Researchers discover cell communication mechanism that drives cancer adaptation

DPAG and Oncology researchers have uncovered a new mechanism by which cancer cells adapt to the stresses they encounter as they grow and respond to therapies

Prof Anna Schuh wins Vice-Chancellor Innovation Award

Anna and her team wins the Teamwork award for their work on improving the outcome of children with blood diseases in sub-Saharan Africa

Using machine-learning approaches to identify blood cancer types

Myeloproliferative Neoplasms (MPNs) are a group of blood cancers that occur when stem cells in the bone marrow develop mutations that lead to over-production of blood cells – either red blood cells in Polycythaemia Vera (PV), or platelets in Essential Thrombocythaemia (ET). This carries an increased risk of developing blood clots, such as in the legs, lungs, heart attacks or strokes.

In myelofibrosis, the most severe of the MPNs, destructive scarring (‘fibrosis’) of the bone marrow develops, leading to failure of the marrow to produce blood cells and severe symptoms. Patients with all MPNs are at higher risk of developing leukaemia, especially patients with myelofibrosis when this develops in over 1 in every 10 patients.

Unfortunately, we do not yet have any drug treatments that can cure these conditions. Treatments for ET and PV aim to control the blood counts and reduce the risk of blood clots. For myelofibrosis, targeted therapies such as ruxolitinib, a JAK inhibitor, can effectively control symptoms, but this does not alter the natural history of the disease and survival remains less than 5-10 years following diagnosis.

In the vast majority of cases, mutations are found in one of 3 genes – JAK2, CALR or MPL. Screening for these is important in MPN diagnosis, however distinguishing between the MPN subtypes requires a careful examination of blood counts and the morphological features of a bone marrow biopsy.

Unfortunately, assessment of the bone marrow is highly subjective, reliant on qualitative observations and there is great variability, even when it is done by expert haematopathologists. In particular, it is very hard to reliably distinguish between a mutation-negative MPN and a ‘reactive’ (non-cancer) bone marrow.

A more accurate method for diagnosis is very much needed, to enable selection of the most appropriate treatment strategy for patients and to determine treatment targets. Megakaryocyte cells or  ‘megas’ – the large, bone marrow cells that produce blood platelets – are very abnormal in all the MPNs and thought to play a key role in the disease pathology. Interestingly, although the gene mutations underlying all 3 MPNs lead to an over production of megas, subtle differences in the appearance and location of these cells within the bone marrow occur in the different MPN subtypes.

To try to improve MPN classification, a team lead by Jens Rittscher (Department of Engineering) and Daniel Royston (Radcliffe Department of Medicine), developed an AI approach to screen and classify MPN cases based on features of the mega cells, discovering new features in their cell size, clustering and internal complexity. Their machine learning approach revealed that there are clear differences between MPN subtypes – the platform was able to more accurately classify patients by assessing subtle morphological differences in the biopsies that could not have been identified by the naked eye.

These findings have been published in Blood Advances. Dr Beth Psaila, a clinician scientist at the MRC Weatherall Institute of Molecular Medicine and a haematology consultant specialising in MPNs said:

“It has long been recognised that a multitude of subtle differences in megakaryocyte morphology can distinguish between the MPN subtypes. However, this means that assessment of bone marrow biopsies is poorly reproducible, sometimes leading to diagnostic uncertainty and inappropriate treatment plans for patients.

“The approach developed here is really exciting for the field, as it is now possible to perform deep phenotyping of megakaryocytes and more accurate disease classification using simple H&E slides which are routinely prepared in all diagnostic facilities. This will be incredibly useful both for research aimed at better understanding the role of megakaryocytes in blood cancers as well as improving diagnosis and treatment pathways for our patients.”

The team hopes that in the future, this work can be combined with other histological assessments to optimise the clinical application of AI approaches, and create a more comprehensive quantitative description of the bone-marrow microenvironment and its cancers.

About the researchers and the study

This work was funded by the NIHR Oxford Biomedical Research Centre and is the result of collaboration between Korsuk Sirinukunwattana (Department of Engineering), Alan Aberdeen (Ground Truth Labs Ltd.), Helen Theissen (Department of Engineering), Jens Rittscher (Department of Engineering) and Daniel Royston (Radcliffe Department of Medicine [NDCLS]).

Jens Rittscher is a Principle Investigator whose research aim is to enhance our understanding of complex biological processes through the analysis of image data that has been acquired at the microscopic scale. Jens develops algorithms and methods that enable the quantification of a broad range of phenotypical alterations, the precise localisation of signalling events, and the ability to correlate such events in the context of the biological specimen.

Korsuk Sirinukunwattana is a postdoctoral research assistant in Rittscher’s group specialised in medical image analysis and computational pathology. His main research interest is the association between tissue morphology and molecular/genetic subtypes in various diseases.

Alan Aberdeen leads Oxford spinout Ground Truth Labs, a company supporting digital pathology research through on-demand analysis, biomarker discovery, and high-quality cohorts.

Helen Theissen is a doctoral research student in Rittscher’s group. Her research focuses on computational methods to characterise cellular subtypes and quantify the bone marrow microenvironment in MPNs.

Daniel Royston is a joint academic & consultant Haematopathologist at Oxford University Hospitals NHS Foundation Trust / Radcliffe Department of Medicine.

Researchers discover mutation that determines treatment efficiency

Weatherall Institute of Molecular Medicine researchers have recently discovered why a class of cancer drugs is beneficial only in a subset of patients

New digital classification method using AI developed for colorectal cancer

A new study from S:CORT demonstrates an easy, cheap way to determine colorectal cancer molecular subtype using AI deep-learning digital pathology technology

Mapping the T-cell landscape of pancreatic cancer

Through analysis of T-cell populations, researchers Drs Enas Abu-Shah & Shivan Sivakumar identify novel therapeutic opportunities in pancreatic cancer patients

QResearch researchers collaborate on two major cancer projects

The Department of Primary Care and Health Sciences recently announced that researchers in the Primary Care Epidemiology Group are joining two landmark projects to combine healthcare data and artificial intelligence to improve cancer diagnosis.

Led by Professor Julia Hippisley-Cox, the team will utilise the QResearch database of routinely collected electronic patient health records for studies on lung and oesophageal cancer diagnosis.

The two projects, announced today as part of a £13m investment from UKRI through their industrial strategy challenge fund, brings together different strengths from academia, charities, digital health and diagnostics companies.

Both projects are part-funded by Cancer Research UK.

DELTA, led by the University of Cambridge, will help to diagnose oesophageal cancer, which has increased 6-fold since the 1990s. Just 15% of people will survive for 5 years or more – often because it’s diagnosed too late.

Barrett’s oesophagus, a condition that can turn into cancer of the oesophagus, is more common in patients who suffer with heartburn. By using a new test for patients with heartburn, called the ‘Cytosponge’, the project aims to diagnose up to 50% of cases of oesophageal cancer earlier, leading to improvements in survival, quality of life and economic benefits for the NHS.

Professor Hippisley-Cox’s team are leading on the clinical epidemiology element of this research programme. The researchers will interrogate the QResearch database with the aim of developing a risk prediction algorithm that will be able to identify those individuals at highest risk of oesophageal cancer for further investigation.

DART (The Integration and Analysis of Data Using Artificial Intelligence to Improve Patient Outcomes with Thoracic Diseases), led by the University of Oxford, will accelerate lung cancer diagnosis, increasing the likelihood that treatment will be successful. See the full story on this announcement here.

Academics, NHS clinicians, the Roy Castle Lung Cancer Foundation and industrial partners (Roche Diagnostics, GE Healthcare, Optellum) will work with the NHS England Lung Health Checks programme to combine clinical, imaging and molecular data for the first time using artificial intelligence algorithms.

Professor Hippisley-Cox’s team will link to data from primary care to better assess risk in the general population to refine the right at-risk individuals to be selected for screening. It is hoped that this research will define a new set of standards for lung cancer screening to increase the number of lung cancers diagnosed at an earlier stage, when treatment is more likely to be successful. Find out more about this project here.

The QResearch database is one of the largest clinical research databases in Europe, covering 35 million patients from 1,500 GP practices throughout the UK. It includes longitudinal data collected over 25 years that is linked at an individual patient level to Hospital Episode Statistics (HES), mortality data and cancer registration (more details here), making it an extremely rich resource for cancer research.

Oxford to lead new programme of AI research to improve lung cancer screening

UK Research and Innovation, Cancer Research UK and industry are investing more than £11 million in an Oxford-led artificial intelligence (AI) research programme to improve the diagnosis of lung cancer and other thoracic diseases.

Professor Fergus Gleeson at the University of Oxford will lead on a programme of research focusing on accelerating pathways for the earlier diagnosis of lung cancer. Lung cancer is the biggest cause of cancer death in the UK and worldwide, with £307 million/year cost to the NHS in England. The earlier that lung cancer is diagnosed, the more likely that treatment will be successful but currently only 16% patients are diagnosed with the earliest stage of the disease. To address this clinical problem, NHS England is launching a £70 million lung cancer screening pilot programme at 10 sites*.

To improve patient care beyond the current screening guidelines, a team of academics from Oxford University, Nottingham University, and Imperial College London; NHS clinicians from Oxford University Hospitals NHS Trust, Nottingham University Hospitals NHS Trust, the Royal Marsden Hospital, the Royal Brompton Hospital, and University College London Hospitals NHS Foundation Trust; and the Roy Castle Lung Cancer Foundation will join forces with three leading industrial partners (Roche Diagnostics, GE Healthcare, Optellum).

Working with the NHS England Lung Health Check programme, clinical, imaging and molecular data will be combined for the first time using AI algorithms with the aim of more accurately and quickly diagnosing and characterising lung cancer with fewer invasive clinical procedures. Algorithms will also be developed to better evaluate risks from comorbidities such as chronic obstructive pulmonary disease (COPD). In addition, this programme will link to data from primary care to better assess risk in the general population to refine the right at-risk individuals to be selected for screening. It is hoped that this research will define a new set of standards for lung cancer screening to increase the number of lung cancers diagnosed at an earlier stage, when treatment is more likely to be successful.

Professor Fergus Gleeson, Chief Investigator for the programme, said

“The novel linking of diagnostic technologies, patient outcomes and biomarkers using AI has the potential to make a real difference to how people with suspected lung cancer are investigated. By differentiating between cancers and non-cancers more accurately based on the initial CT scan and blood tests, we hope to remove the delay and possible harm caused by repeat scans and further invasive tests. If successful, this has the potential to reduce patient anxiety and diagnose cancers earlier to improve survival and save the NHS money.”

This programme builds on the National Consortium of Intelligent Medical Imaging (NCIMI) at the Big Data Institute in Oxford, one of five UK AI Centres of Excellence. The funding, delivered through UK Research and Innovation’s (UKRI’s) Industrial Strategy Challenge Fund, is part of over £13m government investment in ‘data to early diagnosis and precision medicine’ for the research, development and evaluation of integrated diagnostic solutions. UKRI is also partnering with Cancer Research UK, which is making up to a £3m contribution to the cancer-focused projects. The Oxford-led project is one of six awarded from this competition.

Science Minister, Amanda Solloway MP, said:

“Our brilliant scientists and researchers in Oxford are harnessing world-leading technologies, like AI, to tackle some of the most complex and chronic diseases that we face. Tragically, we know that one in two people in the UK will be diagnosed with some form of cancer during their lifetime. The University of Oxford project we are backing today will help ensure more lives are saved and improved by using state of the art technology to identify cancerous tumours in the lung earlier and more accurately.”

Dr Timor Kadir, Chief Science & Technology Officer at Optellum Ltd, commented:

“Three industry leaders – Roche, Optellum and GE – have joined their expertise in molecular diagnostics, imaging and AI to help diagnose and treat lung cancer patients at the earliest possible stage. The programme results will be integrated into Optellum’s AI-driven Clinical Decision Support platform that supports physicians in choosing the optimal diagnostic and treatment procedures for the right patient at the right time.”

Ben Newton, General Manager, Oncology, at GE Healthcare, said:

“We are very pleased to be working with the University of Oxford via the NCIMI project on this important lung cancer research. By extending our existing NCIMI data infrastructure and creating innovative AI solutions to spot comorbid pathologies, we aim to help identify lung diseases earlier in the UK.”

Geoff Twist, Managing Director UK and Ireland and Management Centre European Agents at Roche Diagnostics Ltd, said:

“We are thrilled with this funding award, because it gives us the opportunity to work towards ground-breaking innovation in early diagnosis and because working in partnership is vital to achieve success in the health system. By bringing together the collective knowledge and expertise of these academic, medical and industry partners, this project has the potential to impact patient care globally through new diagnostic solutions in lung cancer.”

Dr Jesme Fox, Medical Director of the Roy Castle Lung Cancer Foundation, said:

“The majority of our lung cancer patients are diagnosed too late for the disease to be cured. We know that we need to be diagnosing lung cancer at an earlier stage, through screening. This innovative project has the potential to revolutionise lung cancer screening, making it more efficient and most importantly, saving lives. Roy Castle Lung Cancer Foundation is delighted to support this Programme”

Professor Xin Lu, co-Director of the CRUK Oxford Centre and Director of the Oxford Centre for Early Cancer Detection, commented:

“I am delighted that this national multi-site collaborative programme will be led from Oxford by Fergus Gleeson. Involving a world-class team of academics, clinicians, local and global industry, and patient representatives, this research is hugely important for accelerating lung cancer detection.”

 

* The 10 NHS England Lung Health Check sites are:

  • North East and Cumbria Cancer Alliance – Newcastle Gateshead CCG
  • Greater Manchester Cancer Alliance – Tameside and Glossop CCG
  • Cheshire and Merseyside Cancer Alliance – Knowsley CCG and Halton CCG
  • Lancashire and South Cumbria Cancer Alliance – Blackburn with Darwen CCG and Blackpool CCG
  • West Yorkshire Cancer Alliance – North Kirklees CCG
  • South Yorkshire Cancer Alliance – Doncaster CCG
  • Humber, Coast and Vale Cancer Alliance – Hull CCG
  • East of England Cancer Alliance – Thurrock CCG and Luton CCG
  • East Midlands Cancer Alliance – Northamptonshire CCG and Mansfield and Ashfield CCG
  • Wessex Cancer Alliance – Southampton CCG

 

Yang Shi joins the Oxford Cancer community

Yang Shi, who joins Ludwig from Harvard University, is a world leader in the field of epigenetics, which explores how chemical modifications to chromatin—the combination of DNA and histone proteins—control the organisation and expression of the human genome. Aberrations in those processes are vital drivers of cancer and underlie many other diseases and disorders.

“Yang has an outstanding track-record of innovative research into the identity and mechanisms of action of chromatin modifiers. We are delighted that Yang is bringing his wealth of experience, international standing and collaborative spirit to lead our cancer epigenetics theme at Ludwig Oxford.”

~ Xin Lu, Director of the Ludwig Oxford Branch.

Shi is widely known for his discoveries regarding a chemical modification, methylation, made to the histone proteins. In 2004, Shi and his colleagues identified and characterised an enzyme, LSD1, that erases methyl marks from histones. Their discovery upended a 40-year-old dogma that considered such modifications irreversible, altering longstanding models of genomic regulation. Shi’s laboratory went on to identify many other histone demethylating enzymes with roles in a diverse array of biological processes. More recently, his group discovered several enzymes that methylate RNA and possibly influence the translation of gene transcripts into proteins.

Shi is applying these fundamental discoveries to the benefit of patients. His group’s work on LSD1 led to the development of LSD1-inhibitors now in clinical trials for the treatment of cancer. More recently, Shi and his colleagues demonstrated that inhibiting LSD1 might also help make otherwise non-responsive tumours susceptible to the checkpoint blockade immunotherapy. His lab is additionally studying the role and therapeutic manipulation of epigenetic modifiers in pediatric high-grade gliomas and acute myeloid leukaemia.

“Yang’s science is of the highest calibre—as rigorous and collaborative as it is original—and we are very excited to have him in the Ludwig community. I’m sure many of our researchers will benefit from his expertise, and that they will be equally generous with their own expertise and support as he explores the implications of his discoveries for cancer biology and the design of new therapies.”

~ Chi Van Dang, Scientific Director of the Ludwig Institute.

Shi obtained his PhD from New York University, completed his postdoctoral training with Thomas Shenk of Princeton University and joined the faculty of Harvard Medical School in 1991, where he was most recently C.H. Waddington Professor of Pediatrics. Shi has received many honours for his contributions to epigenetics and is a fellow of the American Association for the Advancement of Science and a member of the American Academy of Arts and Sciences.

Find out more about Yang’s research.