Academic research community (GECIP)

We are investigating a genetic change that can impact tumour progression and patient survival. Unresectable and metastatic Non-Small Cell Lung Cancers (NSCLC) carrying this genetic aberration have a very poor prognosis. The sponsors of this study are developing a novel therapy for the treatment for this patient population. To this objective, we need to understand if the Genomics England database can support a full Real World Evidence study which will be used to contextualise the results of the clinical trial.

My Personal Therapeutics (MPT) offers the Personal Discovery Process (PDP) technology developed at Mount Sinai Medical Center (NY). PDP is a unique methodology to develop ultra-personalised cancer drug therapies, based on patient’s tumour genomes. The discovery platform is based on that real-world patient tumours are made refractory or resistant to current treatments by variants in multiple genes, including genes not previously associated with cancer. Pentavere is one of Canada’s fastest-growing healthcare technology companies, combining Artificial Intelligence (AI) technologies with clinical expertise to harness the full potential of real-world data at high speed and accuracy. By using AI we aim to dramatically reduce the cost and time to determine the optimum therapy when first-line treatments fail. In this project Pentavere will use its AI engine DARWEN™ to support MPT to analyse complex tumour genomic information, patient characteristics and associated outcomes information from Genomics England lung cancer patient’s database. Once cancer driver genetic events are identified MPT will then create fruit flies (Drosophila) carrying a tumour genetically similar to that of the patient. These fly avatars will be used for high-throughput drug screening. Thus, we will identify drug treatments fully tailored to each patient, with specific tumour genetic constitution

The search for the cancer drivers has mostly focused on protein-coding part of genes,which from only a tiny part of the genome which mostly consists on non coding DNA. We aim at discovery of the noncoding drivers in cancer, i.e. located in UTR, promoter, splice sites and intronic regions, alterations in which may lead to cancer promoting regulatory changes. We will apply a wide variety of statistical methods to the data in order to provide a reliable predictions. The unprecedented size of dataset that is available through the NGRL allows us to achieve previously inaccessible statistical power. As lung cancer is a second leading cause of death, we are particularly interested in studying it, at both subtype level (i.e. adenocarcinoma and squamous cell carcinoma) and as a whole. Our research will involve all cancer participants diagnosed with primary lung cancer.

variants in KRAS play an active role in causing cancer. KRAS is positioned at the intersection of several key cellular pathways and therefore targeting variants in KRAS has proved difficult. We aim to understand more about the KRAS variants themselves as well as the associated changes in the wider cellular pathways that accompany KRAS variants. The hope is that by understanding these complex relationships between KRAS and the pathways it is involved in, we can design better therapies to then target KRAS-mutant cancers.

The 100,000 Genomes Project enables researchers and clinicians to study the DNA sequence of cancers. This cancer genome harbours scars from the mutational processes acting on it called mutational signatures. These distinctive fingerprints can distinguish a cancer caused by smoking from one that is not. We hope to use this data to understand why some ex-smokers go on to develop lung tumours from smoking, and why some develop tumours with seemingly no link to their smoking history. We hope to do this by using the clinical data collected on these patients including how many cigarettes were smoked, how long for and at what age did they stop smoking. The will help us understand more about how lung tumours emerge which will improve our diagnosis and prevention of these tumours.

Immunotherapy is a breakthrough emerging treatment for many different cancer types. Despite promising initial clinical results much work needs to be done to understand which patients will receive the most benefit from these new lines of treatments and how to most efficiently personalise treatment to each patient. We will use whole genomic sequencing (WGS) to examine all regions of the genome for variants that could be recognised by the immune system, and get more accurate measure for the total number of variants in a patient. Additionally, we will combine this analysis with a detailed study on the tumour microenvironment and particularly focus on the subset of patients within the NGRL that receive immunotherapy.

Extrachromosomal DNA is a circular structure of DNA that is present in a cancer cell but not found in chromosomes. This structure can help amplify genes which promote tumour aggressiveness. As the tumour grows, this pattern can become more extreme, contributing to tumour progression and the development of treatment resistance. We plan to use computational methods to identify which lung cancers contain extrachromosomal DNA and whether its presence is related to a host of clinical factors including relapse rates, treatment resistance and survival outcomes.

Lung cancer is the 2nd most common cancer, and the leading cause of cancer deaths. Non-small cell lung cancer (NSCLC) accounts for approximately 85% of lung cancer cases, and prognosis for these patients has historically been very poor. Recently, testing of tumour samples has identified numerous gene alterations among patients with NSCLC. Recent evidence suggests a link between the extent of these gene variants within the tumour (called tumour mutational burden) and the efficacy of certain types of cancer treatments. In this study, we will identify patients who have been diagnosed with NSCLC and included in the GeL database. We will then extract associated hospital records and anti-cancer treatment records for these patients. The study will use “follow” the patients’ journey from diagnosis until the end of the study and examine any potential links between the tumour mutational burden and outcomes in these patients. Findings from this study could potentially demonstrate how genomic data could be used in conjunction with routinely collected clinical data to identify how tumour mutation burden is associated with outcomes and support the development of targeted anti-cancer treatment for patients with NSCLC.

Immunotherapy has emerged as a breakthrough form of new cancer treatment, leading to extended survival in a considerable number of patients. However in some patients immunotherapy is not effective, and other treatment options may be more beneficial. Predictive tests are urgently needed to identify which patients will and won’t benefit from immunotherapy treatment, and in this project we will investigate whole genome sequencing data as a potential tool to make these predictions.

Whilst our understanding of lung cancer has improved over recent years, a large number of diagnosed patients die from the disease. Analysis of the broad range of genetic variants that occur within patient tumours may help identify new treatment strategies, or optimise existing ones.

Lung cancer has the highest death rate among cancer types in the world and non-small-cell lung cancer (NSCLC) is its most common type. Previous studies have already uncovered the complex genomic landscape of this disease, but the clinical importance of intratumor heterogeneity and the understanding of cancer genome evolution has not been characterised. Analysis of mutation and copy number signatures in NSCLC patients should reveal biological processes that are active during tumor development. This will hopefully lead to a better understanding of the evolution of cancer and to new treatment strategies.

The analysis of multi-region whole genome sequencing data has the potential to provide important insights in the evolution of cancer. Whole genome sequencing (WGS) allows the examination of variants and copy number alterations in coding and non-coding regions of the genome. Multi-region sequencing enables the potential to determine the clonal nature of driver events and evolutionary processes. A better understanding of the evolution of cancer will hopefully result in new treatment strategies.

Cancer has a significant global impact remaining a leading cause of mortality and morbidity. Historic management of many cancer subtypes included surgery with the consideration of follow-up chemotherapy for early stage cancers or palliative chemotherapy or radiotherapy for advanced stage disease. However, advances in targeted therapy over the past decade focusing on the molecular and genetic contribution to cancer have changed the way multiple cancers are treated. RAS is the most commonly mutated cancer causing gene, yet despite this there are no direct therapeutic inhibitors of RAS used in the clinic beyond the recently approved KRAS inhibitor sotorasib. This drug works by blocking the action of KRAS, the most common subtype of RAS, in its inactive state. Active KRAS transitions to its inactive state by the aid of a gene called NF1, we therefore hypothesise that certain subgroups of KRAS this may preferentially co-mutate with NF1 to reduce the amount of inactive KRAS, limit the effectiveness of drug and be overall advantageous to cancer development.

This project aims to develop our understanding of RAS and NF1 mutant cancer by looking deeper at this co-mutational relationship as well as deeper exploration of the differential genomic context of RAS and NF1 mutant disease. This ultimately aims to provide insights into molecular stratification, drug vulnerabilities and patient selection for clinical trials, providing insights into developing therapies for this cancer subgroup of unmet need.

Lung and breast cancer are two of the most common cancer types world-wide with a very high death rate. Previous studies have already uncovered the complex genomic landscape of this disease, but little is known about the epigenetic landscape and how those two interact with each other. Analysis of differences in the mutation and copy number distribution relative to replication timing changes between normal and cancer could reveal new gene targets and new biological processes that are active during tumour development. It could also allow us to investigate replication timing as a potential biomarker.

I will test whether millions of inherited genetic markers across the genome are associated with poor outcomes in lung cancer patients (also known as a genome-wide association study). The results of this analysis will be combined with findings from other genetic studies of lung cancer survival. This will increase the chance of finding regions of the genome that truly influence patient outcomes in lung cancer, which could help identify new treatment targets.

Cancer is an evolutionary disease that changes and adapts over time. Cancer cells that are more aggressive/resistant to treatment that develop within tumours can survive and reproduce more easily than the original cancer. High levels of genetic diversity in tumours drives this evolution and is linked to poor patient prognosis and the development of drug resistance, but this diversity also leads to unique vulnerabilities in cancer cells that we can target with drugs.

In this project we will use the tumour sequencing data in the Genomics England dataset to understand what kinds of DNA sequences evolve repeatedly in different tumours from different patients and consistently drive drug resistance. Once we have identified these sequences we can then look in databases of known cancer vulnerabilities to find ways to target them and develop new drugs that prevent drug resistance developing.

The NHS Long-Term Plan for cancer aims to have 55,000 more people each year survive their cancer for five years or more, so the development of therapies that specifically tackle cancer evolution and drug resistance are urgently required.