Biology subjects

Rolfs, F.

Publications and source records attributed to Rolfs, F..

3 recordsLinked to original sources

bioRxiv2022

Multi-omics analysis reveals distinct non-reversion mechanisms of PARPi resistance in BRCA1- versus BRCA2-deficient mammary tumors

BRCA1 and BRCA2 both function in DNA double-strand break repair by homologous recombination (HR). Due to their HR-defect, BRCA1/2-deficient cancers are sensitive to poly(ADP-ribose) polymerase inhibitors (PARPi) but they eventually acquire resistance. Preclinical studies yielded several PARPi resistance mechanisms that do not involve BRCA1/2 reactivation, but their relevance in the clinic remains elusive. To investigate which BRCA1/2-independent mechanisms drive spontaneous resistance in vivo, we combined molecular profiling with functional analysis of the HR status of matched PARPi-naive and PARPi-resistant mouse mammary tumors harboring large intragenic deletions that prevent functional restoration of BRCA1/2. We observed restoration of HR in 64% of PARPi-resistant BRCA1-deficient tumors but none in the PARPi-resistant BRCA2-deficient tumors. Moreover, we found that 53BP1 loss is the prevalent resistance mechanism in HR-proficient BRCA1-deficient tumors, whereas resistance in BRCA2-deficient tumors is mainly induced by the loss of PARG. Our combined multi-omics analysis catalogued additional genes and pathways potentially involved in modulating PARPi response.

cancer biology↗

bioRxiv2020

Feasibility of phosphoproteomics on leftover samples after RNA extraction with guanidinium thiocyanate

In daily practice, different types of biomolecules are usually extracted for large-scale omics analysis with tailored protocols. However, when sample material is limited, an all-in-one strategy is preferable. While lysis of cells and tissues with urea is the accepted standard for phosphoproteomic applications, DNA, RNA and proteins can be simultaneously extracted from small samples using acid guanidinium thiocyanate-phenol-chloroform (AGPC). Use of AGPC for mass spectrometry (MS)-based phosphoproteomics has been reported, but not benchmarked. Here we compared urea-with AGPC-based protein extraction, profiling phosphorylations in the DNA damage response pathway after ionizing irradiation of U2OS cells as proof of principle. On average we identified circa 9000 phosphosites per sample with both extraction methods. Moreover, we observed high similarity of phosphosite characteristics (e.g. 94% shared class 1 identifications) and deduced kinase activities (e.g. ATM, ATR, CHEK1/2, PRKDC). AGPC-based sample extraction can thus replace standard cell lysates for phosphoproteomic workflows and may thus be an attractive way to obtain input material for multiple omics workflows, yielding several data types from a single sample.

molecular biology↗

bioRxiv2018

INKA, an integrative data analysis pipeline for phosphoproteomic inference of active phosphokinases

Identifying (hyper)active kinases in cancer patient tumors is crucial to enable individualized treatment with specific inhibitors. Conceptually, kinase activity can be gleaned from global protein phosphorylation profiles obtained with mass spectrometry-based phosphoproteomics. A major challenge is to relate such profiles to specific kinases to identify (hyper)active kinases that may fuel growth/progression of individual tumors. Approaches have hitherto focused on phosphorylation of either kinases or their substrates. Here, we combine kinase-centric and substrate-centric information in an Integrative Inferred Kinase Activity (INKA) analysis. INKA utilizes label-free quantification of phosphopeptides derived from kinases, kinase activation loops, kinase substrates deduced from prior experimental knowledge, and kinase substrates predicted from sequence motifs, yielding a single score. This multipronged, stringent analysis enables ranking of kinase activity and visualization of kinase-substrate relation networks in a biological sample. As a proof of concept, INKA scoring of phosphoproteomic data for different oncogene-driven cancer cell lines inferred top activity of implicated driver kinases, and relevant quantitative changes upon perturbation. These analyses show the ability of INKA scoring to identify (hyper)active kinases, with potential clinical significance.

systems biology↗