Barriers to working with people affected by cancer: Preliminary qualitative data from a mixed methods exploration of practitioner perspectives in nutritional therapy

Introduction: Cancer is a leading cause of death worldwide. Nutrition may affect occurrence, recurrence and survival rates and many cancer patients and survivors seek individualized nutrition advice. Appropriately skilled nutritional therapy (NT) practitioners may be well-placed to safely provide this advice, but little is known of their perspectives on working with people affected by cancer. This mixed-methods study seeks to explore their views on training, barriers to practice, use of evidence, and other resources, to support the development of safe evidence-based practice. Preliminary data on barriers to practice are reported here. Methods: Two cohorts of NT practitioners were recruited from all UK registered NT practitioners, by an on-line anonymous survey. 84 cancer practitioners (CP) and 165 non-cancer practitioners (NCP) were recruited. Mixed quantitative and qualitative data was collected by the survey. Content analysis was used to analyze qualitative data on the use of evidence, barriers to practice and perceived needs for working with clients with cancer, for further exploration using interviews and focus groups. Preliminary results: For the NCP cohort, exploring themes of perceived barriers to working with people affected by cancer suggested that perceived complexity, risk and need for caution in this area of practice were important barriers. Insufficient specialist knowledge and skills also emerged as barriers. Some NCPs perceived opposition from medical practitioners and other mainstream healthcare professions as an obstacle to starting cancer practice. To overcome these barriers, specialist training emerged as most important. For the CP cohort, in exploring the skills they considered enabled them to undertake cancer work, specialist clinical and technical knowledge emerged strongly. Only 10% CP participants did not want more work with people affected by cancer. 10% CPs reported some NHS referrals, whereas most received clients by self-referral or from other practitioners. When considering barriers that impede their cancer practice, the dominant categories for CPs were hostility or opposition by mainstream oncology professionals, and lack of dialogue and engagement with them. To overcome these barriers, CPs desired engagement with oncology professionals and recognized specialist cancer NT training. For both NCPs and CPs, evidence resources, practice guidelines and practitioner support networks also emerged as potential enablers to cancer practice. Conclusions: This is the first detailed exploration of NT practitioners’ perceived barriers to working with people affected by cancer. Acquiring specialist skills and knowledge appears important to enable NCPs to start cancer work, and for CPs with these skills, the perceived barriers appear foremost in the relationship with mainstream cancer professionals. Further exploration of these themes, and other NT practitioner perspectives on working with people affected by cancer, is underway. This work will inform and support the development of professional practice, training and other resources.

Prior knowledge transfer across transcriptional data sets and technologies using compositional statistics yields new mislabelled ovarian cell line

Here, we describe gene expression compositional assignment (GECA), a powerful, yet simple method based on compositional statistics that can validate the transfer of prior knowledge, such as gene lists, into independent data sets, platforms and technologies. Transcriptional profiling has been used to derive gene lists that stratify patients into prognostic molecular subgroups and assess biomarker performance in the pre-clinical setting. Archived public data sets are an invaluable resource for subsequent in silico validation, though their use can lead to data integration issues. We show that GECA can be used without the need for normalising expression levels between data sets and can outperform rank-based correlation methods. To validate GECA, we demonstrate its success in the cross-platform transfer of gene lists in different domains including: bladder cancer staging, tumour site of origin and mislabelled cell lines. We also show its effectiveness in transferring an epithelial ovarian cancer prognostic gene signature across technologies, from a microarray to a next-generation sequencing setting. In a final case study, we predict the tumour site of origin and histopathology of epithelial ovarian cancer cell lines. In particular, we identify and validate the commonly-used cell line OVCAR-5 as non-ovarian, being gastrointestinal in origin. GECA is available as an open-source R package.

Targeting treatment resistant breast cancer stem cells with FKBPL and its peptide derivative, AD-01

FKBPL and its peptide derivative, AD-01, have already demonstrated well-established inhibitory effects on breast cancer growth and CD44 dependent anti-angiogenic activity1, 2, 3. Since breast cancer stem cells (BCSCs) are CD44 positive, we wanted to explore if AD-01 could specifically target BCSCs. FKBPL stable overexpression or AD-01 treatment were highly effective at reducing the BCSC population measured by inhibiting mammosphere forming efficiency (MFE) in cell lines and primary breast cancer samples from both solid breast tumours and pleural effusions. Flow cytometry, to assess the ESA+/CD44+/CD24- subpopulation, validated these results. The ability of AD-01 to inhibit the self-renewal capacity of BCSCs was confirmed across three generations of mammospheres, where mammospheres were completely eradicated by the third generation (p<0.001). Clonogenic assays suggested that AD-01 mediated BCSC differentiation, with a significant decrease in the number of holoclones and an associated increase in meroclones/paraclones. In support of this, the stem cell markers, Nanog and Oct4 were significantly reduced following AD-01 treatment, whilst transfection of FKBPL-targeted siRNAs led to an increase in these markers and in mammosphere forming potential, highlighting the endogenous role of FKBPL in stem cell signalling. The clinical relevance of this was confirmed using a publically available microarray data set (GSE7390), where, high FKBPL and low Nanog expression were independently associated with improved overall survival in breast cancer patients (log rank test p=0.03; hazard ratio=3.01). When AD-01 was combined with other agents, we observed synergistic activity with the Notch inhibitor, DAPT and AD-01 was also able to abrogate a chemo- and radiotherapy induced enrichment in BCSCs. Importantly, using ‘gold standard’ in vivo limiting dilution assays we demonstrated a delay in tumour initiation and reoccurrence in AD-01 treated xenografts. In summary, AD-01 appears to have dual anti-angiogenic and anti-BCSC activity which will be advantageous as this agent enters clinical trial.

Targeting treatment resistant cancer stem cells with FKBPL and its peptide therapeutics

FKBPL and its peptide derivatives have already demonstrated well-established inhibitory effects on cancer growth and CD44-dependent anti-angiogenic activity. Since cancer stem cells (CSCs) are CD44 positive, we wanted to explore if these therapeutics could specifically target CSCs in breast and ovarian cancer. In a tumoursphere assay, FKBPL stable overexpression or FKBPL-based peptide (AD-01, preclinical peptide or ALM201, clinical peptide candidate) treatment were highly effective at reducing the CSC population measured by inhibiting tumoursphere forming efficiency in breast and ovarian cancer cell lines and primary breast cancer samples from both solid breast tumours and pleural effusions. Flow cytometry, to assess the ESA+/CD44+/CD24- and ALDH+ cell subpopulations representative of CSCs, validated these results. The ability of AD-01 and ALM201 to inhibit the self-renewal capacity of CSCs was confirmed across three generations, eradicating CSC completely by the third generation (p<0.001). Furthermore, clonogenic assay demonstrated that FKBPL-based peptides mediated CSC differentiation, with a significant decrease in the number of CSCs or holoclones and an associated increase in differentiated cancer cells or meroclones/paraclones. In addition, AD-01 treatment in vitro and in vivo led to a significant reduction in the stem cell markers, Nanog, Sox2 and Oct4 protein and mRNA levels; whilst transfection of FKBPL-targeted siRNAs led to an increase in these markers and in tumoursphere forming potential, highlighting the endogenous role of FKBPL in stem cell signalling. The clinical relevance of this was confirmed using a publically available microarray data set (GSE7390), where, high FKBPL and low Nanog expression were independently associated with improved overall survival in breast cancer patients (log rank test p=0.03; hazard ratio=3.01). Additionally, when AD-01 was combined with other agents, we observed additive activity with the Notch inhibitor, DAPT and AD-01 was also able to abrogate a chemo- and radiotherapy induced enrichment in CSCs. Importantly, using gold standard in vivo limiting dilution assays we demonstrated a delay in tumour initiation and reoccurrence in AD-01 treated xenografts. In summary, FKBPL-based peptides appear to have dual anti-angiogenic and anti-CSC activity which will be advantageous as this agent enters clinical trial.

RACIAL/ETHNIC VARIABILITY IN THE EFFECT OF RADIATION ON SECOND PRIMARY THYROID CANCER IN CHILDREN: ANALYSIS USING SURVEILLANCE EPIDEMIOLOGY AND END RESULTS DATA

Increases in pediatric thyroid cancer incidence could be partly due to previous clinical intervention. This retrospective cohort study used 1973-2012 data from the Surveillance Epidemiology and End Results program to assess the association between previous radiation therapy exposure in development of second primary thyroid cancer (SPTC) among 0-19-year-old children. Statistical analysis included the calculation of summary statistics and univariable and multivariable logistic regression analysis. Relative to no previous radiation therapy exposure, cases exposed to radiation had 2.46 times the odds of developing SPTC (95% CI: 1.39-4.34). After adjustment for sex and age at diagnosis, Hispanic children who received radiation therapy for a first primary malignancy had 3.51 times the odds of developing SPTC compared to Hispanic children who had not received radiation therapy, [AOR=3.51, 99% CI: 0.69-17.70, p=0.04]. These findings support the development of age-specific guidelines for the use of radiation based interventions among children with and without cancer.

Biological ageing and colorectal cancer: fetuin A, sirtuins and telomeres at the interface between inflammation, metabolism and cancer

Colorectal cancer is a common, age-associated disease with significant comorbidity and mortality. Biomarkers of ageing may have prognostic or predictive value in colorectal cancer. Fetuin A, members of the sirtuin family of proteins and telomeres have shown promise as potential biomarkers of ageing. AIM: To evaluate these potential biomarkers in the context of colorectal cancer. METHODS: Two cohorts of patients were used. Telomere length was measured in peripheral blood leukocytes (PBL), and for a subset of patients, in normal colorectal and colorectal tumour tissue. Serum fetuin A was measured for these patients and data on clinico-pathological factors of accepted significance in colorectal cancer was collected prospectively. Telomere length in the matched samples of leukocytes, normal colorectal and colorectal tumour tissue was compared. Associations between telomere length in the different tissues, serum fetuin A and clinico-pathological factors of accepted significance in colorectal cancer were evaluated. A systematic review of the literature was performed to examine the evidence for correlation between telomere length in different tissues in humans. Tissue from colorectal tumours from the second cohort patients was mounted in a tissue microarray (TMA) and stained for sirtuin proteins (SIRT2-SIRT7). This TMA also contained tissue from a subset of matched samples of adjacent normal colorectal mucosa. Staining of normal colorectal and colorectal tumour tissue was evaluated by the weighted Histoscore method and compared. The effect of staining in tumour tissue on cancer-specific survival was examined. Associations between Histoscores and clinico-pathological factors of accepted significance in colorectal cancer were assessed. RESULTS: Systematic review of the literature did not show robust evidence of correlation between telomere length in different tissues in humans. Telomere length in peripheral blood leukocytes did not show correlation with telomere length in normal colorectal mucosa, or in colorectal tumour tissue. PBL telomere length was potentially related to the presence of distant metastases. Fetuin A was inversely associated with markers of systemic inflammation and with T stage. Novel nuclear localisation was described for SIRT4 and SIRT5. Protein expression of the sirtuins was reduced in tumour tissue in comparison to normal colorectal mucosa, apart from SIRT3 cytoplasmic and nuclear and SIRT6 nuclear stainng. Lowest and highest quartile SIRT2 expression was associated with worse survival. Sirtuin protein expression levels and localisation correlate with increased systemic inflammation and pathological markers of poor prognosis in tumour tissue. Intercorrelations between sirtuin expression levels in normal tissue are not seen in tumour tissue, possibly indicating a breakdown of signalling and control.

DATA DRIVEN APPROACHES TO IDENTIFY DETERMINANTS OF HEART DISEASES AND CANCER RESISTANCE

Cancer and cardio-vascular diseases are the leading causes of death world-wide. Caused by systemic genetic and molecular disruptions in cells, these disorders are the manifestation of profound disturbance of normal cellular homeostasis. People suffering or at high risk for these disorders need early diagnosis and personalized therapeutic intervention. Successful implementation of such clinical measures can significantly improve global health. However, development of effective therapies is hindered by the challenges in identifying genetic and molecular determinants of the onset of diseases; and in cases where therapies already exist, the main challenge is to identify molecular determinants that drive resistance to the therapies. Due to the progress in sequencing technologies, the access to a large genome-wide biological data is now extended far beyond few experimental labs to the global research community. The unprecedented availability of the data has revolutionized the capabilities of computational researchers, enabling them to collaboratively address the long standing problems from many different perspectives. Likewise, this thesis tackles the two main public health related challenges using data driven approaches. Numerous association studies have been proposed to identify genomic variants that determine disease. However, their clinical utility remains limited due to their inability to distinguish causal variants from associated variants. In the presented thesis, we first propose a simple scheme that improves association studies in supervised fashion and has shown its applicability in identifying genomic regulatory variants associated with hypertension. Next, we propose a coupled Bayesian regression approach -- eQTeL, which leverages epigenetic data to estimate regulatory and gene interaction potential, and identifies combinations of regulatory genomic variants that explain the gene expression variance. On human heart data, eQTeL not only explains a significantly greater proportion of expression variance in samples, but also predicts gene expression more accurately than other methods. We demonstrate that eQTeL accurately detects causal regulatory SNPs by simulation, particularly those with small effect sizes. Using various functional data, we show that SNPs detected by eQTeL are enriched for allele-specific protein binding and histone modifications, which potentially disrupt binding of core cardiac transcription factors and are spatially proximal to their target. eQTeL SNPs capture a substantial proportion of genetic determinants of expression variance and we estimate that 58% of these SNPs are putatively causal. The challenge of identifying molecular determinants of cancer resistance so far could only be dealt with labor intensive and costly experimental studies, and in case of experimental drugs such studies are infeasible. Here we take a fundamentally different data driven approach to understand the evolving landscape of emerging resistance. We introduce a novel class of genetic interactions termed synthetic rescues (SR) in cancer, which denotes a functional interaction between two genes where a change in the activity of one vulnerable gene (which may be a target of a cancer drug) is lethal, but subsequently altered activity of its partner rescuer gene restores cell viability. Next we describe a comprehensive computational framework --termed INCISOR-- for identifying SR underlying cancer resistance. Applying INCISOR to mine The Cancer Genome Atlas (TCGA), a large collection of cancer patient data, we identified the first pan-cancer SR networks, composed of interactions common to many cancer types. We experimentally test and validate a subset of these interactions involving the master regulator gene mTOR. We find that rescuer genes become increasingly activated as breast cancer progresses, testifying to pervasive ongoing rescue processes. We show that SRs can be utilized to successfully predict patients' survival and response to the majority of current cancer drugs, and importantly, for predicting the emergence of drug resistance from the initial tumor biopsy. Our analysis suggests a potential new strategy for enhancing the effectiveness of existing cancer therapies by targeting their rescuer genes to counteract resistance. The thesis provides statistical frameworks that can harness ever increasing high throughput genomic data to address challenges in determining the molecular underpinnings of hypertension, cardiovascular disease and cancer resistance. We discover novel molecular mechanistic insights that will advance the progress in early disease prevention and personalized therapeutics. Our analyses sheds light on the fundamental biological understanding of gene regulation and interaction, and opens up exciting avenues of translational applications in risk prediction and therapeutics.

Study of tRNA modifying enzymes and codon usage bias in cancer

Recent evidences indicate that tRNA modifications and tRNA modifying enzymes may play important roles in complex human diseases such as cancer, neurological disorders and mitochondrial-linked diseases. We postulate that expression deregulation of tRNA modifying enzymes affects the level of tRNA modifications and, consequently, their function and the translation efficiency of their tRNA corresponding codons. Due to the degeneracy of the genetic code, most amino acids are encoded by two to six synonymous codons. This degeneracy and the biased usage of synonymous codons cause alterations that can span from protein folding to enhanced translation efficiency of a select gene group. In this work, we focused on cancer and performed a meta-analysis study to compare microarray gene expression profiles, reported by previous studies and evaluate the codon usage of different types of cancer where tRNA modifying enzymes were found de-regulated. A total of 36 different tRNA modifying enzymes were found de-regulated in most cancer datasets analyzed. The codon usage analysis revealed a preference for codons ending in AU for the up-regulated genes, while the down-regulated genes show a preference for GC ending codons. Furthermore, a PCA biplot analysis showed this same tendency. We also analyzed the codon usage of the datasets where the CTU2 tRNA modifying enzyme was found deregulated as this enzyme affects the wobble position (position 34) of specific tRNAs. Our data points to a distinct codon usage pattern between up and downregulated genes in cancer, which might be caused by the deregulation of specific tRNA modifying enzymes. This codon usage bias may augment the transcription and translation efficiency of some genes that otherwise, in a normal situation, would be translated less efficiently.

EMMA 2--a MAGE-compliant system for the collaborative analysis and integration of microarray data

Background: Understanding transcriptional regulation by genome-wide microarray studies can contribute to unravel complex relationships between genes. Attempts to standardize the annotation of microarray data include the Minimum Information About a Microarray Experiment (MIAME) recommendations, the MAGE-ML format for data interchange, and the use of controlled vocabularies or ontologies. The existing software systems for microarray data analysis implement the mentioned standards only partially and are often hard to use and extend. Integration of genomic annotation data and other sources of external knowledge using open standards is therefore a key requirement for future integrated analysis systems. Results: The EMMA 2 software has been designed to resolve shortcomings with respect to full MAGE-ML and ontology support and makes use of modern data integration techniques. We present a software system that features comprehensive data analysis functions for spotted arrays, and for the most common synthesized oligo arrays such as Agilent, Affymetrix and NimbleGen. The system is based on the full MAGE object model. Analysis functionality is based on R and Bioconductor packages and can make use of a compute cluster for distributed services. Conclusion: Our model-driven approach for automatically implementing a full MAGE object model provides high flexibility and compatibility. Data integration via SOAP-based web-services is advantageous in a distributed client-server environment as the collaborative analysis of microarray data is gaining more and more relevance in international research consortia. The adequacy of the EMMA 2 software design and implementation has been proven by its application in many distributed functional genomics projects. Its scalability makes the current architecture suited for extensions towards future transcriptomics methods based on high-throughput sequencing approaches which have much higher computational requirements than microarrays.

Identifying cancer-related microRNAs based on gene expression data

International audience

Microfluidics-based single-cell functional proteomics microchip for portraying protein signal transduction networks within the framework of physicochemical principles, with applications in fundamental and translational cancer research

Single-cell functional proteomics assays can connect genomic information to biological function through quantitative and multiplex protein measurements. Tools for single-cell proteomics have developed rapidly over the past 5 years and are providing unique opportunities. This thesis describes an emerging microfluidics-based toolkit for single cell functional proteomics, focusing on the development of the single cell barcode chips (SCBCs) with applications in fundamental and translational cancer research.

The microchip designed to simultaneously quantify a panel of secreted, cytoplasmic and membrane proteins from single cells will be discussed at the beginning, which is the prototype for subsequent proteomic microchips with more sophisticated design in preclinical cancer research or clinical applications. The SCBCs are a highly versatile and information rich tool for single-cell functional proteomics. They are based upon isolating individual cells, or defined number of cells, within microchambers, each of which is equipped with a large antibody microarray (the barcode), with between a few hundred to ten thousand microchambers included within a single microchip. Functional proteomics assays at single-cell resolution yield unique pieces of information that significantly shape the way of thinking on cancer research. An in-depth discussion about analysis and interpretation of the unique information such as functional protein fluctuations and protein-protein correlative interactions will follow.

The SCBC is a powerful tool to resolve the functional heterogeneity of cancer cells. It has the capacity to extract a comprehensive picture of the signal transduction network from single tumor cells and thus provides insight into the effect of targeted therapies on protein signaling networks. We will demonstrate this point through applying the SCBCs to investigate three isogenic cell lines of glioblastoma multiforme (GBM).

The cancer cell population is highly heterogeneous with high-amplitude fluctuation at the single cell level, which in turn grants the robustness of the entire population. The concept that a stable population existing in the presence of random fluctuations is reminiscent of many physical systems that are successfully understood using statistical physics. Thus, tools derived from that field can probably be applied to using fluctuations to determine the nature of signaling networks. In the second part of the thesis, we will focus on such a case to use thermodynamics-motivated principles to understand cancer cell hypoxia, where single cell proteomics assays coupled with a quantitative version of Le Chatelier's principle derived from statistical mechanics yield detailed and surprising predictions, which were found to be correct in both cell line and primary tumor model.

The third part of the thesis demonstrates the application of this technology in the preclinical cancer research to study the GBM cancer cell resistance to molecular targeted therapy. Physical approaches to anticipate therapy resistance and to identify effective therapy combinations will be discussed in detail. Our approach is based upon elucidating the signaling coordination within the phosphoprotein signaling pathways that are hyperactivated in human GBMs, and interrogating how that coordination responds to the perturbation of targeted inhibitor. Strongly coupled protein-protein interactions constitute most signaling cascades. A physical analogy of such a system is the strongly coupled atom-atom interactions in a crystal lattice. Similar to decomposing the atomic interactions into a series of independent normal vibrational modes, a simplified picture of signaling network coordination can also be achieved by diagonalizing protein-protein correlation or covariance matrices to decompose the pairwise correlative interactions into a set of distinct linear combinations of signaling proteins (i.e. independent signaling modes). By doing so, two independent signaling modes – one associated with mTOR signaling and a second associated with ERK/Src signaling have been resolved, which in turn allow us to anticipate resistance, and to design combination therapies that are effective, as well as identify those therapies and therapy combinations that will be ineffective. We validated our predictions in mouse tumor models and all predictions were borne out.

In the last part, some preliminary results about the clinical translation of single-cell proteomics chips will be presented. The successful demonstration of our work on human-derived xenografts provides the rationale to extend our current work into the clinic. It will enable us to interrogate GBM tumor samples in a way that could potentially yield a straightforward, rapid interpretation so that we can give therapeutic guidance to the attending physicians within a clinical relevant time scale. The technical challenges of the clinical translation will be presented and our solutions to address the challenges will be discussed as well. A clinical case study will then follow, where some preliminary data collected from a pediatric GBM patient bearing an EGFR amplified tumor will be presented to demonstrate the general protocol and the workflow of the proposed clinical studies.

A Putative Otu Domain-containing Protein 1 Deubiquitinating Enzyme Is Differentially Expressed In Thyroid Cancer And Identifies Less-aggressive Tumours.

This study aimed to identify novel biomarkers for thyroid carcinoma diagnosis and prognosis. We have constructed a human single-chain variable fragment (scFv) antibody library that was selected against tumour thyroid cells using the BRASIL method (biopanning and rapid analysis of selective interactive ligands) and phage display technology. One highly reactive clone, scFv-C1, with specific binding to papillary thyroid tumour proteins was confirmed by ELISA, which was further tested against a tissue microarray that comprised of 229 thyroid tissues, including: 110 carcinomas (38 papillary thyroid carcinomas (PTCs), 42 follicular carcinomas, 30 follicular variants of PTC), 18 normal thyroid tissues, 49 nodular goitres (NG) and 52 follicular adenomas. The scFv-C1 was able to distinguish carcinomas from benign lesions (P=0.0001) and reacted preferentially against T1 and T2 tumour stages (P=0.0108). We have further identified an OTU domain-containing protein 1, DUBA-7 deubiquitinating enzyme as the scFv-binding antigen using two-dimensional polyacrylamide gel electrophoresis and mass spectrometry. The strategy of screening and identifying a cell-surface-binding antibody against thyroid tissues was highly effective and resulted in a useful biomarker that recognises malignancy among thyroid nodules and may help identify lower-risk cases that can benefit from less-aggressive management.

Predictive Value Of Phase I Trials For Safety In Later Trials And Final Approved Dose: Analysis Of 61 Approved Cancer Drugs.

Phase I trials use a small number of patients to define a maximum tolerated dose (MTD) and the safety of new agents. We compared data from phase I and registration trials to determine whether early trials predicted later safety and final dose. We searched the U.S. Food and Drug Administration (FDA) website for drugs approved in nonpediatric cancers (January 1990-October 2012). The recommended phase II dose (R2PD) and toxicities from phase I were compared with doses and safety in later trials. In 62 of 85 (73%) matched trials, the dose from the later trial was within 20% of the RP2D. In a multivariable analysis, phase I trials of targeted agents were less predictive of the final approved dose (OR, 0.2 for adopting ± 20% of the RP2D for targeted vs. other classes; P = 0.025). Of the 530 clinically relevant toxicities in later trials, 70% (n = 374) were described in phase I. A significant relationship (P = 0.0032) between increasing the number of patients in phase I (up to 60) and the ability to describe future clinically relevant toxicities was observed. Among 28,505 patients in later trials, the death rate that was related to drug was 1.41%. In conclusion, dosing based on phase I trials was associated with a low toxicity-related death rate in later trials. The ability to predict relevant toxicities correlates with the number of patients on the initial phase I trial. The final dose approved was within 20% of the RP2D in 73% of assessed trials.

Ki-1/57 And Cgi-55 Ectopic Expression Impact Cellular Pathways Involved In Proliferation And Stress Response Regulation.

Ki-1/57 (HABP4) and CGI-55 (SERBP1) are regulatory proteins and paralogs with 40.7% amino acid sequence identity and 67.4% similarity. Functionally, they have been implicated in the regulation of gene expression on both the transcriptional and mRNA metabolism levels. A link with tumorigenesis is suggested, since both paralogs show altered expression levels in tumor cells and the Ki-1/57 gene is found in a region of chromosome 9q that represents a haplotype for familiar colon cancer. However, the target genes regulated by Ki-1/57 and CGI-55 are unknown. Here, we analyzed the alterations of the global transcriptome profile after Ki-1/57 or CGI-55 overexpression in HEK293T cells by DNA microchip technology. We were able to identify 363 or 190 down-regulated and 50 or 27 up-regulated genes for Ki-1/57 and CGI-55, respectively, of which 20 were shared between both proteins. Expression levels of selected genes were confirmed by qRT-PCR both after protein overexpression and siRNA knockdown. The majority of the genes with altered expression were associated to proliferation, apoptosis and cell cycle control processes, prompting us to further explore these contexts experimentally. We observed that overexpression of Ki-1/57 or CGI-55 results in reduced cell proliferation, mainly due to a G1 phase arrest, whereas siRNA knockdown of CGI-55 caused an increase in proliferation. In the case of Ki-1/57 overexpression, we found protection from apoptosis after treatment with the ER-stress inducer thapsigargin. Together, our data give important new insights that may help to explain these proteins putative involvement in tumorigenic events.

Analysis Of The Contribution Of Immunologically-detectable Her2, Steroid Receptors And Of The Triple-negative" Tumor Status To Disease-free And Overall Survival Of Women With Epithelial Ovarian Cancer."

We assessed associations between steroid receptors including: estrogen-alpha, estrogen-beta, androgen receptor, progesterone receptor, the HER2 status and triple-negative epithelial ovarian cancer (ERα-/PR-/HER2-; TNEOC) status and survival in women with epithelial ovarian cancer. The study included 152 women with primary epithelial ovarian cancer. The status of steroid receptor and HER2 was determined by immunohistochemistry. Disease-free and overall survival were calculated and compared with steroid receptor and HER2 status as well as clinicopathological features using the Cox Proportional Hazards model. A mean follow-up period of 43.6 months (interquartile range=41.4 months) was achieved where 44% of patients had serous tumor, followed by mucinous (23%), endometrioid (9%), mixed (9%), undifferentiated (8.5%) and clear cell tumors (5.3%). ER-alpha staining was associated with grade II-III tumors. Progesterone receptor staining was positively associated with a Body Mass Index≥25. Androgen receptor positivity was higher in serous tumors. In stand-alone analysis of receptor contribution to survival, estrogen-alpha positivity was associated with greater disease-free survival. However, there was no significant association between steroid receptor expression, HER2 status, or TNEOC status, and overall survival. Although estrogen-alpha, androgen receptor, progesterone receptor and the HER2 status were associated with key clinical features of the women and pathological characteristics of the tumors, these associations were not implicated in survival. Interestingly, women with TNEOC seem to fare the same way as their counterparts with non-TNEOC.