Liza A. Vergara

Bio: Liza A. Vergara is an academic researcher from University of Queensland. The author has contributed to research in topics: Pentoxifylline & Medicine. The author has an hindex of 8, co-authored 17 publications receiving 1446 citations.

An expression signature for p53 status in human breast cancer predicts mutation status, transcriptional effects, and patient survival

Abstract: Perturbations of the p53 pathway are associated with more aggressive and therapeutically refractory tumors. However, molecular assessment of p53 status, by using sequence analysis and immunohistochemistry, are incomplete assessors of p53 functional effects. We posited that the transcriptional fingerprint is a more definitive downstream indicator of p53 function. Herein, we analyzed transcript profiles of 251 p53-sequenced primary breast tumors and identified a clinically embedded 32-gene expression signature that distinguishes p53-mutant and wild-type tumors of different histologies and outperforms sequence-based assessments of p53 in predicting prognosis and therapeutic response. Moreover, the p53 signature identified a subset of aggressive tumors absent of sequence mutations in p53 yet exhibiting expression characteristics consistent with p53 deficiency because of attenuated p53 transcript levels. Our results show the primary importance of p53 functional status in predicting clinical breast cancer behavior.

Antibacterial honey for the prevention of peritoneal-dialysis-related infections (HONEYPOT): a randomised trial

Abstract: Summary Background There is a paucity of evidence to guide the best strategy for prevention of peritoneal-dialysis-related infections. Antibacterial honey has shown promise as a novel, cheap, effective, topical prophylactic agent without inducing microbial resistance. We therefore assessed whether daily application of honey at the exit site would increase the time to peritoneal-dialysis-related infections compared with standard exit-site care plus intranasal mupirocin prophylaxis for nasal carriers of Staphylococcus aureus . Methods In this open-label trial undertaken in 26 centres in Australia and New Zealand, participants undergoing peritoneal dialysis were randomly assigned in a 1:1 ratio with an adaptive allocation algorithm to daily topical exit-site application of antibacterial honey plus standard exit-site care or intranasal mupirocin prophylaxis (only in carriers of nasal S aureus ) plus standard exit-site care (control group). The primary endpoint was time to first infection related to peritoneal dialysis (exit-site infection, tunnel infection, or peritonitis). The trial is registered with the Australian New Zealand Clinical Trials Registry, number 12607000537459. Findings Of 371 participants, 186 were assigned to the honey group and 185 to the control group. The median peritoneal-dialysis-related infection-free survival times were not significantly different in the honey (16·0 months [IQR not estimable]) and control groups (17·7 months [not estimable]; unadjusted hazard ratio 1·12, 95% CI 0·83–1·51; p=0·47). In the subgroup analyses, honey increased the risks of both the primary endpoint (1·85, 1·05–3·24; p=0·03) and peritonitis (2·25, 1·16–4·36) in participants with diabetes. The incidences of serious adverse events (298 vs 327, respectively; p=0·1) and deaths (14 vs 18, respectively; p=0·9) were not significantly different in the honey and control groups. 11 (6%) participants in the honey group had local skin reactions. Interpretation The findings of this trial show that honey cannot be recommended routinely for the prevention of peritoneal-dialysis-related infections. Funding Baxter Healthcare, Queensland Government, Comvita, and Gambro.

A tRial Evaluating Mid Cut-Off Value Membrane Clearance of Albumin and Light Chains in HemoDialysis Patients: A Safety Device Study.

Abstract: Background: A new class of dialysis membrane, the mid cut-off (MCO) dialyzer, has been developed to improve the clearance of uremic toxins in hemodialysis (HD). The a tRial Evaluating Mid cut-Off Value membrane clearance of Albumin and Light chains in HemoDialysis patients (REMOVAL-HD) study aimed to determine if regular use of MCO dialyzer was safe and specifically did not result in a significant loss of albumin. Methods: This investigator initiated, crossover, longitudinal, device study was conducted across 9 centers in Australia and New Zealand (n = 89). Participants had a 4-week wash-in with high-flux HD, followed by 24-week intervention with MCO HD and a subsequent 4-week wash-out with high-flux HD. The primary outcome was change in serum albumin between weeks 4 and 28. Secondary outcomes included trends in serum albumin, changes in kappa- and lambda-free light chains (FLC), 6-min walk test (6MWT), malnutrition inflammation score (MIS), restless legs score and quality of life. Results: Participants had a mean age of 66 ± 14 years, 62% were men, 45% were anuric, and 51% had ­diabetes. There was no reduction in serum albumin following treatment with MCO HD (mean reduction –0.7 g/L, 95% CI –1.5 to 0.1). A sustained, unexplained reduction in serum albumin (>25%) was not observed in any participant. A reduction in FLC was observed 2 weeks into MCO HD (lambda-FLC: Δ –9.1 mg/L, 95% CI –14.4 to –3.7; kappa-FLC: Δ –5.7 mg/L, 95% CI –9.8 to –1.6) and was sustained for the rest of the study intervention. Both FLC increased after the cessation of MCO use. There was no improvement in restless legs symptoms, quality of life, 6MWT or MIS scores. Conclusions: Regular HD using the MCO dialyzer did not result in a significant fall in serum albumin. There were no effects on quality of life, functional status or nutrition. Trial Registration: Australian New Zealand Clinical Trials Registry Number (ANZCTRN) 12616000804482.

Identification of human triple-negative breast cancer subtypes and preclinical models for selection of targeted therapies

Abstract: Triple-negative breast cancer (TNBC) is a highly diverse group of cancers, and subtyping is necessary to better identify molecular-based therapies. In this study, we analyzed gene expression (GE) profiles from 21 breast cancer data sets and identified 587 TNBC cases. Cluster analysis identified 6 TNBC subtypes displaying unique GE and ontologies, including 2 basal-like (BL1 and BL2), an immunomodulatory (IM), a mesenchymal (M), a mesenchymal stem–like (MSL), and a luminal androgen receptor (LAR) subtype. Further, GE analysis allowed us to identify TNBC cell line models representative of these subtypes. Predicted “driver” signaling pathways were pharmacologically targeted in these cell line models as proof of concept that analysis of distinct GE signatures can inform therapy selection. BL1 and BL2 subtypes had higher expression of cell cycle and DNA damage response genes, and representative cell lines preferentially responded to cisplatin. M and MSL subtypes were enriched in GE for epithelial-mesenchymal transition, and growth factor pathways and cell models responded to NVP-BEZ235 (a PI3K/mTOR inhibitor) and dasatinib (an abl/src inhibitor). The LAR subtype includes patients with decreased relapse-free survival and was characterized by androgen receptor (AR) signaling. LAR cell lines were uniquely sensitive to bicalutamide (an AR antagonist). These data may be useful in biomarker selection, drug discovery, and clinical trial design that will enable alignment of TNBC patients to appropriate targeted therapies.

Supervised Risk Predictor of Breast Cancer Based on Intrinsic Subtypes

Abstract: Purpose To improve on current standards for breast cancer prognosis and prediction of chemotherapy benefit by developing a risk model that incorporates the gene expression–based “intrinsic” subtypes luminal A, luminal B, HER2-enriched, and basal-like. Methods A 50-gene subtype predictor was developed using microarray and quantitative reverse transcriptase polymerase chain reaction data from 189 prototype samples. Test sets from 761 patients (no systemic therapy) were evaluated for prognosis, and 133 patients were evaluated for prediction of pathologic complete response (pCR) to a taxane and anthracycline regimen. Results The intrinsic subtypes as discrete entities showed prognostic significance (P = 2.26E-12) and remained significant in multivariable analyses that incorporated standard parameters (estrogen receptor status, histologic grade, tumor size, and node status). A prognostic model for node-negative breast cancer was built using intrinsic subtype and clinical information. The C-index estimate for t...

An online survival analysis tool to rapidly assess the effect of 22,277 genes on breast cancer prognosis using microarray data of 1,809 patients.

Abstract: Validating prognostic or predictive candidate genes in appropriately powered breast cancer cohorts are of utmost interest. Our aim was to develop an online tool to draw survival plots, which can be used to assess the relevance of the expression levels of various genes on the clinical outcome both in untreated and treated breast cancer patients. A background database was established using gene expression data and survival information of 1,809 patients downloaded from GEO (Affymetrix HGU133A and HGU133+2 microarrays). The median relapse free survival is 6.43 years, 968/1,231 patients are estrogen-receptor (ER) positive, and 190/1,369 are lymph-node positive. After quality control and normalization only probes present on both Affymetrix platforms were retained (n = 22,277). In order to analyze the prognostic value of a particular gene, the cohorts are divided into two groups according to the median (or upper/lower quartile) expression of the gene. The two groups can be compared in terms of relapse free survival, overall survival, and distant metastasis free survival. A survival curve is displayed, and the hazard ratio with 95% confidence intervals and logrank P value are calculated and displayed. Additionally, three subgroups of patients can be assessed: systematically untreated patients, endocrine-treated ER positive patients, and patients with a distribution of clinical characteristics representative of those seen in general clinical practice in the US. Web address: www.kmplot.com . We used this integrative data analysis tool to confirm the prognostic power of the proliferation-related genes TOP2A and TOP2B, MKI67, CCND2, CCND3, CCNDE2, as well as CDKN1A, and TK2. We also validated the capability of microarrays to determine estrogen receptor status in 1,231 patients. The tool is highly valuable for the preliminary assessment of biomarkers, especially for research groups with limited bioinformatic resources.

An embryonic stem cell–like gene expression signature in poorly differentiated aggressive human tumors

Abstract: Cancer cells possess traits reminiscent of those ascribed to normal stem cells. It is unclear, however, whether these phenotypic similarities reflect the activity of common molecular pathways. Here, we analyze the enrichment patterns of gene sets associated with embryonic stem (ES) cell identity in the expression profiles of various human tumor types. We find that histologically poorly differentiated tumors show preferential overexpression of genes normally enriched in ES cells, combined with preferential repression of Polycomb-regulated genes. Moreover, activation targets of Nanog, Oct4, Sox2 and c-Myc are more frequently overexpressed in poorly differentiated tumors than in well-differentiated tumors. In breast cancers, this ES-like signature is associated with high-grade estrogen receptor (ER)-negative tumors, often of the basal-like subtype, and with poor clinical outcome. The ES signature is also present in poorly differentiated glioblastomas and bladder carcinomas. We identify a subset of ES cell-associated transcription regulators that are highly expressed in poorly differentiated tumors. Our results reveal a previously unknown link between genes associated with ES cell identity and the histopathological traits of tumors and support the possibility that these genes contribute to stem cell-like phenotypes shown by many tumors.

Stromal gene expression predicts clinical outcome in breast cancer

Abstract: Although it is increasingly evident that cancer is influenced by signals emanating from tumor stroma, little is known regarding how changes in stromal gene expression affect epithelial tumor progression. We used laser capture microdissection to compare gene expression profiles of tumor stroma from 53 primary breast tumors and derived signatures strongly associated with clinical outcome. We present a new stroma-derived prognostic predictor (SDPP) that stratifies disease outcome independently of standard clinical prognostic factors and published expression-based predictors. The SDPP predicts outcome in several published whole tumor-derived expression data sets, identifies poor-outcome individuals from multiple clinical subtypes, including lymph node-negative tumors, and shows increased accuracy with respect to previously published predictors, especially for HER2-positive tumors. Prognostic power increases substantially when the predictor is combined with existing outcome predictors. Genes represented in the SDPP reveal the strong prognostic capacity of differential immune responses as well as angiogenic and hypoxic responses, highlighting the importance of stromal biology in tumor progression.