Ensemble analyses improve signatures of tumour hypoxia and reveal inter-platform differences

BACKGROUND: The reproducibility of transcriptomic biomarkers across datasets remains poor, limiting clinical application. We and others have suggested that this is in-part caused by differential error-structure between datasets, and their incomplete removal by pre-processing algorithms. METHODS: To test this hypothesis, we systematically assessed the effects of pre-processing on biomarker classification using 24 different pre-processing methods and 15 distinct signatures of tumour hypoxia in 10 datasets (2,143 patients). RESULTS: We confirm strong pre-processing effects for all datasets and signatures, and find that these differ between microarray versions. Importantly, exploiting different pre-processing techniques in an ensemble technique improved classification for a majority of signatures. CONCLUSIONS: Assessing biomarkers using an ensemble of pre-processing techniques shows clear value across multiple diseases, datasets and biomarkers. Importantly, ensemble classification improves biomarkers with initially good results but does not result in spuriously improved performance for poor biomarkers. While further research is required, this approach has the potential to become a standard for transcriptomic biomarkers

Exploiting the noise: improving biomarkers with ensembles of data analysis methodologies.

BackgroundThe advent of personalized medicine requires robust, reproducible biomarkers that indicate which treatment will maximize therapeutic benefit while minimizing side effects and costs. Numerous molecular signatures have been developed over the past decade to fill this need, but their validation and up-take into clinical settings has been poor. Here, we investigate the technical reasons underlying reported failures in biomarker validation for non-small cell lung cancer (NSCLC).MethodsWe evaluated two published prognostic multi-gene biomarkers for NSCLC in an independent 442-patient dataset. We then systematically assessed how technical factors influenced validation success.ResultsBoth biomarkers validated successfully (biomarker #1: hazard ratio (HR) 1.63, 95% confidence interval (CI) 1.21 to 2.19, P = 0.001; biomarker #2: HR 1.42, 95% CI 1.03 to 1.96, P = 0.030). Further, despite being underpowered for stage-specific analyses, both biomarkers successfully stratified stage II patients and biomarker #1 also stratified stage IB patients. We then systematically evaluated reasons for reported validation failures and find they can be directly attributed to technical challenges in data analysis. By examining 24 separate pre-processing techniques we show that minor alterations in pre-processing can change a successful prognostic biomarker (HR 1.85, 95% CI 1.37 to 2.50, P < 0.001) into one indistinguishable from random chance (HR 1.15, 95% CI 0.86 to 1.54, P = 0.348). Finally, we develop a new method, based on ensembles of analysis methodologies, to exploit this technical variability to improve biomarker robustness and to provide an independent confidence metric.ConclusionsBiomarkers comprise a fundamental component of personalized medicine. We first validated two NSCLC prognostic biomarkers in an independent patient cohort. Power analyses demonstrate that even this large, 442-patient cohort is under-powered for stage-specific analyses. We then use these results to discover an unexpected sensitivity of validation to subtle data analysis decisions. Finally, we develop a novel algorithmic approach to exploit this sensitivity to improve biomarker robustness

Promoter hypomethylation of NY-ESO-1, association with clinicopathological features and PD-L1 expression in non-small cell lung cancer.

Cancer-Testis antigens (CTA) are immunogenic molecules with normal tissue expression restricted to testes but with aberrant expression in up to 30% of non-small cell lung cancers (NSCLCs). Regulation of CTA expression is mediated in part through promoter DNA methylation. Recently, immunotherapy has altered treatment paradigms in NSCLC. Given its immunogenicity and ability to be re-expressed through demethylation, NY-ESO-1 promoter methylation, protein expression and its association with programmed death receptor ligand-1 (PD-L1) expression and clinicopathological features were investigated. Lung cancer cell line demethylation resulting from 5-Aza-2'-deoxycytidine treatment was associated with both NY-ESO-1 and PD-L1 re-expression in vitro but not increased chemosensitivity. NY-ESO-1 hypomethylation was observed in 15/94 (16%) of patient samples and associated with positive protein expression (P < 0.0001). In contrast, PD-L1 expression was observed in 50/91 (55%) but strong expression in only 12/91 (13%) cases. There was no association between NY-ESO-1 and PD-L1 expression, despite resultant re-expression of both by 5-Aza-2'-deoxycytidine. Importantly, NY-ESO-1 hypomethylation was found to be an independent marker of poor prognosis in patients not treated with chemotherapy (HR 3.59, P = 0.003) in multivariate analysis. In patients treated with chemotherapy there were no differences in survival associated with NY-ESO-1 hypomethylation. Collectively, these results provided supporting evidence for the potential use of NY-ESO-1 hypomethylation as a prognostic biomarker in stage 3 NSCLCs. In addition, these data highlight the potential to incorporate demethylating agents to enhance immune activation, in tumours currently devoid of immune infiltrates and expression of immune checkpoint genes

A Pilot Study Comparing HPV-Positive and HPV-Negative Head and Neck Squamous Cell Carcinomas by Whole Exome Sequencing.

Background. Next-generation sequencing of cancers has identified important therapeutic targets and biomarkers. The goal of this pilot study was to compare the genetic changes in a human papillomavirus- (HPV-)positive and an HPV-negative head and neck tumor. Methods. DNA was extracted from the blood and primary tumor of a patient with an HPV-positive tonsillar cancer and those of a patient with an HPV-negative oral tongue tumor. Exome enrichment was performed using the Agilent SureSelect All Exon Kit, followed by sequencing on the ABI SOLiD platform. Results. Exome sequencing revealed slightly more mutations in the HPV-negative tumor (73) in contrast to the HPV-positive tumor (58). Multiple mutations were noted in zinc finger genes (ZNF3, 10, 229, 470, 543, 616, 664, 638, 716, and 799) and mucin genes (MUC4, 6, 12, and 16). Mutations were noted in MUC12 in both tumors. Conclusions. HPV-positive HNSCC is distinct from HPV-negative disease in terms of evidence of viral infection, p16 status, and frequency of mutations. Next-generation sequencing has the potential to identify novel therapeutic targets and biomarkers in HNSCC

A case report and genetic characterization of a massive acinic cell carcinoma of the parotid with delayed distant metastases.

We describe the presentation, management, and clinical outcome of a massive acinic cell carcinoma of the parotid gland. The primary tumor and blood underwent exome sequencing which revealed deletions in CDKN2A as well as PPP1R13B, which induces p53. A damaging nonsynonymous mutation was noted in EP300, a histone acetylase which plays a role in cellular proliferation. This study provides the first insights into the genetic underpinnings of this cancer. Future large-scale efforts will be necessary to define the mutational landscape of salivary gland malignancies to identify therapeutic targets and biomarkers of treatment failure

Pathway-based subnetworks enable cross-disease biomarker discovery.

Biomarkers lie at the heart of precision medicine. Surprisingly, while rapid genomic profiling is becoming ubiquitous, the development of biomarkers usually involves the application of bespoke techniques that cannot be directly applied to other datasets. There is an urgent need for a systematic methodology to create biologically-interpretable molecular models that robustly predict key phenotypes. Here we present SIMMS (Subnetwork Integration for Multi-Modal Signatures): an algorithm that fragments pathways into functional modules and uses these to predict phenotypes. We apply SIMMS to multiple data types across five diseases, and in each it reproducibly identifies known and novel subtypes, and makes superior predictions to the best bespoke approaches. To demonstrate its ability on a new dataset, we profile 33 genes/nodes of the PI3K pathway in 1734 FFPE breast tumors and create a four-subnetwork prediction model. This model out-performs a clinically-validated molecular test in an independent cohort of 1742 patients. SIMMS is generic and enables systematic data integration for robust biomarker discovery

The transcriptomic profile of ovarian cancer grading.

Ovarian carcinoma is the leading cause of gynecological malignancy, with the serous subtype being the most commonly presented subtype. Recent studies have demonstrated that grade does not yield significant prognostic information, independent of TNM staging. As such, several different grading systems have been proposed to reveal morphological characteristics of these tumors, however each yield different results. To help address this issue, we performed a rigorous computational analysis to better understand the molecular differences that fundamentally explain the different grades and grading systems. mRNA abundance levels were analyzed across 334 total patients and their association with each grade and grading system were assessed. Few molecular differences were observed between grade 2 and 3 tumors when using the International Federation of Gynecology and Obstetrics (FIGO) grading system, suggesting their molecular similarity. In contrast, grading by the Silverberg system reveals that grades 1-3 are molecularly equidistant from one another across a spectrum. Additionally, we have identified a few candidate genes with good prognostic information that could potentially be used for classifying cases with similar morphological appearances

Exploiting the noise: improving biomarkers with ensembles of data analysis methodologies.

BackgroundThe advent of personalized medicine requires robust, reproducible biomarkers that indicate which treatment will maximize therapeutic benefit while minimizing side effects and costs. Numerous molecular signatures have been developed over the past decade to fill this need, but their validation and up-take into clinical settings has been poor. Here, we investigate the technical reasons underlying reported failures in biomarker validation for non-small cell lung cancer (NSCLC).MethodsWe evaluated two published prognostic multi-gene biomarkers for NSCLC in an independent 442-patient dataset. We then systematically assessed how technical factors influenced validation success.ResultsBoth biomarkers validated successfully (biomarker #1: hazard ratio (HR) 1.63, 95% confidence interval (CI) 1.21 to 2.19, P = 0.001; biomarker #2: HR 1.42, 95% CI 1.03 to 1.96, P = 0.030). Further, despite being underpowered for stage-specific analyses, both biomarkers successfully stratified stage II patients and biomarker #1 also stratified stage IB patients. We then systematically evaluated reasons for reported validation failures and find they can be directly attributed to technical challenges in data analysis. By examining 24 separate pre-processing techniques we show that minor alterations in pre-processing can change a successful prognostic biomarker (HR 1.85, 95% CI 1.37 to 2.50, P < 0.001) into one indistinguishable from random chance (HR 1.15, 95% CI 0.86 to 1.54, P = 0.348). Finally, we develop a new method, based on ensembles of analysis methodologies, to exploit this technical variability to improve biomarker robustness and to provide an independent confidence metric.ConclusionsBiomarkers comprise a fundamental component of personalized medicine. We first validated two NSCLC prognostic biomarkers in an independent patient cohort. Power analyses demonstrate that even this large, 442-patient cohort is under-powered for stage-specific analyses. We then use these results to discover an unexpected sensitivity of validation to subtle data analysis decisions. Finally, we develop a novel algorithmic approach to exploit this sensitivity to improve biomarker robustness

Comparison of toxicity and outcomes of concurrent radiotherapy with carboplatin/paclitaxel or cisplatin/etoposide in stage III non-small cell lung cancer.

Concurrent chemoradiotherapy (CCRT) has become the standard of care for patients with unresectable stage III non-small cell lung cancer (NSCLC). The comparative merits of two widely used regimens: carboplatin/paclitaxel (PC) and cisplatin/etoposide (PE), each with concurrent radiotherapy, remain largely undefined. Records for consecutive patients with stage III NSCLC treated with PC or PE and ≥60 Gy chest radiotherapy between 2000 and 2011 were reviewed for outcomes and toxicity. Survival was estimated using the Kaplan-Meier method and Cox modeling with the Wald test. Comparison across groups was done using the student's t and chi-squared tests. Seventy-five (PC: 44, PE: 31) patients were analyzed. PC patients were older (median 71 vs. 63 years; P = 0.0006). Other characteristics were comparable between groups. With PE, there was significantly increased grade ≥3 neutropenia (39% vs. 14%, P = 0.024) and thrombocytopenia (10% vs. 0%, P = 0.039). Radiation pneumonitis was more common with PC (66% vs. 38%, P = 0.033). Five treatment-related deaths occurred (PC: 3 vs. PE: 2, P = 1.000). With a median follow-up of 51.6 months, there were no significant differences in relapse-free survival (median PC 12.0 vs. PE 11.5 months, P = 0.700) or overall survival (median PC 20.7 vs. PE 13.7 months; P = 0.989). In multivariate analyses, no factors predicted for improved survival for either regimen. PC was more likely to be used in elderly patients. Despite this, PC resulted in significantly less hematological toxicity but achieved similar survival outcomes as PE. PC is an acceptable CCRT regimen, especially in older patients with multiple comorbidities