Quantitative Assessment of Tissue Biomarkers and Construction of a Model to Predict Outcome in Breast Cancer Using Multiple Imputation

Missing data pose one of the greatest challenges in the rigorous evaluation of biomarkers. The limited availability of specimens with complete clinical annotation and quality biomaterial often leads to underpowered studies. Tissue microarray studies, for example, may be further handicapped by the loss of data points because of unevaluable staining, core loss, or the lack of tumor in the histospot. This paper presents a novel approach to these common problems in the context of a tissue protein biomarker analysis in a cohort of patients with breast cancer. Our analysis develops techniques based on multiple imputation to address the missing value problem. We first select markers using a training cohort, identifying a small subset of protein expression levels that are most useful in predicting patient survival. The best model is obtained by including both protein markers (including COX6C, GATA3, NAT1, and ESR1) and lymph node status. The use of either lymph node status or the four protein expression levels provides similar improvements in goodness-of-fit, with both significantly better than a baseline clinical model. Using the same multiple imputation strategy, we then validate the results out-of-sample on a larger independent cohort. Our approach of integrating multiple imputation with each stage of the analysis serves as an example that may be replicated or adapted in future studies with missing values

Antitumor Activity of Pembrolizumab in Biomarker-Unselected Patients With Recurrent and/or Metastatic Head and Neck Squamous Cell Carcinoma: Results From the Phase Ib KEYNOTE-012 Expansion Cohort.

Purpose Treatment with pembrolizumab, an anti-programmed death-1 antibody, at 10 mg/kg administered once every 2 weeks, displayed durable antitumor activity in programmed death-ligand 1 (PD-L1) -positive recurrent and/or metastatic (R/M) head and neck squamous cell carcinoma (HNSCC) in the KEYNOTE-012 trial. Results from the expansion cohort, in which patients with HNSCC, irrespective of biomarker status, received a fixed dose of pembrolizumab at a less frequent dosing schedule, are reported. Patients and Methods Patients with R/M HNSCC, irrespective of PD-L1 or human papillomavirus status, received pembrolizumab 200 mg intravenously once every 3 weeks. Imaging was performed every 8 weeks. Primary end points were overall response rate (ORR) per central imaging vendor (Response Evaluation Criteria in Solid Tumors v1.1) and safety. Secondary end points included progression-free survival, overall survival, and association of response and PD-L1 expression. Patients who received one or more doses of pembrolizumab were included in analyses. Results Of 132 patients enrolled, median age was 60 years (range, 25 to 84 years), 83% were male, and 57% received two or more lines of therapy for R/M disease. ORR was 18% (95% CI, 12 to 26) by central imaging vendor and 20% (95% CI, 13 to 28) by investigator review. Median duration of response was not reached (range, ≥ 2 to ≥ 11 months). Six-month progression-free survival and overall survival rates were 23% and 59%, respectively. By using tumor and immune cells, a statistically significant increase in ORR was observed for PD-L1-positive versus -negative patients (22% v 4%; P = .021). Treatment-related adverse events of any grade and grade ≥ 3 events occurred in 62% and 9% of patients, respectively. Conclusion Fixed-dose pembrolizumab 200 mg administered once every 3 weeks was well tolerated and yielded a clinically meaningful ORR with evidence of durable responses, which supports further development of this regimen in patients with advanced HNSCC

Expression of Drug Targets in Patients Treated with Sorafenib, Carboplatin and Paclitaxel

Introduction: Sorafenib, a multitarget kinase inhibitor, targets members of the mitogen-activated protein kinase (MAPK) pathway and VEGFR kinases. Here we assessed the association between expression of sorafenib targets and biomarkers of taxane sensitivity and response to therapy in pre-treatment tumors from patients enrolled in ECOG 2603, a phase III comparing sorafenib, carboplatin and paclitaxel (SCP) to carboplatin, paclitaxel and placebo (CP). Methods: Using a method of automated quantitative analysis (AQUA) of in situ protein expression, we quantified expression of VEGF-R2, VEGF-R1, VEGF-R3, FGF-R1, PDGF-Rβ, c-Kit, B-Raf, C-Raf, MEK1, ERK1/2, STMN1, MAP2, EB1 and Bcl-2 in pretreatment specimens from 263 patients. Results: An association was found between high FGF-R1 and VEGF-R1 and increased progression-free survival (PFS) and overall survival (OS) in our combined cohort (SCP and CP arms). Expression of FGF-R1 and VEGF-R1 was higher in patients who responded to therapy ((CR+PR) vs. (SD+PD+ un-evaluable)). Conclusions: In light of the absence of treatment effect associated with sorafenib, the association found between FGF-R1 and VEGF-R1 expression and OS, PFS and response might reflect a predictive biomarker signature for carboplatin/paclitaxel-based therapy. Seeing that carboplatin and pacitaxel are now widely used for this disease, corroboration in another cohort might enable us to improve the therapeutic ratio of this regimen. © 2013 Jilaveanu et al

PDK-1/AKT pathway as a novel therapeutic target in rhabdomyosarcoma cells using OSU-03012 compound

Rhabdomyosarcoma (RMS) is the most common paediatric soft-tissue sarcoma including two major subtypes, alveolar rhabdomyosarcoma (ARMS) and embryonal rhabdomyosarcoma (ERMS). Increasing evidence suggests that oncogenesis of RMS involves multistages of signalling protein dysregulation which may include prolonged activation of serine/threonine kinases such as phosphoinositide-dependant kinase-1 (PDK-1) and AKT. To date, whether PDK-1/AKT pathway is activated in RMS is unknown. This study was to examine phosphorylation status of AKT and to evaluate a novel small molecular inhibitor, OSU-03012 targeting PDK-1 in RMS. We examined phosphorylation levels of AKT using ARMS and ERMS tissue microarray and immunohistochemistry staining. Our results showed phospho-AKTThr308 level is elevated 42 and 35% in ARMS and ERMS, respectively. Phospho-AKTSer473 level is also increased 43% in ARMS and 55% in ERMS. Furthermore, we showed that OSU-03012 inhibits cell viability and induces apoptosis in ARMS and ERMS cell lines (RH30, SMS-CTR), which express elevated phospho-AKT levels. Normal cells are much less sensitive to OSU-03012 and in which no detectable apoptosis was observed. This study showed, for the first time, that PDK-1/AKT pathway is activated in RMS and may play an important role in survival of RMS. PDK-1/AKT pathway may be an attractive therapeutic target for cancer intervention in RMS using OSU-03012

Human epidermal growth factor receptor-2 and estrogen receptor expression, a demonstration project using the residual tissue respository of the Surveillance, Epidemiology, and End Results (SEER) program

In 2001, the National Cancer Institute’s Surveillance, Epidemiology, and End Results (SEER) program established Residual Tissue Repositories (RTR) in the Hawaii, Iowa, and Los Angeles Tumor Registries to collect discarded tissue blocks from pathologic laboratories within their catchment areas. To validate the utility of the RTR for supplementing SEER’s central database, we assessed human epidermal growth factor receptor-2 (HER2) and estrogen receptor expression (ER) in a demonstration project

Automatic Tumor-Stroma Separation in Fluorescence TMAs Enables the Quantitative High-Throughput Analysis of Multiple Cancer Biomarkers

The upcoming quantification and automation in biomarker based histological tumor evaluation will require computational methods capable of automatically identifying tumor areas and differentiating them from the stroma. As no single generally applicable tumor biomarker is available, pathology routinely uses morphological criteria as a spatial reference system. We here present and evaluate a method capable of performing the classification in immunofluorescence histological slides solely using a DAPI background stain. Due to the restriction to a single color channel this is inherently challenging. We formed cell graphs based on the topological distribution of the tissue cell nuclei and extracted the corresponding graph features. By using topological, morphological and intensity based features we could systematically quantify and compare the discrimination capability individual features contribute to the overall algorithm. We here show that when classifying fluorescence tissue slides in the DAPI channel, morphological and intensity based features clearly outpace topological ones which have been used exclusively in related previous approaches. We assembled the 15 best features to train a support vector machine based on Keratin stained tumor areas. On a test set of TMAs with 210 cores of triple negative breast cancers our classifier was able to distinguish between tumor and stroma tissue with a total overall accuracy of 88%. Our method yields first results on the discrimination capability of features groups which is essential for an automated tumor diagnostics. Also, it provides an objective spatial reference system for the multiplex analysis of biomarkers in fluorescence immunohistochemistry

Prioritizing genes associated with prostate cancer development

<p>Abstract</p> <p>Background</p> <p>The genetic control of prostate cancer development is poorly understood. Large numbers of gene-expression datasets on different aspects of prostate tumorigenesis are available. We used these data to identify and prioritize candidate genes associated with the development of prostate cancer and bone metastases. Our working hypothesis was that combining meta-analyses on different but overlapping steps of prostate tumorigenesis will improve identification of genes associated with prostate cancer development.</p> <p>Methods</p> <p>A <it>Z </it>score-based meta-analysis of gene-expression data was used to identify candidate genes associated with prostate cancer development. To put together different datasets, we conducted a meta-analysis on 3 levels that follow the natural history of prostate cancer development. For experimental verification of candidates, we used in silico validation as well as in-house gene-expression data.</p> <p>Results</p> <p>Genes with experimental evidence of an association with prostate cancer development were overrepresented among our top candidates. The meta-analysis also identified a considerable number of novel candidate genes with no published evidence of a role in prostate cancer development. Functional annotation identified cytoskeleton, cell adhesion, extracellular matrix, and cell motility as the top functions associated with prostate cancer development. We identified 10 genes--<it>CDC2, CCNA2, IGF1, EGR1, SRF, CTGF, CCL2, CAV1, SMAD4</it>, and <it>AURKA</it>--that form hubs of the interaction network and therefore are likely to be primary drivers of prostate cancer development.</p> <p>Conclusions</p> <p>By using this large 3-level meta-analysis of the gene-expression data to identify candidate genes associated with prostate cancer development, we have generated a list of candidate genes that may be a useful resource for researchers studying the molecular mechanisms underlying prostate cancer development.</p

The Wnt pathway regulator DKK1 is preferentially expressed in hormone-resistant breast tumours and in some common cancer types

In addition to new tumour antigens, new prognostic and diagnostic markers are needed for common cancers. In this study, we report the expression of Dickkopf-1 (DKK1) in multiple common cancers. This constitutes a comprehensive analysis of the DKK1 expression profile. Dickkopf-1 expression was evaluated by classical and quantitative reverse transcriptase–polymerase chain reaction (RT–PCR) and enzyme-linked immunosorbant assay for protein determination, in cancer lines and clinical specimens of several cancer origins. For breast cancer, expression was correlated with clinicopathological parameters. Dickkopf-1 expression was confirmed in several cancer cell lines derived from breast and other common cancers. Dickkopf-1 protein secretion was documented in breast, prostate and lung cancer lines, but was negligible in melanoma. Analysis of DKK1 expression in human cancer specimens revealed DKK1 expression in breast (21 out of 73), lung (11 out of 23) and kidney cancers (six out of 20). Interestingly, DKK1 was preferentially expressed in oestrogen and progesterone receptor-negative tumours (ER−/PR−; P=0.005) and in tumours from women with a family history of breast cancer (P=0.024). Importantly, DKK1 protein production was confirmed in multiple breast cancer specimens that were positive by RT–PCR. This work establishes DKK1 as a potential prognostic and diagnostic marker for cohorts of breast cancer patients with poor prognosis. Dickkopf-1 may also become a relevant candidate target for immunotherapy of different cancers

Benefits of biomarker selection and clinico-pathological covariate inclusion in breast cancer prognostic models

Introduction: Multi-marker molecular assays have impacted management of early stage breast cancer, facilitating adjuvant chemotherapy decisions. We generated prognostic models that incorporate protein-based molecular markers and clinico-pathological variables to improve survival prediction. Methods: We used a quantitative immunofluorescence method to study protein expression of 14 markers included in the Oncotype DX™ assay on a 638 breast cancer patient cohort with 15-year follow-up. We performed cross-validation analyses to assess performance of multivariate Cox models consisting of these markers and standard clinico-pathological covariates, using an average time-dependent Area Under the Receiver Operating Characteristic curves and compared it to nested Cox models obtained by robust backward selection procedures. Results: A prognostic index derived from of a multivariate Cox regression model incorporating molecular and clinico-pathological covariates (nodal status, tumor size, nuclear grade, and age) is superior to models based on molecular studies alone or clinico-pathological covariates alone. Performance of this composite model can be further improved using feature selection techniques to prune variables. When stratifying patients by Nottingham Prognostic Index (NPI), the most prognostic markers in high and low NPI groups differed. Similarly, for the node-negative, hormone receptor-positive sub-population, we derived a compact model with three clinico-pathological variables and two protein markers that was superior to the full model. Conclusions: Prognostic models that include both molecular and clinico-pathological covariates can be more accurate than models based on either set of features alone. Furthermore, feature selection can decrease the number of molecular variables needed to predict outcome, potentially resulting in less expensive assays.This work was supported by a grant from the Susan G Komen Foundation (to YK)

Mouse mammary tumors display Stat3 activation dependent on leukemia inhibitory factor signaling

Introduction: It has been demonstrated that leukemia inhibitory factor (LIF) induces epithelium apoptosis through Stat3 activation during mouse mammary gland involution. In contrast, it has been shown that this transcription factor is commonly activated in breast cancer cells, although what causes this effect remains unknown. Here we have tested the hypothesis that locally produced LIF can be responsible for Stat3 activation in mouse mammary tumors. Methods: The studies were performed in different tumorigenic and non-tumorigenic mammary cells. The expression of LIF and LIF receptor was tested by RT-PCR analysis. In tumors, LIF and Stat3 proteins were analyzed by immunohistochemistry, whereas Stat3 and extracellular signal-regulated kinase (ERK)1/2 expression and phosphorylation were studied by Western blot analysis. A LIF-specific blocking antibody was used to determine whether this cytokine was responsible for Stat3 phosphorylation induced by conditioned medium. Specific pharmacological inhibitors (PD98059 and Stat3ip) that affect ERK1/2 and Stat3 activation were used to study their involvement in LIF-induced effects. To analyze cell survival, assays with crystal violet were performed. Results: High levels of LIF expression and activated Stat3 were found in mammary tumors growing in vivo and in their primary cultures. We found a single mouse mammary tumor cell line, LM3, that showed low levels of activated Stat3. Incidentally, these cells also showed very little expression of LIF receptor. This suggested that autocrine/paracrine LIF would be responsible for Stat3 activation in mouse mammary tumors. This hypothesis was confirmed by the ability of conditioned medium of mammary tumor primary cultures to induce Stat3 phosphorylation, activity that was prevented by pretreatment with LIF-blocking antibody. Besides, we found that LIF increased tumor cell viability. Interestingly, blocking Stat3 activation enhanced this effect in mammary tumor cells. Conclusion: LIF is overexpressed in mouse mammary tumors, where it acts as the main Stat3 activator. Interestingly, the positive LIF effect on tumor cell viability is not dependent on Stat3 activation, which inhibits tumor cell survival as it does in normal mammary epithelium. © 2007 Quaglino et al.; licensee BioMed Central Ltd.Fil:Quaglino, A. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina.Fil:Schere-Levy, C. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina.Fil:Romorini, L. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina.Fil:Kordon, E.C. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina