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.

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

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

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.

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.

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.

Urothelial cancer proteomics provides both prognostic and functional information.

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.

Shotgun proteomics of archival triple-negative breast cancer samples.

PURPOSE: Triple-negative breast cancer (TNBC) accounts for 15-20% of all breast cancers, and has a worse prognosis compared with hormone receptor-positive disease. Its unfavorable outcome and the lack of hormonal receptors determine the use of adjuvant chemotherapy as part of the standard treatment for these tumors, although several studies have documented that the current standard combination chemotherapy is suboptimal. Therefore, a new functional taxonomy of breast cancer and new targets for therapeutic development are urgently needed. EXPERIMENTAL DESIGN: In this study, we have analyzed the proteome of TNBC applying a high-throughput proteomics approach to routinely archived formalin-fixed, paraffin-embedded tumor tissues. RESULTS: We have been able to identify and quantify more than 1000 protein groups. Some of these proteins are of outstanding interest in the biology and clinical management of this disease, such as CD44 and PARP1. Moreover, we have characterized some signaling pathways that could be related to TNBC genesis and development. CONCLUSION AND CLINICAL RELEVANCE: Our results open up new avenues for the use of proteomics technologies in clinically relevant studies using archival samples. Shotgun LC-MS/MS studies could serve to discover new biomarkers and may provide clues to the genesis of TNBC and underlying molecular alterations.

Gene signatures in breast cancer: current and future uses.

Gene signatures have been developed for estrogen receptor-positive breast cancer to complement pathological factors in providing prognostic information. The 70-gene and the 21-gene signatures identify patients who may not require adjuvant chemotherapy. Gene signatures in triple-negative disease and HER2-positive disease have not been fully developed yet, although studies demonstrate heterogeneity within these subgroups. Further research is needed before genotyping will help in making clinical decisions in triple-negative and HER2-positive disease. Molecular subtyping of breast cancer led to define luminal, basal, and HER2-enriched subtypes, which have specific clinical behavior. This approach may lead to identify new subgroups requiring specific therapies. Standardization of techniques will be required to translate investigations to the clinic.

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.

Comparison of gene expression profiling by reverse transcription quantitative PCR between fresh frozen and formalin-fixed, paraffin-embedded breast cancer tissues.

Recent reports demonstrate the feasibility of quantifying gene expression by using RNA isolated from blocks of formalin-fixed, paraffin-embedded (FFPE) tumor tissue. The development of molecular tests for clinical use based on archival materials would be of great utility in the search for and validation of important genes or gene expression profiles. In this study, we compared the performance of different normalization strategies in the correlation of quantitative data between fresh frozen (FF) and FFPE samples and analyzed the parameters that characterize such correlation for each gene. Total RNA extracted from FFPE samples presented a shift in raw cycle threshold (Cq) values that can be explained by its extensive degradation. Proper normalization can compensate for the effects of RNA degradation in gene expression measurements. We show that correlation between normalized expression values is better for moderately to highly expressed genes whose expression varies significantly between samples. Nevertheless, some genes had no correlation. These genes should not be included in molecular tests for clinical use based on FFPE samples. Our results could serve as a guide when developing clinical diagnostic tests based on RT-qPCR analyses of FFPE tissues in the coming era of treatment decision-making based on gene expression profiling.

Aurora kinases as prognostic biomarkers in ovarian carcinoma.

We investigated the expression of Aurora kinases A and B by immunohistochemistry in 68 ovarian carcinomas to analyze their prognostic value. The amplification of AURKA gene by fluorescence in situ hybridization was also assessed. Overall, 58.8% and 85.3% of ovarian carcinomas showed expression of Aurora A and B, respectively. Amplification of AURKA was found in 27.6% of cases examined. Tumors with Aurora A expression showed a lower rate of recurrence than those tumors without Aurora A expression (65% versus 91.7%, P = .019). In the univariate analysis, patients with Aurora A and B expression showed an increased progression-free survival (P = .023 and .06, respectively, log-rank test) and overall survival (P = .03 and .02, respectively, log-rank test). The multivariate analysis adjusted to optimal surgery by Cox proportional hazards regression showed Aurora A expression as an independent prognostic factor for progression-free survival (P = .03) and overall survival (P = .02). In conclusion, Aurora A expression seems to have a prognostic value in ovarian carcinoma.

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.

High-throughput techniques in breast cancer: a clinical perspective.

High-throughput technologies such as DNA-microarrays, RT-PCR and proteomics can improve the prognostic and predictive information acquired from classical parameters. Unlike information gathered by classical methods, high-throughput technologies can accurately inform clinicians on patient response to adjuvant therapy or those who will resist the effect of that therapy. Studies performed in breast cancer with high-throughput techniques have focused on tumour biology, prognosis, prediction of response to a few agents and, more recently, early diagnosis. However, further refinement is needed before these techniques become part of clinical routine. In the meantime, they will be used in clinical investigation, particularly in the areas of hormonal therapy and adjuvant chemotherapy, where modest improvements in the capacity of prediction can benefit many women. Close cooperation among clinicians, pathologists and basic investigators is essential to take high-throughput techniques to daily practice. New diagnostic tools will be complex but they will provide valuable patient information.