Study Design - Rare Cancers Genomics

Multidisciplinary and multi-omics molecular characterisation of rare cancers

Rare Cancers Genomics

Rare cancers (those with an incidence below 6 out of 100,000 per year) account for ~25–30% of all cancer diagnoses and 25% of cancer deaths, representing a substantial burden of disease. However, basic science research, clinical trials and approval of new therapies for rare cancers are lacking. This translates into a worse prognosis for patients with a rare cancer than for those with common cancers. As research evolves and common cancers are reclassified based on molecular and genomic markers into rare subtypes, the novel ways in which rare cancers are being studied will be extended to all cancer types. Consequently, with the number of rare cancers increasing (currently rising by 0.5% annually), finding more appropriate solutions for diagnosing, managing and studying rare cancers is essential (Barker & Scott Nat Rev Cancer 2019).

Taking advantage of the expertise provided by a multidisciplinary team, and by means of state-of-the-art computational analyses (including machine learning and AI) as well as exceptional bio-repositories, our research aims at providing a better understanding of the molecular characteristics of rare cancers.

We focus our research on the following three specific aims :

 
 
 

Identify

Identify the molecular characteristics that may inform the carcinogenesis and aetiology of these cancers
 

Provide

Provide the necessary data to generate a more clinically relevant classification of tumours
 

Improve

Improve the clinical management by identifying and validating novel candidate diagnostic, prognostic, and predictive biomarkers

To achieve our aims we follow different approaches:

1)

Perform integrative multi-omics molecular analyses of large bio-repositories with good quality of samples and detailed pathological, clinical and epidemiological annotations

2)

Integrate big data generated from multiple large-scale genomics initiatives to expedite the translation of this research to the classification of tumours

3)

Assess the clinical value of the candidate biomarkers

4)

Review and identify new morphological characteristics using image-based AI and integrate them with the molecular data

5)

Use state-of-the-art in vitro organoid models to study the cancer initiation and progression