Layer Analysis Based on RNA-Seq Reveals Molecular Complexity of Gastric Cancer.

Gastric adenocarcinoma (GA) is a significant global health issue with poor prognosis, despite advancements in treatment. Although molecular classifications, such as The Cancer Genome Atlas (TCGA), provide valuable insights, their clinical utility remains limited. We performed a multi-layered functional analysis using TCGA RNA sequencing data to better define molecular subtypes and explore therapeutic implications.

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.

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.

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.

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.

Protein content of blood-derived extracellular vesicles: An approach to the pathophysiology of cerebral hemorrhage.

Extracellular vesicles (EVs) participate in cell-to-cell paracrine signaling and can be biomarkers of the pathophysiological processes underlying disease. In intracerebral hemorrhage, the study of the number and molecular content of circulating EVs may help elucidate the biological mechanisms involved in damage and repair, contributing valuable information to the identification of new therapeutic targets. The objective of this study was to describe the number and protein content of blood-derived EVs following an intracerebral hemorrhage (ICH).

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.

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.

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.

Identification of Carcinogenesis and Tumor Progression Processes in Pancreatic Ductal Adenocarcinoma Using High-Throughput Proteomics.

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive disease with an overall 5-year survival rate of just 5%. A better understanding of the carcinogenesis processes and the mechanisms of the progression of PDAC is mandatory. Fifty-two PDAC patients treated with surgery and adjuvant therapy, with available primary tumors, normal tissue, preneoplastic lesions (PanIN), and/or lymph node metastases, were selected for the study.

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.

Description of the genetic variants identified in a cohort of patients diagnosed with localized anal squamous cell carcinoma and treated with panitumumab.

Squamous cell carcinoma is the most frequent histologic type of anal carcinoma. The standard of care since the 1970s has been a combination of 5-fluorouracil, mitomycin C, and radiotherapy. This treatment is very effective in T1/T2 tumors (achieving complete regression in 80-90% of tumors).

Bayesian networks established functional differences between breast cancer subtypes.

Breast cancer is a heterogeneous disease. In clinical practice, tumors are classified as hormonal receptor positive, Her2 positive and triple negative tumors. In previous works, our group defined a new hormonal receptor positive subgroup, the TN-like subtype, which had a prognosis and a molecular profile more similar to triple negative tumors.

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.

miRNA profiling in renal carcinoma suggest the existence of a group of pro-angionenic tumors in localized clear cell renal carcinoma.

Renal cell carcinoma comprises a variety of entities, the most common being the clear-cell, papillary and chromophobe subtypes. These subtypes are related to different clinical evolution; however, most therapies have been developed for clear-cell carcinoma and there is not a specific treatment based on different subtypes. In this study, one hundred and sixty-four paraffin samples from primary nephrectomies for localized tumors were analyzed.

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.

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

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. Proteomics data from breast tumors were obtained by mass-spectrometry.

Melanoma proteomics suggests functional differences related to mutational status.

Melanoma is the most lethal cutaneous cancer. New drugs have recently appeared; however, not all patients obtain a benefit of these new drugs. For this reason, it is still necessary to characterize melanoma at molecular level.

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.

A novel approach to triple-negative breast cancer molecular classification reveals a luminal immune-positive subgroup with good prognoses.

Triple-negative breast cancer is a heterogeneous disease characterized by a lack of hormonal receptors and HER2 overexpression. It is the only breast cancer subgroup that does not benefit from targeted therapies, and its prognosis is poor. Several studies have developed specific molecular classifications for triple-negative breast cancer.

Probabilistic graphical models relate immune status with response to neoadjuvant chemotherapy in breast cancer.

Breast cancer is the most frequent tumor in women and its incidence is increasing. Neoadjuvant chemotherapy has become standard of care as a complement to surgery in locally advanced or poor-prognosis early stage disease. The achievement of a complete response to neoadjuvant chemotherapy correlates with prognosis but it is not possible to predict who will obtain an excellent response.

Molecular characterization of breast cancer cell response to metabolic drugs.

Metabolic reprogramming is a hallmark of cancer. It has been described that breast cancer subtypes present metabolism differences and this fact enables the possibility of using metabolic inhibitors as targeted drugs in specific scenarios. In this study, breast cancer cell lines were treated with metformin and rapamycin, showing a heterogeneous response to treatment and leading to cell cycle disruption.

Functional proteomics outlines the complexity of breast cancer molecular subtypes.

Breast cancer is a heterogeneous disease comprising a variety of entities with various genetic backgrounds. Estrogen receptor-positive, human epidermal growth factor receptor 2-negative tumors typically have a favorable outcome; however, some patients eventually relapse, which suggests some heterogeneity within this category. In the present study, we used proteomics and miRNA profiling techniques to characterize a set of 102 either estrogen receptor-positive (ER+)/progesterone receptor-positive (PR+) or triple-negative formalin-fixed, paraffin-embedded breast tumors.

Prediction of adjuvant chemotherapy response in triple negative breast cancer with discovery and targeted proteomics.

Background: Triple-negative breast cancer (TNBC) accounts for 15-20% of all breast cancers and usually requires the administration of adjuvant chemotherapy after surgery but even with this treatment many patients still suffer from a relapse. The main objective of this study was to identify proteomics-based biomarkers that predict the response to standard adjuvant chemotherapy, so that patients at are not going to benefit from it can be offered therapeutic alternatives.

Methods: We analyzed the proteome of a retrospective series of formalin-fixed, paraffin-embedded TNBC tissue applying high-throughput label-free quantitative proteomics.

Characterisation of the triple negative breast cancer phenotype associated with the development of central nervous system metastases.

Aims: Breast cancer (BC) is the most frequent tumour in women, representing 20-30% of all malignancies, and continues to be the leading cause of cancer deaths among European women. Triple-negative (TN) BC biological aggressiveness is associated with a higher dissemination rate, with central nervous system (CNS) metastases common. This study aims to elucidate the association between gene expression profiles of PTGS2, HBEGF and ST6GALNAC5 and the development of CNS metastases in TNBC.