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.

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.

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.

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.

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.

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. 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.

Synergistic effect of antimetabolic and chemotherapy drugs in triple-negative breast cancer

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.

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.

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). 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.

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. 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.

Comprehensive Characterization of the Mutational Landscape in Localized Anal Squamous Cell Carcinoma

Anal squamous cell carcinoma (ASCC) is a rare neoplasm. Chemoradiotherapy is the standard of care, with no therapeutic advances achieved over the past three decades. Thus, a deeper molecular characterization of this disease is still necessary. We analyzed 46 paraffin-embedded tumor samples from patients diagnosed with primary ASCC by exome sequencing. A bioinformatics approach focused in the identification of high-impact genetic variants, which may act as drivers of oncogenesis, was performed. The relation between genetics variants and prognosis was also studied. The list of high-impact genetic variants was unique for each patient. However, the pathways in which these genes are involved are well-known hallmarks of cancer, such as angiogenesis or immune pathways. Additionally, we determined that genetic variants in BRCA2, ZNF750, FAM208B, ZNF599, and ZC3H13 genes are related with poor disease-free survival in ASCC. This may help to stratify the patient's prognosis and open new avenues for potential therapeutic intervention. In conclusion, sequencing of ASCC clinical samples appears an encouraging tool for the molecular portrait of this disease.

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.

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.

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. 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.

Abstract P3-08-42: Disease-free survival prognostic signature in triple-negative breast cancer based on high-throughput proteomics data

Introduction Triple negative breast cancer (TNBC) accounts for 15-20% of the breast cancer and is characterized by an aggressive phenotype and worst prognosis. TNBC does not benefit from any targeted therapy, so further characterization would be needed to define subgroups with potential therapeutic value. Material and methods 125 TNBC paraffin samples were analyzed using high-throughput proteomics based on SWATH-MS. Survival analyses and a prognostic predictor were done using BRB Array Tools. Proteins related with disease-free survival were established and, then, a prognostic signature was built based on their p-values. Results and discussion Using SWATH-MS, 1,206 proteins were identified in a cohort of 125 TNBC tumors. Of these 1,206 proteins, 29 proteins were related with disease-free survival. In addition, a prognostic signature based on the expression of two proteins, RMB3 and NIPSNAP1, was defined. The predictor split our population into a low-risk and a high-risk group (p=0.0002, HR= 6.519). Multivariate analysis showed that the prognostic signature based on the expression of these two proteins supplied significant information to the clinical parameters. Conclusion SWATH-MS proteomics demonstrates its utility in the analysis of TNBC paraffin samples. Moreover, this proteomics data allows us to build a prognostic signature based on the expression of two proteins (RBM3 and NIPSNAP1). This prognostic signature could be used in the future to identify a population with a high-risk of relapse that may be directed to a clinical trial.

Citation Format: Pilar Zamora Auñon, Silvia García Adrián, Lucia Trilla-Fuertes, Angelo Gámez-Pozo, Guillermo Prado-Vázquez, Andrea Zapater-Moros, Mariana Díaz- Almirón, Rocío López Vacas, Cristina Chiva, Cristina Chiva, Eduard Sabidó, Enrique Espinosa Arranz, Juan Ángel Fresno Vara. Disease-free survival prognostic signature in triple-negative breast cancer based on high-throughput proteomics data [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P3-08-42.

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 β-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.

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.

Proteomics characterisation of central nervous system metastasis biomarkers in triple negative breast cancer

Background

Breast cancer (BC) is the most frequent tumour in women. Triple negative tumours (TNBC)-which are associated with minor survival rates-lack markers predictive of response to anticancer drugs. Triple negative tumours frequently metastasise to the central nervous system (CNS).

Objective

The main objective of this study was to study differences in tumour protein expression between patients with CNS metastases and those without this kind of spread, and propose new biomarkers.

Methods

A retrospective study was performed. Targeted proteomics and statistical analyses were used to identify possible biomarkers.

Results

Proteins were quantified by a targeted proteomics approach and protein expression data were successfully obtained from 51 triple negative formalin-fixed paraffin-embedded samples. ISG15, THBS1 and AP1M1 were identified as possible biomarkers related with CNS metastasis development.

Conclusions

Three possible biomarkers associated with CNS metastases in TNBC tumours were identified: ISG15, THBS1 and AP1M1. They may become markers predicting the appearance of CNS infiltration in triple negative BC.

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. 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.

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. The genetic causes and molecular effects of this differential response were characterized by means of 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 such as transcription. Moreover, flux balance analysis using protein expression values showed 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 diverse approaches provide complementary information and allow us to suggest hypotheses about the response to drugs that target metabolism and their mechanisms of action.

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. 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<0.01). A reduced 5-protein profile with prognostic value was identified and its prediction performance was assessed in an independent targeted proteomics experiment and a publicly available transcriptomics dataset. Predictor P5 including peptides from proteins RAC2, RAB6A, BIEA and IPYR was the best performance protein combination in predicting relapse after adjuvant chemotherapy in TNBC patients.

CONCLUSIONS

This study identified a protein combination signature that complements histopathological prognostic factors in TNBC treated with adjuvant chemotherapy. The protein signature can be used in paraffin-embedded samples, and after a prospective validation in independent series, it could be used as predictive clinical test in order to recommend participation in clinical trials or a more exhaustive follow-up.

Combined Label-Free Quantitative Proteomics and microRNA Expression Analysis of Breast Cancer Unravel Molecular Differences with Clinical Implications

Better knowledge of the biology of breast cancer has allowed the use of new targeted therapies, leading to improved outcome. High-throughput technologies allow deepening into the molecular architecture of breast cancer, integrating different levels of information, which is important if it helps in making clinical decisions. microRNA (miRNA) and protein expression profiles were obtained from 71 estrogen receptor-positive (ER(+)) and 25 triple-negative breast cancer (TNBC) samples. RNA and proteins obtained from formalin-fixed, paraffin-embedded tumors were analyzed by RT-qPCR and LC/MS-MS, respectively. We applied probabilistic graphical models representing complex biologic systems as networks, confirming that ER(+) and TNBC subtypes are distinct biologic entities. The integration of miRNA and protein expression data unravels molecular processes that can be related to differences in the genesis and clinical evolution of these types of breast cancer. Our results confirm that TNBC has a unique metabolic profile that may be exploited for therapeutic intervention.

PTRF/cavin-1 and MIF proteins are identified as non-small cell lung cancer biomarkers by label-free proteomics

With the completion of the human genome sequence, biomedical sciences have entered in the “omics” era, mainly due to high-throughput genomics techniques and the recent application of mass spectrometry to proteomics analyses. However, there is still a time lag between these technological advances and their application in the clinical setting. Our work is designed to build bridges between high-performance proteomics and clinical routine. Protein extracts were obtained from fresh frozen normal lung and non-small cell lung cancer samples. We applied a phosphopeptide enrichment followed by LC-MS/MS. Subsequent label-free quantification and bioinformatics analyses were performed. We assessed protein patterns on these samples, showing dozens of differential markers between normal and tumor tissue. Gene ontology and interactome analyses identified signaling pathways altered on tumor tissue. We have identified two proteins, PTRF/cavin-1 and MIF, which are differentially expressed between normal lung and non-small cell lung cancer. These potential biomarkers were validated using western blot and immunohistochemistry. The application of discovery-based proteomics analyses in clinical samples allowed us to identify new potential biomarkers and therapeutic targets in non-small cell lung cancer.

Protein phosphorylation analysis in archival clinical cancer samples by shotgun and targeted proteomics approaches

Protein phosphorylation affects most eukaryotic cellular processes and its deregulation is considered a hallmark of cancer and other diseases. Phosphoproteomics may enable monitoring of altered signaling pathways as a means of stratifying tumors and facilitating the discovery of new drugs. Unfortunately, the development of molecular tests for clinical use is constrained by the limited availability of fresh frozen, clinically annotated samples. Here we report phosphopeptide analysis in human archival formalin-fixed, paraffin-embedded (FFPE) cancer samples based on immobilized metal affinity chromatography followed by liquid chromatography coupled with tandem mass spectrometry and selected reaction monitoring techniques. Our results indicate the equivalence of detectable phosphorylation rates in archival FFPE and fresh frozen tissues. Moreover, we demonstrate the applicability of targeted assays for phosphopeptide analysis in clinical archival FFPE samples, using an experimental workflow suitable for processing and analyzing large sample series. This work paves the way for the application of shotgun and targeted phosphoproteomics approaches in clinically relevant studies using archival clinical samples.

Gene profiling in breast cancer: time to move forward

Gene signatures may complement clinical and pathological factors to predict prognosis and response to therapy in patients with breast cancer, and can also sub-classify these tumours into entities with different biology and treatment requirements. A number of prognostic gene signatures are commercially available at this moment and two of them have entered phase III evaluation. Specific signatures are also being assessed to predict response to a number of drug therapies. The combined use of prognostic, predictive and subtype-defining signatures will guide therapeutic decisions in the future and will facilitate development of targeted drugs in specific groups of patients. However, cost-utility issues and some technical limitations have hindered widespread adoption of gene profiling. Gene signatures will become part of the routine clinical workup only if they help making clinical decisions. The first step to achieve this will consist of the inclusion of gene signatures in the design of clinical trials with new drugs.

MALDI profiling of human lung cancer subtypes

BACKGROUND

Proteomics is expected to play a key role in cancer biomarker discovery. Although it has become feasible to rapidly analyze proteins from crude cell extracts using mass spectrometry, complex sample composition hampers this type of measurement. Therefore, for effective proteome analysis, it becomes critical to enrich samples for the analytes of interest. Despite that one-third of the proteins in eukaryotic cells are thought to be phosphorylated at some point in their life cycle, only a low percentage of intracellular proteins is phosphorylated at a given time.

METHODOLOGY/PRINCIPAL FINDINGS

In this work, we have applied chromatographic phosphopeptide enrichment techniques to reduce the complexity of human clinical samples. A novel method for high-throughput peptide profiling of human tumor samples, using Parallel IMAC and MALDI-TOF MS, is described. We have applied this methodology to analyze human normal and cancer lung samples in the search for new biomarkers. Using a highly reproducible spectral processing algorithm to produce peptide mass profiles with minimal variability across the samples, lineal discriminant-based and decision tree-based classification models were generated. These models can distinguish normal from tumor samples, as well as differentiate the various non-small cell lung cancer histological subtypes.

CONCLUSIONS/SIGNIFICANCE

A novel, optimized sample preparation method and a careful data acquisition strategy is described for high-throughput peptide profiling of small amounts of human normal lung and lung cancer samples. We show that the appropriate combination of peptide expression values is able to discriminate normal lung from non-small cell lung cancer samples and among different histological subtypes. Our study does emphasize the great potential of proteomics in the molecular characterization of cancer.

Angiogenesis-related gene expression profile with independent prognostic value in advanced ovarian carcinoma

Background

Ovarian carcinoma is the most important cause of gynecological cancer-related mortality in Western societies. Despite the improved median overall survival in patients receiving chemotherapy regimens such as paclitaxel and carboplatin combination, relapse still occurs in most advanced diseased patients. Increased angiogenesis is associated with rapid recurrence and decreased survival in ovarian cancer. This study was planned to identify an angiogenesis-related gene expression profile with prognostic value in advanced ovarian carcinoma patients.

Methodology/Principal Findings

RNAs were collected from formalin-fixed paraffin-embedded samples of 61 patients with III/IV FIGO stage ovarian cancer who underwent surgical cytoreduction and received a carboplatin plus paclitaxel regimen. Expression levels of 82 angiogenesis related genes were measured by quantitative real-time polymerase chain reaction using TaqMan low-density arrays. A 34-gene-profile which was able to predict the overall survival of ovarian carcinoma patients was identified. After a leave-one-out cross validation, the profile distinguished two groups of patients with different outcomes. Median overall survival and progression-free survival for the high risk group was 28.3 and 15.0 months, respectively, and was not reached by patients in the low risk group at the end of follow-up. Moreover, the profile maintained an independent prognostic value in the multivariate analysis. The hazard ratio for death was 2.3 (95% CI, 1.5 to 3.2; p<0.001).

Conclusions/Significance

It is possible to generate a prognostic model for advanced ovarian carcinoma based on angiogenesis-related genes using formalin-fixed paraffin-embedded samples. The present results are consistent with the increasing weight of angiogenesis genes in the prognosis of ovarian carcinoma.