Biology subjects

Prosz, A.

Publications and source records attributed to Prosz, A..

5 recordsLinked to original sources

bioRxiv2026

Identification of an ERCC2 mutation associated mutational signature of nucleotide excision repair deficiency in targeted panel sequencing data

Next generation sequencing based mutational signatures are frequently used to identify tumors with specific DNA repair deficiencies for targeted therapeutic strategies. Although mutational signatures are most commonly derived from whole exome (WES) or whole genome sequencing (WGS) data, more patients currently undergo tumor sequencing using more limited targeted panels that typically encompass several hundred cancer-associated genes. Identifying clinically relevant mutational signatures from targeted panel data requires new approaches capable of deriving signatures from the more limited sequencing data. Here, we derive and validate a panel sequencing-based composite mutational signature associated with nucleotide excision repair (NER) deficiency induced by inactivating ERCC2 mutations in bladder cancer. Using publicly available panel sequencing data, we find that ERCC2 wild type (WT) bladder cancer cases that have high levels of this mutational signature respond better to neoadjuvant platinum therapy and have improved overall survival compared to ERCC2 WT cases with low levels of the signature. We also find that other solid tumor types with ERCC2 mutations also show the characteristic mutational signature seen in NER-deficient ERCC2-mutant bladder cancers, suggesting a novel approach to therapeutically target these ERCC2-mutant solid tumors beyond bladder cancer.

bioinformatics↗

bioRxiv2023

Replicative stress in gastroesophageal adenocarcinoma is associated with chromosomal instability and sensitivity to DNA damage response inhibitors.

Gastroesophageal adenocarcinoma (GEA) is an aggressive, often lethal, malignancy that displays marked chromosomal instability (CIN). To understand adaptive responses that enable CIN, we analyzed paired normal, premalignant, and malignant gastric lesions from human specimens and a carcinogen-induced mouse model, observing activation of replication stress, DNA damage response (DDR), and cell cycle regulator p21 in neoplastic progression. In GEA cell lines, expression of DDR markers correlated with ploidy abnormalities, including high-level focal amplifications and whole-genome duplication (WGD). Moreover, high expression of DNA damage marker H2AX correlated with CIN, WGD, and inferior patient survival. By developing and implementing a composite diagnostic score that incorporates TP53 mutation status, ploidy abnormalities, and H2AX expression, among other genomic information, we can identify GEA cell lines with enhanced sensitivity to DDR pathway inhibitors targeting Chk1/2 and Wee1. Anti-tumor properties were further augmented in combination with irinotecan (SN38) but not gemcitabine chemotherapy. These results implicate specific DDR biomarkers and ploidy abnormalities as diagnostic proxy that may predict premalignant progression and response to DDR pathway inhibitors.

cancer biology↗

bioRxiv2023

Nucleotide excision repair deficiency is a targetable therapeutic vulnerability in clear cell renal cell carcinoma.

PurposeDue to a demonstrated lack of DNA repair deficiencies, clear cell renal cell carcinoma (ccRCC) has not benefitted from targeted synthetic lethality-based therapies. We investigated whether nucleotide excision repair (NER) deficiency is present in an identifiable subset of ccRCC cases that would render those tumors sensitive to therapy targeting this specific DNA repair pathway aberration. Experimental DesignWe used functional assays that detect UV-induced 6-4 pyrimidine-pyrimidone photoproducts to quantify NER deficiency in ccRCC cell lines. We also measured sensitivity to irofulven, an experimental cancer therapeutic agent that specifically targets cells with inactivated transcription-coupled nucleotide excision repair (TC-NER). In order to detect NER deficiency in clinical biopsies, we assessed whole exome sequencing data for the presence of an NER deficiency associated mutational signature previously identified in ERCC2 mutant bladder cancer. ResultsFunctional assays showed NER deficiency in ccRCC cells. Irofulven sensitivity increased in some cell lines. Prostaglandin reductase 1 (PTGR1), which activates irofulven, was also associated with this sensitivity. Next generation sequencing data of the cell lines showed NER deficiency-associated mutational signatures. A significant subset of ccRCC patients had the same signature and high PTGR1 expression. ConclusionsccRCC cell line based analysis showed that NER deficiency is likely present in this cancer type. Approximately 10% of ccRCC patients in the TCGA cohort showed mutational signatures consistent with ERCC2 inactivation associated NER deficiency and also substantial levels of PTGR1 expression. These patients may be responsive to irofulven, a previously abandoned anticancer agent that has minimal activity in NER-proficient cells. Translational relevanceDNA repair deficiencies can be therapeutically targeted by synthetic lethal-based strategies in cancer. However, clear cell renal cell carcinoma (ccRCC) has not benefitted from this therapeutic approach due to a lack of evidence for the presence of specific DNA repair pathway deficiencies. Here, we demonstrate that ccRCC harbors a therapeutically targetable DNA repair pathway aberration, nucleotide excision repair (NER) deficiency. ccRCC cell lines displayed robust signs of NER deficiency as determined by functional assays and some of these cell lines also displayed NER deficiency induced mutational signatures. These cell lines are also sensitive to irofulven, an abandoned anticancer agent that creates DNA lesions which can only be repaired by the NER pathway. We estimate that up to 10% of ccRCC cases may respond to NER-directed therapy with irofulven based on NER deficiency associated mutational signatures and PTGR1 expression levels, which is an enzyme required to activate irofulven.

cancer biology↗

bioRxiv2022

Diagnostic tool to identify and treat DNA repair deficient gastroesophageal adenocarcinomas

Background and aimsDNA repair deficiency is a common feature of cancer. Homologous recombination (HR) and nucleotide excision repair (NER) are the two most frequently disabled DNA repair pathways in solid tumors. HR deficient breast, ovarian, pancreatic and prostate cancers respond well to platinum chemotherapy and PARP inhibitors. However, the frequency of DNA repair pathway deficiency in gastric and esophageal adenocarcinoma (GEA) still lacks diagnostic and functional validation. Furthermore, whether DNA repair deficient GEA have enhanced responsiveness to platinum chemotherapy and sensitivity to PARP inhibitors is not well characterized. MethodsUsing whole exome and genome sequencing data, we measured various HR deficiency-associated mutational signatures in patient specimen of gastric, esophageal and colorectal cancer specimens and gastric cancer cell lines. Gold-standard immunofluorescence assays were used to confirm HR and NER deficiency in cancer cell lines. The relationship between PARP inhibitor treatment and tumor response was evaluated in patients with gastric cancer. Drug sensitivity was determined using standard in vitro cell culture assays. Single-cell RNA-sequencing was performed to evaluate gastric cancer response to commonly used chemotherapeutics. ResultsWe found that a significant subset of GEA, but very few colorectal tumors, show evidence of HR deficiency by mutational signature analysis (HRD score). Gastric cancer cell lines with high HRD mutational signature scores demonstrated functional HR deficiency by RAD51 assay and increased sensitivity to platinum and PARP inhibitors. There was a positive association between HRD scores and tumor response in patients with gastric cancer treated with a PARP inhibitor on a clinical trial. A gastric cancer cell line with strong sensitivity to cisplatin showed HR proficiency but exhibited NER deficiency by DDB2 proteo-probe assay. Single-cell RNA-sequencing revealed that, in addition to inducing general apoptosis, cisplatin treatment triggered ferroptosis in a NER-deficient gastric cancer, which may explain the outlier sensitivity. ConclusionA subset of upper gastrointestinal tumors have genomic features of HR and NER deficiency and therefore may be more likely to benefit from platinum chemotherapy and PARP inhibition.

cancer biology↗

bioRxiv2021

A billion synthetic 3D-antibody-antigen complexes enable unconstrained machine-learning formalized investigation of antibody specificity prediction

Machine learning (ML) is a key technology for accurate prediction of antibody-antigen binding. Two orthogonal problems hinder the application of ML to antibody-specificity prediction and the benchmarking thereof: The lack of a unified ML formalization of immunological antibody specificity prediction problems and the unavailability of large-scale synthetic benchmarking datasets of real-world relevance. Here, we developed the Absolut! software suite that enables parameter-based unconstrained generation of synthetic lattice-based 3D-antibody-antigen binding structures with ground-truth access to conformational paratope, epitope, and affinity. We formalized common immunological antibody specificity prediction problems as ML tasks and confirmed that for both sequence and structure-based tasks, accuracy-based rankings of ML methods trained on experimental data hold for ML methods trained on Absolut!-generated data. The Absolut! framework thus enables real-world relevant development and benchmarking of ML strategies for biotherapeutics design. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=195 SRC="FIGDIR/small/451258v3_ufig1.gif" ALT="Figure 1"> View larger version (86K): org.highwire.dtl.DTLVardef@4c3a6aorg.highwire.dtl.DTLVardef@8f1deborg.highwire.dtl.DTLVardef@1f55ec6org.highwire.dtl.DTLVardef@2135dd_HPS_FORMAT_FIGEXP M_FIG The software framework Absolut! enables (A,B) the generation of virtually arbitrarily large numbers of synthetic 3D-antibody-antigen structures, (C,D) the formalization of antibody specificity as machine learning (ML) tasks as well as the exploration of ML strategies for real-world antibody-antigen binding or paratope-epitope prediction. C_FIG HighlightsO_LISoftware framework Absolut! to generate an arbitrarily large number of synthetic 3D-antibody-antigen structures that contain biological layers of antibody-antigen binding complexity that render ML predictions challenging C_LIO_LIImmunological antibody specificity prediction problems formalized as machine learning tasks for which the in silico complexes are immediately usable as benchmark datasets C_LIO_LIExploration of machine learning prediction accuracy as a function of architecture, dataset size, choice of negatives, and sequence-structure encoding C_LIO_LIRelative ML performance learnt on Absolut! datasets transfers to experimental datasets C_LI

immunology↗