A fuzzy gene expression-based computational approach improves breast cancer prognostication

A fuzzy computational approach that takes into account several molecular subtypes in order to provide more accurate breast cancer prognosi

Comparison of prognostic gene expression signatures for breast cancer

<p>Abstract</p> <p>Background</p> <p>During the last years, several groups have identified prognostic gene expression signatures with apparently similar performances. However, signatures were never compared on an independent population of untreated breast cancer patients, where risk assessment was computed using the original algorithms and microarray platforms.</p> <p>Results</p> <p>We compared three gene expression signatures, the 70-gene, the 76-gene and the Gene expression Grade Index (GGI) signatures, in terms of predicting distant metastasis free survival (DMFS) for the individual patient. To this end, we used the previously published TRANSBIG independent validation series of node-negative untreated primary breast cancer patients. We observed agreement in prediction for 135 of 198 patients (68%) when considering the three signatures. When comparing the signatures two by two, the agreement in prediction was 71% for the 70- and 76-gene signatures, 76% for the 76-gene signature and the GGI, and 88% for the 70-gene signature and the GGI. The three signatures had similar capabilities of predicting DMFS and added significant prognostic information to that provided by the classical parameters.</p> <p>Conclusion</p> <p>Despite the difference in development of these signatures and the limited overlap in gene identity, they showed similar prognostic performance, adding to the growing evidence that these prognostic signatures are of clinical relevance.</p

Comprehensive evaluation of methodology to assess abundance of immune infiltrates in breast cancer

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Improvement of the clinical applicability of the Genomic Grade Index through a qRT-PCR test performed on frozen and formalin-fixed paraffin-embedded tissues

BACKGROUND: Proliferation and tumor differentiation captured by the genomic grade index (GGI) are important prognostic indicators in breast cancer (BC) especially for the estrogen receptor positive (ER+) disease. The aims of this study were to convert this microarray index to a qRT-PCR assay (PCR-GGI), which could be realized on formalin fixed paraffin embedded samples (FFPE), and to assess its prognostic performance and predictive value of clinical benefit in early and advanced ER+ BC patients treated with tamoxifen. METHODS: The accuracy and concordance of the PCR-GGI with the GGI was assessed using BC patients for which frozen and FFPE tissues as well as microarray data were available (n = 19). The evaluation of the prognostic value of the PCR-GGI was assessed on FFPE material using a consecutive series of 212 systemically treated early BC patients. The predictive performance for tamoxifen benefit was assessed using two ER+ BC populations treated either with adjuvant tamoxifen only (n = 77+139) or first-line tamoxifen for advanced disease (n = 270). RESULTS: The PCR-GGI is based on the expression of 8 genes (4 representative of the GGI and 4 reference genes). A significant correlation was observed between the microarray-derived GGI and the qRT-PCR assay using frozen (rho = 0.95, p &lt; 10E-06) and FFPE material (rho = 0.89, p &lt; 10E-06). The prognostic performance of the PCR-GGI was confirmed on FFPE samples (HRunivar. = 1.89; [95CI:1.01-3.54], p = 0.05). The PCR-GGI further identified two subgroups of patients with statistically different time to distant metastasis free survival (DMFS) across the two cohorts of ER+ BC patients treated with adjuvant tamoxifen. Additionally, the PCR-GGI was associated with response to tamoxifen in the advanced setting (HRunivar. = 1.98; [95CI:1.51-2.59], p = 6.9E-07). CONCLUSION: PCR-GGI recapitulates in an accurate and reproducible manner the performances of the GGI using frozen and FFPE samples

Consistent metagenes from cancer expression profiles yield agent specific predictors of chemotherapy response

Genome scale expression profiling of human tumor samples is likely to yield improved cancer treatment decisions. However, identification of clinically predictive or prognostic classifiers can be challenging when a large number of genes are measured in a small number of tumors.Journal ArticleResearch Support, N.I.H. ExtramuralResearch Support, Non-U.S. Gov'tSCOPUS: ar.jinfo:eu-repo/semantics/publishe

Breast cancer diagnosed during pregnancy is associated with enrichment of non-silent mutations, mismatch repair deficiency signature and mucin mutations

Breast cancer diagnosed during pregnancy (BCP) is a rare and highly challenging disease. To investigate the impact of pregnancy on the biology of breast cancer, we conducted a comparative analysis of a cohort of BCP patients and non-pregnant control patients by integrating gene expression, copy number alterations and whole genome sequencing data. We showed that BCP exhibit unique molecular characteristics including an enrichment of non-silent mutations, a higher frequency of mutations in mucin gene family and an enrichment of mismatch repair deficiency mutational signature. This provides important insights into the biology of BCP and suggests that these features may be implicated in promoting tumor progression during pregnancy. In addition, it provides an unprecedented resource for further understanding the biology of breast cancer in young women and how pregnancy could modulate tumor biology

The Gene expression Grade Index: a potential predictor of relapse for endocrine-treated breast cancer patients in the BIG 1–98 trial

<p>Abstract</p> <p>Background</p> <p>We have previously shown that the Gene expression Grade Index (GGI) was able to identify two subtypes of estrogen receptor (ER)-positive tumors that were associated with statistically distinct clinical outcomes in both untreated and tamoxifen-treated patients. Here, we aim to investigate the ability of the GGI to predict relapses in postmenopausal women who were treated with tamoxifen (T) or letrozole (L) within the BIG 1–98 trial.</p> <p>Methods</p> <p>We generated gene expression profiles (Affymetrix) and computed the GGI for a matched, case-control sample of patients enrolled in the BIG 1–98 trial from the two hospitals where frozen samples were available. All relapses (cases) were identified from patients randomized to receive monotherapy or from the switching treatment arms for whom relapse occurred before the switch. Each case was randomly matched with four controls based upon nodal status and treatment (T or L). The prognostic value of GGI was assessed as a continuous predictor and divided at the median. Predictive accuracy of GGI was estimated using time-dependent area under the curve (AUC) of the ROC curves.</p> <p>Results</p> <p>Frozen samples were analyzable for 48 patients (10 cases and 38 controls). Seven of the 10 cases had been assigned to receive L. Cases and controls were comparable with respect to menopausal and nodal status, local and chemotherapy, and HER2 positivity. Cases were slightly older than controls and had a larger proportion of large, poorly differentiated ER+/PgR- tumors. The GGI was significantly and linearly related to risk of relapse: each 10-unit increase in GGI resulted in an increase of approximately 11% in the hazard rate (p = 0.02). Within the subgroups of patients with node-positive disease or who were treated with L, the hazard of relapse was significantly greater for patients with GGI at or above the median. AUC reached a maximum of 78% at 27 months.</p> <p>Conclusion</p> <p>This analysis supports the GGI as a good predictor of relapse for ER-positive patients, even among patients who receive L. Validation of these results, in a larger series from BIG 1–98, is planned using the simplified GGI represented by a smaller set of genes and tested by qRT-PCR on paraffin-embedded tissues.</p

Reporting on patient’s body mass index (BMI) in recent clinical trials for patients with breast cancer:a systematic review

Background: The proportion of patients with breast cancer and obesity is increasing. While the therapeutic landscape of breast cancer has been expanding, we lack knowledge about the potential differential efficacy of most drugs according to the body mass index (BMI). Here, we conducted a systematic review on recent clinical drug trials to document the dosing regimen of recent drugs, the reporting of BMI and the possible exclusion of patients according to BMI, other adiposity measurements and/or diabetes (leading comorbidity of obesity). We further explored whether treatment efficacy was evaluated according to BMI. Methods: A search of Pubmed and ClinicalTrials.gov was performed to identify phase I-IV trials investigating novel systemic breast cancer treatments. Dosing regimens and exclusion based on BMI, adiposity measurements or diabetes, documentation of BMI and subgroup analyses according to BMI were assessed. Results: 495 trials evaluating 26 different drugs were included. Most of the drugs (21/26, 81%) were given in a fixed dose independent of patient weight. BMI was an exclusion criterion in 3 out of 495 trials. Patients with diabetes, the leading comorbidity of obesity, were excluded in 67/495 trials (13.5%). Distribution of patients according to BMI was mentioned in 8% of the manuscripts, subgroup analysis was performed in 2 trials. No other measures of adiposity/body composition were mentioned in any of the trials. Retrospective analyses on the impact of BMI were performed in 6 trials. Conclusions: Patient adiposity is hardly considered as most novel drug treatments are given in a fixed dose. BMI is generally not reported in recent trials and few secondary analyses are performed. Given the prevalence of patients with obesity and the impact obesity can have on pharmacokinetics and cancer biology, more attention should be given by investigators and study sponsors to reporting patient’s BMI and evaluating its impact on treatment efficacy and toxicity.</p

Reporting on patient’s body mass index (BMI) in recent clinical trials for patients with breast cancer:a systematic review

Background: The proportion of patients with breast cancer and obesity is increasing. While the therapeutic landscape of breast cancer has been expanding, we lack knowledge about the potential differential efficacy of most drugs according to the body mass index (BMI). Here, we conducted a systematic review on recent clinical drug trials to document the dosing regimen of recent drugs, the reporting of BMI and the possible exclusion of patients according to BMI, other adiposity measurements and/or diabetes (leading comorbidity of obesity). We further explored whether treatment efficacy was evaluated according to BMI. Methods: A search of Pubmed and ClinicalTrials.gov was performed to identify phase I-IV trials investigating novel systemic breast cancer treatments. Dosing regimens and exclusion based on BMI, adiposity measurements or diabetes, documentation of BMI and subgroup analyses according to BMI were assessed. Results: 495 trials evaluating 26 different drugs were included. Most of the drugs (21/26, 81%) were given in a fixed dose independent of patient weight. BMI was an exclusion criterion in 3 out of 495 trials. Patients with diabetes, the leading comorbidity of obesity, were excluded in 67/495 trials (13.5%). Distribution of patients according to BMI was mentioned in 8% of the manuscripts, subgroup analysis was performed in 2 trials. No other measures of adiposity/body composition were mentioned in any of the trials. Retrospective analyses on the impact of BMI were performed in 6 trials. Conclusions: Patient adiposity is hardly considered as most novel drug treatments are given in a fixed dose. BMI is generally not reported in recent trials and few secondary analyses are performed. Given the prevalence of patients with obesity and the impact obesity can have on pharmacokinetics and cancer biology, more attention should be given by investigators and study sponsors to reporting patient’s BMI and evaluating its impact on treatment efficacy and toxicity.</p

Strengthening Geriatric Expertise in Swiss Nursing Homes: Intercare Implementation Study Protocol

Nursing home (NH) residents with complex care needs ask for attentive monitoring of changes and appropriate in-house decision making. However, access to geriatric expertise is often limited with a lack of geriatricians, general practitioners, and/or nurses with advanced clinical skills, leading to potentially avoidable hospitalizations. This situation calls for the development, implementation, and evaluation of innovative, contextually adapted nurse-led care models that support NHs in improving their quality of care and reducing hospitalizations by investing in effective clinical leadership, geriatric expertise, and care coordination. DESIGN An effectiveness-implementation hybrid type 2 design to assess clinical outcomes of a nurse-led care model and a mixed-method approach to evaluate implementation outcomes will be applied. The model development, tailoring, and implementation are based on the Consolidated Framework for Implementation Research (CFIR). SETTING NHs in the German-speaking region of Switzerland. PARTICIPANTS Eleven NHs were recruited. The sample size was estimated assuming an average of .8 unplanned hospitalizations/1000 resident days and a reduction of 25% in NHs with the nurse-led care model. INTERVENTION The multilevel complex context-adapted intervention consists of six core elements (eg, specifically trained INTERCARE nurses or evidence-based tools like Identify, Situation, Background, Assessment and Recommendation [ISBAR]). Multilevel implementation strategies include leadership and INTERCARE nurse training and support. MEASUREMENTS The primary outcomes are unplanned hospitalizations/1000 care days. Secondary outcomes include unplanned emergency department visits, quality indicators (eg, physical restraint use), and costs. Implementation outcomes included, for example, fidelity to the model's core elements. CONCLUSION The INTERCARE study will provide evidence about the effectiveness of a nurse-led care model in the real-world setting and accompanying implementation strategies