bluuebunny/crossref_metadata_2025_split huggingface dataset:
Discovery of a biomarker signature that predicts upgrading or upstaging in patients with low-risk prostate cancer.
Authors: Erho Nicholas , Vergara Ismael A. , Buerki Christine , Ghadessi Mercedeh , Crisan Anamaria , Sierocinski Thomas , Haddad Zaid , Zimmermann Benedikt , Harko Sebastian , Sosu-Sedzorme Worlanyo , Triche Timothy J. , Davicioni Elai 
Published: February 2017

37 Background: More than 90% of patients diagnosed with organ-confined prostate cancer (PCa) choose upfront definitive treatment (e.g., radical surgery) even though many are excellent candidates for delayed therapy (i.e., active surveillance [AS]). Therefore, patients may suffer from the adverse effects of treatment without gaining any benefit. Biomarker signatures that predict tumour aggressiveness are promising tools for identification of patients suited for AS. In this study, we use a transcriptome-wide assay to develop a biomarker signature for patients assessed as low risk at diagnosis who are upgraded or upstaged following radical prostatectomy (RP). Methods: Gene expression data of 56 RP samples from the Memorial Sloan Kettering Oncogenome Project (GSE21034) which met the low risk criteria (i.e., biopsy Gleason score (GS) ≤ 6, clinical stage T1 or T2A, and pre-operative PSA (pre-op PSA) ≤ 10 ng/ml) were used to develop the signature. Of these tumors, 31 underwent upgrading or upstaging (defined by pathological GS ≥ 7 or a pathological tumor stage > T3A). In the training set (n = 29) a median fold difference filter (MFD > 1.4) was applied to select features. The top 16 t-test ranked features were modelled with a K-nearest-neighbor (KNN) classifier (k = 3) which predicts upgrading/upstaging events. Results: The KNN was applied to the test set (n = 27) and achieved an area under the receiver operating characteristic curve (AUC) of 0.93, significantly better discrimination than pre-op PSA (AUC = 0.52) or tumor stage (AUC = 0.63). Compared to the null model’s accuracy of 56%, the KNN correctly predicts 81% (p-value < 0.005) of the upgrading/upstaging events. In multivariable analysis with pre-op PSA, tumor stage, and age at diagnosis, the KNN remained the only significant (p < 0.05) factor with an odds ratio of 2.7. Conclusions: A 16 marker signature was identified from RP specimens and shown to accurately segregate true low risk patients from those which transitioned to higher risk. Validation studies of this signature in prospectively designed cohorts of active surveillance candidates are underway to determine if the molecular signature can improve treatment and management decisions for low risk PCa patients.
Detection of alpha-methylacyl-CoA racemase (AMACR), a biomarker of prostate cancer, in patient blood samples using a nanoparticle electrochemical biosensor.
Authors: Cooney Matthew M. , Thompson Cheryl L. , Lin Po-Yuan , Cheng Kai-Lun , McGuffin-Cawley James D. , Shieu Fuh-Sheng , Samia Anna C. , Gupta Sanjay , Liu Chung-Chiun 
Published: February 2017

41 Background: A promising prostate cancer biomarker, alpha-methylacyl-CoA racemase (AMACR), has demonstrated the ability to distinguish cancer from healthy and benign cells with high sensitivity and specificity. However the lack of a good clinical assay has limited its translation into the clinic. Here we report on the development of a single use disposable biosensor for AMACR detection. Methods: This is a very inexpensive, small, single-use disposable sensor that requires only a drop of plasma and connects to a portable device. The biosensor utilizes the reaction of pristanic acid with a substrate that includes AMACR to produce Trans-2,3-dehydropritanayl-CoA plus H2O2. The biosensor utilizes iridium oxide nanoparticle catalyst to oxidize the H2O2 produced in the above metabolic pathway. Thus the oxidation of H2O2 yields a measurable current to quantify AMACR in the sample. This is the first in vitro assay method for AMACR based on this reaction mechanism. Results: In our study including plasma from 9 healthy males, 10 patients with high grade prostatic intraepithelial neoplasia and 5 prostate cancer patients we show 100% accuracy in separating prostate cancer patients from controls as well as those with benign prostate conditions. Conclusions: Our data provides strong evidence for the ability of this biosensor to perform as a reliable assay for prostate cancer detection and diagnosis.
Correlation of elevated ALCAM shedding with patient outcome in colorectal cancer.
Authors: Hansen Amanda Georgia , Freeman Tanner , Washington Mary Kay , Fan Kang-Hsien , Shyr Yu , Beauchamp R. Daniel , Zijlstra Andries 
Published: February 2017

46 Background: ALCAM (Activated Leukocyte Cell Adhesion Molecule) is a cell-cell adhesion protein that has been highlighted as a putative biomarker for the progression of many cancers, including colorectal cancer (CRC). Current prognosis for CRC patients predominantly rely on pathologic staging classification. Although TNM staging successfully stratifies high-risk patients, there is significant variability in the rate of disease progression within each stage. Previous studies evaluating ALCAM as a biomarker for predicting colorectal cancer patient survival have published conflicting and inconclusive results. The aim of this present study was to investigate the prognostic value of ALCAM shedding, quantified using a dual stain that detects both the extracellular and the intracellular domains in formalin-fixed tissue, in colorectal patients. Methods: A novel dual-immunohistological detection of ALCAM shedding was performed on tissue microarrays constructed from 75 colorectal primary tumors and 9 healthy control specimens. Tissue was assessed by CellProfiler image analysis to obtain quantitative estimates of ALCAM shedding, defined as the detection of the intracellular domain the absence of the extracellular domain of ALCAM. Two tailed p values ≤ 0.05 were considered to be statistically significant. Results: ALCAM shedding was elevated in 33/79 (41.8%) of tissue from CRC patients compared to healthy controls (p=0.0239). Univariate analysis of CRC patients, stages I-IV, revealed a statistical significance correlating high ALCAM shedding with worse overall survival (p=0.0401). Moreover, ALCAM shedding is a predictor of overall survival (p=0.432) and more notably, disease-specific survival in stage II patients (p=0.0098). Conclusions: Detection of ALCAM shedding allows for stratification of patients according to their risk for poor long-term outcome. Considering that ALCAM is altered in a number of malignancies, the clinical correlation of ALCAM shedding to patient outcome is likely to extend beyond CRC to other cancers.
Examining the reliability of surrogate tissue predictability of drug efficacy in reconstituted human 3D skin models containing melanomas.
Authors: Parekh Palak R. , Redon Christophe E. , Weyemi Urbain , Bonner William 
Published: February 2017

49 Background: Melanoma has a poor survival rate in advanced stages due to the lack of efficacious approved treatments. However, melanoma’s dermal location enables the application of human skin models. These models mimic many important characteristics of skin in vivo and obviate the use of invasive procedures. Monitoring gamma-H2AX foci to assess DNA double-strand break (DSB) levels in reconstituted human 3D skin tissue harboring melanoma tumor cells may provide insights into important questions in cancer research. In addition to the differential toxicities of anti-cancer agents on the various cell types, we examine how the early drug response of normal tissues might serve as surrogates for predicting treatment efficacy on the tumors. Methods: Commercially available reconstituted 3D human skin tissue consists of dermal fibroblast and epidermal keratinocyte layers with malignant melanoma A375 cells which have been cultured to form a multilayer, highly differentiated epidermis containing various stages of cutaneous melanoma malignancy. Drugs representative of several different categories were studied, including bleomycin (a radiomimetic), camptothecin (a topoisomerase I inhibitor), temozolomide (a DNA alkylating agent), AZD6244 (a MEK inhibitor), Vorinostat (a HDAC inhibitor) and cisplatin (a DNA intercalatingagent). DNA DSB levels were monitored by gamma-H2AX foci formation and apoptosis by TUNEL assay. Tumor growth was monitored by H&E staining. Results: A wide range of responses in both tumor and normal tissue by various drugs were observed, including tumor regression and normal tissue toxicity. The early responses of normal tissues depended on the treatment, providing insights into the use of surrogate tissues as predictors of drug efficacy and later tumor regression. Conclusions: Measuring DNA DSB damage in reconstituted 3D skin tissue is an attractive model for studying the efficacy and toxicity of genotoxic drugs against melanoma. If validated, such models would help reduce the use of animals for preclinical studies.
Hypothesis-free biomarker discovery platform evolving into a companion diagnostic using bioinformatics.
Authors: Pedersen Gitte 
Published: February 2017

52 Background: Cancer drug discovery is hypothesis driven focused on addressing specific mechanisms; however the patient’s individual tumor harbor much more diversity and therefore the biomarkers behind the hypothesis are often insufficient in explaining the differences in individual drug response. A Danish Private Public Partnership is developing biomarkers for cancer prognostics under a $30 million grant. The Danish National Biobank was established by another $20 million grant and hosts approximately 15 million biological samples associated with cradle-to-grave electronic medical records. Methods: Proposing a hypothesis-free biomarker-discovery platform using intelligent target-filtering technology integrated with sequencing short reads on a small chip addressing the sample prep and data analysis bottlenecks in current sequencing platforms. By using bioinformatics on existing sequence data it is possible to optimize the proposed strategy and develop novel diagnostic applications. The intelligent target-filtering process generates a single-stranded tag library. Because the tags are single stranded, it is possible to detect all possible tags using a universal high-throughput platform. The cost per sample is low, the content/test very high, and hypotheses free. This approach represents a paradigm shift in the biomarker-discovery process, saving significant time and money while increasing the probability of success. The diagnostic becomes a bioinformatics exercise that allows the drug discovery company to optimize the definition of responders. Results: The result of the digital gene expression experiment was compared to RNA-seq and Affymetrix whole genome array. Conclusions: The platform offers a more cost-effective way of providing an open and hypothesis free platform compared to sequencing. The platform also offers cost-effective open-ended solution for such diverse applications as expression profiling, genome wide methylation profiling, genome wide duplication/deletion analysis, environmental microbiome profiling, and single gene mutation detection with only relatively minor adjustments in how the single stranded tags are extracted from the sample.
Moving next-generation sequencing into the clinical realm: Detection of somatic mutations in cancer by targeted amplicon sequencing.
Authors: Lih Chih-Jian , Forbes Thomas , Mehaffey Michele , Sause Eric , Sims David , McGregor Paul , Conley Barbara A. , Kummar Shivaani , Williams Paul M. 
Published: February 2017

60 Background: Molecular targeted therapies are increasingly important in treating cancer patients; robust analytically validated clinical assays are required for patient selection in early-stage clinical trials. The goal of Molecular Characterization Laboratory (MoCha) is to develop clinical diagnostic assays using next generation sequencing methods to support clinical studies in DCTD (CTEP). Methods: We developed a custom assay for somatic mutation detection using Fluidigm access array technology for amplicon generation followed by sequencing with the Illumina Miseq. A panel of 62 amplicons covering 6 Kb genomic regions was designed to detect 92 DNA loci, including common therapeutically actionable targets, in 37 genes. Analytical studies were performed using genomic DNA samples from fresh or formalin fixed cancer cell-lines and a normal hapmap individual (CEPH). We subsequently applied this assay to characterize DNA samples from both tumor tissues and blood specimens from ovarian cancer patients. Results: The assay detected known variants in both frozen and fixed DNA samples reproducibly with high sensitivity and specificity (<2%). Using a series of positive control plasmid spikes mixed into a normal reference CEPH DNA at pre-defined copy number ratios, we verified the assay is sensitive to detect variants at 5% allelic frequency with a minimum 400 X coverage. We identified somatic mutations in TP53 and PIK3CA in a few patients, and a germ-line variant D1583N in ATM genes occurring in one-third of tested patients. Conclusions: We developed and validated a next generation sequencing assay suitable for patient selection for clinical trials. Plans are to correlate sequencing and clinical results when clinical data are available.
Prognostic and diagnostic significance of epigenetic alterations of p16 and DAPK1 promoter sequences in patients with epithelial ovarian carcinoma (EOC).
Authors: Mir Ab Rashid , Najar Imtiyaz Ahmad , Yadav Prasant , Javed Jamsheed , Zubari Mariyam , Farooq Shazia , Gandhi Gauri , Ray Prakash C. , Dholariya Sagar , Saxnena Alpana 
Published: February 2017

62 Background: Novel strategies for early detection of EOC, the most common and second most lethal cancer in Indian women, are urgently needed. Silencing tumor suppressor genes via DNA methylation has established hypermethylation as one of the most frequent molecular alterations that may initiate and drive many types of human neoplasia including EOC. To determine the alterations of tumor suppressor gene DAPK1 and p16INK4A in EOC patients to explore the possibilities of identifying potential minimally invasive markers in blood of the patients, which could help in the clinical practice as a diagnostic and prognostic marker. Methods: Fifty EOC patients with primary epithelial ovarian cancer were selected for the study; these patients were followed for a median of 20 months. Genomic DNA extracted from fresh peripheral blood and serum followed by sodium bisulfate modification. The DAPK1 and p16 methylation was detected using methylation-specific PCR (MSP). The DAPK1 and p16 methylation status was correlated with age, stage,menopause, Ca125.5 and clinic pathological features. Results: The frequencies of DAPK1 and p16 methylation in EOC patients was found to be 68% and 84% respectively . Aberrant methylation of DAPK1 and p16 was associated with age at diagnosis (p = 0.043) .The significant association was seen with age,menopause. Patients with high methylation indices had poor prognosis (p<0.001, Hazards ratio=14.58) with age (p = 0.043), and tumor stage (p = 0.033). Aberrant methylation of DAPK1 and p16 was strongly associated with EOC patients (p = 0.037 respectively). Conclusions: our results that the methylated loci of TSGs (DAPK1 and p16) may be employed as clinically useful biomarkers for prognosis and diagnosis of EOC noninvasively using readily available body fluid by MS-PCR and proved to be efficient and cost-effective method.
A 19-gene prognostic GEP signature to determine metastatic risk associated with thymomas.
Authors: Gokmen-Polar Yesim , Goswami Chirayu Pankaj , Cook Robert W. , Oelschlager Kristen M. , Maetzold Derek , Vladislav Ioan Tudor , Shirar Kristen L. , Kesler Kenneth , Loehrer Patrick J. , Badve Sunil S. 
Published: February 2017

68 Background: Therapeutic decisions for patients with thymomas are made on tumor stage as histologic classification is of limited prognostic value. A molecular prognostic tool may better predict the likelihood of metastasis in thymomas and enable better management of patients. Methods: Reverse-transcriptase-polymerase-chain-reaction (RT-PCR) assay for 23 genes (19 test and 4 reference genes) was performed on archival paraffin-embedded primary thymomas (n=36). The gene expression levels were used to compute gene expression profile (GEP) signature and classify tumors into low or high risk. The GEP was validated in an independent test set (n=75) of patients. Results: We developed and validated a 19-gene prognostic GEP signature for predicting metastatic behavior in thymomas. Using radial basis machine (RBM) modeling in the training set, both 5-year and 10-year MFS rates were 85% and 22% for predicted low risk and high risk of metastasis (p<0.0005, log-rank), respectively. For the validation set, 5-year MFS rates were 93% and 35% for predicted low and high risk patients (p<0.0018 log-rank), respectively. The 5-year MFS rates for Masaoka stage in the validation set were 53% and 41% for stage I/II and stage III/IV(p<0.0433 log-rank), respectively. In a multivariate Cox model, the GEP was the only independent predictor of metastasis free survival compared to age at diagnosis and stage (p=0.048). Conclusions: Using archival tissues, we identified and validated a 19-gene signature that predicts likelihood of metastases of thymomas more accurately than traditional staging. The signature provides insights for both patient stratification and targeted therapy.
Disruption of protocadherin function and correlation with metastasis and cancer progression in TCGA patients.
Authors: Shima James , Delaney Joseph , Umesh Anita , Park Jenny , Wall Gregory , Su Qiaojuan , Shekar Mamatha , Kupershmidt Ilya 
Published: February 2017

70 Background: Protocadherins (PCDH) are a large family of calcium-dependent cell–cell adhesion molecules involved in tissue morphogenesis. Inactivation of PCDH locus genes are implicated in a number of oncogenic processes—hypermethylation of the PCDH gamma locus is present in Wilms’ tumors and arises after tumor development and the epigenetic silencing of PCDHGA11 occurs in the course of parenchymal invasion by astrocytomas. Assessment of PCDH function may help guide treatment decisions and provide a marker for metastasis in specific cancers. Methods: Identification of altered PCDH locus genes in TCGA patient samples was performed using the NextBio Clinical platform which provides access to the genomic and clinical data available in the TCGA dataset. The NextBio Research platform was used to correlate curated public experimental data and identify deleterious genomic changes associated with the PCDH locus and disease. Results: A nearly 3-fold increase in the frequency of potentially detrimental somatic mutations in PCDHGB4 was observed in breast cancer patients with extensive lymph node metastases (N3) compared to those with a lower degree of lymph node metastasis (N0-N2) (p value = 0.015). No other cancers appeared to have genomic changes deleterious to PCDHGB4 function, including ovarian and uterine cancer. Using the NextBio Research platform, breast cancer was found to be the 3rd most correlated disease with PCDHGB4, strongly correlating with RNA downregulation, DNA hypermethylation, and copy number loss. Other deleterious genomic changes in the PCDH locus were identified in ovarian (PCDHG), renal (PCDHA), and lung (PCDHA) cancers. Conclusions: Using the NextBio Clinical and Research platforms to analyze available TCGA data, disruption of PCDH function was found to correlate with increased metastasis only in certain cancers and with specific locus-disease pairings. Examination of PCDH locus perturbations may provide insight into a patient’s risk of developing metastatic disease. Further examination of the tissue specific alteration in PCDH genes may also aid in identifying unique cell adhesion pathways leading to metastasis in distinct tissues.
Use of quantitative histomorphometrics to classify disease progression in HPV-positive squamous cell carcinoma.
Authors: Ali Sahirzeeshan , Lewis James S. , Madabhushi Anant 
Published: February 2017

73 Background: Oropharyngeal squamous cell carcinoma (SCC) is now recognized as distinct because of its strong association with high-risk human papillomavirus (HPV) and favorable prognosis. Although these tumors have some specific morphologic features, none have been shown to predict prognosis, particularly on small specimens such as biopsies. Methods: We present a novel cluster cell graph (CCG) that is computationally efficient and provides an effective tool to quantitatively describe histopathologic images according to the spatial distribution and clustering of cells. CCG is generated by firstly automatically identifying all cells/nuclei within the histologic image and then identifying nuclear clusters. These clusters are then represented via a single node corresponding to center of mass of the cluster. Any pair of nodes is connected via a link if the probability of such an association is greater than a pre-defined threshold, the probability being calculated as a decaying function of the Euclidean distance between node pairs. Once links have been identified between cluster nodes, subgraphs are constructed as series of topological features defined on each node of the subgraph, i.e., local graph metrics (e.g. clustering coefficients and skewness of edge lengths). Results: We leverage CCG to capture structural characteristics of HPV-related (p16+) oropharyngeal SCC (oSCC) from formalin-fixed, paraffin-embedded tissue microarray images. A series of quantitative histomorphometric descriptors are extracted for each image and used to train a random forest (RF) classifier to distinguish patients with p16+ oSCC who develop disease progression from those that do not. Over 25 runs of 3-fold cross validation in a cohort of 140 studies (116 non-progressors and 26 progressors) the RF classifier yielded a mean accuracy of over 87% in distinguishing between progressors and non-progressors. Conclusions: The ability to quantitatively describe the spatial distribution/clustering of nuclei via CCG allows for building of classifiers that can distinguish between progressors and non-progressors in p16+ oSCC tissue images. The approach should be extensible to outcome prediction for other cancers as well.