Prognostic factors in patients with advanced non-small cell lung cancer : data from the phase III FLEX study

The FLEX study demonstrated that the addition of cetuximab to chemotherapy significantly improved overall survival in the first-line treatment of patients with advanced non-small cell lung cancer (NSCLC). In the FLEX intention to treat (ITT) population, we investigated the prognostic significance of particular baseline characteristics. Individual patient data from the treatment arms of the ITT population of the FLEX study were combined. Univariable and multivariable Cox regression models were used to investigate variables with potential prognostic value. The ITT population comprised 1125 patients. In the univariable analysis, longer median survival times were apparent for females compared with males (12.7 vs 9.3 months); patients with an Eastern Cooperative Oncology Group performance status (ECOG PS) of 0 compared with 1 compared with 2 (13.5 vs 10.6 vs 5.9 months); never smokers compared with former smokers compared with current smokers (14.6 vs 11.1 vs 9.0); Asians compared with Caucasians (19.5 vs 9.6 months); patients with adenocarcinoma compared with squamous cell carcinoma (12.4 vs 9.3 months) and those with metastases to one site compared with two sites compared with three or more sites (12.4 months vs 9.8 months vs 6.4 months). Age (<65 vs ≥65 years), tumor stage (IIIB with pleural effusion vs IV) and percentage of tumor cells expressing EGFR (<40% vs ≥40%) were not identified as possible prognostic factors in relation to survival time. In multivariable analysis, a stepwise selection procedure identified age (<65 vs ≥65 years), gender, ECOG PS, smoking status, region, tumor histology, and number of organs involved as independent factors of prognostic value. In summary, in patients with advanced NSCLC enrolled in the FLEX study, and consistent with previous analyses, particular patient and disease characteristics at baseline were shown to be independent factors of prognostic value. The FLEX study is registered with ClinicalTrials.gov, number NCT00148798. © 2012 Elsevier Ireland Ltd.

Spatio-temporal modelling of cancer data in Queensland using Bayesian methods

Cancer is the leading contributor to the disease burden in Australia. This thesis develops and applies Bayesian hierarchical models to facilitate an investigation of the spatial and temporal associations for cancer diagnosis and survival among Queenslanders. The key objectives are to document and quantify the importance of spatial inequalities, explore factors influencing these inequalities, and investigate how spatial inequalities change over time. Existing Bayesian hierarchical models are refined, new models and methods developed, and tangible benefits obtained for cancer patients in Queensland. The versatility of using Bayesian models in cancer control are clearly demonstrated through these detailed and comprehensive analyses.

Investigating local relationships between trace elements in soils and cancer data

Medical geology research has recognised a number of potentially toxic elements (PTEs), such as arsenic, cobalt, chromium, copper, nickel, lead, vanadium, uranium and zinc, known to influence human disease by their respective deficiency or toxicity. As the impact of infectious diseases has decreased and the population ages, so cancer has become the most common cause of death in developed countries including Northern Ireland. This research explores the relationship between environmental exposure to potentially toxic elements in soil and cancer disease data across Northern Ireland. The incidence of twelve different cancer types (lung, stomach, leukaemia, oesophagus, colorectal, bladder, kidney, breast, mesothelioma, melanoma and non melanoma(NM) both basal and squamous, were examined in the form of twenty-five coded datasets comprising aggregates over the 12 year period from 1993 to 2006. A local modelling technique,geographically weighted regression (GWR) is usedto explore the relationship between environmental exposure and cancer disease data. The results show comparisons of the geographical incidence of certain cancers (stomach and NM squamous skin cancer) in relation to concentrations of certain PTEs (arsenic levels in soils and radon were identified). Findings from the research have implications for regional human health risk assessments.

Model selection for prognostic time-to-event gene signature discovery with applications in early breast cancer data

Model selection between competing models is a key consideration in the discovery of prognostic multigene signatures. The use of appropriate statistical performance measures as well as verification of biological significance of the signatures is imperative to maximise the chance of external validation of the generated signatures. Current approaches in time-to-event studies often use only a single measure of performance in model selection, such as logrank test p-values, or dichotomise the follow-up times at some phase of the study to facilitate signature discovery. In this study we improve the prognostic signature discovery process through the application of the multivariate partial Cox model combined with the concordance index, hazard ratio of predictions, independence from available clinical covariates and biological enrichment as measures of signature performance. The proposed framework was applied to discover prognostic multigene signatures from early breast cancer data. The partial Cox model combined with the multiple performance measures were used in both guiding the selection of the optimal panel of prognostic genes and prediction of risk within cross validation without dichotomising the follow-up times at any stage. The signatures were successfully externally cross validated in independent breast cancer datasets, yielding a hazard ratio of 2.55 [1.44, 4.51] for the top ranking signature.

Failure-Free Survival and Radiotherapy in Patients With Newly Diagnosed Nonmetastatic Prostate Cancer: Data From Patients in the Control Arm of the STAMPEDE Trial

Importance: The natural history of patients with newly diagnosed high-risk nonmetastatic (M0) prostate cancer receiving hormone therapy (HT) either alone or with standard-of-care radiotherapy (RT) is not well documented. Furthermore, no clinical trial has assessed the role of RT in patients with node-positive (N+) M0 disease. The STAMPEDE Trial includes such individuals, allowing an exploratory multivariate analysis of the impact of radical RT.

Objective: To describe survival and the impact on failure-free survival of RT by nodal involvement in these patients.

Design, Setting, and Participants: Cohort study using data collected for patients allocated to the control arm (standard-of-care only) of the STAMPEDE Trial between October 5, 2005, and May 1, 2014. Outcomes are presented as hazard ratios (HRs) with 95% CIs derived from adjusted Cox models; survival estimates are reported at 2 and 5 years. Participants were high-risk, hormone-naive patients with newly diagnosed M0 prostate cancer starting long-term HT for the first time. Radiotherapy is encouraged in this group, but mandated for patients with node-negative (N0) M0 disease only since November 2011.

Exposures: Long-term HT either alone or with RT, as per local standard. Planned RT use was recorded at entry.

Main Outcomes and Measures: Failure-free survival (FFS) and overall survival.

Results: A total of 721 men with newly diagnosed M0 disease were included: median age at entry, 66 (interquartile range [IQR], 61-72) years, median (IQR) prostate-specific antigen level of 43 (18-88) ng/mL. There were 40 deaths (31 owing to prostate cancer) with 17 months' median follow-up. Two-year survival was 96% (95% CI, 93%-97%) and 2-year FFS, 77% (95% CI, 73%-81%). Median (IQR) FFS was 63 (26 to not reached) months. Time to FFS was worse in patients with N+ disease (HR, 2.02 [95% CI, 1.46-2.81]) than in those with N0 disease. Failure-free survival outcomes favored planned use of RT for patients with both N0M0 (HR, 0.33 [95% CI, 0.18-0.61]) and N+M0 disease (HR, 0.48 [95% CI, 0.29-0.79]).

Conclusions and Relevance: Survival for men entering the cohort with high-risk M0 disease was higher than anticipated at study inception. These nonrandomized data were consistent with previous trials that support routine use of RT with HT in patients with N0M0 disease. Additionally, the data suggest that the benefits of RT extend to men with N+M0 disease.

Trial Registration: clinicaltrials.gov Identifier: NCT00268476; ISRCTN78818544.

A Comparative Analysis of Predictive Learning Algorithms on High-Dimensional Microarray Cancer Data

This research evaluates pattern recognition techniques on a subclass of big data where the dimensionality of the input space (p) is much larger than the number of observations (n). Specifically, we evaluate massive gene expression microarray cancer data where the ratio κ is less than one. We explore the statistical and computational challenges inherent in these high dimensional low sample size (HDLSS) problems and present statistical machine learning methods used to tackle and circumvent these difficulties. Regularization and kernel algorithms were explored in this research using seven datasets where κ < 1. These techniques require special attention to tuning necessitating several extensions of cross-validation to be investigated to support better predictive performance. While no single algorithm was universally the best predictor, the regularization technique produced lower test errors in five of the seven datasets studied.

Identifying cancer subtypes in glioblastoma by combining genomic, transcriptomic and epigenomic data

We present a nonparametric Bayesian method for disease subtype discovery in multi-dimensional cancer data. Our method can simultaneously analyse a wide range of data types, allowing for both agreement and disagreement between their underlying clustering structure. It includes feature selection and infers the most likely number of disease subtypes, given the data. We apply the method to 277 glioblastoma samples from The Cancer Genome Atlas, for which there are gene expression, copy number variation, methylation and microRNA data. We identify 8 distinct consensus subtypes and study their prognostic value for death, new tumour events, progression and recurrence. The consensus subtypes are prognostic of tumour recurrence (log-rank p-value of $3.6 \times 10^{-4}$ after correction for multiple hypothesis tests). This is driven principally by the methylation data (log-rank p-value of $2.0 \times 10^{-3}$) but the effect is strengthened by the other 3 data types, demonstrating the value of integrating multiple data types. Of particular note is a subtype of 47 patients characterised by very low levels of methylation. This subtype has very low rates of tumour recurrence and no new events in 10 years of follow up. We also identify a small gene expression subtype of 6 patients that shows particularly poor survival outcomes. Additionally, we note a consensus subtype that showly a highly distinctive data signature and suggest that it is therefore a biologically distinct subtype of glioblastoma. The code is available from https://sites.google.com/site/multipledatafusion/

Optimized approach to decision fusion of heterogeneous data for breast cancer diagnosis.

As more diagnostic testing options become available to physicians, it becomes more difficult to combine various types of medical information together in order to optimize the overall diagnosis. To improve diagnostic performance, here we introduce an approach to optimize a decision-fusion technique to combine heterogeneous information, such as from different modalities, feature categories, or institutions. For classifier comparison we used two performance metrics: The receiving operator characteristic (ROC) area under the curve [area under the ROC curve (AUC)] and the normalized partial area under the curve (pAUC). This study used four classifiers: Linear discriminant analysis (LDA), artificial neural network (ANN), and two variants of our decision-fusion technique, AUC-optimized (DF-A) and pAUC-optimized (DF-P) decision fusion. We applied each of these classifiers with 100-fold cross-validation to two heterogeneous breast cancer data sets: One of mass lesion features and a much more challenging one of microcalcification lesion features. For the calcification data set, DF-A outperformed the other classifiers in terms of AUC (p < 0.02) and achieved AUC=0.85 +/- 0.01. The DF-P surpassed the other classifiers in terms of pAUC (p < 0.01) and reached pAUC=0.38 +/- 0.02. For the mass data set, DF-A outperformed both the ANN and the LDA (p < 0.04) and achieved AUC=0.94 +/- 0.01. Although for this data set there were no statistically significant differences among the classifiers' pAUC values (pAUC=0.57 +/- 0.07 to 0.67 +/- 0.05, p > 0.10), the DF-P did significantly improve specificity versus the LDA at both 98% and 100% sensitivity (p < 0.04). In conclusion, decision fusion directly optimized clinically significant performance measures, such as AUC and pAUC, and sometimes outperformed two well-known machine-learning techniques when applied to two different breast cancer data sets.

Harnessing the complexity of gene expression data from cancer: from single gene to structural pathway methods

High-dimensional gene expression data provide a rich source of information because they capture the expression level of genes in dynamic states that reflect the biological functioning of a cell. For this reason, such data are suitable to reveal systems related properties inside a cell, e.g., in order to elucidate molecular mechanisms of complex diseases like breast or prostate cancer. However, this is not only strongly dependent on the sample size and the correlation structure of a data set, but also on the statistical hypotheses tested. Many different approaches have been developed over the years to analyze gene expression data to (I) identify changes in single genes, (II) identify changes in gene sets or pathways, and (III) identify changes in the correlation structure in pathways. In this paper, we review statistical methods for all three types of approaches, including subtypes, in the context of cancer data and provide links to software implementations and tools and address also the general problem of multiple hypotheses testing. Further, we provide recommendations for the selection of such analysis methods.

Approaches and Strategy for Cancer Research and Surveillance Data: Integration, Information Pipeline, Data Models, and Informatics Opportunities

Thesis (Ph.D.)--University of Washington, 2016-04

Inferring lung cancer risk factor patterns through joint Bayesian spatio-temporal analysis

Background: Preventing risk factor exposure is vital to reduce the high burden from lung cancer. The leading risk factor for developing lung cancer is tobacco smoking. In Australia, despite apparent success in reducing smoking prevalence, there is limited information on small area patterns and small area temporal trends. We sought to estimate spatio-temporal patterns for lung cancer risk factors using routinely collected population-based cancer data. Methods: The analysis used a Bayesian shared component spatio-temporal model, with male and female lung cancer included separately. The shared component reflected exposure to lung cancer risk factors, and was modelled over 477 statistical local areas (SLAs) and 15 years in Queensland, Australia. Analyses were also run adjusting for area-level socioeconomic disadvantage, Indigenous population composition, or remoteness. Results: Strong spatial patterns were observed in the underlying risk factor exposure for both males (median Relative Risk (RR) across SLAs compared to the Queensland average ranged from 0.48-2.00) and females (median RR range across SLAs 0.53-1.80), with high exposure observed in many remote areas. Strong temporal trends were also observed. Males showed a decrease in the underlying risk across time, while females showed an increase followed by a decrease in the final two years. These patterns were largely consistent across each SLA. The high underlying risk estimates observed among disadvantaged, remote and indigenous areas decreased after adjustment, particularly among females. Conclusion: The modelled underlying exposure appeared to reflect previous smoking prevalence, with a lag period of around 30 years, consistent with the time taken to develop lung cancer. The consistent temporal trends in lung cancer risk factors across small areas support the hypothesis that past interventions have been equally effective across the state. However, this also means that spatial inequalities have remained unaddressed, highlighting the potential for future interventions, particularly among remote areas.