A Fujimoto

A Gonzalez-Perez

A Landau

AN Bullock

B Vogelstein

DC García-Olmo

F Mouliere

G Perkins

G Sozzi

I Kinde

I Varela

J Ferlay

JB Hiatt

K Wang

LA Diaz

LB Alexandrov

M Gerlinger

M Greaves

MA Chapman

N The Cancer Genome Atlas Research

P Martinez

S Misale

S Nik-Zainal

S-J Dawson

SM Jhiang

ST Sherry

T Forshew

Y Dennis Lo

Y Sato

Scientific Reports

Pierre Martinez

Nicholas McGranahan

Nicolai Juul Birkbak

Marco Gerlinger

Charles Swanton

Crossref

Computational optimisation of targeted DNA sequencing for cancer detection

Despite recent progress thanks to next-generation sequencing technologies, personalised cancer medicine is still hampered by intra-tumour heterogeneity and drug resistance. As most patients with advanced metastatic disease face poor survival, there is need to improve early diagnosis. Analysing circulating tumour DNA (ctDNA) might represent a non-invasive method to detect mutations in patients, facilitating early detection. In this article, we define reduced gene panels from publicly available datasets as a first step to assess and optimise the potential of targeted ctDNA scans for early tumour detection. Dividing 4,467 samples into one discovery and two independent validation cohorts, we show that up to 76% of 10 cancer types harbour at least one mutation in a panel of only 25 genes, with high sensitivity across most tumour types. Our analyses demonstrate that targeting "hotspot" regions would introduce biases towards in-frame mutations and would compromise the reproducibility of tumour detection

Computational optimisation of targeted DNA sequencing for cancer detection.

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