Biology subjects

Dabke, K.

Publications and source records attributed to Dabke, K..

4 recordsLinked to original sources

bioRxiv2025

The mutational landscape defines the proteome and spatial organization of tumor, stroma and immune cells in ovarian cancer

High-grade serous ovarian cancer (HGSOC) is a highly aggressive and lethal form of ovarian cancer. Challenges to diagnosis and treatment include a lack of effective screening methods for early detection, the absence of cancer-specific symptoms, and the development of chemoresistance. The genomic instability of HGSOC, further complicated by homologous recombination deficiency (HRD), leads to heterogeneity in HGSOC tumors and patient response to treatment. This makes it challenging to develop a single, effective diagnostic and treatment approach for this disease. Proteogenomic studies have provided some insight into HGSOC biology, but a deeper understanding of the tumor proteome through chemotherapy and disease recurrence is needed. Here we have profiled the proteome of tumors from 29 HGSOC patients before and after multiple rounds of chemotherapy. The proteome of HGSOC tumors remained unchanged for individual patients even after numerous rounds of chemotherapy. Differential expression analysis revealed known and novel proteins associated with chemoresistance and HRD status, further supported by similar changes at the genetic and epigenetic levels. We found that HRD affected proteins related to immune pathways. HRD was also associated with more shared T cell receptor (TCR) CDR3 repertoires of tumor-infiltrating T cells and increased neoantigen counts. ERAP1, a protein involved in peptide trimming before antigen presentation to immune cells via MHC-class I, was overexpressed in homologous recombination proficient (HRP) tumors and negatively correlated to neoantigen count. Its potential role in immune suppression in HRP tumors makes it an attractive therapeutic target that may be effective in combination with immunotherapy, particularly for HRP tumors. 26-plex immunostaining of HGSOC whole tissues further revealed significant differences in the spatial proximities of immune cells to each other and to tumor cells based on HRD status. Through proteomic and imaging analysis, this work has shown that the immune landscape of HGSOC tumors is influenced by homologous recombination status and identified candidate drivers of HGSOC biology.

cancer biology↗

bioRxiv2025

The mutational and clonality profile of HGSOC is established early in tumor development and conserved throughout therapy resistance

High grade serous ovarian cancer (HGSOC) is the most lethal gynecologic malignancy, killing more than 9,000 women each year in the United States alone. Nearly 80% of patients with HGSOC tumors will experience recurrence within 5 years, but little is known about the mechanisms that drive this process. Intratumor heterogeneity is believed to be a key feature of recurrence and resistance in HGSOC tumors, with early studies reporting diverse and complex mechanisms of the seeding of metastatic sites, including metastasis reseeding the primary tumor. Few studies have investigated temporal changes to clonality and structural variants through disease recurrence and the development of chemoresistance as surgical debulking is not frequently performed at this later stage. We performed multi-omic profiling in paired chemo-naive and chemoresistant tumors from 32 HGSOC patients to investigate the mutational and genomic landscape of disease progression. Somatic mutation profiles were largely conserved through disease progression. Mutational burdens did not significantly differ across recurrence but were driven by homologous recombination repair deficiency status. Clonal composition and dynamics were measured through variant allele frequency alterations as tumors progressed from primary chemo-naive to recurrent chemo-resistant tumors. A novel candidate driver gene, MDC1, from the homologous recombination repair pathway, was significantly mutated and over-represented in patients with homologous recombination proficient tumors, with somatic mutations clustered in a single exon. Tumor evolution and phylogeny revealed that few changes in clonal abundance and complexity occur across the disease course in these patients. Taking structural variants into account, homologous recombination repair proficient (HRP) tumors tend to be polyclonal while homologous recombination repair deficient (HRD) tumors tend to be monoclonal, accompanied by a longer progression-free survival than the HRP patients. Three distinct classes of tumors were identified by structural variant signature analysis: tumors defined by DNA losses, tumors defined by DNA gains, and tumors defined by copy number neutral changes, which were largely defined by HRD status. Each class displayed distinct regions of the chromosome that were frequently affected by large scale SV events (>5Mb). Although no regions were frequently altered in recurrent tumors, GO analysis revealed that recurrent tumors have a significantly reduced immune response, which was not seen in the primary tumors. Ultra long read sequencing validated a majority of the SVs identified in short read sequencing and identified additional SVs undetected by short reads. These analyses identify that the phenotype of high grade serous ovarian tumors as defined by mutation and clonality profiles is established early in disease development and remain largely unchanged through chemotherapy and recurrence. This, when considered with the significant inter-patient heterogeneity identified in HGSOC, demonstrates the need for personalized therapies based on tumor profiling.

genomics↗

bioRxiv2024

Identification of Disease-relevant, Sex-based Proteomic Differences in iPSC-derived Vascular Smooth Muscle

The prevalence of cardiovascular disease varies with sex, and the impact of intrinsic sex-based differences on vasculature is not well understood. Animal models can provide important insight into some aspects of human biology, however not all discoveries in animal systems translate well to humans. To explore the impact of chromosomal sex on proteomic phenotypes, we used iPSC-derived vascular smooth muscle cells from healthy donors of both sexes to identify sex-based proteomic differences and their possible effects on cardiovascular pathophysiology. Our analysis confirmed that differentiated cells have a proteomic profile more similar to healthy primary aortic smooth muscle than iPSCs. We also identified sex-based differences in iPSC- derived vascular smooth muscle in pathways related to ATP binding, glycogen metabolic process, and cadherin binding as well as multiple proteins relevant to cardiovascular pathophysiology and disease. Additionally, we explored the role of autosomal and sex chromosomes in protein regulation, identifying that proteins on autosomal chromosomes also show sex-based regulation that may affect the protein expression of proteins from autosomal chromosomes. This work supports the biological relevance of iPSC-derived vascular smooth muscle cells as a model for disease, and further exploration of the pathways identified here can lead to the discovery of sex-specific pharmacological targets for cardiovascular disease. SignificanceIn this work, we have differentiated 4 male and 4 female iPSC lines into vascular smooth muscle cells, giving us the ability to identify statistically-significant sex-specific proteomic markers that are relevant to cardiovascular disease risk (such as PCK2, MTOR, IGFBP2, PTGR2, and SULTE1).

molecular biology↗

bioRxiv2020

A Simple Optimization Workflow to Enable Precise and Accurate Imputation of Missing Values in Proteomic Datasets

Missing values in proteomic data sets have real consequences on downstream data analysis and reproducibility. Although several imputation methods exist to handle missing values, there is no single imputation method that is best suited for a diverse range of data sets and no clear strategy exists for evaluating imputation methods for large-scale DIA-MS data sets, especially at different levels of protein quantification. To navigate through the different imputation strategies available in the literature, we have established a workflow to assess imputation methods on large-scale label-free DIA-MS data sets. We used two distinct DIA-MS data sets with real missing values to evaluate eight different imputation methods with multiple parameters at different levels of protein quantification; dilution series data set and an independent data set with actual experimental samples. We found that imputation methods based on local structures within the data, like local least squares (LLS) and random forest (RF), worked well in our dilution series data set whereas, imputation methods based on global structures within the data, like BPCA performed well in our independent data set. We also found that imputation at the most basic level of protein quantification - fragment level-improved accuracy and number of proteins quantified. Overall, this study indicates that the most suitable imputation method depends on the overall structure and correlations of proteins within the data set and can be identified with the workflow presented here.

bioinformatics↗