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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.

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, IdiPAZ, Grant/Award Number: Jesús Antolín Garciarena Fellowship; European Proteomics Infrastructure Consortium, Grant/Award Number: 823839, Horizon 2020 Programme

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 the clinical practice. The purpose of this study is to deepen into 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. Tree 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, Wnt signalling pathway and extracellular functions. Immune-high patients, with a higher expression of immune-related genes and genes involved in viral mimicry response may be benefit for 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 for searching new therapeutic targets and specific therapeutic strategies in CRC disease.Simple summaryColorectal cancer is a heterogeneous disease. Several efforts have been done to characterize this heterogeneity but they have not impact in clinic. In this work, we used a novel analysis approach based on identifying layers of information using expression data from colorectal tumors and characterize three different layers of information: one layer related to adhesion with prognostic value, one related to immune characteristics and one related to molecular features. The molecular layer divided colorectal tumors in stem cell, Wnt, metabolic, and extracellular groups. These molecular groups suggested some possible therapeutic targets for each group. Additionally, immune characteristics divided tumors in tumors with a high expression of immune and viral mimicry response genes and with a low expression, suggesting immunotherapy and viral mimicry related therapies as suitable for these immune-high patients.

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.

Càncer; Genètica mèdica; Marcadors predictius Cáncer; Genética Médica; Marcadores predictivos Cancer; Medical genetics; Predictive markers 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). However, in T3/T4 tumors, the 3-year relapse free survival rate is only 50%. The VITAL trial aimed to assess the efficacy and safety of panitumumab in combination with this standard treatment. In this study, 27 paraffin-embedded samples from the VITAL trial and 18 samples from patients from daily clinical practice were analyzed by whole-exome sequencing and the influence of the presence of genetic variants in the response to panitumumab was studied. Having a moderate- or high-impact genetic variant in PIK3CA seemed to be related to the response to panitumumab. Furthermore, copy number variants in FGFR3, GRB2 and JAK1 were also related to the response to panitumumab. These genetic alterations have also been studied in the cohort of patients from daily clinical practice (not treated with panitumumab) and they did not have a predictive value. Therefore, in this study, a collection of genetic alterations related to the response with panitumumab was described. These results could be useful for patient stratification in new anti-EGFR clinical trials. LT-F is supported by the Spanish Economy and Competitiveness Ministry (DI-15-07614).

The triple-negative breast cancer (TNBC) subtype comprises approximately 15% of all breast cancers and is associated with poor long-term outcomes. Classical chemotherapy remains the standard of treatment, with toxicity and resistance being major limitations. TNBC is a high metabolic group, and antimetabolic drugs are effective in inhibiting TNBC cell growth. We analyzed the combined effect of chemotherapy and antimetabolic drug combinations in MDA-MB-231, MDA-MB-468 and HCC1143 human TNBC cell lines. Cells were treated with each drug or with drug combinations at a range of concentrations to establish the half-maximal inhibitory concentrations (IC50). The dose-effects of each drug or drug combination were calculated, and the synergistic or antagonistic effects of drug combinations were defined. Chemotherapy and antimetabolic drugs exhibited growth inhibitory effects on TNBC cell lines. Antimetabolic drugs targeting the glycolysis pathway had a synergistic effect with chemotherapy drugs, and antiglycolysis drug combinations also had a synergistic effect. The use of these drug combinations could lead to new therapeutic strategies that reduce chemotherapy drug doses, decreasing their toxic effect, or that maintain the doses but enhance their efficacy by their synergistic effect with other drugs, María I. Lumbreras-Herrera and Andrea Zapater-Moros are supported by Consejería de Educación e Investigación de la Comunidad de Madrid (IND2018/BMD-9262). Elena López-Camacho is supported by the Spanish Economy and Competitiveness Ministry (PTQ2018–009760). This work is supported by an unrestricted grant from Roche

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. Proteins were extracted from small punches and analyzed by LC-MS/MS using data-independent acquisition. Proteomics data were analyzed using probabilistic graphical models, allowing functional characterization. Comparisons between groups were made using linear mixed models. Three proteomic tumor subtypes were defined. T1 (32% of patients) was related to adhesion, T2 (34%) had metabolic features, and T3 (34%) presented high splicing and nucleoplasm activity. These proteomics subtypes were validated in the PDAC TCGA cohort. Relevant biological processes related to carcinogenesis and tumor progression were studied in each subtype. Carcinogenesis in the T1 subtype seems to be related to an increase of adhesion and complement activation node activity, whereas tumor progression seems to be related to nucleoplasm and translation nodes. Regarding the T2 subtype, it seems that metabolism and, especially, mitochondria act as the motor of cancer development. T3 analyses point out that nucleoplasm, mitochondria and metabolism, and extracellular matrix nodes could be involved in T3 tumor carcinogenesis. The identified processes were different among proteomics subtypes, suggesting that the molecular motor of the disease is different in each subtype. These differences can have implications for the development of future tailored therapeutic approaches for each PDAC proteomics subtype., Cancers, 14 (10), ISSN:2072-6694

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.

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.

Triple negative breast cancer (TNBC) accounts for 15-20% of all breast carcinomas and it is clinically characterized by an aggressive phenotype and bad prognosis. TNBC does not benefit from any targeted therapy, so further characterization is needed to define subgroups with potential therapeutic value. In this work, the proteomes of one hundred twenty-five formalin-fixed paraffin-embedded samples from patients diagnosed with triple negative breast cancer were analyzed by mass spectrometry using data-independent acquisition. Hierarchical clustering, probabilistic graphical models and Significance Analysis of Microarrays were used to characterize molecular groups. Additionally, a predictive signature related with relapse was defined. Two molecular groups with differences in several biological processes as glycolysis, translation and immune response, were defined in this cohort, and a prognostic signature based on the abundance of proteins RBM3 and NIPSNAP1 was defined. This predictor split the population into low-risk and high-risk groups. The differential processes identified between the two molecular groups may serve to design new therapeutic strategies in the future and the prognostic signature could be useful to identify a population at high-risk of relapse that could be directed to clinical trials.

Covid-19, caused by the SARS-CoV-2 virus, has reached the category of a worldwide pandemic. Even though intensive efforts, no effective treatments or a vaccine are available. Molecular characterization of the transcriptional response in Covid-19 patients could be helpful to identify therapeutic targets. In this study, RNAseq data from peripheral blood mononuclear cell samples from Covid-19 patients and healthy controls was analyzed from a functional point of view using probabilistic graphical models. Two networks were built: one based on genes differentially expressed between healthy and infected individuals and another one based on the 2,000 most variable genes in terms of expression in order to make a functional characterization. In the network based on differentially expressed genes, two inflammatory response nodes with different tendencies were identified, one related to cytokines and chemokines, and another one related to bacterial infections. In addition, differences in metabolism, which were studied in depth using Flux Balance Analysis, were identified. SARS-CoV2-infection caused alterations in glutamate, methionine and cysteine, and tetrahydrobiopterin metabolism. In the network based on 2,000 most variable genes, also two inflammatory nodes with different tendencies between healthy individuals and patients were identified. Similar to the other network, one was related to cytokines and chemokines. However, the other one, lower in Covid-19 patients, was related to allergic processes and self-regulation of the immune response. Also, we identified a decrease in T cell node activity and an increase in cell division node activity. In the current absence of treatments for these patients, functional characterization of the transcriptional response to SARS-CoV-2 infection could be helpful to define targetable processes. Therefore, these results may be relevant to propose new treatments.

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. 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. 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. 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. 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.

Introduction Breast cancer is one of the most prevalent cancers in the world. Traditionally, early breast cancer treatment is based on surgery and, after surgery, hormone treatment or chemotherapy. However, the neoadjuvant treatment is increasingly used. Metabolomics is the most recent “omics” which allows quantify metabolites into blood patient samples. Coupled with computational analyses it could be possible to study differential metabolomics patterns and associate them with neoadjuvant response. Material and methods Blood plasma samples from patients with breast cancer treated with neoadjuvant chemotherapy were used to perform metabolomics experiments. One sample before the treatment (basal) and one sample after the chemotherapy (post-treatment) were analyzed and clinical data regarding response (complete response or partial response) was also collected. Metabolomics experiments were performed using liquid chromatography coupled with mass-spectrometry. Bayesian network and class comparison analyses were used to establish differential metabolic patterns between conditions. Additionally, a response prediction model based on logistic regression was build using metabolomics data from basal samples. Results and discussion A network showing the relationships between metabolites was build. Comparing metabolite measurements between complete response and partial response tumors in basal samples, 19 metabolites showed a differential quantification between both types of responses. Moreover, one of these metabolites is linoleic acid, previously described as a biomarker of complete response in neoadjuvant treatment in breast cancer. Using these 19 differential metabolites, a response predictive model was build. According to this model, it is possible to predict response to neoadjuvant treatment based on the amount of one metabolite, still only identified by its mass and charge. On the other hand, comparing basal and post-treatment samples, the network showed differential metabolomics patterns. These differential metabolites could be used as predictive biomarkers of response. Conclusion This study is a proof of concept that using a new “omics” technique such as metabolomics in blood samples, coupled with computational analyses, it is possible to identify differential metabolomics patterns between complete and partial response or basal and post-treatment samples and design predictive models of response These results could facilitate in the future the implementation of blood-based tests into the clinical routine. Citation Format: Zamora P, Trilla-Fuertes L, Zapater-Moros A, Gámez-Pozo A, Prado-Vázquez G, Ferrer-Gómez M, Díaz- Almirón M, López Vacas R, Espinosa E, Fresno Vara JA. A pilot study of metabolomics biomarkers in breast cancer tumors treated with neoadjuvant therapy [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr P5-12-14.

e16519 Background: Prognosis of localized clear cell renal cell carcinoma (ccRCC) patients is estimated by nomograms based on clinico-pathological factors. However, clinical guidelines do not recommend their use. Recently, pembrolizumab has demonstrated benefit in disease-free survival (DFS) in medium-high risk localized ccRCC, increasing the need for better patient stratification tools. In this study, we have defined and validated a molecular signature, Bio-miR, based on the expression of nine microRNAs, with prognostic value in ccRCC. Methods: The discovery and two validation cohorts (Leeds-UK and Spain) consisted of patients with resected localized (stage Ib-III) ccRCC and no adjuvant therapy. miRNA expression was analyzed using microarrays and validated using qPCR in FFPE nephrectomy tissues. Cox regression was used to define the best microRNAs combination to predict risk of relapse. Results: In the discovery cohort (n = 71), DFS at 5 years was 93.9% amongst Bio-miR-defined low-risk patients and 61.6% in high-risk patients (HR = 6.9 (3.4-42.9), p < 0.001). Cancer-specific survival at 5 years was 95.7% and 86.4% in low- and high-risk patients, respectively (HR = 7.7 (1.7-35.1), p < 0.01). Bio-miR compared favorably with different histopathological factors and UISS and Karakiewicz´s nomograms. In the Leeds validation cohort (n = 75/95 passing qPCR quality control), patients defined as low-risk had a 5-year DFS rate of 94% versus 62% in high-risk defined disease. In this cohort Bio-miR was able to divide the Leibovich intermediate-risk population into two groups with divergent five-year DFS rates (100% vs 71%). In the Spanish validation cohort (n = 180), DFS rates at 5 years were 82.9% in the low-risk group and 58.7% in the high-risk group (HR = 2.4 (1.4-4.4); p < 0.005). Applying the inclusion criteria from the phase III KEYNOTE-564 trial, Bio-miR identifies a small low-risk population who could be spared adjuvant treatment. Conversely, amongst patients excluded from the study due to low-risk features, Bio-miR defines a high-risk population (DFS at five years of 50%) who should be prioritized for adjuvant therapy. Conclusions: We define and validate a nine-microRNA based signature capable of dichotomizing patients with localized ccRCC into low- and high-risk groups based on risk of relapse. Importantly, Bio-miR acts independently of tumor stage and grade and could, therefore, help refine the selection of patients for adjuvant therapy as well as inform the design of future adjuvant ccRCC trials.

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. The molecular analysis of the tumor offers a unique opportunity to unveil predictive factors. In this work, gene expression profiling in 279 tumor samples from patients receiving neoadjuvant chemotherapy was performed and probabilistic graphical models were used. This approach enables addressing biological and clinical questions from a Systems Biology perspective, allowing to deal with large gene expression data and their interactions. Tumors presenting complete response to neoadjuvant chemotherapy had a higher activity of immune related functions compared to resistant tumors. Similarly, samples from complete responders presented higher expression of lymphocyte cell lineage markers, immune-activating and immune-suppressive markers, which may correlate with tumor infiltration by lymphocytes (TILs). These results suggest that the patient’s immune system plays a key role in tumor response to neoadjuvant treatment. However, future studies with larger cohorts are necessary to validate these hypotheses.

BACKGROUND Breast cancer is a diverse and heterogeneous disease. The use of neoadjuvant treatments has improved the prognosis of localized breast cancer. However, molecular basis of neoadjuvant treatment response and resistance remains unknown. Clinical data has uncovered the existence of different tumor responses to neoadjudvant chemotherapy, allowing the classification of patients in different groups. Gene expression profile description of the different patient groups provide essential information in the clinical decision making as well as to allow a deeper knowledge of this disease. MATERIALS AND METHODS A breast cancer tumor dataset was obtained from the Gene Expression Omnibus (GSE41998) and from a phase II trial (NCT00455533). 279 tumors from previously untreated women with primary invasive breast adenocarcinoma were included in this study. Whole genome gene expression profiling was performed using Affymetrix GeneChip gene expression microarrays.Differentially expressed genes were chosen selecting 3000 more variable probes among all patients and were used to construct four networks of gene functional interactions, one for all tumors and three for each molecular subtype independently. Functional structure was performed using probabilistic graphical models with local minimum Bayesian Information Criterion. Data analyses were carried out using MeV, BRB Array Tools, R, Cytoscape software suites and DAVID web tools. RESULTS Regardless of tumor molecular subtype, tumors showing a complete response to treatment showed higher "Immune response (MHCII)", "Immune response (chemotaxis)", "Immune response (B cell)“ and "Immune response (Interferon)” nodes activities compared to resistant tumors (stable disease tumors). These differences are also observed when analyzing tumor molecular subgroups (Luminal A, Luminal B and Basal-like) separately. Moreover, complete response tumors, showed significantly higher levels of lymphocytic cell lineage markers (CD4, CD8 and CD20). CONCLUSION This type of approach allows seeing differences at biological process levels rather than at the individual gene level.Tumors that respond to neoadjuvant treatment showed higher “Immune” nodes activity than resistant tumors and these differences were also showed in analyses stratified by molecular subtype. Besides, complete response tumors presented higher values of lymphocyte cell lineage markers which might suggest a greater amount of tumor-infiltrating lymphocytes (TILs). These results can suggest that patients' immune system could play an important role in the response to neoadjuvant chemotherapy treatment. Citation Format: Zamora Auñón P, Zapater-Moros A, Trilla-Fuertes L, Gamez-Pozo A, Prado-Vázquez G, Llorente-Armijo S, Lopez-Vacas R, Main P, Espinosa Arranz E, Fresno-Vara JA. A functional approach to the molecular basis of neoadjuvant treatment response in breast cancer [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr P6-15-12.

Carcinoma anal de cèl·lules escamoses; Biologia molecular; Proteòmica Anal squamous cell carcinoma; Molecular biology; Proteomics Carcinoma anal de células escamosas; Biología molecular; Proteómica 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. This study was supported by the Instituto de Salud Carlos III, Spanish Economy and Competitiveness Ministry, Spain and co-sponsored by the FEDER program, “Una forma de hacer Europa” (PI15/01310), a Roche Farma grant, Cátedra UAM-Amgen and a grant of Grupo Español Multidisciplinar en Cáncer Digestivo (GEMCAD1403). LT-F is supported by the Spanish Economy and Competitiveness Ministry (DI-15–07614). GP-V is supported by the Consejería de Educación, Juventud y Deporte of Comunidad de Madrid (IND2017/BMD7783); AZ-M is supported by Jesús Antolín Garciarena fellowship from IdiPAZ. The authors have declared a conflict of interest. JAFV and AG-P are shareholders in Biomedica Molecular Medicine SL. LT-F and GP-V are employees of Biomedica Molecular Medicine SL. JC has received honoraria for scientific consulting (as speaker and advisory roles) from Novartis, Pfizer, Ipsen, Exelixis, Bayer, Eisai, Advanced Accelerator Applications, Amgen, Sanofi and Merck Serono and research support from Eisai, Novartis, Ipsen, Astrazeneca, Pfizer and Advanced Accelerator Applications. IG has received honoraria and/or travel expenses from Roche, Sanofi, Merck, Servier, Amgen and Sirtflex, and for advisory role from Merck and Sanofi. JF has received consulting and advisory honoraria from Amgen, Ipsen, Eissai, Merck, Roche and Novartis; research funding from Merck, and travel and accommodation expenses from Amgen and Servier. The other authors declare no conflicts of interest.

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. In this study, proteomics and Bayesian networks were used to characterize protein relationships in 96 breast tumor samples. Components obtained by these methods had a clear functional structure. The analysis of these components suggested differences in processes such as mitochondrial function or extracellular matrix between breast cancer subtypes, including our new defined subtype TN-like. In addition, one of the components, mainly related with extracellular matrix processes, had prognostic value in this cohort. Functional approaches allow to build hypotheses about regulatory mechanisms and to establish new relationships among proteins in the breast cancer context., PLoS ONE, 15 (6), ISSN:1932-6203

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. MiRNAs were isolated and measured by microRNA arrays. Significance Analysis of Microarrays and Consensus Cluster algorithm were used to characterize different renal subtypes. The analyses showed that chromophobe renal tumors are a homogeneous group characterized by an overexpression of miR 1229, miR 10a, miR 182, miR 1208, miR 222, miR 221, miR 891b, miR 629-5p and miR 221-5p. On the other hand, clear cell renal carcinomas presented two different groups inside this histological subtype, with differences in miRNAs that regulate focal adhesion, transcription, apoptosis and angiogenesis processes. Specifically, one of the defined groups had an overexpression of proangiogenic microRNAs miR185, miR126 and miR130a. In conclusion, differences in miRNA expression profiles between histological renal subtypes were established. In addition, clear cell renal carcinomas had different expression of proangiogenic miRNAs. With the emergence of antiangiogenic drugs, these differences could be used as therapeutic targets in the future or as a selection method for tailoring personalized treatments.

Aims: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 beta-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.Conclusions:Flux activities summarize Flux Balance Analysis data and can be associated with prognosis in 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.

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. However, these molecular subtypes have had little impact in the clinical setting. Gene expression data and clinical information from 494 triple-negative breast tumors were obtained from public databases. First, a probabilistic graphical model approach to associate gene expression profiles was performed. Then, sparse k-means was used to establish a new molecular classification. Results were then verified in a second database including 153 triple-negative breast tumors treated with neoadjuvant chemotherapy. Clinical and gene expression data from 494 triple-negative breast tumors were analyzed. Tumors in the dataset were divided into four subgroups (luminal-androgen receptor expressing, basal, claudin-low and claudin-high), using the cancer stem cell hypothesis as reference. These four subgroups were defined and characterized through hierarchical clustering and probabilistic graphical models and compared with previously defined classifications. In addition, two subgroups related to immune activity were defined. This immune activity showed prognostic value in the whole cohort and in the luminal subgroup. The claudin-high subgroup showed poor response to neoadjuvant chemotherapy. Through a novel analytical approach we proved that there are at least two independent sources of biological information: cellular and immune. Thus, we developed two different and overlapping triple-negative breast cancer classifications and showed that the luminal immune-positive subgroup had better prognoses than the luminal immune-negative. Finally, this work paves the way for using the defined classifications as predictive features in the neoadjuvant scenario.

Metabolomics has a great potential in the development of new biomarkers in cancer. In this study, metabolomics and gene expression data from breast cancer tumor samples were analyzed, using (1) probabilistic graphical models to define associations using quantitative data without othera prioriinformation; and (2) Flux Balance Analysis and flux activities to characterize differences in metabolic pathways. 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.

BackgroundMuscle-invasive bladder tumors are associated with 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.ObjectiveThe aim of this work is to characterize muscle-invasive bladder tumors at molecular levels using computational analyses.Design, Settings and ParticipantsThe TCGA cohort of muscle-invasive bladder cancer patients was used to describe these tumors.Outcome Measurements and Statistical AnalysisProbabilistic 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 functional level.ResultsLuminal and Basal groups were identified, and an immune molecular layer with independent value was also described. Luminal tumors had 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 androgen receptor.This fact points to androgen receptor as a therapeutic target in this group. Basal tumors were highly proliferative according to Flux Balance Analysis, which make these tumors good candidates for neoadjuvant chemotherapy. Immune-high group had higher expression of immune biomarkers, suggesting that this group may benefit from immune therapy.ConclusionsOur approach, based on layer analyses, established a Luminal group candidate for androgen receptor inhibitor therapy, a proliferative Basal group which seems to be a good candidate for chemotherapy, and an immune-high group candidate for immunotherapy.Patient SummaryMuscle-invasive bladder cancer has a poor prognosis in spite of appropriate therapy. Therefore, it is still necessary to characterize these tumors to propose new therapeutic targets. In this work we used computational analyses to characterize these tumors and propose treatments.

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 has a prognosis and a molecular profile more similar to triple negative tumors. In this study, proteomics and Bayesian networks were used to characterize protein relationships in 106 breast tumor samples. Components obtained by these methods had a clear functional structure. The analysis of these components suggested differences in processes such as metastasis or proliferation between breast cancer subtypes, including our new subtype TN-like. In addition, one of the components, mainly related with metastasis, had prognostic value in this cohort. Functional approaches allow to build hypotheses about regulatory mechanisms and to establish new relationships among proteins in the breast cancer context.Author SummaryBreast cancer classification in the clinical practice is defined by three biomarkers (estrogen receptor, progesterone receptor and HER2) into hormone receptor positive, HER2+ and triple negative breast cancer (TNBC). Our group recently described a new ER+ subtype with molecular characteristics and prognosis similar to TNBC. In this study we propose a mathematical method, the Bayesian networks, as a useful tool to study protein interactions and differential biological processes in breast cancer subtypes, characterizing differences in relevant processes such as proliferation or metastasis and associated them with patient prognosis.

Traditionally, bladder cancer has been classified based on histology features. Recently, some works have proposed a molecular classification of invasive bladder tumors. To determine whether proteomics can define molecular subtypes of muscle invasive urothelial cancer (MIUC) and allow evaluating the status of biological processes and its clinical value. 58 MIUC patients who underwent curative surgical resection at our institution between 2006 and 2012 were included. Proteome was evaluated by high-throughput proteomics in routinely archive FFPE tumor tissue. New molecular subgroups were defined. Functional structure and individual proteins prognostic value were evaluated and correlated with clinicopathologic parameters. 1,453 proteins were quantified, leading to two MIUC molecular subgroups. A protein-based functional structure was defined, including several nodes with specific biological activity. The functional structure showed differences between subtypes in metabolism, focal adhesion, RNA and splicing nodes. Focal adhesion node has prognostic value in the whole population. A 6-protein prognostic signature, associated with higher risk of relapse (5 year DFS 70% versus 20%) was defined. Additionally, we identified two MIUC subtypes groups. Prognostic information provided by pathologic characteristics is not enough to understand MIUC behavior. Proteomics analysis may enhance our understanding of prognostic and classification. These findings can lead to improving diagnosis and treatment selection in these patients.

Metabolic reprogramming is a hallmark of cancer. We and other authors have previously shown that breast cancer subtypes present metabolism differences. In this study, breast cancer cell lines were treated with metformin and rapamycin. The response was heterogeneous across various breast cancer cells, leading to cell cycle disruption in specific conditions. The molecular effects of these treatments were characterized using sublethal doses, SNP genotyping and mass spectrometry-based proteomics. Protein expression was analyzed using probabilistic graphical models, showing that treatments elicit various responses in some biological processes, providing insights into cell responses to metabolism drugs. Moreover, a flux balance analysis approach using protein expression values was applied, showing that predicted growth rates were comparable with cell viability measurements and suggesting an increase in reactive oxygen species response enzymes due to metformin treatment. In addition, a method to assess flux differences in whole pathways was proposed. Our results show that these various approaches provide complementary information, which can be used to suggest hypotheses about the drugs’ mechanisms of action and the response to drugs that target metabolism.

The aim of the study was to explore the molecular differences between melanoma tumor subtypes, based on BRAF and NRAS mutational status. Fourteen formalin-fixed, paraffin- embedded melanoma samples were analyzed using a high-throughput proteomics approach, coupled with probabilistic graphical models and Flux Balance Analysis, to characterize these differences. Proteomics analyses showed differences in expression of proteins related with fatty acid metabolism, melanogenesis and extracellular space between BRAF mutated and BRAF non-mutated melanoma tumors. Additionally, probabilistic graphical models showed differences between melanoma subgroups at biological processes such as melanogenesis or metabolism. On the other hand, Flux Balance Analysis predicts a higher tumor growth rate in BRAF mutated melanoma samples. In conclusion, differential biological processes between melanomas showing a specific mutational status can be detected using combined proteomics and computational approaches.

Introduction Muscle-invasive bladder cancer (MIBC) is associated with a high risk of relapse and metastasis. The standard treatment consists of radical cystectomy followed by cisplatin-based chemotherapy. Therefore, the molecular characterisation of MIBC in order to find new therapeutic targets is still necessary. Material and methods In this study, using The Cancer Genome Atlas (TCGA) cohort, functional structure and layer analyses were done to characterise MIBC tumours at functional level. Functional structure was developed using probabilistic graphical models and gene ontology analyses. Layer analyses were based on sparse k-means and Consensus Cluster Algorithm (CCA). Sparse k-means assigns a weight to each variable, based on its relevance in the sample classification. Then, a CCA using variables that were selected by the sparse K-means method was applied to define the optimum number of groups for each case. This sparse k-means-CCA workflow was performed successive times to explore the existence of independent informative molecular layers. Once relevant genes for one molecular layer are identified, these genes were removed from the dataset and the sparse k-means-CCA workflow was performed again, allowing the identification of different layers of molecular information and establishing different classifications based on various molecular features. Gene ontology analyses were done for each layer. Results and discussions A probabilistic graphical model with a functional structure was obtained. Sixteen layers were functionally characterised and the first three layers were analysed in depth. The first layer defined luminal and basal groups. Luminal tumours had a better prognosis than basal ones. Strikingly, luminal tumours showed higher expression of the androgen receptor, suggesting that this group is a good candidate for receiving treatment with androgen receptor inhibitors. The second layer established two groups based on genes related to extracellular matrix function. Lastly, the third layer defined two groups on the basis of immune information. The immune high group had a better prognosis and was characterised by higher expression of CTLA4 and PDL1, so this group may be a candidate for receiving immunotherapy. Conclusion Using probabilistic graphical models and layer analyses it is possible to establish independently a molecular and an immune classification. Using these approaches, we also proposed a specific treatment for luminal tumours (androgen receptor inhibitors) and for immune high tumours (immunotherapy).

Background The molecular analysis of melanoma has improved our understanding of the disease. The Cancer Genome Atlas (TCGA) Network proposed the molecular classification of melanoma in three subtypes: keratin-high, immune-high and membrane-low. However, this classification has not translated into therapeutic advances so far. Immunotherapy has contributed to improve survival, yet the mechanisms explaining differences in efficacy have not been elucidated. The aim of this study is to characterize the immune status of melanoma tumors through gene expression, and to analyze if these differences have an impact in the response to immunotherapy. Methods A probabilistic graphical model, followed by successive sparse k-means and consensus cluster analyses, was used to classify melanoma tumor samples from the TCGA cohort. Findings were translated into a cohort of patients treated with anti-PD1 antibodies (GSE78220, Hugo W et al) as a validation dataset. Results A probabilistic graphical model, including the 2,971 more variable genes from 472 melanoma samples from the TCGA dataset was built, and the resulting graph was processed to seek functional structures. Sparse k-means selected 119 genes. Gene ontology analysis showed that these genes were mainly related with immune processes. Immune genes split the population into two groups with different immune status. The so-called immune-high group included 232 patients (49%) and the immune-low group groups 238 patients (51%). The validation dataset GSE78220 provided mRNA expression in melanomas being treated with anti-PD-1 antibodies (28 biopsies belonging to 27 patients). The immune layer was translated to the new cohort by centroid method: 9 patients had immune-low tumors, whereas the remaining 18 had immune-high tumors. Kaplan Meier analysis using the clinical data from the GSE78220 cohort found a favorable response in patients with immune-low tumors (90% long-term survival). Conclusions We found a gene signature related to the tumor immune status that split the TCGA cohort in two groups. When applied to a cohort of patients treated with anti-PD1 antibodies, the group with immune-low tumors had 90% of long-term survival. Legal entity responsible for the study The authors. Funding Has not received any funding. Disclosure All authors have declared no conflicts of interest.

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. We identified several protein signatures with predictive value, which were validated with quantitative targeted proteomics in an independent cohort of patients and further evaluated in publicly available transcriptomics data. Results Using univariate Cox analysis, a panel of 18 proteins was significantly associated with distant metastasis-free survival of patients (p, PLoS ONE, 12 (6), ISSN:1932-6203

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. Protein expression-based probabilistic graphical models and flux balance analyses revealed that some ER+/PR+ samples had a protein expression profile similar to that of triple-negative samples and had a clinical outcome similar to those with triple-negative disease. This probabilistic graphical model-based classification had prognostic value in patients with luminal A breast cancer. This prognostic information was independent of that provided by standard genomic tests for breast cancer, such as MammaPrint, OncoType Dx and the 8-gene Score, The authors would like to acknowledge funding from grants PI12/00444, PI12/01016 and PI15/01310 from the Instituto de Salud Carlos III, Spanish Economy and Competitiveness Ministry, Spain, and co-funded by the FEDER program, “Una forma de hacer Europa”. This study has also been supported by the PRIME-XS project, grant agreement number 262067, funded by the EU’s Seventh Framework Program for Research. AG-P and RL-V are supported by Instituto de Salud Carlos III, and the Spanish Economy and Competitiveness Ministry grants, CA12/00258 and CA12/00264, respectively. . We want to particularly acknowledge the patients in this study for their participation and to the IdiPAZ, I+12 and O+EHUN Biobanks for the generous gifts of clinical samples used in this study. LT-F is supported by the Spanish Economy and Competitiveness Ministry (DI-15-07614). IdiPAZ, I+12 and O+EHUN Biobanks are supported by Instituto de Salud Carlos III, Spanish Economy and Competitiveness Ministry (RD09/0076/00073, RD09/0076/00118 and RD09/0076/00140, respectively) and FarmaIndustria, through the Cooperation Program in Clinical and Translational Research of the Community of Madrid and Basque Autonomous Community

Background Reprogramming of metabolism is a hallmark in cancer. In previous works we observed differences in glucose metabolism between tumors from different breast cancer subtypes, suggesting the possibility to use drugs against metabolism in this disease. Flux Balance Analysis (FBA) is widely used to study biochemical networks, allowing to predict growth rates and to simulate drug response. Material and methods Breast cancer cell lines and different drugs against metabolic targets were evaluated with dose-response curves, and pharmacological parameters for each condition were calculated. Proteomics data from breast cancer cells lines treated with sub-lethal doses and controls were obtained applying a mass spectrometry-based approach. Differences in protein expression between treated vs. control were assessed. An FBA approach using the human metabolic reconstruction Recon2 and including the protein expression values from perturbation experiments was also applied. Model predictions were validated using dynamic FBA and growth rate for each sample was estimated. With the aim to compare the activity of the different pathway fluxes between control and treated cells, flux activity was calculated for each condition and for each pathway and response predictive models were performed. Results Drug response was diverse across different breast cancer cells. Mass spectrometry from cell samples allows identifying and quantifying 4,114 proteins. FBA predicted that growth rates decrease in treated cells vs. control, as observed in cell viability assays. Dynamic FBA showed that our model correctly reflects cell growth rates. Finally, using flux activities, it is possible to build models which could predict response against these drugs. Conclusions Proteomics provide insights of the mechanisms responsible of cells' response to metabolism drugs. A validated computational model able to predict tumor growth using data from proteomics was developed. Model predicts growth rates and also dysregulation of biological processes triggered by drug treatment. Moreover, these computational approaches could be used to propose new mechanisms of action and effects of metabolic drugs. Acknowledgments This work was supported by grant PI15/01310 from Instituto de Salud Carlos III, Spanish Economy and Competitiveness Ministry, Spain and co-funded by FEDER program, “Una forma de hacer Europa”. L.T.-F is supported by Spanish Economy and Competitiveness Ministry grant DI-15-07614. Competing interest JAFV, AG-P and EE are stakeholders of Biomedica Molecular Medicine S.L. and Biomedica Molecular Medicine Ltd. LT-F is an employee of Biomedica Molecular Medicine S.L. The authors have declared no other conflict of interest. Citation Format: Zamora Auñón P, Trilla-Fuertes L, Díaz-Almirón M, Gamez-Pozo A, Prado-Vázquez G, Zapater-Moros A, Llorente-Armijo S, Gaya Romero F, Espinosa Arranz E, Fresno-Vara JA. Computational modeling predicts drugs response to targeting metabolism in breast cancer cells [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr P1-02-06.

Background: Triple negative breast cancer (TNBC) is described by lack of ER and PR expression and HER2 overexpression. This subgroup has no targeted therapies and its prognosis its worse than other breast cancer subtypes. The Cancer Stem Cell hypothesis lies in two ideas; first, that breast cancers can initiate in different cell types, which can be any epithelial stem cells or any of their progeny, and, that this original cell will give the tumor a specific molecular profile. Our work proposes a division in molecular groups of TNBC looking for this tissue of origin molecular profile through gene expression data and probabilistic graphical models analyses. Material and methods: TNBC gene expression data was obtained from GSE31519 (n=494). 2000 most variable genes were selected for subsequent analysis. A functional network was built using a probabilistic graphical model approach. Functional nodes were defined, and its function was explored by Gene Ontology using DAVID. Then, a new molecular classification was generated using the activity of functional nodes and k-means. Subgroups were characterized and compared with previous TNBC molecular classifications. Results: Probabilistic graphical models defined a functional structure comprising 27 functional nodes. We found some Luminal, some Basal and a claudin-enriched node. Based on these nodes molecular subgroups were defined, following the cancer stem cell hypothesis. Thus, four subtypes: (Luminal Androgen receptor (LAR), basal, claudin-low and claudin-high were defined matching tumor origin characteristics with those in the actual tumor sample. Tumors with low expression of claudins (CLDN-low subtype) had been differentiated in the first steps of the mammary epithelial development. Tumors with high expression of claudins (CLDN-High) had been originated at the second step of the development. Next step in the development are basal-epithelial cells, which will generate Basal subtype of TNBC. And finally, the last step in development is the differentiation to luminal cell, which is the origin of the Luminal subtype. Immune status, determined by immune functional nodes, showed prognostic value (p>0.05). Finally, we compared our classification with previous ones defined by Lehmann, Burstein and the PAM50. Table 1Cellular ClassificacionTumor sizeGradeNodal T1RestG3G1 or G2N0N1Basal761631994516835CLDN-High227315194CLDN-Low103221202811LAR115429333618Total9927628010325168 Conclusion: Functional networks can provide a relevant molecular knowledge which complements the TNBC classification. From this approach we establish a new classification taking into account the cancer stem cell hypothesis. Besides, this deep knowledge will allow a more accurate prediction of outcome and can also be used for diagnostic purposes and therapy selection. Citation Format: Zamora-Auñón P, Trilla-Fuertes L, Diaz-Almiron M, Gamez-Pozo A, Prado-Vázquez G, Zapater-Moros A, Llorente-Armijo S, Gaya Romero F, Espinosa Arranz E, Fresno-Vara JA. Triple negative breast cancer classification according to cancer stem cell hypothesis [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr P6-07-07.

Introduction Breast cancer is one of the most prevalent cancers in the world. In previous works we observed differences in glucose metabolism between breast cancer subtypes, suggesting that metabolism plays an important role in this disease. Flux Balance Analysis (FBA) is widely used to study metabolic networks, allowing predicting growth rates or the rate of production of a given metabolite. Material and methods Proteomics data from 96 breast cancer tumors were obtained applying a high-throughput proteomics approach to routinely archive formalin-fixed, paraffin-embedded tumor tissue. Proteomics tumor data were analyzed using the human metabolic reconstruction Recon2 and FBA. The tumor growth rate for each tumor was calculated. In order to analyze fluxes from the different metabolic pathways, flux activities were calculated as the sum of the fluxes of each reaction in each pathway defined in the Recon2. Then, flux activities were used to build prognostic models. Results and discussion Using the results obtained from FBA in the proteomics dataset, flux activities were calculated for each pathway. Employing these flux activities, a prognostic signature was built. Flux activities of vitamin A, tetrahydrobiopterin metabolism and beta-alanine metabolism pathways split our population into a low and a high risk group (p=0.044). Conclusion Vitamine A, beta-alanine and tetrahydrobiopterin metabolism flux activities could be used to predict relapse risk. Flux activities is a method proposed in a previous work to study response against drugs that now also demonstrated its utility in summarizing FBA data and is associated with prognosis. Citation Format: Zamora P, Trilla-Fuertes L, Gámez-Pozo A, Prado-Vázquez G, Zapater-Moros A, Ferrer-Gómez M, Díaz- Almirón M, López Vacas R, Espinosa E, Fresno Vara JA. Computational metabolism modeling predicts risk of relapse in breast cancer patients [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr P2-02-13.

Introduction Colorectal cancer (CRC) is the fourth cause of cancer death in the world. The Colorectal Cancer Subtyping Consortium (CRCSC) defined four consensus molecular subtypes (CMS): immune, canonical, metabolic and mesenchymal. This study bases on the hypothesis that there are two independent biological layers, as our group has previously seen in other malignancies, such as Triple negative breast cancer, melanoma or bladder cancer. So, the aim of this studio is to put the colorrectal cancer subtypes in context with that hypothesis. Material and methods Colorectal cancer tumour datasets were obtained from Gene Expression Omnibus (GEO). A merged database was constructed, and limma R package was used to remove batch effect. A probabilistic graphical model, followed by successive sparse k-means and consensus cluster analyses were used to classify CRC tumour samples from the merged dataset. Results and discussions Through this approach, three independent classifications, immune, molecular and adhesion-related, were established. Molecular classification showed different groups of tumours that present differential expression of diverse colorectal biomarkers. The immune classification further divided patients into high immune activity and low immune activity subgroups. And the adhesion-related assignment divides patients in two subgroups with prognostic value. We showed that immune and adhesion-related layers, when studied together, add its prognostic value defining low risk and high risk subgroups. We finally compare our classifications with the consensus molecular classification. Conclusion Molecular information obtained using a novel analytical approach to classify colorectal cancer tumour samples allowed us to establish new classifications based on different biological layers. We have demonstrated that our classifications based on the immune and the adhesion-related layers have prognostic value. Ultimately, to deepen the knowledge of CRC makes it possible to propose new therapeutic strategies and could also be used for diagnostic and prognostic purposes.

Introduction Breast cancer is one of the most prevalent cancers in the world. Traditionally, early breast cancer treatment is based on surgery and, after surgery, hormone treatment or chemotherapy. However, the neoadjuvant treatment is increasingly used. Metabolomics is the most recent ‘omics’ which allows quantify metabolites into blood patient samples. Coupled with computational analyses it could be possible to study differential metabolomics patterns and associate them with neoadjuvant response. Material and methods Blood plasma samples from patients with breast cancer treated with neoadjuvant chemotherapy were used to perform metabolomics experiments. One sample before the treatment (basal) and one sample after the chemotherapy (post-treatment) were analysed and clinical data regarding response (complete response or partial response) was also collected. Metabolomics experiments were performed using liquid chromatography coupled with mass-spectrometry.). Bayesian network and class comparison analyses were used to establish differential metabolic patterns between conditions. Additionally, a response prediction model based on logistic regression was build using metabolomics data from basal samples. Results and discussions A network showing the relationships between metabolites was build. Comparing metabolite measurements between complete response and partial response tumours in basal samples, 19 metabolites showed a differential quantification between both types of responses. Moreover, one of these metabolites is linoleic acid, previously described as a biomarker of complete response in neoadjuvant treatment in breast cancer. Using these 19 differential metabolites, a response predictive model was build. According to this model, it is possible to predict response to neoadjuvant treatment based on the amount of one metabolite, still only identified by its mass and charge. On the other hand, comparing basal and post-treatment samples, the network showed differential metabolomics patterns. These differential metabolites could be used as predictive biomarkers of response. Conclusion This study is a proof of concept that using a new ‘omics’ technique such as metabolomics in blood samples, coupled with computational analyses, it is possible to identify differential metabolomics patterns between complete and partial response or basal and post-treatment samples and design predictive models of response These results could facilitate in the future the implementation of blood-based tests into the clinical routine.

Introduction Breast cancer is one of the most prevalent cancers in the world. In previous works we observed differences in glucose metabolism between breast cancer subtypes, suggesting that metabolism plays an important role in this disease. Flux Balance Analysis (FBA) is widely used to study metabolic networks, allowing predicting growth rates or the rate of production of a given metabolite. Material and methods Proteomics data from 96 breast cancer tumours were obtained applying a high-throughput proteomics approach to routinely archive formalin-fixed, paraffin-embedded tumour tissue. Proteomics tumour data were analysed using the human metabolic reconstruction Recon2 and FBA. The tumour growth rate for each tumour was calculated. In order to analyse fluxes from the different metabolic pathways, flux activities were calculated as the sum of the fluxes of each reaction in each pathway defined in the Recon2. Then, flux activities were used to build prognostic models. Results and discussions Using the results obtained from FBA in the proteomics dataset, flux activities were calculated for each pathway. Employing these flux activities, a prognostic signature was built. Flux activities of vitamin A, tetrahydrobiopterin metabolism, and beta-alanine metabolism pathways split our population into a low and a high-risk group (p=0.044). Conclusion Vitamine A, beta-alanine and tetrahydrobiopterin metabolism flux activities could be used to predict relapse risk. Flux activities is a method proposed in a previous work to study response against drugs that now also demonstrated its utility in summarising FBA data and is associated with prognosis.

Introduction Melanoma is the most lethal malignancy of the skin. The Cancer Genome Atlas (TCGA) Network proposed a molecular classification of melanoma, consisting in three subtypes: keratin-high, immune-high and membrane-low. However, this classification has not been translated into therapeutic advances yet. The aim of this study is to characterise molecular differences of melanoma at biological and molecular levels and propose a novel molecular classification with clinical impact. Material and methods A novel approach based on the existence of different molecular informative layers was used in this study to establish independent sets of information. A probabilistic graphical model, followed by successive sparse k-means and consensus cluster analyses were used to classify melanoma tumour samples from the TCGA cohort. Then, molecular classification was validated in another public cohort: GSE65904. Results and discussions We established that there were at least two different kinds of molecular information: an immune layer and a histological layer. Immune high and immune low groups were established based on immune layer information. Keratin low-proliferation low, melanogenesis high-membrane low, melanogenesis low-membrane high and keratin high groups were established based on molecular layer information. This suggests that the information related to the immune system is independent from other molecular features and is distributed among the groups established by the TCGA classification. Besides, the immune and histological assignments showed different clinical outcomes in another dataset, identifying two immune groups with prognostic value. Conclusion In this work we proposed a novel analytical approach based on informative molecular layers. In this way, two independent classifications, an immune-based and a histological-based classification, were established. Immune classification showed prognostic value in an independent cohort. Besides, the histological and the immune layers may deserve additional research for define new potential targeted therapies.