verrucous leukoplakia lesions and from the mucosa of 5 healthy individuals for sequencing using RNAseq technology. Using bioinformatic methods, we investigated gene expression and enrichment differences between patients both with and without the disorder. We applied network biology methods to investigate functional relations among those genes that were differentially deregulated. RESULTS: We detected 140 differentially expressed genes with distinct roles in immune surveillance, tissue and organ morphogenesis, development, and organization. Of these 140 genes, 111 have been previously described as cancer expression biomarkers, being oral squamous cell carcinoma the most represented type of cancer among them. Of these 140 genes, 26 were prioritized for further investigation as biomarkers using larger sample sizes. CONCLUSIONS: The gene expression patterns of healthy and unhealthy patients differed in 140 genes whose deregulation has a functional impact on normal functioning of the immune system. This immune expression profile provides a plausible hypothesis to explain the transformation to oral squamous cell carcinoma observed in 6 of the 10 assayed cases. CLINICAL RELEVANCE: By determining the molecular bases of the proliferative verrucous leukoplakia disorder and identifying early biomarkers of malignancy, this can allow us to develop new treatment strategies.

Computational models applied to metabolomics data hints at the relevance of glutamine metabolism in breast cancer.

BACKGROUND: Metabolomics has a great potential in the development of new biomarkers in cancer and it has experiment recent technical advances. METHODS: In this study, metabolomics and gene expression data from 67 localized (stage I to IIIB) breast cancer tumor samples were analyzed, using (1) probabilistic graphical models to define associations using quantitative data without other a priori information; and (2) Flux Balance Analysis and flux activities to characterize differences in metabolic pathways. RESULTS: On the one hand, both analyses highlighted the importance of glutamine in breast cancer. Moreover, cell experiments showed that treating breast cancer cells with drugs targeting glutamine metabolism significantly affects cell viability. On the other hand, these computational methods suggested some hypotheses and have demonstrated their utility in the analysis of metabolomics data and in associating metabolomics with patient's clinical outcome. CONCLUSIONS: Computational analyses applied to metabolomics data suggested that glutamine metabolism is a relevant process in breast cancer. Cell experiments confirmed this hypothesis. In addition, these computational analyses allow associating metabolomics data with patient prognosis.

Isotopologue Multipoint Calibration for Proteomics Biomarker Quantification in Clinical Practice.

Targeted proteomics has become the method of choice for biomarker validation in human biopsies due to its high sensitivity, reproducibility, accuracy, and precision. However, for targeted proteomics to be transferred to clinical routine there is the need to reduce its complexity, make its procedures simpler, increase its throughput, and improve its analytical performance. Here we present the Isotopologue Multipoint Calibration (ImCal) quantification strategy, which uses a mix of isotopologue peptides to generate internal multipoint calibration curves for each individual sample and to accurately quantify biomarker peptides in clinical applications without the need of expert supervision. ImCal relies on the use of five different isotopically-labelled peptides of different nominal mass mixed at different concentrations to be used as an internal calibration curve for each endogenous peptide. The use of internal multipoint calibration curves is well-suited for the generation of ready-to-use biomarker kits for clinical applications as it is compatible with both high- and low-resolution mass spectrometers and different levels of endogenous peptide, it eliminates the need for blank matrixes required in external curves, it allows the evaluation of matrix effects and the valid quantification range in each individual sample, and it does not require expert adjustment. We used the ImCal method to quantify HER2 in 35 breast cancer formalin-fixed paraffin-embedded patient samples, revealing a high degree of heterogeneity among patients, which contrasts with the homogeneous immunohistochemistry patient classification. Our work illustrates how an improvement of mass spectrometry methods for biomarker quantification can provide fine-grain patient stratification, and thus better disease diagnostic and prognosis.

Biological molecular layer classification of muscle-invasive bladder cancer opens new treatment opportunities.

BACKGROUND: Muscle-invasive bladder tumors are associated with a high risk of relapse and metastasis even after neoadjuvant chemotherapy and radical cystectomy. Therefore, further therapeutic options are needed and molecular characterization of the disease may help to identify new targets. The aim of this study was to characterize muscle-invasive bladder tumors at the molecular level using computational analyses. METHODS: The TCGA cohort of muscle-invasive bladder cancer patients was used to describe these tumors. Probabilistic graphical models, layer analyses based on sparse k-means coupled with Consensus Cluster, and Flux Balance Analysis were applied to characterize muscle-invasive bladder tumors at a functional level. RESULTS: Luminal and Basal groups were identified, and an immune molecular layer with independent value was also described. Luminal tumors showed decreased activity in the nodes of epidermis development and extracellular matrix, and increased activity in the node of steroid metabolism leading to a higher expression of the androgen receptor. This fact points to the androgen receptor as a therapeutic target in this group. Basal tumors were highly proliferative according to Flux Balance Analysis, which makes these tumors good candidates for neoadjuvant chemotherapy. The Immune-high group showed a higher degree of expression of immune biomarkers, suggesting that this group may benefit from immune therapy. CONCLUSIONS: Our approach, based on layer analyses, established a Luminal group candidate for therapy with androgen receptor inhibitors, a proliferative Basal group which seems to be a good candidate for chemotherapy, and an immune-high group candidate for immunotherapy.

Computational metabolism modeling predicts risk of distant relapse-free survival in breast cancer patients.

Aim: Differences in metabolism among breast cancer subtypes suggest that metabolism plays an important role in this disease. Flux balance analysis is used to explore these differences as well as drug response. Materials & methods: Proteomics data from breast tumors were obtained by mass-spectrometry. Flux balance analysis was performed to study metabolic networks. Flux activities from metabolic pathways were calculated and used to build prognostic models. Results: Flux activities of vitamin A, tetrahydrobiopterin and ß-alanine metabolism pathways split our population into low- and high-risk patients. Additionally, flux activities of glycolysis and glutamate metabolism split triple negative tumors into low- and high-risk groups. Conclusion: Flux activities summarize flux balance analysis data and can be associated with prognosis in cancer.

Targeting cancer stem cell OXPHOS with tailored ruthenium complexes as a new anti-cancer strategy.

BACKGROUND: Previous studies by our group have shown that oxidative phosphorylation (OXPHOS) is the main pathway by which pancreatic cancer stem cells (CSCs) meet their energetic requirements; therefore, OXPHOS represents an Achille's heel of these highly tumorigenic cells. Unfortunately, therapies that target OXPHOS in CSCs are lacking. METHODS: The safety and anti-CSC activity of a ruthenium complex featuring bipyridine and terpyridine ligands and one coordination labile position (Ru1) were evaluated across primary pancreatic cancer cultures and in vivo, using 8 patient-derived xenografts (PDXs). RNAseq analysis followed by mitochondria-specific molecular assays were used to determine the mechanism of action. RESULTS: We show that Ru1 is capable of inhibiting CSC OXPHOS function in vitro, and more importantly, it presents excellent anti-cancer activity, with low toxicity, across a large panel of human pancreatic PDXs, as well as in colorectal cancer and osteosarcoma PDXs. Mechanistic studies suggest that this activity stems from Ru1 binding to the D-loop region of the mitochondrial DNA of CSCs, inhibiting OXPHOS complex-associated transcription, leading to reduced mitochondrial oxygen consumption, membrane potential, and ATP production, all of which are necessary for CSCs, which heavily depend on mitochondrial respiration. CONCLUSIONS: Overall, the coordination complex Ru1 represents not only an exciting new anti-cancer agent, but also a molecular tool to dissect the role of OXPHOS in CSCs. Results indicating that the compound is safe, non-toxic and highly effective in vivo are extremely exciting, and have allowed us to uncover unprecedented mechanistic possibilities to fight different cancer types based on targeting CSC OXPHOS.

DICER1, DROSHA and miRNAs in patients with non-small cell lung cancer: implications for outcomes and histologic classification.

The clinical and functional significance of RNA-interference machinery in lung cancer is poorly understood. Besides, microRNAs (miRNA) have the potential to serve both as biomarkers and therapeutic agents, by personalizing diagnosis and therapy. In this study, we investigated whether the expression levels of DICER1 and DROSHA, components of the RNA-interference machinery, can predict survival, and whether the miRNA expression profiles can differentiate histologic subtypes in non-small cell lung cancer (NSCLC). Levels of DICER1, DROSHA and five different miRNAs were measured in NSCLC specimens (N = 115) by qRT-PCR assay and correlated with clinical outcomes. Low expression of DROSHA was associated with an increased median survival (154.2 versus 39.8 months, P = 0.016). Also, high DROSHA expression was associated with decreased median survival in the following subgroups: adenocarcinoma (P = 0.011), grade III tumors (P = 0.038) and low-stage patients (P = 0.014). In multivariate analyses, we found two independent predictors of reduced disease-specific survival: high DROSHA expression [hazards ratio = 2.24; P = 0.04] and advanced tumor stage (hazards ratio = 1.29, P = 0.02). In general, the overall tumor miRNA expression was downregulated in our cohort compared with normal tissues. Expression levels of hsa-let-7a (P = 0.005) and miR-16 (P = 0.003) miRNA were significantly higher in squamous cell carcinoma than in adenocarcinoma samples. This study supports the value of the expression profiling of the components of the miRNA-processing machinery in the prognosis of NSCLC patients, especially DROSHA expression levels. In addition, differential expression of miRNAs, such as hsa-let-7a and miR-16 may be helpful tools in the histologic subclassification of NSCLC.

A Novel Molecular Analysis Approach in Colorectal Cancer Suggests New Treatment Opportunities.

Colorectal cancer (CRC) is a molecular and clinically heterogeneous disease. In 2015, the Colorectal Cancer Subtyping Consortium classified CRC into four consensus molecular subtypes (CMS), but these CMS have had little impact on clinical practice. The purpose of this study is to deepen the molecular characterization of CRC. A novel approach, based on probabilistic graphical models (PGM) and sparse k-means-consensus cluster layer analyses, was applied in order to functionally characterize CRC tumors. First, PGM was used to functionally characterize CRC, and then sparse k-means-consensus cluster was used to explore layers of biological information and establish classifications. To this aim, gene expression and clinical data of 805 CRC samples from three databases were analyzed. Three different layers based on biological features were identified: adhesion, immune, and molecular. The adhesion layer divided patients into high and low adhesion groups, with prognostic value. The immune layer divided patients into immune-high and immune-low groups, according to the expression of immune-related genes. The molecular layer established four molecular groups related to stem cells, metabolism, the Wnt signaling pathway, and extracellular functions. Immune-high patients, with higher expression of immune-related genes and genes involved in the viral mimicry response, may benefit from immunotherapy and viral mimicry-related therapies. Additionally, several possible therapeutic targets have been identified in each molecular group. Therefore, this improved CRC classification could be useful in searching for new therapeutic targets and specific therapeutic strategies in CRC disease.

Proteomics Characterization of Clear Cell Renal Cell Carcinoma.

PURPOSE: To explore the tumor proteome of patients diagnosed with localized clear cell renal cancer (ccRCC) and treated with surgery. MATERIAL AND METHODS: A total of 165 FFPE tumor samples from patients diagnosed with ccRCC were analyzed using DIA-proteomics. Proteomics ccRCC subtypes were defined using a consensus cluster algorithm (CCA) and characterized by a functional approach using probabilistic graphical models and survival analyses. RESULTS: We identified and quantified 3091 proteins, including 2026 high-confidence proteins. Two proteomics subtypes of ccRCC (CC1 and CC2) were identified by CC using the high-confidence proteins only. Characterization of molecular differences between CC1 and CC2 was performed in two steps. First, we defined 514 proteins showing differential expression between the two subtypes using a significance analysis of microarrays analysis. Proteins overexpressed in CC1 were mainly related to translation and ribosome, while proteins overexpressed in CC2 were mainly related to focal adhesion and membrane. Second, a functional analysis using probabilistic graphical models was performed. CC1 subtype is characterized by an increased expression of proteins related to glycolysis, mitochondria, translation, adhesion proteins related to cytoskeleton and actin, nucleosome, and spliceosome, while CC2 subtype showed higher expression of proteins involved in focal adhesion, extracellular matrix, and collagen organization. CONCLUSIONS: ccRCC tumors can be classified in two different proteomics subtypes. CC1 and CC2 present specific proteomics profiles, reflecting alterations of different molecular pathways in each subtype. The knowledge generated in this type of studies could help in the development of new drugs targeting subtype-specific deregulated pathways.

Client Applications and Server-Side Docker for Management of RNASeq and/or VariantSeq Workflows and Pipelines of the GPRO Suite.

The GPRO suite is an in-progress bioinformatic project for -omics data analysis. As part of the continued growth of this project, we introduce a client- and server-side solution for comparative transcriptomics and analysis of variants. The client-side consists of two Java applications called "RNASeq" and "VariantSeq" to manage pipelines and workflows based on the most common command line interface tools for RNA-seq and Variant-seq analysis, respectively. As such, "RNASeq" and "VariantSeq" are coupled with a Linux server infrastructure (named GPRO Server-Side) that hosts all dependencies of each application (scripts, databases, and command line interface software). Implementation of the Server-Side requires a Linux operating system, PHP, SQL, Python, bash scripting, and third-party software. The GPRO Server-Side can be installed, via a Docker container, in the user's PC under any operating system or on remote servers, as a cloud solution. "RNASeq" and "VariantSeq" are both available as desktop (RCP compilation) and web (RAP compilation) applications. Each application has two execution modes: a step-by-step mode enables each step of the workflow to be executed independently, and a pipeline mode allows all steps to be run sequentially. "RNASeq" and "VariantSeq" also feature an experimental, online support system called GENIE that consists of a virtual (chatbot) assistant and a pipeline jobs panel coupled with an expert system. The chatbot can troubleshoot issues with the usage of each tool, the pipeline jobs panel provides information about the status of each computational job executed in the GPRO Server-Side, while the expert system provides the user with a potential recommendation to identify or fix failed analyses. Our solution is a ready-to-use topic specific platform that combines the user-friendliness, robustness, and security of desktop software, with the efficiency of cloud/web applications to manage pipelines and workflows based on command line interface software.

Circulating extracellular vesicles promote recovery in a preclinical model of intracerebral hemorrhage.

Circulating extracellular vesicles (EVs) are proposed to participate in enhancing pathways of recovery after stroke through paracrine signaling. To verify this hypothesis in a proof-of-concept study, blood-derived allogenic EVs from rats and xenogenic EVs from humans who experienced spontaneous good recovery after an intracerebral hemorrhage (ICH) were administered intravenously to rats at 24 h after a subcortical ICH. At 28 days, both treatments improved the motor function assessment scales score, showed greater fiber preservation in the perilesional zone (diffusion tensor-fractional anisotropy MRI), increased immunofluorescence markers of myelin (MOG), and decreased astrocyte markers (GFAP) compared with controls. Comparison of the protein cargo of circulating EVs at 28 days from animals with good vs. poor recovery showed down-expression of immune system activation pathways (CO4, KLKB1, PROC, FA9, and C1QA) and of restorative processes such as axon guidance (RAC1), myelination (MBP), and synaptic vesicle trafficking (SYN1), which is in line with better tissue preservation. Up-expression of PCSK9 (neuron differentiation) in xenogenic EVs-treated animals suggests enhancement of repair pathways. In conclusion, the administration of blood-derived EVs improved recovery after ICH. These findings open a new and promising opportunity for further development of restorative therapies to improve the outcomes after an ICH.

Multi-omics Characterization of Response to PD-1 Inhibitors in Advanced Melanoma.

Immunotherapy improves the survival of patients with advanced melanoma, 40% of whom become long-term responders. However, not all patients respond to immunotherapy. Further knowledge of the processes involved in the response and resistance to immunotherapy is still needed. In this study, clinical paraffin samples from fifty-two advanced melanoma patients treated with anti-PD-1 inhibitors were assessed via high-throughput proteomics and RNA-seq. The obtained proteomics and transcriptomics data were analyzed using multi-omics network analyses based on probabilistic graphical models to identify those biological processes involved in the response to immunotherapy. Additionally, proteins related to overall survival were studied. The activity of the node formed by the proteins involved in protein processing in the endoplasmic reticulum and antigen presentation machinery was higher in responders compared to non-responders; the activity of the immune and inflammatory response node was also higher in those with complete or partial responses. A predictor for overall survival based on two proteins (AMBP and PDSM5) was defined. In summary, the response to anti-PD-1 therapy in advanced melanoma is related to protein processing in the endoplasmic reticulum, and also to genes involved in the immune and inflammatory responses. Finally, a two-protein predictor can define survival in advanced disease. The molecular characterization of the mechanisms involved in the response and resistance to immunotherapy in melanoma leads the way to establishing therapeutic alternatives for patients who will not respond to this treatment.

Clinico-Pathological Features, Outcomes and Impacts of COVID-19 Pandemic on Patients with Early-Onset Colorectal Cancer: A Single-Institution Experience.

BACKGROUND: The rising incidence of colorectal cancer (CRC) among young patients is alarming. We aim to characterize the clinico-pathological features and outcomes of patients with early-onset CRC (EOCRC), as well as the impacts of COVID-19 pandemic. METHODS: We included all patients with pathologically confirmed diagnoses of CRC at Hospital Universitario La Paz from October 2016 to December 2021. The EOCRC cut-off age was 50 years old. RESULTS: A total of 1475 patients diagnosed with CRC were included, eighty (5.4%) of whom had EOCRC. Significant differences were found between EOCRC and later-onset patients regarding T, N stage and metastatic presentation at diagnosis; perineural invasion; tumor budding; high-grade tumors; and signet ring cell histology, with all issues having higher prevalence in the early-onset group. More EOCRC patients had the RAS/ BRAF wild type. Chemotherapy was administered more frequently to patients with EOCRC. In the metastatic setting, the EOCRC group presented a significantly longer median OS. Regarding the COVID-19 pandemic, more patients with COVID-19 were diagnosed with metastatic disease (61%) in the year after the lockdown (14 March 2020) than in the pre-pandemic EOCRC group (29%). CONCLUSIONS: EOCRC is diagnosed at a more advanced stage and with worse survival features in localized patients. More patients with EOCRC were diagnosed with metastatic disease in the year after the COVID-19 pandemic lockdown. The long-term consequences of COVID-19 are yet to be determined.

MiRNAs in renal cell carcinoma.

MicroRNAs (miRNAs) are small RNA sequences that act as post-transcriptional regulatory genes to control many cellular processes through pairing bases with a complementary messenger RNA (mRNA). A single miRNA molecule can regulate more than 200 different transcripts and the same mRNA can be regulated by multiple miRNAs. In this review, we highlight the importance of miRNAs and collect the existing evidence on their relationship with kidney cancer.