Machine Learning Methods for Cancer Immunology

Tumours are highly heterogeneous collections of tissues characterised by a repertoire of heavily mutated and rapidly proliferating cells. Evading immune destruction is a fundamental hallmark of cancer, and elucidating the contextual basis of tumour-infiltrating leukocytes is pivotal for improving immunotherapy initiatives. However, progress in this domain is hindered by an incomplete characterisation of the regulatory mechanisms involved in cancer immunity. Addressing this challenge, this thesis is formulated around a fundamental line of inquiry: how do we quantitatively describe the immune system with respect to tumour heterogeneity? Describing the molecular interactions between cancer cells and the immune system is a fundamental goal of cancer immunology. The first part of this thesis describes a three-stage association study to address this challenge in pancreatic ductal adenocarcinoma (PDAC). Firstly, network-based approaches are used to characterise PDAC on the basis of transcription factor regulators of an oncogenic KRAS signature. Next, gene expression tools are used to resolve the leukocyte subset mixing proportions, stromal contamination, immune checkpoint expression and immune pathway dysregulation from the data. Finally, partial correlations are used to characterise immune features in terms of KRAS master regulator activity. The results are compared across two independent cohorts for consistency. Moving beyond associations, the second part of the dissertation introduces a causal modelling approach to infer directed interactions between signaling pathway activity and immune agency. This is achieved by anchoring the analysis on somatic genomic changes. In particular, copy number profiles, transcriptomic data, image data and a protein-protein interaction network are integrated using graphical modelling approaches to infer directed relationships. Generated models are compared between independent cohorts and orthogonal datasets to evaluate consistency. Finally, proposed mechanisms are cross-referenced against literature examples to test for legitimacy. In summary, this dissertation provides methodological contributions, at the levels of associative and causal inference, for inferring the contextual basis for tumour-specific immune agency.This PhD was supported by the Cancer Research UK and Engineering and Physical Sciences Research Council Imaging Centre in Cambridge and Manchester (C197/A16465

Differences in survival and mutational burden between subtypes.

<p><b>A.</b> Kaplan–Meier survival curves of the different tumour subgroups using the ICGC cohort. Numbers of subjects at risk at the start of each time interval are shown above the <i>x</i>-axis. The groups overall showed significant survival differences (logrank <i>p</i>-value = 1.8e–4). More specifically, Hedeghog/Wnt group HR = 1.73, 95% CI 1.1 to 2.72, coxPH test <i>p</i>-value = 0.018; Notch group HR = 0.62, 95% CI 0.42 to 0.93, coxPH test <i>p</i>-value = 0.019; when compared to the cell cycle group and after correcting for gender, age, and tumour stage. <b>B.</b> Kaplan–Meier survival curves of the different tumour subgroups using the TCGA cohort for subsets of individuals that did or did not receive adjuvant targeted therapy treatment. Numbers of subjects at risk at the start of each time interval are shown above the <i>x</i>-axis. Hedeghog/Wnt group HR = 4.12, 95% CI 1.2 to 13.8, coxPH test <i>p</i>-value = 0.02; when compared to the cell cycle group and after correcting for gender, age, tumour stage, and radiation therapy indicator. <b>C.</b> Mutations in key genes and pathways in pancreatic cancer. The upper and middle panels show the frequency of altered samples by copy number changes (gains refer to amplifications of >5 copies); the bottom panel shows the frequency of altered samples by nonsilent single nucleotide variants, small insertions, or deletions with moderate-to-high biological effect.</p

Subtypes show different immune activity.

<p><b>A.</b> Bar plot showing the Pearson partial correlation t-statistic difference between Hedgehog and Notch for ssGSEA pathway enrichment scores significantly associated with one subtype or the other. <b>B.</b> Boxplots from the ESTIMATE analysis showing the variation in the stromal and immune content between the Hedgehog, Notch, and cell cycle subgroups for both the ICGC and TCGA cohorts. <b>C.</b> Bar plot showing the difference in Pearson partial correlation coefficient difference between Hedgehog and Notch for estimated CIBERSORT leukocyte cell fractions significantly associated with one subtype or the other. A negative difference highlights strong association with Hedgehog, whereas a positive value indicates a strong association with Notch.</p

Clustering of master regulators into different functional groups.

<p>Heat maps showing the similarity between the samples in the <b>A</b>. ICGC and <b>B</b>. TCGA cohorts as measured by “signature distance” between the MRs activity profiles [<a href="http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1002223#pmed.1002223.ref027" target="_blank">27</a>]. Unsupervised analysis identified three classes of tumours with differential activities of the three identified disease processes: cell cycle (pink), Hedgehog/Wnt (blue), and Notch (green).</p

Oncogenic <i>KRAS</i> is regulated by three groups of master regulators.

<p><b>A.</b> Volcano plot showing the magnitude of the differential gene expression between murine mock ductal cells and murine cre ductal cells (with activated oncogenic <i>Kras</i>). Each dot represents one probe with detectable expression in both conditions. The coloured dots mark the threshold (<i>p</i> < 0.05 and log2 fold-change > 1) for defining a gene as differentially expressed. <b>B.</b> Ras GTPase assay shows increased GTPase activity in cre cells blotted with pan-Ras antibody (M, mock; c, cre). <b>C.</b> Visual representation of master regulators (MRs) identified with msVIPER analysis (<i>p</i> < 0.01). The nodes in the networks represent the 55 master regulators (large dots) and the corresponding inferred targets (smaller dots). The edges in the network represent the regulatory relationship between regulators and the inferred targets. The colours highlight the community structure of the network identified via greedy optimization of modularity. The three groups of nodes correspond to a total of 27 master regulators and represent three distinct disease processes enriched for cell cycle (pink), Hedgehog/Wnt signalling (blue), and Notch signalling (green) pathways. <b>D.</b> For the 27 MRs in the three core processes, the heat map shows their activity (first column) and differential expression in the <i>KRAS</i> signature (second column) as obtained by Virtual Inference of Protein-activity by Enriched Regulon (VIPER) analysis. “Expression” refers to the differential expression value after <i>KRAS</i> induction (cell line experiment). The colour code of in the heat map corresponds to the t-statistic value obtained after limma differential expression analysis, with blue representing down-regulated genes and red representing up-regulated genes after <i>KRAS</i> activation. “Activity” refers to the differential protein activity value after <i>KRAS</i> induction with red or blue representing activation or inactivation, respectively. The protein activity score is quantitatively inferred by the aREA algorithm in VIPER by systematically analysing expression of genes coexpressed with the transcription factor (TF).</p

Patterns of Immune Infiltration in Breast Cancer and Their Clinical Implications: A Gene-Expression-Based Retrospective Study

<div><p>Background</p><p>Immune infiltration of breast tumours is associated with clinical outcome. However, past work has not accounted for the diversity of functionally distinct cell types that make up the immune response. The aim of this study was to determine whether differences in the cellular composition of the immune infiltrate in breast tumours influence survival and treatment response, and whether these effects differ by molecular subtype.</p><p>Methods and Findings</p><p>We applied an established computational approach (CIBERSORT) to bulk gene expression profiles of almost 11,000 tumours to infer the proportions of 22 subsets of immune cells. We investigated associations between each cell type and survival and response to chemotherapy, modelling cellular proportions as quartiles. We found that tumours with little or no immune infiltration were associated with different survival patterns according to oestrogen receptor (ER) status. In ER-negative disease, tumours lacking immune infiltration were associated with the poorest prognosis, whereas in ER-positive disease, they were associated with intermediate prognosis. Of the cell subsets investigated, T regulatory cells and M0 and M2 macrophages emerged as the most strongly associated with poor outcome, regardless of ER status. Among ER-negative tumours, CD8+ T cells (hazard ratio [HR] = 0.89, 95% CI 0.80–0.98; <i>p</i> = 0.02) and activated memory T cells (HR 0.88, 95% CI 0.80–0.97; <i>p</i> = 0.01) were associated with favourable outcome. T follicular helper cells (odds ratio [OR] = 1.34, 95% CI 1.14–1.57; <i>p</i> < 0.001) and memory B cells (OR = 1.18, 95% CI 1.0–1.39; <i>p</i> = 0.04) were associated with pathological complete response to neoadjuvant chemotherapy in ER-negative disease, suggesting a role for humoral immunity in mediating response to cytotoxic therapy. Unsupervised clustering analysis using immune cell proportions revealed eight subgroups of tumours, largely defined by the balance between M0, M1, and M2 macrophages, with distinct survival patterns by ER status and associations with patient age at diagnosis. The main limitations of this study are the use of diverse platforms for measuring gene expression, including some not previously used with CIBERSORT, and the combined analysis of different forms of follow-up across studies.</p><p>Conclusions</p><p>Large differences in the cellular composition of the immune infiltrate in breast tumours appear to exist, and these differences are likely to be important determinants of both prognosis and response to treatment. In particular, macrophages emerge as a possible target for novel therapies. Detailed analysis of the cellular immune response in tumours has the potential to enhance clinical prediction and to identify candidates for immunotherapy.</p></div

Survival plots highlighting the patient subgroup with tumours containing little or no immune infiltration by CIBERSORT <i>p</i>-value.

<p>Depicted <i>p</i>-values are from log-rank tests. ER, oestrogen receptor; T-regs, T regulatory cells.</p

Hierarchical clustering of all samples based on immune cell proportions.

<p>Stacked bar charts of samples ordered by cluster assignment. NK cells, natural killer cells.</p

Survival plots by cluster separately for ER-positive and ER-negative disease.

<p>Depicted <i>p</i>-values are from log-rank tests. ER, oestrogen receptor.</p