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The genomic imprint of cancer therapies helps timing the formation of metastases.

Eszter NémethMarcin KrzystanekLilla ReinigerDezső RibliOrsolya PipekZsófia SztupinszkiTibor GlaszIstván CsabaiJudit MoldvayZoltan SzallasiDávid Szüts
Published in: International journal of cancer (2019)
A retrospective determination of the time of metastasis formation is essential for a better understanding of the evolution of oligometastatic cancer. This study was based on the hypothesis that genomic alterations induced by cancer therapies could be used to determine the temporal order of the treatment and the formation of metastases. We analysed the whole genome sequence of a primary tumour sample and three metastatic sites derived from autopsy samples from a young never-smoker lung adenocarcinoma patient with an activating EGFR mutation. Mutation detection methods were refined to accurately detect and distinguish clonal and subclonal mutations. In comparison to a panel of samples from untreated smoker or never-smoker patients, we showed that the mutagenic effect of cisplatin treatment could be specifically detected from the base substitution mutations. Metastases that arose before or after chemotherapeutic treatment could be distinguished based on the allele frequency of cisplatin-induced dinucleotide mutations. In addition, genomic rearrangements and late amplification of the EGFR gene likely induced by afatinib treatment following the acquisition of a T790M gefitinib resistance mutation provided further evidence to tie the time of metastasis formation to treatment history. The established analysis pipeline for the detection of treatment-derived mutations allows the drawing of tumour evolutionary paths based on genomic data, showing that metastases may be seeded well before they become detectable by clinical imaging.
Keyphrases
  • small cell lung cancer
  • squamous cell carcinoma
  • epidermal growth factor receptor
  • mass spectrometry
  • genome wide
  • machine learning
  • case report
  • newly diagnosed
  • photodynamic therapy
  • big data