Accelerator Award: How to apply

Applications for the accelerator award are invited annually. To apply, you first need to submit an expression of interest. If successful, we'll then help you develop your proposal and team further, with seed funding and collaboration facilitation, before you submit a full application.

 

Application process

Application process

JAN 2019

Expression of interest

Submit an outline of your proposal for shortlisting by our committee

FEB 2019

Online hosting and seed funding

If shortlisted, we'll give you £5,000 to develop your application, and we'll promote your abstract to help you find collaborators

MAY 2019

Full application
 

Submit a formal, costed proposal

JUNE 2019

Peer review and interview

We may request written peer review, and you'll be invited to present to the committee.

AUTUMN 2019

Funding announcement

We'll publicise the new projects that we've funded

Shortlisted teams

Our committee have now reviewed the outline applications and we've awarded seed funding to 14 teams to develop their proposals further. Browse the shortlisted teams below, and contact the PI if you think you may be able to contribute.

Ignacio Melero, Fundación para la Investigación Médica Aplicada

Radiotherapy exerts therapeutic effects at least in part dependent on the immune system and that may control tumour lesions outside the irradiation field (the so-called abscopal effects). Synergistic combinations of radiotherapy and immunotherapy have been reported preclinically and hinted in case reports and early clinical trials. This project aims to test intratumour injection of immunotherapy agents and external radiotherapy to assess efficacy and mechanisms of action. In mice bearing multifocal tumours a variety of agents will be injected in to the irradiated tumor that chiefly include pathogen-denoting molecular patterns (TLR-agonists, STING-agonists, etc), immunomodulatory monoclonal antibodies (anti-CD40, anti-CTLA4, anti-CD137, anti-OX40, anti-TGFbeta, anti-CD25, etc). 

The main objective is to comparatively identify the most powerful treatment strategy, addressing the immune mechanisms of action underlying efficacy and resitance. Three clinical trials are proposed encompassing radiotherapy (SABRT or Proton beam) in conjunction with intratumoral injection of pathogen-denoting molecular patterns already undergoing clinical development of intralesional delivery as immunotherapy agents. Two clinical-grade compounds will be tested, the TLR-3/MDA5 agonist BO112 (manufactured and developed by Bioncotech) and the TLR9 agonist SD-101 (manufactured and developed by Dynavax). BO112 will be tested in conjunction with radiotherapy for liver metastases of patients suffering four frequent solid malignancies (NSCLC, Gastric cancer, colorectal cancer, Triple-negative breast cancer) as well as for non-resectable squamous head and neck cancer in combination with proton beam radiotherapy. SD-101 will also be tested in conjunction with radiotherapy but in this case for resectable head and neck cancer cases as a window-of-opportunity neoadjuvant trial, thus providing surgical specimens for analyses. Clinical follow-up will be performed coordinately with translational studies in pre and on-treatment biopsy specimens.

Partners include experts in preclinical and clinical radiotherapy joining efforts with tumor immunologists and clinical oncologists. Collaborative efforts among partners in mouse and human tissue samples will include gene-expression profiling, TCR-sequencing and multiplex tissue immunofluorescence, among other techniques based on the multidisciplinary expertise of the different partners. Overall, LIRT seeks to study and implement synergistic strategies of radio-immunotherapy in an attempt to generate in-situ cancer vaccines and minimize systemic side effects. For this purpose, we are taking a double translational and "back-translational" approach.

Robin Jones, The Institute of Cancer Research

Background

Soft tissue sarcomas (STS) are a group of rare cancers for which surgery is the standard treatment for localised disease. However ~50% of patients who undergo surgery ultimately relapse leading to poor outcomes. Neoadjuvant chemotherapy (NCT) offers the potential to improve cure rates by targeting micrometastases and reducing the risk of relapse. However, there is currently no consensus on the use of NCT in STS patient management and a lack of robust biomarkers to predict NCT response.

We posit that the complexity and heterogeneity characteristic of STS necessitates a holistic analysis approach that incorporates a host of distinct but related clinical and molecular features simultaneously. We propose to exploit data-driven methods such as machine learning (ML) and artificial intelligence (AI) to distil these complex multifactorial features and accelerate the identification of candidate predictive biomarkers for NCT response in STS.

Aims

The vision is to deliver datasets, data mining tools and patient-derived models that will drive the development of a stratified medicine approach for personalised NCT in high-risk STS patients. This will be achieved through 3 interrelated programs.1)Develop a common data platform to enable standardised clinicopathological and imaging data collection and curation of STS clinical specimens. 2)Generate molecular datasets and models that shed light on the biological processes impacting tumour response to NCT and 3)Develop a suite of data mining tools to facilitate analysis and integration of clinical and molecular data for use by the sarcoma community.

Methods

A standardised data platform incorporating digital pathology, imaging and clinical data will be implemented by the consortium. Comprehensive multi-omic analysis of pre- and post-NCT tissues will also be undertaken. In parallel, patient-derived models will be generated to enable assessment of STS drug responses and identification of genetic dependencies. Data analytics pipelines such as ML, AI and mathematical modelling will be developed to mine the clinical and molecular data.

How the results of this research will be used

By mining the comprehensive knowledgebase generated as part of this Award, we will provide a resource for the community to develop next-generation biomarkers incorporating genomic and digital pathology data; and utilise radiomic/radiogenomic workflows to enhance the accuracy of non-invasive evaluation of tumour response, both of which will have direct impact on clinical practice. Additionally, data integration will greatly expand our knowledge into the biological processes impacting responses to NCT in STS, with potential for revealing new targets to overcome NCT resistance for future therapeutic development.

Maria Blasco, Centro Nacional de Investigaciones Oncologicas

Recent advances in drug development have largely impacted the lives of cancer patients, bringing hope, quality of life and a future for patients and their families. However, how to selectively kill cancer cells remains one of the biggest challenges in oncology. In this regard, a recent promising approach has been to develop synthetic lethal strategies that exploit the presence of genomic instability in cancer cells. While this approach has already led to notable successes such as the use of PARP inhibitors for the treatment of BRCA mutant tumours, this approach is still in its initial stage and there is still great potential to identify new therapies based on this rationale.

We here present a comprehensive proposal oriented to the development of new cancer therapies that target genomic instability in cancer. To do so, we integrate a cohesive team of top level-scientists from Spain, UK and Italy, with strong expertise in relevant areas such as genomic instability and DNA damage responses, telomere biology and genetic screenings oriented to the discovery of synthetic lethal interactions. Moreover, the team includes a robust expertise in academic drug discovery—facilitated by cryo-EM—with experience in bringing these products to industrial development and clinical trials. Notably, besides its research output, this proposal will also have a significant impact through the training of new scientists in the development of new medicines.

This project has the potential to lead to new drugs and/or druggable candidates that can be developed into novel therapies for cancer patients. In addition, the results generated here will likely encourage the drug development industry to integrate or co-develop some of the discoveries made by the Consortium, maximizing the chances that these will be translated into actual treatments. Given that tumours with high levels of genomic instability are associated with poor prognosis, this proposal addresses a key aspect of oncological research. 

Marcello Deraco, Fondazione IRCCS - Istituto Nazionale dei Tumori – Milano

Pseudomyxoma peritonei (PMP) is a rare condition of mainly appendiceal origin characterized by peritoneal dissemination of mucinous tumor and, if untreated, is ultimately fatal. PMP represents a perfect paradigm of peritoneal-based malignancy amenable to intensive locoregional treatment. In fact, dramatic survival improvements have been associated with complete cytoreductive surgery and hypertermic intraperitoneal chemotherapy (CRS-HIPEC).

The most common histological variants of PMP according to PSOGI classification are Low-grade Peritoneal Mucinous Carcinomatosis and high-grade Peritoneal Mucinous Carcinomatosis. This histological classification is the current most important prognostic factor. However prognosis is still unpredictable and 20-40% of low-grade disease succumb to rapidly progressive recurrent disease. Conversely, a small but significant proportion of high grade disease experiences favorable course despite the worst histology. Furthermore, the use of adjuvant systemic anticancer treatment is not standardized and PMP biology is still largely unknown. The goals of the present project are:

  • build a multicentric cohort of PMPs through a web-platform for collection of genomic/trascriptomic and clinical data as well as for creating a repository of tissue specimens and 3D-derived cultures;
  • generate global DNA/RNA-seq data from 200 retrospective cases available among the centres. Similar profiles will be obtained from 150 cases/year for 5 years from all the centres involved. 
  • develop 3D-cellular models of PMP. Benefiting from previous experience on 3D models (organoids) of colorectal carcinomatosis we will develop similar ones that will serve to better understand PMP biology;
  • establish common bio-banks for tissue specimens, blood and 3D models in each centre;
  • validate the prognostic value of a new transcriptional signature and of a new composite prognostic score based on gene expression profiling and PMP stromal and cellularity features that we have successfully tested in a small retrospective series. These signatures will discriminate between high and low-risk disease and, in spite of pathological features, predict with high sensitivity patient’s prognosis.
  • validate “druggable targets”, and to test new drugs or drug combinations. Specifically, we will assess the suitability of a preclinical platform, in which PMP organoids are subject to drug screening for selecting therapeutic candidates. The 3D models will also allow to validate potential new therapeutic targets emerging from molecular characterization of PMP by using genetic and pharmacological approaches.

The project will generate NGS-analyses on about 950 PMPs, as well as a considerable number of 3D models, sharable among the partners and open to external groups.

Carlo Gambacorti-Passerini, University of Milan-Bicocca

The long-term outcome of cancer remains unsatisfactory for a large fraction of patients. A better understanding of the driving oncogenic lesions at the personalized level could impact on patients survival. Early mutational events are recognized as true drivers of tumor development. Moreover, as neoplastic cells are characterized by genetic instability and accumulation of genetic alterations, they adapt to unfavourable conditions, including anticancer therapies, as demonstrated by long-term failure of targeted therapies in various contexts. The impact of mutational load on targeted therapy outcome was recently shown in chronic myeloid leukaemia (CML): patients with multiple oncogenic mutations are more likely to fail imatinib treatment.

Recent developments in single-cell analysis techniques offer the opportunity to study cell population heterogeneity behind resistance to cancer therapies. We propose to develop a theragnostic tool based on the genetic characterization of neoplastic cells at single-cell level, in order to direct therapeutic choices and predict outcomes, using two disease models, i.e. ALK+ anaplastic large cell lymphoma (ALCL) and atypical CML (aCML).

When resistant to front-line chemotherapy, ALCL prognosis is very poor. The ALK inhibitor crizotinib has shown therapeutic activity in these patients with 80% response rates. Unfortunately, approximately 1/3 of patients relapse. As there exist no pre-treatment characteristics in ALK+ ALCL patients that allow us to predict short term vs. durable response to crizotinib, we hypothesize that molecular markers should be explored as response predictors. Our hypothesis is that the genetic status of the tumor can provide this information. For this reasons, we propose (i) to apply single-cell transcriptomics/epigenomics to characterize neoplastic cells; and (ii) to analyse the mutational load in ALCL samples by sequencing their exome. We aim to develop a molecular signature classifier to discriminate patients at high versus low risk of failing crizotinib.

In aCML patients, no targeted treatment is yet available and prognosis is dismal. Several genes have been implicated as potential drivers of transformation, often co-existing in the same tumor. We aim to develop a single-cell genotyping tool that will allow to reconstruct the clonal hierarchy of the disease and identify potential therapeutic targets, at the individual patient level. The early development of mutations and their actionability will be studied. Finally, the synergism of blocking different targets inside the same cell will be studied. The final tool will be represented by an algorithm that will comprise all this information and will indicate a treatment strategy based on the individual patient mutational landscape.

Paul French, Imperial College London

Drug resistance is a major challenge for cancer therapy, arising from heterogeneous cellular behaviours within tumours, where initially identical clonal cells can mutate and adapt to diverse microenvironments. This complexity is rarely addressed in standard assays that typically measure the average response of cell populations in highly artificial contexts and fail to account for outlier cells that may drive drug resistance. There is increasing interest in more complex 3D tissue models, such as patient-derived organoids (PDO) that better recapitulate the complexity of the in vivo context compared to conventional high throughput assays of homogenous 2D cell cultures. However, increasing the physiological complexity of cancer models makes them harder to image – limiting opportunities for high throughput (phenotypic) screening.

We aim to explore the trade-off between complexity of 3D cancer models and power of assays (in terms of single cell resolution and throughput) by developing modular open source automated instrumentation optimised for 3D imaging of complex cell cultures with a range of optical properties. We will also explore cell culture and sample preparation (e.g. labelling, mounting, clearing) techniques to enable researchers to optimise 3D assays to address their specific cancer biology questions.

The proposed automated 3D imaging instrumentation will provide quantitative single cell-resolved readouts of drug-target engagement and responses in fixed and live cell cancer models. It will extend the commercial state-of-the-art with modules for much faster 3D imaging, e.g. of cell morphology, dynamics and migration in multiwell plate arrays, alongside the ability to tune performance between speed and imaging depth for larger and/or more scattering PDO. We will develop open source instrumentation and software for 3D image acquisition and analysis and will develop and validate robust and reproducible protocols and SOPs for culturing and assaying PDO.

Following instrument development at Imperial and the Crick, we will utilise and validate the Accelerator capabilities with Edinburgh, the ICR and the IRB Barcelona through exemplar cancer biology assays, e.g. determining which cells within a heterogeneous cancer population are effectively killed by chemotherapy and which of the persisting cells are responsible for disease recurrence. We will also explore the role of the tumour microenvironment for quiescent resistant tumour cell sub-populations. To better understand side-effects of chemotherapy, we will assay the effects of chemotherapy on the regenerative capacity of stem cell models of normal tissue. A PhD cohort trained in these technologies will help introduce them to the cancer biology and drug discovery communities.

Luis Paz-Ares, Fundación para la investigación biomédica del Hospital 12 de Octubre (H12O)

Background

Lung cancer is the leading cause of cancer-related deaths worldwide. Depiste recent advanced in targeted therapy, no specific therapies against KRAS, the most common driver mutation in NSCLC, are available. Thereby, there is an urgent need to identify novel strategies to treat these tumours. Recent therapeutic proposals target upstream or downstream kinases, but their effect is limited due to either reduced therapeutic window or the plasticity of KRAS signalling to engage alternative pathways. In this sense, an integrative resource of data generated from cell lines, genetically-defined mouse models and patients will be critical to develop novel targeted and immune therapies.

Aims

We aim to build a multinational (UK, Italy and Spain) and multiparametric platform combining complete information from KRAS-driven NSCLC patients with experimental models (cell lines library, patient-derived organoids and xenografts, and GEM bearing KRAS-driven lung tumours) in order to design prevention and also tailored targeted or immune therapeutic strategies. We will use this platform to address most urgent questions on current targets, mechanisms of resistance, and new targets including immunotherapy. In addition, the platform will be available to the entire scientific community for further research.

Methods

An open library (KLUNG) containing samples and models derived from patients will be generated and characterized at the genomic (DNA and RNAseq) and immune (inmunohistochemistry, flow cytometry, high-dimensional immune profiling technologies) levels. We will perform a comprehensive characterization of KRAS-driven NSCLC (genomic, transcriptomic and immune) from early carcinogenesis steps to late stages of disease. Tumor subtypes with distinctive molecular and immune profiles will be then defined. Sensitivity or resistance mechanisms will be studied using genomic-wide CRISPR-based screens in NSCLC cells, and possible therapies will be validated in KRAS-driven mouse tumor models, PDX and clinical samples. In addition, KRAS-mutant mice will be used to model the response to immunotherapies alone, including those cell based, or in combination with complementary therapies.

How the results of this research will be used.

Data and reagents generate from these ambitious project will be used to identify and validate new targets, and to establish correlations between genomic data and the response to targeted therapies or immunotherapies in mouse models or clinical trials. Moreover, samples from KLUNG open library will be virtually included in a shared single database that will be made public so additional consortia interested in NSCLC research can take advantage of them.

Alberto Orfao, Centro de Investigación del Cáncer de Salamanca (CIC)

Despite research efforts on investigating early cancer stages are key for successful adoption and implementation of both preventive and early therapeutic measures, no large collaborative research efforts exist today that aim at dissecting the very early, preclinical stages of haematological cancers by focusing on the “healthy” population.

Here we propose to set-up a Network aimed at creating a collaborative research initiative and open-access platforms on early-cancer detection/prevention focused on two disease models (ECRIN-M3): monoclonal B-cell lymphocytosis (MBL)-chronic lymphocytic leukemia (CLL) and monoclonal gammopathy of undetermined significance (MGUS)-multiple myeloma (MM).

The proposal aims at: i)creating key (open-access) research infrastructures (a research network, high-quality biobank and a detailed genetic map of MBL and MGUS tumor cells and subjects), ii)develop novel standardized minimally-invasive and affordable laboratory protocols for fast sensitive diagnostic screening of MBL and MGUS, iii)provide associated educational materials and tools, and iv)foster scientific collaboration to perform research on tumor cells and the tumor (immune) microenvironment in MBL (vs CLL) and MGUS (vs MM) cases.

The proposal is organized in 6 related work-packages (WP) aimed at: i) optimal management and coordination of the project (WP1), ii) recruitment and biobanking of samples (WP2) after iii) screening for MBL and MGUS in large cohorts of healthy subjects from the general population (WP3), iv) performing genetic analyses of the recruited individuals and identified MBL-MGUS tumor cells vs CLL and MM patients/tumor cells (WP4), v) identifying the key signaling pathways involved in survival and growth of such tumor cells (WP5) and vi) disseminating, translating and exploiting the results achieved, including the tools and open-access infrastructures developed in the project.

To successfully develop the project a multidisciplinary and complementary research team consisting of 10 research groups from three different institutions in Spain, Italy and the United Kingdom which altogether have outstanding prior research experience in MBL, MGUS and the technologies involved, has been formed.

The ultimate goal of the proposal is to accelerate research and foster the development, transfer and adoption of novel preventive measures, early diagnosis and monitoring procedures, and to anticipate potential beneficial therapeutic interventions for MBL-CLL and MGUS-MM subjects-patients, as a model also for other hematological and non-hematological cancers.

Luis Marti-Bonmati, Hospital Universitari i Politècnic La Fe

Tumour biopsy plays a key role in cancer diagnosis and treatment. However, biopsy has many downsides, such as invasiveness, undersampling the complexity of the lesion and poor patient tolerance of the procedure, which limits serial sampling. These limitations have pushed researchers to seek to develop virtual procedures that are able to accurately complement baseline diagnosis and enable decision making for optimal management.

The benefits of a virtual biopsy approach are many, such as precise and personalised information in a time and cost saving manner. Nevertheless, more accurate and validated knowledge is required to develop tools that are able to effectively complement physical biopsy, so that the end goals of a personalised medicine approach in oncology are achieved.

The VIOLIN (Virtual biopsy from rectal cancer imaging) project aims to integrate multimodality multiparametric imaging biomarkers in a radiomic feature-based model together with clinical data to generate predictors for personalised management of individual rectal cancer patients. Tissue characterisation is generated by supervised and unsupervised structured machine learning models applied to both source images and high-dimensional data. This solution, obtained by high-tech engineering techniques, will use images through an international centralised cloud-based repository.
To characterise biological processes in a voxel-wise high-spatial resolution approach, first, second and third order radiomics features (including signal analysis and imaging biomarkers) will be extracted from different imaging modalities using a multitude of image analysis and modelling tools to create a specific cancer radiomic signature.

Project outputs

  • An innovative machine learning based technology applicable to routine clinical practice using real-world data from different institutions.
  • A clinical decision support tool to help in defining tumor behaviour, predict treatment response and patients’ prognosis through radiomic signatures. 

Project expected achievements

  • Create an international centralised secured-cloud image repository
  • Develop and validate a 3D convolutional neural network for automatic tumor segmentation
  • Extract reproducible and relevant radiomics profiles from CT and MR data
  • Develop a set of diagnostic, predictive and prognostic models through a statistics and machine learning approach, both supervised and unsupervised
  • Validate the models on external prospective datasets 
  • Make the technology available to institutions and clinical trials as an open-access solution.

The VIOLIN project will greatly impact on the current status of oncologic medical imaging, moving disruptively towards personalised medicine in the scenario of rectal cancer.

Kevin Blyth, University of Glasgow

Background

Malignant Pleural Mesothelioma (MPM) is an incurable cancer of the lining of the lung. It is strongly associated with asbestos exposure but there is typically a long latency between exposure and development of disease, and the factors that drive or permit this evolution are poorly understood. The recent genomic and transcriptomic characterisation of MPM provides new opportunities for accelerated research, but also major challenges for drug development. Tumour suppressor loss appears consistent (e.g. BAP1, NF-2, CDKN2A) but the overall mutation rate is low, particularly in druggable oncogenes. Clinical trials of immune check-point inhibitors and targeted therapies have disappointed and a better understanding of the key dynamic events is now required to translate this static landscape into effective treatment. Landmark longitudinal studies in mice show that certain genomic events predate malignant transformation providing a pre-clinical rationale for both precision prediction and appropriately-targeted early treatment.

Aims

PREDICT-Meso will establish an international network of researchers in the UK, Spain & Italy, focused on creating longitudinal human cohorts transitioning from a pre-malignant inflammatory phenotype to MPM, aligned with state-of-the-art pre-clinical work-flows dedicated to target identification, in vitro & in vivo models, drug-screening, target-drug validation, multiomic risk-profiling and response tool validation.

Methods

Prospective and retrospective tissue collection will be focused in the UK where MPM incidence is currently the highest in the world. A shared tissue bank will be interrogated using panel-based genomic, transcriptomic & immune landscape mapping. Dynamic biological events associated with MPM evolution will be considered potential therapeutic targets, and reproduced in bespoke network-derived cell-lines and organoids within world-leading laboratories. The effects of target-drug combinations identified through high-throughput drug screening will be validated in vitro & in vivo using a unique, immunocompetent, mouse model that combines intra-pleural asbestos injection with flexible induction of target MPM genes. Prospectively collected tissue will be annotated with radiomic, proteomic and metabolic data to power effective risk-profiling. Data and knowledge will be shared via an open-access website, the ICGC-ARGO initiative, a series of seminars and schools and a PhD training program.

How the results of this research will be used

PREDICT-Meso will generate validated target-drug combinations ready for clinical trials, a large biobank containing currently unavailable tissues, multiomic risk-profiling, and a range of validated in vitro & in vivo models. These datasets and tools will dramatically accelerate research in time to address an imminent global epidemic, primarily affecting the BRIC countries, with whom links will be strengthened.

Paolo Ghia, Universita Vita-Salute San Raffaele

BACKGROUND

Nanoparticles (NPs) represent ideal tools to improve cancer care as they could help address key unmet clinical needs in cancer such as specific delivery of anti-cancer drugs to the primary and metastatic tumor sites, thus minimizing systemic toxicity. Challenging limitations to be overcome include: i) low tumour-to-normal discrimination of targeting with a preferential accumulation of NPs in filter organs and in phagocytic immune cells; ii) suboptimal tumor penetration; iii) poor controlled release properties.

AIMS

The main goal of our consortium (NANOMET) is to overcome the current limitations of nanotechnology, by transforming these challenges into opportunities towards the optimization of NPs for an effective treatment of cancer. We aim at establishing and implementing a new nanotechnology design and testing platform, to accelerate the clinical translation of nanomedicine for the targeting and treatment of primary and metastatic tumours.

METHODS

NANOMET will improve selectivity, penetration and controlled release of their pharmacological payload, using well-established FDA-approved polymers but also innovative polymers. This will be achieved by functionalizing NPs with i) selected ligands for molecules expressed by target cells (both cancer and immune cells); ii) penetration peptides and molecules to manipulate angiogenesis or disrupt the extracellular matrix; iii) complement inhibitors to generate immune-safe NPs. A comprehensive characterization and optimization of the NPs will be performed using available state-of-the-art technologies.

To identify the most biocompatible materials, all NPs will be scrutinized in an ad hoc platform allowing to systematically test their potentially harmful effects, with special emphasis on inflammation and complement activation. This will include both in vitro (immune cells) and in vivo (zebrafish) assays through a phenotypic, functional and morphological analysis.
Finally, immunocompetent preclinical models, available at the participating institutions, of solid primary cancers (lung, head and neck), premetastatic and metastatic melanoma (MetAlert model), and haematological tumours (B-cell lymphomas) will be utilized to assess anti-tumour properties of drug-loaded NPs, targeting either the tumour cells or the immune microenvironment, in the latter case with the aim of reverting immunotolerance.

HOW THE RESULTS OF THIS RESEARCH WILL BE USED

Successful implementation of the NANOMET proposal will provide the scientific community with new potential anti-cancer nanodrugs executing their therapeutic effect at the right location. Ideally, the NANOMET platform aims to grow in the following years to address virtually all types of cancer and all types of NPs, with the final goal to facilitate the translation of nanotechnology into the clinical arena for the benefit of the patients.

Caroline Dive, University of Manchester

Background

Clinical Research lags behind many industries in using digital technology to drive faster and smarter decisions. Phase 1/Experimental Medicine trials in particular would benefit from digitisation. In this “learning” phase of development these trials frequently require in-flight adaptation which would benefit from near-real time data capture and visualisation to drive faster and smarter decisions.

Phase 1 colleagues particularly in Asia are making significant strides to digitise experimental cancer medicine trials. Sponsors are willing to pay a premium in Phase 1 for near-real time access to patient data, with the benefit this yields in faster and smarter iterative decisions that adapt the study population, drug posology, combination agent and predictive/pharmacodynamic biomarker thereby providing the investigational medicinal product the best chance of success.

Aims

  1. Scientific: Through the digitisation of experimental cancer medicine trials, the translatability of pre-clinical observations to clinical trials will increase by reducing false-negatives arising from insensitive experimental methodology
  2. Capability/Infrastructure: To remain a competitive experimental medicine network in Europe to sponsors whose key driver in Phase 1 is to have near-real time access to data from patients recruited and to hypothesis generate and test from the emerging clinical trial dataset

Methods

A network of Experimental Cancer Medicine Centres in Spain, Italy and UK and coordinated nationally, will develop and test digital methods to

  • acquire per-protocolled assessments in hospital
  • push per-protocolled assessments into patient’s homes
  • determine if continuous monitoring of patients provides more informative data
  • enable investigators to be automatically alerted to emerging safety signals, and find trials matching the patient’s molecular and clinical characteristics
  • enable sponsors (both academic and commercial) to iteratively hypothesis generate and test on the emerging dataset
  • provide patients the opportunity to become co-researchers by access to their own data

How the results of this research will be used

Successful digital methods of proven utility will be accessed through open-source software by all participating centre

  1. Providing investigators and sponsors near-real time access to more complete and less error-prone patient datasets in a format of intuitive visualisations and alerts to safety signals
  2. Enabling patients (whose average survival is 6 months) flexibility to provide per-protocolled assessments from home, and to review their own data in the context of the trial as a whole
  3. Upskilling the EU network of Phase 1 centres to conduct the increasing number of clinical trials appraising new digital technologies (devices); to be the go-to place for conducting such trials

Michael Dustin, University of Oxford

We will accelerate develop of a new mode of immunotherapy that fills an open and large niche between the checkpoint antibody therapies and the more specific, but challenging to implement, live T cell immunotherapies. This will be based on sub-100 nm extracellular vesicles (ECV) that combine immunological specificity with potent effector mechanisms. We have discovered that T cells naturally generate ECVs in response to cancer-associated peptide-MHC complexes that combine the T cell receptor (TCR) with cytotoxic (FasL, perforin and granzyme) effector mechanisms that displace antigen specific effector function. Chimeric antigen receptor expressing T cells (CAR-T) cells also release similar targeted ECV. The team of Dustin (T cell ECV), Baldari (effector delivery), Alarcon (TCR), Wood (ECV engineering), Cerundulo (immunotherapy) and Dushek (modelling) will focus on a goal of using these natural structures as a templates for engineering high yield cell lines to produce therapeutic exosome-ECV (E-ECV) based therapeutics with a high specificity index, the ratio of specific to non-specific activity toward different cell types. Exosomes are known to be safe, non-immunogenic and can be engineered to target tissue compartments, including the central nervous system. They can be made with yields compatible with production of therapeutic antibodies at clinical grade. We envision a modular platform in which specificity and effector function can be independently designed, tested and the combined to increase the specificity index for specific tumors. We will initially target glioblastoma, as its underserved by immunotherapies and as a CNS tumour provides an excellent convergence with goals of the Wood lab for CNS targeted gene therapies using exosomes. On this note, we will focus our effort on protein cargo of E-ECV, but will also monitor and manipulate the nucleic acid content to fully exploit the delivery potential of targeted E-ECVs. We will also select E-ECV properties to minimize toxicity, anti-drug immune responses, resist immunosuppressive TME and avoid tumour escape mechanisms by combining multiple effector mechanisms. Different targeting modules can be combined with a single effector module to target different tumours or variants within a single cancer in a patient. Once compete E-ECV are developed and are put into production for pre-clinical mouse models we will follow a drug development process guided by experience of three of the co-investigators with biotechnology companies developing therapeutics. The wider community can participate by contributing targeting modules (TCR and CARs) and we will seek patient involvement to help guide developments useful to their community.

Benjamin Chain, University College London

The remarkable advances in immunotherapy of cancer have raised the possibility that many cancers may ultimately respond to manipulation of the immune system. However, there remain enormous challenges to increase the efficacy, scope and precision of current immunotherapeutic approaches, alone or in combination with other forms of therapy. To address this challenge research groups worldwide are investing resources in charting the immunological landscape in patients with a variety of cancers. Increasingly, these research programs produce high dimensional data in multiple modalities: flow cytometry and in situ profiling, proteomics/peptidomics, bulk and single cell transcriptomics, T cell and B cell receptor repertoire analysis, genomics and clinical data, often including in vivo tissue imaging. However, we have reached a point where there is general consensus that our ability to generate new data outstrips our ability to integrate and interpret them.

To overcome this barrier to progress, we propose to harness the spectacular advances in artificial intelligence (AI) which have already led to a revolution in diverse areas including retail, finance, security, and increasingly medicine. The diverse data modalities involved, and the centrality of discovering mechanistically interpretable interactions that can lead to interventions, poses a genuine methodological challenge for the AI community, and will require new computational approaches and a new cadre of researchers who span the frontier between immunology, oncology and computer science. In this application we identify successful researchers from the AI community, and build a programme to integrate them, their research groups and their methods into immuno-oncology. The programme which will deliver these outcomes, will involve intensive training of immunologists in the theory and practice of AI, and conversely training of computational scientists in the theory and practice of immunology and oncology. To achieve this ambitious aim it is essential to immerse individuals in a genuinely interdisciplinary environment for prolonged periods. The inter-disciplinarity will ensure cutting-edge AI solutions are applied to key research questions, and that the solutions are delivered so as to have maximum impact on the wider immune-oncology community so as to improve the efficacy, scope and precision of cancer immunotherapy. It will also create a cohort of younger scientists (PhD students and early career post-doctoral researchers) who will spearhead growing research activity long after the specific funding of the Accelerator finishes. 

Key dates

Call opens

20 September 2018

EOI deadline

10 January 2019

Triage meeting

15 February

Full application deadline

9 May

Committee meeting

25–27 June

Announcement of awards

TBC

Step-by-step details

Before you begin your application, you must contact the Research Funding Manager to discuss your proposal, and we recommend you do this at least 6 weeks before the deadline. We will open an application form for you on our electronic Grants Management System (eGMS).

Once your application is opened, your Expression of Interest must be submitted online using eGMS by the PI. If you have not applied for a grant with us before, you will first need to set up a profile in eGMS.

You will need to submit:

  • Application overview – full details of the Principal Investigator, proposed title, start date and duration of award.

  • Expression of Interest application form using the template in eGMS.

  • Letters of support from:

    • Agreed Co-Investigators and Collaborators.
    • The lead host institution:
      • The Director, if a CRUK Centre or an FC AECC centre
      • The equivalent senior figure (e.g. Head of Institute or Faculty) if the host institution is based in Italy

Expressions of Interest will be assessed by our international Accelerator Award Committee. All applicants will receive written feedback.

Download the Expression of Interest guidelines (PDF)

By submitting an EOI, you agree that, if shortlisted, a summary of the team’s proposal and the names of researchers involved may be hosted online and in materials produced by the funders to promote the scheme. This will provide an opportunity for researchers with complementary and/or desired skills to contact the PI and discuss potential collaboration.

If shortlisted, you will receive £5,000 of seed funding. This will be awarded to the named PI at the lead Host Institution. These funds can be used for:

  • Meetings/workshops with the collaborators to define and develop the final proposal

  • Project management expertise to assist with planning of the proposal

You will need to submit your full application through our electronic Grants Management System (eGMS).

The table below gives a summary of the information we need you to include in your full application.

Section of Application

What it should cover

Application overview

Title, supporting roles, applicant information, abstract

Additional information

Additional research information, research classification, biomarker research

Financial details

Details of salaries, running expenses, and equipment for the whole team

Research proposal

The overall vision and collaboration proposal for the team

Research Features template

Justification of costs, data and sample sharing, cell lines, details of animal studies, patient involvement information, intellectual property

Supplementary budget information (if applicable)

Details of salaries, running expenses and equipment for the whole team in the relevant currency

Heads of Terms (only for UK-based teams with no Co-Investigators in Spain or Italy)

Non-binding agreement principles to form basis of Research Collaboration Agreement.

Host Institution approval

Letter of support from the Lead Host Institution and each Co-Investigators’s Host Institution

Letters of support (optional)

From each named Collaborator, a letter of support/agreement to provide technology, resource or expertise

Letters of compliance (mandatory if applicable)

To be provided by the relevant Principal Investigator or Co- Investigators for any animal or clinical research to be conducted outside the UK

Response to Committee feedback (optional)

Letter in response to the Committee’s feedback on the team’s Expression of Interest

For full details of the what we need for each section, plus templates, guidance on costs, and how to use eGMS, download the full application guidelines.

Download full application guidelines (PDF)

Peer review

Once you have submitted your full application, the committee may request additional expertise from relevant experts in the field in the form of written peer review. If written peer review is requested by the committee, comments will be provided to you in advance of their committee interview.

Interview

You will be required to attend an interview by the Accelerator Award Committee at CRUK’s Head Office in London in June 2019. Specific details will be given closer to the time.

Applicant teams will be required to attend in person and should include the Principal Investigator and up to three key co-investigators or collaborators (one of which may be the project manager if in place).

You will be expected to give a short presentation (10–15 mins) and then take part in a question and answer session.

The presentation could include an overview of the award, the key outputs and how they will be delivered, the strengths of the team and how they are able to deliver their proposal.

Assessment criteria

The Accelerator Awards Committee will consider:

Scientific potential and rationale

  • The proposal meets a distinct need of the cancer research community and provides a compelling rationale for large scale investment in this area.
  • The team has presented a clear vision on how the proposal meets this need, and is supported by underpinning science.
  • The outputs of the proposal will enable the scientific community to accelerate progress in cancer research, ultimately leading to patient and population benefit.
  • The team clearly articulates the scope of the proposal with regard to the research questions the outputs could help to answer.

Operational efficacy

  • The proposal includes a clear process(es) for making the outputs (including knowledge and expertise) as discoverable and accessible as possible to the scientific community.
  • The ambition of the award is challenging, but deliverable through a collaborative approach.
  • The financial request is reasonable and builds on the existing facilities available through the collaboration.
  • There are appropriate governance structures in place (including project management and assigned accountability).
  • A set of work packages that complement each other is presented, with key milestones and clear deliverables identified throughout the duration of the project.
  • Potential challenges have been identified and contingency plans are available.
  • Consideration has been given to realistic sustainability plans to support the outputs of the proposal beyond the funding period.

Capability of the team

  • The team has the right collection of skills and expertise required to deliver the proposal.
  • By bringing these people and institutions together, a synergistic proposal has been put forward that would not be possible in isolation.
  • The Principal Investigator has a strong track record and is capable of leading a collaboration and proposal of this scale.
  • All team members have a clear role and bring value to the proposal. Each is essential to the success of the proposal.
  • Demonstrates a genuinely collaborative approach, with no one party dominating the proposal or receiving a disproportionate amount of funding.
  • The team is dynamic and capable of responding to internal and external scientific developments.

Funding award

You will be informed of the Committee’s decision as soon as possible after the Committee meeting, and written feedback will soon follow.

If you are successful, you will then be awarded your grant subject to agreement to the Accelerator Award Terms and Conditions.

You will be expected to have a steering board meeting either before or within the first month of the project start date to adjust your project plan in light of any feedback from the Committee. You will be assigned a CRUK, AECC or AIRC representative who will represent the partnership at Steering Board meetings.

Publicity

CRUK, AIRC and FC AECC will lead on all national and regional press and PR for the shortlisting and funding announcement of the Accelerator Awards. You must ensure that all Co-Investigators, Collaborators and their PR/Press Office teams are also made aware of this and do not do any proactive PR or social media (including blogs) about the Awards prior to the announcement. You and your network will be welcome to use CRUK/AIRC/FC AECC’s press materials for your websites once the media embargo is lifted.

Funding committee reviewing your application

Chair

Professor David Livingston – Harvard University

Members

Professor Rene Medema – Netherlands Cancer Institute

Professor Elisabeth De Vries – University Medical Center Groningen

Professor Jacqueline Lees – Koch Institute, Massachusetts Institute of Technology

Professor Jason Lewis – Memorial Sloan Kettering Cancer Centre

Professor Frank McCormick – UCSF Helen Diller Family CCC

Professor Mechthild Krause – Carl Gustav Carus University

Professor Alexander Lazar – MD Anderson

Professor Fabrice André – Institut Gustave Roussy

Contact us

CRUK office

Bouran Sohrabi

acceleratoraward@cancer.org.uk

Tel: 0203 469 8343

AIRC office

Luana Grimolizzi

acceleratoraward@airc.it

AECC office

In partnership with

A partnership between CRUK and Associazione Italiana per la Ricerca sul Cancro (AIRC) and Fundación Científica de la Asociacion Española Contra el Cáncer (FC AECC)