Sir Peter MacCallum Department of Oncology - Theses
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ItemPan-cancer reconstruction of clonal evolution in 1,800 patients using the discrete time-branching process( 2020)Intra and inter-tumour heterogeneity poses a challenge for associating molecular and immunohistochemical markers with clinical outcomes. Sequencing technologies has enabled detailed assessment of tumour heterogeneity, facilitating the genomic characterisation of tumours. Whilst such technologies have revealed mutational landscapes and have identified key driver alterations for tumorigenesis, pan-cancer clonal evolution reconstructions are lacking. In order to bridge this gap, I used discrete-time branching models to derive biological insights into tumour progression and reconstructed the clonal evolution in 1,800 patients, successfully linking mutations with growth patterns of disease progression. I first modified a discrete time-branching process to account for individual clonal subpopulations and derived analytical solutions for expectation and variance of both clonal and tumour expansions. Additionally, I derived the expected time for any given clone to successfully expand as \hat{\tau}, and with the use of these analytical solutions, I showed the likely driver and clonal compositions of the tumours and their phylogenies. Secondly, I generated a database of results from four different versions of time-branching process models that covered multiple parameters. Here I identified how an increase in diversity arises by both increased mutation rate and reduced fitness. I further corroborated that total number of drug resistant cells is directly proportional to lineage extinction probability (\delta) and tumour size as shown in previous studies. I also showed that this effect can be extrapolated to other types of functional passenger mutations involved in cancer-specific mortality. Moreover, I showed how commonly used sequencing cut-offs limit the accurate inference of tumour’s average selective advantage and driver mutation rate. Thirdly, I identified that a minimum distance metric can provide accurate fits of simulated cancer cell fractions to real patient tumour data. This metric showed at least 80% accuracy to identify the initial parameters of s and u and at least 40% accuracy to recover the correct evolutionary trajectory. Fourthly, I applied this fitting procedure to reconstruct the evolutionary trajectories of 1,800 tumours from different cancer sequencing studies. The best fits derived suggests that the most likely parameters for the evolution of solid tumours are high driver mutation rates and weak driver effects of fitness. Fifthly, using The Cancer Genome Atlas cohort, I identified an association between predicted degree of clonality and survival, and found branched topologies are common in malignancies with adverse prognosis. In the TRACERx non-small-cell lung cancer cohort, I identified that clonal reconstructions agreed with previously reported phylogenies. Additionally, using data from the Breast International Group 1-98, I identified the role of tumour fitness in determining clinical outcome, and the evolutionary dynamics of TP53 and PIK3CA mutations conducive to distant metastasis. Finally, using data from a metastatic melanoma patient collected through the CASCADE melanoma study, I was able to propose a pattern of dissemination from the primary to metastatic sites in the liver and brain based on the phylogenies recovered from my data-fitting procedure. This study demonstrates the power of the discrete-time branching process in reconstructing tumour evolution, and its potential to uncover insights in the dynamics of tumour growth that are missed by current methods.
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ItemCirculating tumour DNA analysis for personalised care in breast cancer( 2020)Phenotypic diversity of breast cancers poses insurmountable challenges in the treatment of this lethal disease. Recent advances in next generation sequencing have led to unprecedented insight into the genomic landscape underlying breast tumours. This has resulted in burgeoning development of targeted treatments and predictive biomarkers, several of which have recently demonstrated clinical activity. However, key challenges hinder optimal application. On the background of extensive molecular heterogeneity, most biomarkers represent minority patient subpopulations, hampering clinical development. Furthermore, considerable genomic evolution of breast tumours impacts accuracy of genomic characterisation that is thus far heavily reliant on the sequencing of non-contemporaneous and invasive tumour tissue biopsies. Finally, stratification to genomically-matched targeted therapies also fails to fulfil the extent of its promise. In many cases relentless tumour growth remains unperturbed, while in others resistance ultimately develops. Crucially, molecular mechanisms underlying resistance remain poorly understood, while follow-on treatment options are often poorly defined. Central to the promise of personalised medicine is the robust and accurate characterisation of the tumour genome. Minimal invasiveness and convenience of circulating tumour DNA (ctDNA) analysis, with ability to detect tumour genomic aberrations from a blood draw, highly recommends this approach. Recent technological advances have paved the way to a range of clinical applications, with evolving potential for ctDNA analysis to address the continuum of challenges posed to precision medicine throughout patient management. Toward this end, extensive clinical development is required, while prevailing technological hurdles need to be addressed. This thesis explores a multi-faceted and rigorous approach towards the integration of ctDNA analysis in the management of breast cancer patients. Firstly, the development and validation of multiple assays (allele-specific and NGS-based) tailored to breast cancers, enabled comprehensive genomic analysis with in-built flexibility to be readily applicable to a variety of clinical scenarios. Subsequent establishment of a prospective ctDNA-based molecular profiling program across a large cohort of metastatic breast cancer (mBC) patients demonstrated feasibility of real-time analysis in the clinical setting across a range of genomic targets of variable abundance. Importantly, integration of longitudinal testing in this program throughout patient management demonstrated capacity for ctDNA analysis to reflect genomic evolution in real-time to optimise precision-guided patient management. Finally, exploratory longitudinal ctDNA analysis for the study of resistance mechanisms to CDK4/6i, constituting a novel class of targeted compounds for breast cancer, highlights established and novel resistance markers. Indeed, this study also serves to demonstrate a workable framework for ctDNA analysis as a highly effective approach for the de novo elucidation of resistance mechanisms to novel targeted agents that is relevant across cancer types.
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ItemAutomated discovery of interacting genomic events that impact cancer survival by using data mining and machine learning techniques( 2020)Rapid advancement in genomic technologies has driven down the cost of sequencing significantly. This efficiency has enabled large-scale cancer genomic studies to be conducted, generating a vast amount of data across different levels of omics variables. However, the tasks to extract new knowledge and information from this enormous volume of data present unique challenges. These analyses often require the application of specialised techniques for data mining, integration and interpretation to provide valuable insights. With the rise of machine learning adoption in recent decades, many advanced computational algorithms based on artificial intelligence techniques have also been proposed to analyse these genomics data. Although some of these applications have led to clinically relevant conclusions, many others are still relying on incomplete prior knowledge, or limited to only a selected number of features. These limitations raise the general question about the broader applicability of machine learning in the field of cancer genomics. This research addresses this question by assessing the application of machine learning techniques in the context of breast cancer genomics data. This assessment includes a comprehensive evaluation of computational methods for predicting cancer driver genes and the development of a novel deep learning approach for identifying breast cancer subtypes. The evaluation result of driver gene prediction algorithms suggests that the selection of the best method to be applied to a dataset will primarily be driven by the objectives of the study and the characteristics of the dataset. All of the evaluated approaches could identify well- studied genes, but not all of them performed as well on smaller datasets, subtype-specific cohorts, and in discovering novel genes. To examine the benefit of a more complex machine learning model, this thesis also presents a novel deep learning approach that integrates multi-omics data for predicting various breast cancer’ biomarkers and molecular subtypes. This method combines a semi-supervised autoencoder for dimensionality reduction, and a supervised multitask learning setup for the classifications. Taking an input of gene expression, somatic point mutation and copy number data, the algorithm predicts the ER-Status, HER2-Status and molecular subtypes of breast cancer samples. Further survival analysis of the outputs from this deep learning approach indicates that the predicted subtypes show a stronger correlation with patient prognosis compared to the original PAM50 label. While the outputs from machine learning algorithms still require further validation, the adoption of these complex computational methods in cancer genomics will become increasingly common. Collectively, the results from this thesis suggest that the machine learning analysis of ‘omics data hold great potential in automating the discovery of clinically- relevant molecular features.
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ItemDeveloping new therapies to treat advanced prostate cancer( 2020)Prostate cancer (PCa) is one of the most diagnosed tumours in Australia, pertaining to more than 30% of all cancer detection (AIHW 2020). Most of these diagnoses are correlated to men with early, localised stages of the disease that are easily treated with radical proctectomy surgery and/or radiation. One third of these patients will relapse with their disease progressing to the either the recurrent localised stage or the metastatic stage. These advanced cases will have to undergo Androgen Deprivation Therapy (ADT), as hormone dependence is a key driver of prostate tumorigenesis. ADT will help increase patient survival by halting cancer progression, while minimising symptoms. Although this type of therapy can initially alleviate tumour burden, patients will inevitably be subjected to cancer recurrence where their tumours continue to progress despite castrate levels of androgens. An observed second-order effect of ADT is that it creates a therapeutic selection pressure within PCa, leading to the proliferation and establishment of cells that are resistant to androgen ablation. These hormone-refractory cells lead to the development of Castration Resistant Prostate Cancer (CRPC), a stage of the disease that ultimately leads to death. Despite the development of second-generation ADT or chemotherapeutic agents, no effective therapies have been developed yet to combat this lethal stage of the disease. Therefore, there is a need for therapies that can target pathways and mechanisms that are not related to androgen signalling. MYC overexpression is one of the most frequently characterised genomic aberrations within men with CRPC. MYC mRNA overexpression is found in 30% of men with localised disease and in 85% of men with CRPC. Although MYC seems like a prime therapeutic target to combat PCa, previous attempts at developing MYC inhibitors have failed due to major off-target effects making these therapies dangerous. The overexpression of MYC is tightly correlated with increased ribosome biogenesis because MYC-driven tumours have an inherent need for protein synthesis to sustain infinite cell proliferation. The rate limiting step of ribosome biogenesis is the transcription of a ribosome RNA precursor by RNA polymerase I within the nucleolus. MYC orchestrates the over-transcription of this precursor by tightly interacting with Pol I associated components. PIM kinases and the PI3K pathways also tightly interact with MYC in order to increase its stability and prevent its degradation. It can be inferred from this that MYC-driven tumours are highly proliferative, lack any type of cell cycle regulation, and are constitutively producing proteins to sustain their uncontrolled growth. Administration of CX-5461, an RNA Pol I inhibitor, was followed by potent anti-proliferative activity and cell cycle arrest within various in vitro and in vivo models of prostate cancer. Strikingly, the combination of Pol I inhibition along with CX-6258, a PIM kinase inhibitor, was characterised by potent reduction in invasive lesions within two mouse models of prostate cancer. Although both drugs combined seemed to have potential additive effect, little is understood about the mechanism of action of these two inhibitors together. Therefore, one of the main aims of this thesis is to understand the efficacy of this combination. Both drugs will be tested on different subtypes of human prostate cancer in the explant settings in order to correlate patient-drug response with specific genomic or pathological profiles. A further goal for this project will be to utilize mass spectrometry to characterise the main phosphorylation events that occur as a result of the administration of these inhibitors, both alone and in combination. This will further the understanding of the mechanistic action of both drugs combined. Due to the limited efficacy of CX-5461 as a single agent, PMR-116, a novel Pol I inhibitor, will also be tested within different models of prostate cancer to evaluate as a potential anti-cancer drug. The final aim of this thesis will be to combine CX-5461 with targeted 177Lu-PSMA-617 therapy, as the latter has recently been shown to potentiate radiation-induced DNA damage. Through the various aims of this thesis, we hope to find and characterise the efficacy of RNA polymerase I therapies on prostate cancer both as single agents or combined therapies.
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ItemInvestigating the metabolic consequences resulting from the oncogenic activation of the PI3K-AKT pathway( 2020)The phosphoinositide 3-kinase (PI3K) pathway is one of the most commonly activated pathways in a variety of cancers and it has recently been highlighted as one of the primary modulators of cell metabolism. This has opened promises and challenges for the development of therapeutic strategies to target metabolism in cancer cells harbouring mutations in the PI3K pathway. Through an inducible “exon switch” approach, our laboratory has previously generated mice ubiquitously expressing a mutation in Pik3ca, the gene coding for the subunit p110alpha of PI3K. By using this mouse model (UbCreERPik3caH1047R) our laboratory has shown that mutations in the PI3K pathway lead to dramatic severe defects in glucose homeostasis resulting in hypoglycaemia and hypoinsulinemia. In this thesis the causes responsible for the metabolic dysfunction observed in these mutant mice are investigated. This thesis provides evidence that mutations in the PI3K pathway lead to increased glucose uptake by the tissues, inhibition of hepatic gluconeogenesis and inhibition of insulin release from the pancreas. Previous studies performed on the UbCreERPik3caH1047R mouse model have also shown increased body weight and organomegaly. This thesis demonstrates that the increase in body weight, resulting from the activation of the Pik3caH1047R mutation was not associated with adiposity. On the contrary, mutations in the PI3K pathway determine loss of body fat and increased lipolysis in the adipose tissue of mice, whilst the tissue growth is associated to hypertrophy or hyperplasia. Furthermore, oncogenic activation of the Pik3caH1047R mutation in vivo leads to alteration of the respiratory exchange rate and energy expenditure of the mice and stimulates browning of the adipose tissue. This thesis also shows that activation of the PI3K pathway alters the expression of genes and proteins involved in metabolic pathways, and that these alterations are organ-specific, therefore opening promises for customising treatments to individual patients.
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ItemImaging and blood biomarkers: towards personalized medicine in head and neck cancer( 2020)Head and neck cancer is the 6th most common malignancy, accounting for approximately 4% of malignancies, and 1 – 2% of cancer-related deaths. Radiation therapy utilises high energy radiation to kill cancer cells. In head and neck cancer, radiotherapy is one of the main treatment modalities, particularly in curative-intent treatments. Despite advancements in imaging and radiation treatment planning and delivery, the prescribed dose and radiation treatment workflow remained unchanged and is largely ‘one size fits all’. Similarly, the survivorship program for patients with head and neck cancer is ‘one size fits all’, often one standard institutional follow up schedule for all patients treated for head and neck cancer, regardless of expected risk of treatment-related late toxicities, patients’ subsequent risk of recurrence and second malignancy. This thesis focuses on the value and efficacy of imaging and blood biomarkers in improving treatment personalisation in patients with head and neck cancer. In chapters 1 and 2, I explored the use of imaging and blood markers in the pre-, during, and post-radiotherapy settings to further improve risk stratification. In chapter 1, I investigated the potential use of readily available and ‘cheap’ blood biomarkers (neutrophil and lymphocyte counts) as predictors of subsequent outcomes in a large cohort of patients with oropharyngeal cancer. In chapter 2, I designed and conducted a prospective observational study to systematically characterize the kinetics of gross tumour volume and apparent diffusion coefficient (ADC) changes observed in magnetic resonance imaging (MRI) and circulating tumour cells (CTCs) counts during radiotherapy in patients with head and neck squamous cell cancer. In the survivorship period (Chapter 3), I evaluated the effectiveness of current surveillance program and investigated the potential use of PET imaging and alternative imaging frequencies to improve the cost-effectiveness of the survivorship program. In this chapter, I found that 70% of disease recurrence occur within 2 years and the probability of a surveillance imaging detecting a recurrence in an asymptomatic patient with no adverse clinical finding is very low. Furthermore, in patients with human papillomavirus (HPV)-related oropharyngeal cancer, achieving a complete response on post-treatment PET imaging has a negative predictive value of any subsequent recurrence of 92%, so the yield of surveillance imaging is very low in this group. Using a partially observed Markov decision model, a potentially effective surveillance program with less frequent imaging was propositioned in this chapter. Finally, in chapter 4, I assessed the potential use of re-irradiation in the era of modern imaging and new radiation treatment techniques including intensity-modulated radiotherapy (IMRT), proton therapy and stereotactic body radiotherapy (SBRT). I showed the value of different imaging modality (dual energy CT and MRI) in target delineation in patients who had previous radiation. In addition, I demonstrated that the local control rate for each treatment technique is similar. Although wide field radiotherapy (IMRT and proton therapy) had improve disease-specific survival, treatment with these techniques are longer (typically 6 to 7 weeks) and had higher toxicity rates than SBRT (delivered over 5 treatments).
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ItemIdentification and Characterisation of the Cellular and Molecular Changes in Intestinal Metaplasia Associated with Progression to Gastric Cancer( 2020)Gastric cancer (GC) patients are mainly asymptomatic and present at an advanced stage with a 5-year survival rate of only 20-30% in most countries. It is crucial to gain greater insight into the process of gastric carcinogenesis in order to develop tools that will allow improved patient stratification and targeted surveillance. The relationship between intestinal metaplasia (IM), a key premalignant lesion in gastric carcinogenesis, and the diffuse type of gastric cancer (DGC) was investigated using data mining of GC patient pathology reports and existing gene expression data. IM and DGC were shown to be associated both histologically and molecularly in a proportion of cases suggesting that IM might be a precursor lesion to a subset of DGC cases. Complete and incomplete IM subtypes, the latter being associated with a greater risk of progression, were characterised at the molecular level using gene expression data acquired from macro-dissected IM tissue. Complete IM was associated with genes highly expressed in the small intestine whereas incomplete IM was associated with genes expressed both in the colon and in GC suggesting it is molecularly closer to a state of malignancy. Using OPAL multiplex immunohistochemistry, the macrophage and T cell landscape in IM was investigated. In addition to traditional “M1-like” (IRF8) and “M2-like” (CD163, CD206) macrophage populations, a novel hybrid subgroup “M1/2” containing macrophages expressing both M1 and M2 markers in different combinations was identified. Overall, complete IM was characterised by M2 macrophages and greater levels of T cell infiltration whereas incomplete IM was characterised by M1/2 macrophages and fewer CD8 and double negative (DN) T cells. Spatial cell analysis showed significantly fewer CD8 and DN T cells in the vicinity of incomplete IM epithelial cells suggesting reduced immune-surveillance may play a key role in progression to dysplasia. IM subtyping is affected by intra-observer and inter-observer variation. To address this, the potential of CD10 and Das1 as biomarkers for subtyping complete and incomplete IM was investigated. Overall CD10 was shown to be an outstanding biomarker for complete IM and Das1 was shown to have potential as an additional risk associated biomarker. GC animal models are associated with long duration and high cost. Optimised protocols for growing and characterising gastrointestinal organoids from gastroscopy biopsies were developed. IM organoid cultures were successfully established and characterised. Additionally a human gastrointestinal organoid biobank was created. In conclusion this thesis offers potential evidence of IM as a precursor lesion to DGC, characterises the molecular differences between complete and incomplete IM and shows key differences in the innate and adaptive immune system between IM subtypes and how these may play a role in progression to GC. It also identifies two biomarkers with potential clinical utility for subtyping IM and describes the methodology for growing IM organoids with future potential as a model for studying gastric carcinogenesis. Finally future studies are required to gain further insight into the cellular and molecular evolution of gastric carcinogenesis which should lead to better management of patients with premalignant lesions and ultimately to the prevention of GC.
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ItemCDK12 and CDK13 cooperatively regulate RNA polymerase II elongation and alternative polyadenylation of mRNA( 2020)Transcription driven by RNA Polymerase II (POLII) is a multi-step process that is strictly regulated by Cyclin dependent kinases (CDKs) at multiple checkpoints. Compared to the regulation of transcription initiation and pause release, the roles of CDKs in regulating transcription elongation remain poorly defined. To investigate the individual and shared roles of CDK12 and CDK13 in transcription regulation, a set of cell lines containing analog-sensitive variants of CDK12 and CDK13 were constructed using CRISPR-Cas9 homology-directed repair (HDR) technology. Multiple Next-Generation Sequencing (NGS) based assays including RNA-Seq, ChIP-Seq, PRO-Seq, TT-Seq were utilised to provide a comprehensive characterisation of the consequences of acute inhibition of CDK12, CDK13 or both kinases using these analog sensitive CDK12 and CDK13 cell lines. Selective inhibition of CDK12 or CDK13 led to various molecular responses including selective changes in gene expression, alternations in polyadenylation site usage as well as mild reduction in POLII elongation rate and processivity. In contrast, dual inhibition of CDK12 and CDK13 caused dramatic changes in transcription genome-wide, including the induction of widespread alternative polyadenylation events, reduction in POLII elongation rates and processivity, substantial changes in gene expression, as well as the near complete loss of POLII Ser2 phosphorylation. These observations illustrated that both CDK12 and CDK13 are regulators of POLII transcription elongation. Furthermore, the substantial differences between selective and simultaneous inhibition of CDK12 and CDK13 revealed the redundant and individual roles of CDK12 and CDK13 in maintaining global transcription elongation. To identify substrates of CDK12 and CDK13 that might be responsible for the phenotypes caused by CDK12 and CDK13 inhibition, phospho-proteomic analysis was performed to identify putative CDK12 and CDK13 substrates. The analysis revealed that CDK12 and CDK13 shared multiple substrates and functional redundancy between CDK12 and CDK13 in phosphorylating these substrates was identified. In order to identify the putative substrates that were responsible for the transcriptional changes upon CDK12 and CDK13 inhibition, a novel siRNA screen method “mini-bulk” CEL-Seq2 siRNA screen was developed and utilised. The screen revealed that SF3B1 and SRRM2 could be the potential substrates of CDK12 and CDK13 that were partially responsible for the transcriptional phenotype caused by the dual inhibition of CDK12 and CDK13, as depletion of SF3B1 and SRRM2 led to similar differential gene expression and alternative polyadenylation profiles as CDK12 and CDK13 inhibition. Finally, as the phospho-proteomic analysis also revealed that CDK12 and CDK13 might regulate phosphorylation of multiple translation regulators, the effect of CDK12 and CDK13 inhibition on protein translation was also investigated. Both nascent protein labelling as well as polysome profiling revealed that CDK12 and CDK13 function was required to maintain global translation. In conclusion, this thesis explored the role of CDK12 and CDK13 in POLII driven transcription and protein translation. CDK12 and CDK13 were shown to cooperatively regulate POLII transcription elongation processivity and alternative polyadenylation, potentially through regulating POLII Ser2 phosphorylation and the phosphorylation of other CDK12 and CDK13 substrates.
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ItemHippo pathway target genes and transcription factors in organ growth control( 2020)The Hippo pathway, first discovered in the fruit fly Drosophila melanogaster, is an evolutionarily conserved regulator of organ growth. The Hippo pathway controls organ growth by regulating the nuclear activity of the transcriptional coactivator Yorkie (Yki). Yki binds to sequence-specific transcription factors to regulate target gene expression, including the TEAD/TEF family transcription factor Scalloped (Sd). It was recently discovered that the Hippo pathway controls the growth of organs in Drosophila by balancing transcriptional activation mediated by Yki and Sd, and transcriptional repression mediated by Tondu-domain- containing Growth Inhibitor (Tgi) and Sd. If this fine balance is disrupted and Yki is hyperactivated, organs can grow beyond their normal size. Importantly, deregulation of Hippo signalling drives tumorigenesis in a variety of human cancers. The high degree of conservation of the Hippo pathway throughout the animal kingdom makes the fruit fly a powerful model organism to understand how this pathway regulates tissue growth during development and cancer. The Hippo pathway regulates the expression of genes involved in cell proliferation, survival, stemness, differentiation, and migration. However, we lack a clear understanding of the full transcriptional program that the Hippo pathway regulates in growing tissues beyond a small number of well-studied target genes. In this thesis, I aimed to deepen our understanding of the transcriptional program that the Hippo pathway controls in the growing Drosophila eye. To do this, I addressed three key questions: 1) What are the target genes of Yki, Sd and Tgi? 2) What gene expression changes occur as a result of hyperactive Yki? 3) What additional transcriptional regulators control Hippo pathway target gene expression and eye growth? 1) Using targeted DamID-seq, I identified target genes of Yki, Sd and Tgi in growing Drosophila eye discs. I found a very high degree of overlapping target genes between Yki, Sd, and Tgi, indicating that Sd is the key transcription factor that mediates binding of both Yki and Tgi to their target gene loci. Additionally, I found a strong enrichment of the AP-1 binding motif in Yki, Sd, and Tgi target genes, suggesting that these transcription factors co-regulate many target genes. 2) I performed comprehensive analyses of Yki, Sd, and Tgi target genes, and the changes in gene expression and chromatin accessibility caused by Yki hyperactivation. This highlighted key biological processes and signalling pathways that the Hippo pathway could regulate during eye growth, including: MAPK pathway, glutathione S-transferases (GSTs), cuticle development, extracellular matrix (ECM), and retinal determination and differentiation. 3) Using targeted DamID-seq, I found that the Drosophila AP-1 transcription factor Jra/Jun shares 71% of its target genes with Yki and/or Sd. However, using genetic studies, I showed that AP-1 transcription factors do not regulate physiological Drosophila eye growth, and are not essential regulators of Hippo pathway target gene expression. The results presented in this thesis provide new insights into the target genes and transcriptional program that is regulated by the Hippo pathway during Drosophila eye growth. They have identified previously unexplored biological processes that the Hippo pathway might regulate in the eye (e.g. cuticle development), and reinforced known roles for the Hippo pathway (e.g. cell fate specification). My findings also extend our understanding of the functional links between AP-1 and the Hippo pathway in growing organs. Importantly, the data generated in this thesis will provide a rich resource for researchers interested in studying the direct target genes of both Hippo signalling and AP-1 transcription factors.
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ItemNovel biomarkers for melanoma immunotherapy( 2020)Traditionally, metastatic melanoma had a dismal prognosis, but the recent advent of immune checkpoint inhibitors (ICI) has extended survival from months to years for some patients. There is an urgent need to identify prognostic and predictive biomarkers for melanoma patients treated with ICI, given that only a minority of patients respond, coupled with the potential treatment related toxicities. This thesis aimed to investigate clinical factors, functional PET imaging and tumour immune profiling as candidate biomarkers for ICI in patients with melanoma. Firstly, Chapter 3 focused on baseline performance status as a biomarker for outcome following anti-PD-1. The hypothesis was that unlike cytotoxic chemotherapy, baseline performance status was not correlated with outcome following ICI, owing to its distinct mechanism of action. However, in the cohort of 91 patients treated with anti-PD-1 at Peter MacCallum Cancer Centre, poor performance status was correlated with poor survival and low response rate to anti-PD-1. Furthermore, patients with poor performance status were more likely to be hospitalised and more likely to die in hospital. Patient characteristics and blood parameters were further examined in Chapter 4, but specific to a cohort of patients with melanoma brain metastases. Melanoma commonly metastasise to the central nervous system and this is associated with extremely poor survival. Recently, combination ICI has resulted in intracranial responses and durable survival. Most of the existing literature in biomarkers in melanoma brain metastases also predates the introduction of ICI, therefore investigation of biomarkers in patients with melanoma brain metastases treated with ICI is needed. A post-hoc analysis of patients with melanoma brain metastases as part of the phase II Anti-PD1 Brain Collaboration study was performed to identify possible predictors of clinical outcome or toxicity. In this study, patients were randomised to receive either nivolumab monotherapy or nivolumab in combination with ipilimumab. High C-reactive protein, a marker of systemic inflammation, was correlated with poor survival. Treatment with combination ICI, hypernatraemia and increased body mass index were associated with higher likelihood of severe toxicity at 120 days, whereas CRP was not associated with higher toxicity. The thesis then went on to examine the role of FDG PET functional imaging as a source of biomarkers for outcome following ICI in Chapter 5. Baseline pre-treatment tumoural FDG-PET avidity (measured by SUVmax or metabolic tumour volume) as well as FDG-avidity in the immune system (measured by spleen to liver ratio) were assessed in relation to survival outcomes. Interestingly, tumoural PET avidity was not correlated with survival, whereas high spleen to liver ratio was correlated with poor survival after ipilimumab. This was subsequently validated in a combined cohort of patients from two separate European centres. High spleen to liver ratio was correlated with low albumin in a multivariate analysis, thus suggesting a possible association with systemic inflammation. Early on-treatment PET (EOT PET) were assessed in a small subset of 16 patients, and several challenges were identified that may limit the use of FDG PET in this early juncture as a biomarker for outcome after ICI. In-depth characterisation of tumoural immune landscape is crucial to improving the understanding of melanoma immuno-biology, with potential implications for biomarker development. Chapter 6 aimed to compare the immune profile of UV related skin cancers (melanoma, cutaneous squamous cell carcinoma and Merkel cell carcinoma) using orthogonal methods of bulk RNA-sequencing and multi-spectral immunohistochemistry. The three skin cancers showed distinct immune landscapes, with melanoma having a significantly higher intratumoural T cell infiltrate compared to Merkel cell carcinoma, whereas PD-L1 density was highly variable across three skin cancers. Transcriptomic analyses of melanoma samples with high PD-L1 density were associated with upregulation of genes related to leucocyte proliferation, migration and adaptive immune responses, in contrast to MCC samples with high PD-L1 density, where such a signature was not observed. Lastly, an in-depth case study of six patients highlighted how multi-factorial biomarkers such as clinical factors, functional PET imaging, baseline blood parameters, and multi-spectral immunohistochemistry can be applied together. In conclusion, this thesis evaluated multi-factorial biomarkers including clinical, functional imaging and tumoural immune profiling biomarkers. These studies add to the evolving literature on biomarkers associated with ICI treatment. It is envisaged that with time, these complementary methods of understanding the patient and tumoural immune environment can aid rational selection of immune based therapies for patients with advanced melanoma.