Evolutionary signatures of human cancers revealed via genomic analysis of over 35,000 patients

Diletta Fontana, Ilaria Crespiatico, Valentina Crippa, Federica Malighetti, Matteo Villa, Fabrizio Angaroni, Luca De Sano, Andrea Aroldi, Marco Antoniotti, Giulio Caravagna, Rocco Piazza, Alex Graudenzi (), Luca Mologni and Daniele Ramazzotti ()
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Diletta Fontana: University of Milano-Bicocca
Ilaria Crespiatico: University of Milano-Bicocca
Valentina Crippa: University of Milano-Bicocca
Federica Malighetti: University of Milano-Bicocca
Matteo Villa: University of Milano-Bicocca
Fabrizio Angaroni: University of Milano-Bicocca
Luca De Sano: University of Milano-Bicocca
Andrea Aroldi: University of Milano-Bicocca
Marco Antoniotti: University of Milano-Bicocca
Giulio Caravagna: University of Trieste
Rocco Piazza: University of Milano-Bicocca
Alex Graudenzi: University of Milano-Bicocca
Luca Mologni: University of Milano-Bicocca
Daniele Ramazzotti: University of Milano-Bicocca

Nature Communications, 2023, vol. 14, issue 1, 1-18

Abstract: Abstract Recurring sequences of genomic alterations occurring across patients can highlight repeated evolutionary processes with significant implications for predicting cancer progression. Leveraging the ever-increasing availability of cancer omics data, here we unveil cancer’s evolutionary signatures tied to distinct disease outcomes, representing “favored trajectories” of acquisition of driver mutations detected in patients with similar prognosis. We present a framework named ASCETIC (Agony-baSed Cancer EvoluTion InferenCe) to extract such signatures from sequencing experiments generated by different technologies such as bulk and single-cell sequencing data. We apply ASCETIC to (i) single-cell data from 146 myeloid malignancy patients and bulk sequencing from 366 acute myeloid leukemia patients, (ii) multi-region sequencing from 100 early-stage lung cancer patients, (iii) exome/genome data from 10,000+ Pan-Cancer Atlas samples, and (iv) targeted sequencing from 25,000+ MSK-MET metastatic patients, revealing subtype-specific single-nucleotide variant signatures associated with distinct prognostic clusters. Validations on several datasets underscore the robustness and generalizability of the extracted signatures.

Date: 2023
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DOI: 10.1038/s41467-023-41670-3

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