Preclinical Molecular Imaging Group

Dr Gabriela Kramer-Marek’s group uses cutting-edge biomedical imaging techniques to gain information about the way particular genes drive cancer progression.

Our group’s long-term goal is to develop specific biomarkers for detecting cancers and to evaluate these biomarkers in pre-clinical cancer models

Notwithstanding the remarkable clinical success of mAb-based treatment regimens, not all patients benefit from them. This can be attributed, at least in part, to the complexity of the tumour microenvironment and its considerable heterogeneity both in terms of the tumour and non-tumour cell components. These phenomena represent a huge challenge in identifying predictive biomarkers and stratifying patient populations for personalised therapy approaches.

Therefore, there is an urgent need to develop assays that will help in three ways:

  1. accurate patient selection
  2. understanding intrinsic resistance mechanisms or the emergence of acquired resistance following treatment initiation and
  3. choosing the most effective combination regimen in circumstances in which single-agent therapies are insufficiently effective.

Currently, the baseline expression level of antigens targeted by therapeutic mAbs can be analysed by methods such as: immunohistochemistry (IHC), flow cytometry, proteomics, or next-generation sequencing of tumour tissues acquired at diagnostic biopsy or intra-operatively. These techniques aid our understanding of how cancer cells adapt to treatment and become resistant, but such methods are inherently invasive, prone to sampling errors caused by inter- and intra-tumour heterogeneity of receptor expression within analysed biopsy specimens and do not lend themselves readily to repeated sampling.

Positron emission tomography (PET), using radiolabelled mAbs, antibody fragments or engineered protein scaffolds (immuno-PET), has the potential to acquire information non-invasively and can be highly complementary to analyses based on tissue acquisition. Accordingly, immuno-PET agents might accurately identify the presence and accessibility of the target and provide a rapid assessment of tumour response to a variety of treatments in a timely fashion (e.g. within 1-2 weeks of treatment initiation).

Furthermore, immuno-PET agents can provide information about the heterogeneity of both target expression and therapeutic response, which are increasingly recognised as key factors in treatment resistance. This especially relates to patients with advanced disease in whom target expression may vary from site to site and a biopsy of a single local or metastatic deposit may not accurately reflect the situation across the entire disease burden. Although introduction of immuno-PET into routine clinical practice may add complexity and increase costs, with appropriate use this imaging modality has the potential to identify patients likely to benefit from therapy and assess the efficacy of novel target-specific drugs.

Against this background, our research focuses on the development and characterisation of targeted-PET radiotracers, including protein-based theranostic agents that enable smart monitoring of immunotherapies and expand opportunities for personalised medicine approaches.

Early diagnosis and individualized therapy have been recognized as crucial for the improvement of cancer treatment outcome. While proper molecular characterization of individual tumour types facilitates choice of the right therapeutic strategies, early assessment of tumour response to therapy could allow the physicians to discontinue ineffective treatment and offer the patient a more promising alternative. Therefore, the role of molecular imaging in elucidating molecular pathways involved in cancer progression and the ability to select the most effective therapy based on the unique biologic characteristics of the patient and the molecular properties of a tumour are undoubtedly of paramount importance.

The mission of this group is to investigate innovative imaging probes and apply them to novel orthotopic or metastatic models that are target driven, to gain information of the way particular oncogenes drive cancer progression through signalling pathways that can be imaged in vivo and, correlate it with target level ex vivo. Such an approach enables non-invasive assessment of biochemical target levels, target modulation and provides opportunities to optimize the drug dosing for maximum therapeutic effect, which leads to the development of better strategies for the more precise delivery of medicine.

The long term goal of our research is to develop specific imaging cancer biomarkers, especially for positron emission tomography (PET) as well as optical imaging and, evaluate these biomarkers in pre-clinical cancer models. Significant efforts are directed towards validating biomarkers for early prediction of treatment response, with the focus on new targeted therapies (such as inhibition of cell signalling pathways).

Our initial portfolio of imaging agents include radiolabelled affibody molecules, TK inhibitors and, conventional tracers that monitor universal markers of tumour physiology.

We are actively supported by other groups from the Division of Radiotherapy and Imaging as well as the Division of Cancer Therapeutics. Moreover, our close association with The Royal Marsden NHS Foundation Trust enables rapid translation of our research to early clinical studies and ensures a fast transition of know-how from the research laboratory to the patient bedside.

Dr Gabriela Kramer-Marek

Group Leader:

Preclinical Molecular Imaging Gabriela Kramer-Marek

Dr Gabriela Kramer-Marek is investigating new ways of molecular imaging in order to predict an individual patient’s response to treatment. Before moving to the ICR, she developed a new approach for non-invasive assessment of HER2 expression in breast cancer.

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Researchers in this group

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Phone: 020 3437 6376

Email: [email protected]

Location: Sutton

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Phone: +44 20 3437 6785

Email: [email protected]

Location: Sutton

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Phone: 020 3437 6356

Email: [email protected]

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Phone: +44 20 3437 6857

Email: [email protected]

Location: Sutton

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Phone: 020 3437 4549

Email: [email protected]

Location: Sutton

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Dr Gabriela Kramer-Marek's group have written 63 publications

Most recent new publication 2/2025

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Recent discoveries from this group

24/02/25

Researchers have developed an artificial intelligence-based model that can help pathologists grade certain lung cancer tumours and predict patients’ outcomes. It does this by assessing the growth patterns in tumour samples, which can vary significantly among patients.

In a recent study, the grading achieved using the model, which is called ANORAK (pyrAmid pooliNg crOss stReam Attention networK), was predictive of disease-free survival (DFS). DFS is the length of time between treatment for lung adenocarcinoma – the most common type of non-small cell lung cancer (NSCLC) – and the return of signs or symptoms.

In the longer term, this model could help clinicians determine how best to treat each of their patients based on the likely progression of their cancer. Improved decision-making at the treatment stage could lead to better outcomes overall in this type of lung cancer. With recently introduced cancer screening programmes significantly increasing the identification of early-stage lung cancer, improving these treatment decisions is more urgent than ever before.

ANORAK was primarily created by scientists at The Institute of Cancer Research, London, with the work funded by a Cancer Research UK Career Establishment Award. The paper has been published in the journal Nature Cancer.

Challenges in tumour grading in lung adenocarcinoma

Lung adenocarcinoma will typically adopt one of six types of growth pattern, with a single tumour often combining multiple types. A global grading system known as the International Association for the Study of Lung Cancer (IASLC) system proposes that these growth patterns predict the extent to which a patient is at risk of disease progression or recurrence.

The combination of distinct pattern types that may appear within a tumour can make it difficult for pathologists to determine a person's outlook, as can the fact that the appearance of each type of pattern varies across a spectrum. The difficulties encountered in defining and quantifying the broad range of growth patterns means that different pathologists may not make the same decision when it comes to grading a tumour.

The risk of suboptimal or inconsistent grading by pathologists is that it leads to inadequate or inappropriate treatment for some patients, potentially putting them at risk of poorer outcomes.

Developing the deep learning model ANORAK

Although previous studies have applied deep learning models to the classification of growth patterns in lung adenocarcinoma, these models have not taken into account the detailed morphological structure of the patterns. What is more, they have not been able to provide automated IASLC grading.

In this study, the researchers developed an AI method, ANORAK, that was able to distinguish between the six types of lung adenocarcinoma growth pattern by assessing them at the pixel level. They applied it to 5,540 tumour samples on diagnostic slides, which came from a total of 1,372 people with the disease. The model was able to improve patient risk stratification, with patients identified as having IASLC grade 1 or 2 tumours having significantly longer DFS than those identified as having IASLC grade 3 tumours.

To further validate the ANORAK model, the team compared the information it provided with manual grading information from three pathologists. The AI grading was comparable with the pathologist grading across the board, even slightly outperforming it for one cohort of patients.

By referring to previous studies, the researchers were also able to show that the extent to which the AI and manual gradings agreed on the predominant growth pattern in a tumour was consistent with the level of agreement between different pathologists. Overall, the team concluded that AI grading added prognostic value, particularly in early-stage lung adenocarcinoma, where treatment decisions can be difficult.

In the second part of the study, the researchers considered four specific scenarios that pathologists might find particularly challenging. These included cases with four or more diagnostic slides per tumour and cases with highly diversified growth patterns. The ANORAK model performed well in each of the four scenarios, leading the team to conclude that their method could help pathologists grade growth patterns even when the case is more demanding than usual.

Finally, the researchers focused on the most common of the six growth patterns: the acinar pattern. Using ANORAK, they were able to achieve a better understanding of the shapes and structures of the acinar pattern. They also determined correlations between various acinar subtypes and different tumour characteristics, including some associated with a poor outlook.

Some achievements are only possible using AI

First author Dr Xiaoxi Pan, then a Postdoctoral Training Fellow in the Computational Pathology and Integrative Genomics Group at the Institute of Cancer Research (ICR), said:

“Diagnostic inaccuracies and variability among pathologists are longstanding issues in lung adenocarcinoma. Our study is the first to implement the IASLC grading system with an AI-powered tool and validate the prognostic values on two distinct cohorts.

“Our AI method enables the precise and automated quantification of unique growth patterns within a tumour, thereby inferring the predominant pattern and grading. It has also identified previously undiscovered morphological and spatial features of certain tissues that were not achievable using existing algorithms or human observations.”

Second author Dr Khalid Abdul Jabbar, who was a postdoc in the same group at the ICR at the time, said:

“We were delighted that our AI method not only matched but also consistently enhanced the prognostic value of grading in early-stage tumours, including in challenging scenarios. Its capability to identify high-risk tumours could aid the selection of patients for additional treatment in a reproducible manner, free from variability among observers.

“Additionally, in the research setting, it has the potential to integrate with genetic data, which could lead to new biotechnical tools and deeper insights into tumour evolution.”

The researchers have already planned further studies to incorporate genetic data into their work, with the aim of better understanding how tumours progress and how this is influenced by the cells and tissues surrounding them. The team will also be trialling ANORAK in larger groups of people with early-stage lung adenocarcinoma to provide further evidence of its effectiveness.