Use cases addressing challenges in cancer research to feed the database
The Mission Board and the Europe’s Beating Cancer Plan have identified scientific questions that need to be addressed to improve our UNderstanding of CANcer. 4.UNCAN.eu will propose mechanisms to use data (collected under the Horizon Europe programmes, Cancer Mission actions, the Europe’s Cancer Beating Plan and use cases defined by UNCAN.eu) to generate innovative solutions to save life and improve the quality of cancer survivor life.
Explore the boundaries between healthy and malignant cells to better prevent and detect cancers
Define the role of age and sex/gender in cancer mechanisms and treatment
Improve the quality of life of patients alive with cancer and cancer survivors
Overcome therapeutic resistance in established cancers
Prevention and early detection
Explore the blurred boundary between healthy and malignant cells to better prevent and detect cancers.
Cancer is commonly seen as a consequence of somatic evolution and clonal selection. In the recent years, the boundaries between healthy and malignant cells were blurred when genetic tools revealed the presence, in healthy tissues, of small clones defined by somatic variants in cancer driver genes. Some mechanisms (e.g., tissue homeostasis and architecture) limit the expansion of these clones in healthy tissues. Their accumulation with age, combined with other factors (e.g., stromal cell senescence) may promote cancer development through diverse trajectories i.e., either one of these clone toggles to a malignant tumour, or an inflammatory climate induced by these clones, and lifestyle-related toxic insults, promote the independent emergence of a cancer.
Rejuvenating strategies eradicating non-malignant clones in healthy tissues might preserve their tumour suppressive properties and decrease the risk of cancer development, while better identification of the negative impact of specific ecosystems, either individual (genetic, epigenetic, age, disabilities, socioeconomic disadvantage) or collective (chemicals, pathogens, radiations), on cancer emergence may suggest strategies to improve disease prevention.
Another benefit of better understanding early steps of oncogenesis will be the timely detection of early-stage cancers diagnosis. As proposed by the LifeTime Initiative (https://lifetime-initiative.eu), characterization of cell types (transformed cells and their environment) and states in early-stage cancers requires the computational analysis of single-cell multi-omics and images collected longitudinally from a number of patients and patient-derived experimental models during the progression from health to disease.
Establishing an atlas of pre-neosplatic and early stage neoplastic lesions, cells, and biomarkers (e.g., circulating tumour DNA) will fuel innovative early diagnosis strategies and drive an interceptive medicine applied to the eradication of early-stage cancer with otherwise lethal potential
Tumour-patient interactions across ages and genders
1. Paediatric cancers
per year in Europe
major types, many sub types
related to predisposition syndromes
Pediatric cancers are typical examples in which data sharing is critical as these diseases are rare and heterogeneous.
Very often, driver events are conditioned on the developmental stage in which the tumour arises, linking developmental biology to cancer research and treatment e.g., how immune system development impedes anticancer therapeutic initiatives such as immunotherapy remains poorly known.
Cancer predisposition syndromes account for ~10% of childhood cancers. For 90%, the causes are unknown and the low mutational burden typically observed in these tumours should not be confused with simplicity in their underlying mechanisms.
Most cancers arise over the age of 60
Most patients over 75 excluded from clinical trials
Most experimental cancer models ignore the role of ageing
Aeging is, by far, the most important risk factor for many types of cancer in humans, i.e., most cancers arise in individuals over the age of 60.
The mechanistic links between cancer and ageing are beginning to emerge as we are entering an exciting era in which rapidly increasing insights in the complex biology of ageing generates novel pharmaceutical approaches that may reduce ageing-associated late-onset human diseases including cancer, and strive to make people healthier longer. Considerable difficulties can be anticipated as to translate ageing research into cancer prevention (e.g., ageing associated cancer risk exhibits organ-specific temporal signatures, thus may require tissue specific interventions), but the potential rewards may outweigh the risks.
For example, elimination of cancer cells by induction of acute senescence has emerged as a concept in anticancer therapy, i.e., senolytic drugs combined with senescence-inducing drugs could maximize cancer treatment efficacy.
Also, compared to civil age-based treatment choice, longitudinal and mechanistic studies on ageing biological marks including proteins, epigenetics and metabolites will better stratify ageing cancer patients.
3. Sex and gender
335.9 cancers per 100 000 male inhabitants
193.9 cancers per 100 000 female inhabitants
Mortality rate higher in males
Differences in therapeutic response and toxicity
Hormones affect cancer risk
The mortality rate is 25% higher for males compared to females, sex disparities are apparent across a range of non-reproductive cancers and vary by age, and there are significant sex differences in therapeutic response and toxicity for many cancer types. Nevertheless, differences based on sex / gender are among the least studied factors affecting cancer emergence and therapeutic response.
Sex differences may arise due to a combination of environmental, genetic, and epigenetic factors, as well as differences in gene regulation and expression.
In addition, cancer risk and outcome is poorly explored in the 0.3–1.2% of the adult population in Europe identified as transgender (trans) or gender diverse.
Studying how hormones affect both cis and trans populations could improve our understanding of cancer for everyone.
UNCAN.eu will be committed to improve our understanding of the drivers of differences based on sex and gender and how their interactions with treatment could improve cancer outcome.
Quality of life of patients alive with cancer and cancer survivors
1.4 million cancer survivors
Risk or premature ageing
Evidence-based guidelines are needed
The consequence of decreasing cancer mortality entails a rapidly increasing number of cancer survivors (and patients leaving with cancer) reaching 1.4 million. The needs of this population require a novel and adapted level of understanding and research.
The best models for providing survivor care (including management of the adverse health effects and emotional toll on family members, friends, and caregivers that are part of the broad definition of survivorship) remain to be defined. Survivorship encompasses multiple aspects that could benefit from basic and translational research programmes. With few exceptions, these are not evidence-based guidelines for the follow-up care of cancer survivors.
An emerging concept is that some cancer treatments cause premature or accelerated ageing in survivors, thus already mentioned research on ageing and cancer may generate innovative approaches to prevent the late effects of cancer treatment.
Studies encompassing hypothesis-driven approaches that explore the predictive value of inflammatory and metabolic biomarkers, and discovery approaches identifying novel markers through harnessing genomic, proteomic, metabolomic and imaging on the basis of the latest scientific advances in the areas of statistics, artificial intelligence and machine learning, will generate a personalization of survivorship care to be validated in a clinical trial.
Therapeutic resistance in established cancers
Resistance to drugs and radiation therapy is the main limiting factor to achieving cancer cure. Eventual resistance of metastatic cancers to existing therapeutic approaches is still observed in a majority of metastatic tumours.
Therapeutic resistance usually involves a combination of several parameters that include:
Tumor volume and growth
To overcome resistance in difficult-to-cure cancers, multiple solutions have been proposed:
Improve the use of existing therapeutics
Monitore the response
Explore synthetic lethal vulnerabilities
Better understanding the ecosystem formed by the tumour and the host at local, regional and global levels might generate technological and pharmacological advances to monitor and manage difficult-to-cure cancers.