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Biology subjects

De Marchi, T.

Publications and source records attributed to De Marchi, T..

2 recordsLinked to original sources

Evolution of ipsilateral breast cancer decoded by proteogenomics

Ipsilateral breast tumor recurrence (IBTR) is a clinically important event, where an isolated in-breast recurrence is a potentially curable event but also associated with an increased risk of distant metastases and breast cancer death. It currently remains unclear if IBTRs are associated with molecular changes that can be explored as a resource for precision medicine strategies targeting locally recurring breast cancer. Here, we employed a recently developed proteogenomics workflow to analyze a cohort of 27 primary breast cancers and their matched IBTRs by whole genome sequencing, RNA sequencing, and mass spectrometry-based proteomics to define proteogenomic features of tumor evolution. Analysis of mutational signatures, copy number changes, and cancer specific mutations revealed a relationship with estrogen and progesterone receptor statuses and increased levels of genetic change. This in turn altered the re-programming of the transcriptome and proteome towards a recurring molecular disease phenotype with high replicating capacity and a higher degree of genomic instability possibly enhanced by high expression of APOBEC3B. In conclusion, this study defines how primary breast tumors differentially evolve into different ipsilateral recurrent malignancies depending on their key biomarker status, suggesting that further enhancing the genomic instability in some tumors could serve as an alternative treatment option.

cancer biology↗

A Proteogenomic workflow reveals distinct molecular phenotypes related to breast cancer appearance

Proteogenomics approaches have enabled the generation of extensive information levels when compared to single omics technology studies, although burdened by massive experimental efforts. Here, we developed four improvements of a data independent acquisition mass spectrometry proteogenomics workflow to reveal distinct molecular phenotypes related to breast cancer appearance. We confirm mutational processes detectable at the protein level and highlight quantitation and pathway complementarity between RNA and protein data. Our analyses also validated previously established enrichments of estrogen receptor-dependent molecular features relating to transcription factor expression, and provided evidence for molecular differences related to the presence of mammographic appearances in spiculated tumors. In addition, several transcript-protein pairs displayed radically different abundance correlations depending on the overall clinical and pathological properties of the tumor. These results demonstrate that there are differentially regulated protein networks in clinically relevant sample groups, and that these protein networks influence both cancer biology as well as the abundance of potential biomarkers and drug targets.

systems biology↗