A software for the identification of somatic variants in cancers. 


TiNCan is a software built for the identification of somatic variants in cancers. The algorithm is able to identify somatic variants in multiple cancers (particularly blood cancers) that can be missed using current state-of-the-art variant calling methods. 

TiNCan recognises that the identification of the present somatic variants from tumour sequence data and matched normal samples could be improved by considering the tumour in normal (TiN) contamination. Each potential variant is assessed using filters and a Bayesian probability model to determine the likelihood of it being a tumour-associated somatic variant as opposed to a germline variation.


This technology doesn’t require a “normal” matched sample as it compares tumour and ‘normal’ samples from the same patient but assumes there is ‘tumour in normal’ (TiN) contamination.


  • It is a reliable tool for detecting, cataloguing and interpreting somatic mutations; to inform reliable diagnosis, patient stratification and potential target identification.
  • It does not require a “normal” matched sample as it compares tumour and ‘normal’ samples from the same patient but assumes there is ‘tumour in normal’ (TiN) contamination
  • Genomic screening that involves detecting somatic mutations  

Comparable Technologies

Detecting, cataloguing and interpreting the somatic mutations that drive the transformation from normal cells to malignant cells is essential to develop our understanding of cancer. In order to unambiguously identify somatic mutations in a cancer genome, many current approaches require the knowledge of the corresponding germline single nucleotide polymorphisms (SNPs), which is obtained using a matched normal sample (typically a blood sample). This is problematic for haematological malignancies as obtaining germline DNA from such patients is very difficult.


A combination of lifestyle choices and an ageing population is thought to be responsible for the increased incidence of blood cancers. In current clinical practice, many treatment decisions are based on patients’ predicted outcomes, whether that is judged by stage, grade or genetics.

Risk stratification of patients by incorporating genomic data more accurately predicts clinical outcomes. This can inform patients about their future prognosis but also aid clinicians in management decisions such as monitoring frequency, and treatment options so that toxic and/or expensive treatments such as bone marrow transplants are only given to patients who will benefit, reducing unnecessary suffering and saving on healthcare costs without compromising overall survival rates, respectively. 

Dr Jyoti Nangalia Is Principal Investigator and a Consultant Haematologist interested in studying human blood samples across lifespan from development to ageing and precancer to cancer. Her group has a particular interest in myeloproliferative neoplasms and has already developed several tools that are already creating an impact in the clinic.

Related publication: https://www.nature.com/articles/s41586-021-04312-6

Intellectual property

The Wellcome Sanger Institute is offering non-exclusive licenses to the algorithm for further development or commercial use.



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