LAST UPDATED:
4 May 2023
Written by:
Executive Director, Computational Pathology, Oncology R&D
Vice President, Image Data Sciences, Computational Pathology, Oncology R&D
At AstraZeneca, we’re pioneering new computational pathology approaches, combining digital pathology and big data with cutting-edge artificial intelligence to enhance patient selection and enable more personalised treatments, with the ultimate goal of improving patient outcomes.
Beyond the microscope: computational pathology
In 1838, German pathologist Johannes Müller published the first known description of cancers observed down a microscope.1 Nearly two centuries later, the basic process for cancer diagnosis remains more or less the same - slices of tumour tissue fixed, stained and magnified for human viewing - although the tools and technologies have improved significantly since Müller’s time.
Over the past two decades we have seen the advent of technologies, such as high-resolution slide scanning, creating digital images of immunohistochemically stained slides that can be easily viewed and shared.2
Advances in data science and computing now allow us to integrate digital pathology images with genomic, radiological, clinical and other data, applying deep learning algorithms to gain novel insights.
By extracting and analysing objective and clinically relevant information from these complex data sets, we can ‘see’ more than the human eye ever could to understand disease and guide treatment.
Known as computational pathology, this approach uses the power of artificial intelligence to enhance patient selection in oncology3 – a key part of our precision medicine strategy.
Learn more about how we are utilising computational pathology to revolutionise the development of cancer therapies and diagnostics in the video below:
Transformational medicines need transformational diagnostics
As our understanding of disease and the options to treat it increases, so does the need for a precision medicine approach to ensure that we target the right patient with the right medicine, earlier in their disease.
For example, antibody-drug conjugates (ADCs) rely on the unique targeting ability of monoclonal antibodies to kill cancer cells and reduce damage to normal cells.4 Similarly, immunotherapies are based on knowledge about the tumour microenvironment and the defensive mechanisms that cancer cells use to escape the immune system.5
Conventional pathology depends on manual, subjective scoring of tissue biomarkers to help us understand disease progression and select the best treatments for patients. In addition, it is usually based on a limited subset of cells within a sample.
Computational methods that capture more quantitative information about underlying tumour biology are critical for supporting the evolving requirements of clinical decision-making when it comes to these novel therapies.6
The computational pathology tools we are developing at AstraZeneca allow us to analyse hundreds of thousands of cells per slide in unprecedented detail, much faster than previously possible.
This generation of rich datasets gives us an enhanced view of what’s happening inside a tumour, quantifying tissue-based biomarkers to help us select patients most likely to respond to treatment.
Quantitative Continuous Scoring: a new frontier in diagnostics
Quantitative Continuous Scoring, or QCS, is our novel, fully automated computational pathology solution.7 It harnesses artificial intelligence to provide detailed data about biomarkers in digital whole slide images acquired from immunohistochemically stained cancer tissues.7
Not only does QCS look for the presence or absence of a biomarker, but it also quantifies the intensity of staining and its location in sub-cellular compartments, such as the membrane, cytosol and nucleus, and analyses the broader spatial organisation of the tissue.8 This information is particularly relevant for drugs like ADCs that are taken up inside cells, helping us to predict their impact on bystander cells and giving additional insights into their mechanism of action.
Right now, we’re pioneering the use of QCS within our clinical trial portfolio, with regulatory approval as a first-in-class AI-driven diagnostic as a future goal. We are also exploring its use in multiple indications, such as non-small cell lung cancer and hepatocellular carcinoma, and envisage it as a valuable future tool for patient selection in clinical trials and cancer care.
Seeing more from every slide
Computational pathology enables us to go much further than a single snapshot of a slide, moving towards a multi-layered, fully integrated ‘geographical map’ of the tumour and its microenvironment. Our ambition is to apply these tools across our portfolio, supporting drug development and enhancing patient selection for clinical trials in oncology.
It’s an incredibly exciting time for the field. We have new tools, novel drugs and digital approaches that are opening up access to this technology. We’re also seeing growing enthusiasm and positive moves in the regulatory environment for these novel diagnostics, suggesting the time is right for a step change.
Collaboration with pathologists, instrument manufacturers and the wider pathology community is critical to establishing routine, AI-driven diagnostics and making this powerful technology more widely available for the benefit of patients in clinical trials. We are working with global partners to help establish the diagnostic laboratory infrastructure needed to make this happen.
In addition to bringing benefits to patients by providing more in-depth and accurate diagnoses, this technology will also free up pathologists’ time to focus on more complex cases where they can add impactful analysis and value.
At AstraZeneca, we believe that computational pathology will underpin the development of the next generation of cancer therapies and diagnostics, changing the way we do clinical research and ultimately leading towards new cures or improved patient outcomes.
