Quantifying, Understanding and Predicting Differences Between Planned and Delivered Dose to Organs at Risk in Head & Neck Cancer Patients Undergoing Radical Radiotherapy to Promote Intelligently Targeted Adaptive Radiotherapy

Introduction: Radical radiotherapy (RT) is an effective but toxic treatment for head and neck cancer (HNC). Contemporary radiotherapy techniques sculpt dose to target disease and avoid organs at risk (OARs), but anatomical change during treatment mean that the radiation dose delivered to the patient – delivered dose (DA), is different to that anticipated at planning – planned dose (DP). Modifying the RT plan during treatment – Adaptive Radiotherapy (ART) – could mitigate these risks by reducing dose to OARs. However, clinical data to guide patient selection for, and timing of ART, are for lacking. Methods: 337 patients with HNC were recruited to the Cancer Research UK VoxTox study. Demographic, disease and treatment data were collated, and both DP and DA to organs at risk (OARs) were computed from daily megavoltage CT image guidance scans, using an open-source deformable image registration package (Elastix). Toxicity data were prospectively collected. Relationships between DP, DA and late toxicities were investigated with univariate, and logistic regression normal tissue complication probability (NTCP) modelling approaches. A sub-study of VoxTox recruited 18 patients who had MRI scans before RT fractions 1, 6, 16, and 26. Changes in salivary gland volumes and relative apparent diffusion coefficient (ADC) values were measured and related to toxicity events. Results: Spinal cord dose differences were small, and not predicted by weight loss or shape change. Mean DA to all other OARs was higher than DP; factors predicting higher DA included primary disease site, concomitant therapy, shape change and advanced neck disease. Nine patients (3.7%) saw DA>DP by 2Gy to more than half of the OARs assessed. These patients all had received bilateral neck RT for N-stage 2b oropharyngeal cancer. Strong uni- and multivariate relationships between OAR dose and toxicity were seen. Differences between DA and DP-based dose-toxicity models were minimal, and not statistically significant. On MRI, both parotid and submandibular glands shrank during treatment, whilst relative ADC rose. Relationships with toxicity were inconclusive. Conclusions: Small differences between OAR DP and DA mean that DA-based toxicity prediction models confer negligible additional benefit at the population level. Factors such as primary disease sub-site, concomitant systemic therapy, staging and shape change may help to select the patients that do develop clinically significant dose differences, and would benefit most from ART for toxicity reduction

Machine learning using radiomics and dosiomics for normal tissue complication probability modeling of radiation-induced xerostomia

In routine clinical practice, the risk of xerostomia is typically managed by limiting the mean radiation dose to parotid glands. This approach used to give satisfying results. In recent years, however, several studies have reported mean-dose models to fail in the recognition of xerostomia risk. This can be explained by a strong improvement of overall dose conformality in radiotherapy due to recent technological advances, and thereby a substantial reduction of the mean dose to parotid glands. This thesis investigated novel approaches to building reliable normal tissue complication probability (NTCP) models of xerostomia in this context. For the purpose of the study, a cohort of 153 head-and-neck cancer patients treated with radiotherapy at Heidelberg University Hospital was retrospectively collected. The predictive performance of the mean-dose to parotid glands was evaluated with the Lyman-Kutcher-Burman (LKB) model. In order to examine the individual predictive power of predictors describing parotid shape (radiomics), dose shape (dosiomics), and demographic characteristics, a total of 61 different features was defined and extracted from the DICOM files. These included the patient’s age and sex, parotid shape features, features related to the dose-volume histogram, the mean dose to subvolumes of parotid glands, spatial dose gradients, and three-dimensional dose moments. In the multivariate analysis, a variety of machine learning algorithms was evaluated: 1) classification methods, that discriminated patients between a high and a low risk of complication, 2) feature selection techniques, that aimed to select a number of highly informative covariates from a large set of predictors, 3) sampling methods, that reduced the class imbalance, 4) data cleaning methods, that reduced noise in the data set. The predictive performance of the models was validated internally, using nested cross-validation, and externally, using an independent patient cohort from the PARSPORT clinical trial. The LKB model showed fairly good performance on mild-to-severe (G1+) xerostomia predictions. The corresponding dose-response curve revealed that even small doses to parotid glands increase the risk of xerostomia and should be kept as low as possible. For the patients who did develop moderate-to-severe (G2+) xerostomia, the mean dose was not an informative predictor, even though the efficient sparing of parotid glands allowed to achieve low G2+ xerostomia rates. The features describing the shape of a parotid gland and the shape of a dose proved to be highly predictive of xerostomia. In particular, the parotid volume and the spatial dose gradients in the transverse plane explained xerostomia well. The results of the machine learning algorithms comparison showed that a particular choice of a classifier and a feature selection method can significantly influence predictive performance of the NTCP model. In general, support vector machines and extra-trees achieved top performance, especially for the endpoints with a large number of observations. For the endpoints with a smaller number of observations, simple logistic regression often performed on a par with the top-ranking machine learning algorithms. The external validation showed that the analyzed multivariate models did not generalize well to the PARSPORT cohort. The only features that were predictive of xerostomia both in the Heidelberg (HD) and the PARSPORT cohort were the spatial dose gradients in the right-left and the anterior-posterior directions. Substantial differences in the distribution of covariates between the two cohorts were observed, which may be one of the reasons for the weak generalizability of the HD models. The results presented in this thesis undermine the applicability of NTCP models of xerostomia based only on the mean dose to parotid glands in highly conformal radiotherapy treatments. The spatial dose gradients in the left-right and the anterior-posterior directions proved to be predictive of xerostomia both in the HD and the PARSPORT cohort. This finding is especially important as it is not limited to a single cohort but describes a general pattern present in two independent data sets. The performance of the sophisticated machine learning methods may indicate a need for larger patient cohorts in studies on NTCP models in order to fully benefit from their advantages. Last but not least, the observed covariate-shift between the HD and the PARSPORT cohort motivates, in the author’s opinion, a need for reporting information about the covariate distribution when publishing novel NTCP models

New approaches for unsupervised transcriptomic data analysis based on Dictionary learning

The era of high-throughput data generation enables new access to biomolecular profiles and exploitation thereof. However, the analysis of such biomolecular data, for example, transcriptomic data, suffers from the so-called "curse of dimensionality". This occurs in the analysis of datasets with a significantly larger number of variables than data points. As a consequence, overfitting and unintentional learning of process-independent patterns can appear. This can lead to insignificant results in the application. A common way of counteracting this problem is the application of dimension reduction methods and subsequent analysis of the resulting low-dimensional representation that has a smaller number of variables. In this thesis, two new methods for the analysis of transcriptomic datasets are introduced and evaluated. Our methods are based on the concepts of Dictionary learning, which is an unsupervised dimension reduction approach. Unlike many dimension reduction approaches that are widely applied for transcriptomic data analysis, Dictionary learning does not impose constraints on the components that are to be derived. This allows for great flexibility when adjusting the representation to the data. Further, Dictionary learning belongs to the class of sparse methods. The result of sparse methods is a model with few non-zero coefficients, which is often preferred for its simplicity and ease of interpretation. Sparse methods exploit the fact that the analysed datasets are highly structured. Indeed, a characteristic of transcriptomic data is particularly their structuredness, which appears due to the connection of genes and pathways, for example. Nonetheless, the application of Dictionary learning in medical data analysis is mainly restricted to image analysis. Another advantage of Dictionary learning is that it is an interpretable approach. Interpretability is a necessity in biomolecular data analysis to gain a holistic understanding of the investigated processes. Our two new transcriptomic data analysis methods are each designed for one main task: (1) identification of subgroups for samples from mixed populations, and (2) temporal ordering of samples from dynamic datasets, also referred to as "pseudotime estimation". Both methods are evaluated on simulated and real-world data and compared to other methods that are widely applied in transcriptomic data analysis. Our methods convince through high performance and overall outperform the comparison methods

STATISTICAL METHODS FOR INFERRING GENETIC REGULATION ACROSS HETEROGENEOUS SAMPLES AND MULTIMODAL DATA

As clinical datasets have increased in size and a wider range of molecular profiles can be credibly measured, understanding sources of heterogeneity has become critical in studying complex phenotypes. Here, we investigate and develop statistical approaches to address and analyze technical variation, genetic diversity, and tissue heterogeneity in large biological datasets. Commercially available methods for normalization of NanoString nCounter RNA expression data are suboptimal in fully addressing unwanted technical variation. First, we develop a more comprehensive quality control, normalization, and validation framework for nCounter data, benchmark it against existing normalization methods for nCounter, and show its advantages on four datasets of differing sample sizes. We then develop race-specific and genetic ancestry-adjusted tumor transcriptomic prediction models from germline genetics in the Carolina Breast Cancer Study (CBCS) and study the performance of these models across ancestral groups and molecular subtypes. These models are employed in a transcriptome-wide association study (TWAS) to identify four novel genetic loci associated with breast-cancer specific survival. Next, we extend TWAS to a novel suite of tools, MOSTWAS, to prioritize distal genetic variation in transcriptomic predictive models with two multi-omic approaches that draw from mediation analysis. We empirically show the utility of these extensions in simulation analyses, TCGA breast cancer data, and ROS/MAP brain tissue data. We develop a novel distal-SNPs added-last test, to be used with MOSTWAS models, to prioritize distal loci that give added information, beyond the association in the local locus around a gene. Lastly, we develop DeCompress, a deconvolution method from gene expression from targeted RNA panels such as NanoString, which have a much smaller feature space than traditional RNA expression assays. We propose an ensemble approach that leverages compressed sensing to expand the feature space and validate it on data from the CBCS. We conduct extensive benchmarking of existing deconvolution methods using simulated in-silico experiments, pseudo-targeted panels from published mixing experiments, and data from the CBCS to show the advantage of DeCompress over reference-free methods. We lastly show the utility of in-silico cell-type proportion estimation in outcome prediction and eQTL mapping.Doctor of Philosoph