Seed funded teams

map of India and map of UK

In March 2020, the 7 successful teams were awarded seed funding grants of up to 30k (approx. 26 lakhs).

The teams will have 8 months to prepare and submit an application for a Programme Award grant of up to £1.5million (approx. Rs 13.1 crores) over 4 years.

The teams:

  • will be tackling 5 of our 7 research challenges
  • are from a range of institutions across India & the UK
  • will address several different cancer types
  • are formed of new and existing collaborations

You can read more about each of our teams below.

South Asian Breast Cancer Risk Prediction, Genetic testing and Health Management (SURAKSHA)

Challenge 1: Prevention

Challenge 1 prevention icon - a data chart with magnifying glass

Identify and quantify cancer risk factors to better understand regional variations in incidence, enabling new approaches to cancer prevention

Meet the team

Lead applicants Usha Menon, Ranjit Manchanda, SVS Deo and Nitya Wadhwa

Lead applicants: Usha Menon (top left), University College London, SVS Deo (top right), All India Institute of Medical Sciences, Ranjit Manchanda (bottom left), Queen Mary University of London and Nitya Wadhwa (bottom right), Translational Health Science & Technology Institute

The team will build on complementary strengths in breast cancer research in India and the UK and draw on expertise in genetic sequencing/bioinformatics. They will also collaborate with individuals with knowledge of developing tools for healthcare professionals, clinical implementation of risk management and patient health behaviour.

The team is a newly formed collaboration with a broad range of skills and expertise. Various individuals have worked together previously on collaborative projects, but this will be the first time that the four lead applicants will be working together on a research grant.

The team is seeking collaborations with gynaecologists and gynaecological cancer team at the participating hospitals so that we can build robust ovarian cancer risk-management pathways. If you have the relevant expertise and are interested in working on this project please contact us at: and we will pass the enquiry along to the lead applicants.

Research abstract

Breast-cancer is the commonest female malignancy in India and UK with 150,000 and 54,541 new cases respectively in 2016. Significant differences exist in BC-incidence rates between India and UK and among Indian states, probably due to variations in the prevalence of known and novel, and undiscovered risk-factors. There is limited data on the associations between RFs and BC in South-Asian populations. Validated breast cancer risk-prediction models (e.g. BOADICEA/CANRISK developed for women of European ancestry) along with unselected genetic-testing of breast cancer patients can improve morbidity and mortality through well-established early detection/prevention interventions.

The goal is to improve identification of Indian women at increased breast cancer-risk who can benefit from affordable and effective early detection/prevention interventions. We will:

  • Validate and identify key breast cancer risk factors and customise ‘the ‘BOADICEA-V.5’ BC-risk prediction-algorithm
  • Adapt “CANRISK” interface-tool to facilitate Indian user-friendly data collection/risk-assessment and co-production of India-specific risk-communication tools
  • Assess the acceptability/satisfaction/psychological-wellbeing/uptake of screening and prevention options of unselected genetic testing/mainstreaming of breast cancer patients and cascade testing of relatives of women with clinically actionable mutations
  • Evaluate cost-effectiveness and identify affordable cost-thresholds/solutions for India

We will undertake a multi-centre female invasive breast cancer case (1800)-control (1800) study across India. Following genetic counselling/informed consent, data on putative breast cancer risk factors, blood samples for genetic testing (high/moderate penetrance breast cancer genes and single-nucleotide-polymorphisms) and bio-banking will be collected. Data will be used to estimate and validate effect-size of breast cancer risk-factors and customise ‘BOADICEA-V.5’ for South-Asian women.

Genetic counsellors will return positive results of clinically actionable pathogenic-mutations (BRCA1/BRCA2/RAD51C/RAD51D/PALB2) in-person and offer cascade testing to first/second-degree relatives. Mutation carriers will be referred to relevant clinical teams for appropriate breast/ovarian cancer risk-management. The research will incorporate extensive stakeholder and patient-public engagement for aim-2. Validated and customised questionnaires (baseline/7-days/9-months) will be used to evaluate impact of mainstreaming and unselected genetic-testing (aim-3). Cost-effectiveness analyses will be undertaken.

This research will enable India and other South-Asian populations to use contemporaneous cutting-edge risk-stratified approaches to improve breast cancer control. Data sharing with national and international consortia will facilitate discovery of novel breast cancer risk factors.

An affordable point-of-care molecular cytology platform for oral cancer diagnosis

Challenge 2: Early Detection

Challenge 2 early detection icon - a magnifying glass

Devise affordable screening tools to improve early detection of cancer

Meet the team

Lead applicants Moni Abraham Kuriakose and Satheesh Prabhu

Lead applicants: Moni Kuriakose (left), Cochin Cancer Research Centre and Satheesh Prabhu (right), Oxford University NHS Foundation Trusts

The project will establish a core consortium of physicians and scientists with complementary expertise in India and the UK, alongside infrastructure for carrying out multi-centric translational research. The collaboration between the Indian and the UK lead applicant is a newly formed one. However, within the Indian institutions, the lead applicant, Dr Kuriakose, and the co-investigators Dr Suresh, Dr Pandya and Dr Pillai are long standing collaborators.

If you are interested in this project and feel you could add value to the team, please contact us at: and we can pass the enquiry along to the lead applicants.

Research abstract

Early detection of oral cancer is essential to down-stage the disease and improve treatment outcome. Currently in UK and India, less than 20% oral cancers are diagnosed at early stage. Non-specific clinical features and lack of compliance to biopsy are recognized as leading causes of delayed diagnosis. A minimally-invasive, affordable, pathology-based, point-of-care (PoC) diagnostic for oral cancer is an unmet need. Brush biopsy with conventional cytology is ineffective due to lack of objective criteria and high inter-observer variations. We have previously demonstrated the feasibility of an automated tele-cytology platform and in parallel, validated a multiplexed molecular-cytology assay for oral cancer. Herein, we propose an affordable, minimally-invasive, molecular-cytology based PoC diagnostic for oral cancer by integrating the molecular cytology/deep-learning platform with an in-house tele-cytology prototype (AMURA: Advanced Molecular-diagnostic Unit for Real-time Analysis-of-cells).

  • Aim 1: Develop AMURA-based molecular cytology system

    • Upgrading of AMURA for automated acquisition of fluorescent images
    • Optimisation of multiplex cytology/AMURA integration
  • Aim 3: Field testing of upgraded AMURA-Molecular cytology detection system with deep-learning
  • Aim 2: Establish a robust cytology score for diagnosis of high-risk oral lesions based on a deep-learning algorithm
    • Evaluation of efficacy of AMURA in delineating high-risk pre-cancer/cancer using manual image analysis; development of the image analysis algorithm
    • Integration of the deep-learning algorithm with AMURA/establishment of the cytology score

In the first objective, AMURA will be upgraded and marker (SNA-1/CD44/Parpi) profiling in brush biopsy samples of dysplastic/neoplastic/benign lesions carried out. As a next step, image analysis algorithm will be developed and integrated with the platform. In the final objective, a multi-centric field study will be adopted to evaluate its accuracy/robustness.

The platform developed will be a PoC assay amenable to commercialisation for improving cytology-based diagnosis in oral cancer and other tumours (cervical/breast/thyroid).

Devise affordable screening tools to improve early detection of cancer

Challenge 2: Early Detection

Challenge 2 early detection icon - a magnifying glass

Devise affordable screening tools to improve early detection of cancer

Meet the team

Lead applicants Arnie Purushotham and Rengaswamy Sankaranarayanan

Lead applicants: Arnie Purushotham (left), King's College London and Rengaswamy Sankaranarayanan (right), RTI International

The multi-disciplinary team consists of experts in public health, epidemiology, cervical cancer, machine learning and artificial intelligence, statistics and health economics from across India and UK. The lead applicants are long standing collaborators and have forged links, utilising existing connections, with the partners/co-investigators on this project and have worked with them to define the research objectives for the proposal.

If you are interested in this project and feel you could add value to the team, please contact us at: and we can pass the enquiry along to the lead applicants.

Research abstract

Cervical cancer is a major public health challenge in India. An urgent need prevails for rapid, accurate, affordable, screening and triage tools to identify cervical pre-cancerous lesions.

To evaluate:

  • QPOCTM high-risk (HR)‐HPV genotyping Assay (QuantumDx), a rapid, affordable, simple-to use, point‐of‐care multiplexed molecular diagnostic compared to Digene Hybrid Capture 2 (HC2) HPV DNA Test (Qiagen)
  • Assisted Visual Examination by NSV (N‐ave), an AI-based cervical imaging and computational analytical device compared to HC2 as a screening test
  • Accuracy of N-ave in triaging HPV+ve women
  • Accuracy of Gynaecam (IIT Bombay), a low-cost imaging device, in detecting cervical lesions in HPV+ve and/or N-ave positive women
  • Costs of QPOCTM, HC2 tests, N-ave as a screening and triaging test and Gynaecam testing.

This will be a cross-sectional study involving 16,000 women aged 30-65 years. Participants will receive QPOCTM, HC2 and N-ave tests at baseline. HPV+ve and/or N-ave positive women will be tested using Gynaecam and findings documented, following which, each positive woman will have cervical biopsy (diagnostic loop excision in women in whom squamo-columnar junction is invisible). Accuracy and predictive values of QPOCTM, HC2, N-ave and Gynaecam will be calculated using histology as reference standard. Women negative on two HPV detection tests and N-ave assessment will be assumed to be negative for CIN 2+ lesions to estimate test accuracies. All women with high grade cervical pre-cancer or cancer will receive treatment. All screen-positive women including treated women will be reviewed at 1 year. Costs of all testing and screening will be compared in comprehensive economic analyses.c analyses.

Implementing affordable, easy‐to‐use and accurate point‐of‐care HPV testing and AI‐based imaging will have a significant impact on mortality rates and quality of life indices with immediate economic benefits to society.

Digital health and community health workers in rural India: a novel strategy to tackle the challenge of achieving earlier cancer

Challenge 3: Early Diagnosis

Challenge 3 early diagnosis icon - a stethoscope

Identify affordable approaches to improve early diagnosis of symptomatic cancers 

Meet the team

Lead applicants Ravi Kannan and Toral Gathani

Lead applicants: Ravi Kannan (left), Cachar Cancer Hospital & Research Centre and Toral Gathani (right), University of Oxford 

The project brings together a multidisciplinary collaboration between academia, healthcare, government and industry in India and the UK, with complementary research strengths to tackle these challenges. The lead applicants are long-standing collaborators who have worked together on various successful studies.

If you are interested in this project and feel you could add value to the team, please contact us at: and we can pass the enquiry along to the lead applicants.


Research abstract

In India, the highest incidences of cancer are reported in the North-Eastern states. Patients tend to present late, resulting in poor survival and greater costs (to the individual and system). Low levels of knowledge of cancer signs, symptoms and care provision, in communities and in community health workers (CHWs), combined with a complex healthcare system (multiple providers and different financing arrangements) contribute to late presentation. Mobile health applications (mHealth) targeted at CHWs have been shown to improve outcomes in other non-communicable diseases in low resource settings.

  1. To facilitate early diagnosis of common cancers by designing and implementing a mHealth application for use by CHWs to support onward healthcare seeking behaviour of individuals through:
    Providing CHWs and their communities with knowledge about cancer presentation
    b. Providing CHWs with information about local cancer care services, treatment and financing available
  2. To evaluate the clinical and cost benefit of the mHealth application for individuals and healthcare system
  3. To establish a sustainable research collaboration to strengthen research capacity in South Assam

This mixed methods study will be located in South Assam. We will conduct semi-structured interviews with CHWs and individuals presenting to local cancer facilities to identify reasons for delays in seeking healthcare, and financial barriers; we will survey CHWs to assess their knowledge, attitudes and beliefs about the symptoms and diagnosis of common cancers, and provision of local cancer care. We will combine these data to develop a mHealth application, through an established industry partnership, for use by CHWs and will use a controlled non-randomised study to test its effectiveness.

The research will address grassroots community cancer care and is aligned to sustainable development goals. It is scalable across this region (and beyond) to increase early diagnosis rates of common cancers saving lives and money.

Predicting treatment response using non-black box machine learning based pathology and radiology image analysis

Challenge 4: Computational Approaches

challenge 4 computational approaches icon - cognitive thinking

Develop computational approaches that can reduce the cost of cancer care delivery

Meet the team

Lead applicants Anita Grigoriadis and Swapnil Rane

Lead applicants: Anita Grigoriadis (left), King's College London and Swapnil Rane (right), Tata Memorial Hospital 

This consortium brings together leading experts in pathology, oncology, imaging, data mining and transparent AI. While there is a long-standing history of collaboration between the host institutions, King’s College London and Tata Memorial Hospital, and key team-members on each side have worked together, the lead applicants and their proposed team will be forming a new collaboration.

As the teams intention is to deliver widely applicable, affordable, robust and privacy-preserving AI tools, they are looking to:

  • Enhance the diversity of the study population by sourcing patient data from geographically distinct locations
  • Co-opt members of academia and industry who are experts in the areas of distributed computing, biomedical engineering and imaging, and Data-Lake management

If you have the relevant expertise and are interested in working on this project please contact us at: and we will pass the enquiry along to the lead applicants.

Research abstract

Around 1.7 million new breast cancers and 0.65 million head and neck squamous cell carcinomas (HNSCC) are diagnosed worldwide, with a significant increase in incidence, cancer-associated morbidity and mortality in both India and the UK. A significant proportion of these are patients are not operable at presentation and receive either neoadjuvant therapy (breast cancer) or chemoradiation/radiation (HNSCC). Only 20% of patients will show (near) complete resolution of tumour. The lack of (near-)complete response is predictive of future recurrences. Current treatment paradigms are insufficient to identify patients who will benefit from these therapies and thereby results in excessive costs, non-optimal use of cancer health services and delay in delivery of curative treatment.

We aim to develop robust and explainable (non-black box) artificial intelligence (AI) tools for prediction of treatment response, by utilising pre-therapy radiology and histopathology images, both independently and concurrently, acquired as a part of standard care.

Our consortium brings together leading experts in pathology, oncology, imaging, data mining and transparent AI. We will develop methods for a privacy-preserving machine learning framework based on routine histology and radiology images from stage-matched, biological group-matched and treatment-matched cohorts of invasive breast cancer patients receiving neoadjuvant therapy, and HNSCC patients who have received primary chemo/radiation or neoadjuvant therapy. Across sites, retrospective archival data will be used to initially build the infrastructure, generate analytical pipelines, establish standardised approaches and provide testable models, while prospectively collected data will refine AI pipelines for the risk-prediction models.

The developed software will assist the clinicians for further patient stratification of breast cancers and HNSCC and improving matching of patients and their tumours to the chemotherapeutic regimes to ensure maximum cost-effectiveness. In parallel, an exemplar of a privacy-preserving framework for data collection, access, sharing and interoperability will be delivered.

Intermittent PARP inhibitor in recurrent ovarian cancer (IPIROC)

Challenge 6: Treatment

Challenge 6 treatment icon

Improve the affordability of effective cancer treatments

Meet the team

Lead applicants Asima Mukhopadhyay and Nicola Curtin

Lead applicants: Asima Mukhopadhyay (left), Chittaranjan National Cancer Institute and Nicola Curtin (right), Newcastle University

The lead applicants have been collaborating since 2008, and this project would be their 6th successful grant application within last 6 years. The team will bring together a range of skills from gynaecological and medical oncologists, clinical pharmacologists, expertise in conducting early/late phase PARPi clinical trials and statisticians. The project will include collaboration with experts in health economics, drug discovery, experimental therapeutics and computational artificial intelligence. Alongside this they will work with representatives from global clinical trials consortiums and patient advocacy/focus groups in UK and India.

If you are interested in this project and feel you could add value to the team, please contact us at: and we can pass the enquiry along to the lead applicants.

Research abstract

Ovarian cancer  is the 3rd commonest, costliest and deadliest women’s cancer in India. Recently, PARP inhibitors (PARPi) have revolutionised the treatment of ovarian cancer (applicants are major contributor to this development) and is approved/publicly funded in UK/other western countries. However, the current recommended daily scheduling (based on a maximally tolerated but not biologically optimal dosing) is unaffordable for most women in India/LMIC countries (5000-7000 GBP/month) leading to health inequality. PARPi-related haematological toxicities especially in women with lower body-weight/pre-existing anaemia, will be a challenge for most Indian women. Our pre-clinical data showed that a single dose of PARPi (rucaparib) inhibited PARP in tumour tissues for ≥7 days and weekly dosing had equivalent anti-tumour activity to daily dosing. Our ongoing preclinical work in ovarian cancer aims to identify the most suitable PARPi for less intense dosing. We propose to ask the question keeping in the theme of this call “Do shorter or alternative treatment schedules provide similar or better outcomes with less toxicity?”.

Ultimately, we aim to provide proof of concept evidence justifying the first clinical trial of a PARPi at a less intense dosing schedule that would be non-inferior but more tolerable and cost-effective.

  1. Phase 0 study (UK, n=20) to determine duration of PARP inhibition after a single dose
  2. Phase 2 study (India, KolGo Trg research group) to study i. QOL-adjusted toxicity and progression-free survival and ii Cost-effectiveness of intermittent PARPi treatment in recurrent platinum-sensitive ovarian cancer
  3. A GCLP validated PD biomarker for PARP inhibition and a low cost functional HRD assay will be optimised for use in PARPi trials (technology development/transfer)

Results from this study will be used to develop a series of Phase 2/3 studies comparing the best intermittent/low-dose PARPi schedule versus standard therapy/daily recommended PARPi schedules in ovarian cancer in low-resource settings.

Abbreviated immune checkpoint inhibition following radical treatment across the mucosal squamous cell cancers – IMPART

Challenge 6: Treatment

Challenge 6 treatment icon

Improve the affordability of effective cancer treatments



Meet the team

Lead applicants Duncan Gilbert and Lalit Kumar

Lead applicants: Duncan Gilbert (left), University College London and Lalit Kumar (right), All India Institute for Medical Science

The group will develop a core team of oncologists between India and the UK with extensive experience in treating these cancers, a proven track record in developing and delivering clinical trials and experience in immune-oncology.

If you are interested in this project and feel you could add value to the team, please contact us at: and we can pass the enquiry along to the lead applicants.

Research abstract

Cancers of the mucosal surfaces (particularly cervix, and head and neck) account for ~20% cancers in India. Although prevention strategies are underway it will be 20+ years before major benefits are seen. These cancers typically present with locally advanced disease treated with chemo-radiotherapy; overall survival is only 50% at 3 years. Immune checkpoint inhibitor (ICI) drugs have revolutionised treatment for some tumours and offer the best opportunity to improve outcomes for mucosal cancers over the next 10 years. Current approaches investigate each cancer separately using >1-year ICI, an arbitrary length of time perhaps driven by financial rather than biological rationale making their use less likely in both India and the UK.

Assembling a multidisciplinary team of clinicians and triallists across India and the UK we aim to demonstrate the efficacy of abbreviated consolidation ICI in improving outcomes across the mucosal cancers in the context of the Indian (and subsequently UK) populations. Initial seed funding will develop the clinical, organisational, patient related, and regulatory frameworks required to address and answer this question.

A randomised controlled basket trial investigating the addition of a short course of consolidation immune checkpoint inhibition to radical treatment in locally advanced mucosal cancers.

If successful, IMPART will improve outcomes for the mucosal cancers, a major unmet health need of global importance. We will have contributed significantly to the infrastructure and ability to successfully conduct subsequent clinical trials in this setting, demonstrating to the global medical community the feasibility of conducting complex clinical trials within India and ‘unlock’ the huge potential of this underutilised partnership. Furthermore, we will have demonstrated that new drugs can and should be used in India. Finally IMPART will provide a strong model for a raft of future academia/industry partnerships.

Get in touch

If you are interested in being involved in one of the projects or would like to know more about the initiative please contact Dr Deborah Robinson at