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.

Fontana, D., Crespiatico, I., Crippa, V., Malighetti, F., Villa, M., Angaroni, F., et al. (2023). Evolutionary signatures of human cancers revealed via genomic analysis of over 35,000 patients. NATURE COMMUNICATIONS, 14(1) [10.1038/s41467-023-41670-3].

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

Fontana, D;Crespiatico, I;Crippa, V;Malighetti, F;Villa, M;Angaroni, F;Aroldi, A;Antoniotti, M;Caravagna, G;Piazza, R;Graudenzi, A
;
Mologni, L;Ramazzotti, D
2023

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.
Articolo in rivista - Articolo scientifico
Cancer evolution, Cancer data science, Personalized medicine
English
25-set-2023
2023
14
1
5982
open
Fontana, D., Crespiatico, I., Crippa, V., Malighetti, F., Villa, M., Angaroni, F., et al. (2023). Evolutionary signatures of human cancers revealed via genomic analysis of over 35,000 patients. NATURE COMMUNICATIONS, 14(1) [10.1038/s41467-023-41670-3].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/440199
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