Biology subjects

Yavuz, B. R.

Publications and source records attributed to Yavuz, B. R..

6 recordsLinked to original sources

bioRxiv2026

Single-Cell Mapping of Malignant Signaling Networks Guides Drug Combinations

Single-cell breast cancer atlases reveal malignant, immune, and stromal diversity; however, how the recurrent signaling pathways drive untreated malignant-cell states and could inform combination therapy remains unclear. Here, we analyzed 15,753 malignant cells from untreated primary breast tumors using a cell-resolved network framework. Individual transcriptomes were projected onto a protein-protein interaction network, partitioned into Leiden communities, and annotated by pathway enrichment. Pathway recurrence was evaluated against matched null models preserving community size, protein-network degree, and gene detection rate. Before null correction, recurrent pathways included PI3K/AKT, MAPK, JAK/STAT, and HIF-1 (hypoxia-inducible factor 1) signaling. After correction, HIF-1 emerged as the dominant recurrent signal across patients, indicating convergence of diverse upstream pathways on a shared hypoxia- and stress-adaptive malignant-cell program. The recurrent JAK/STAT, cAMP, glucagon, oxytocin, and thyroid hormone signaling suggest inflammatory, metabolic, and endocrine crosstalk. These findings support rational drug combinations targeting HIF-1 together with upstream PI3K/AKT/mTOR, MAPK, or JAK/STAT signaling.

bioinformatics↗

bioRxiv2025

Cellular Mechanisms Underlying Melanoma Brain Metastasis

Brain metastasis is a major contributor to melanoma-related mortality, arising from both genomic alterations and dynamic adaptations to the brain microenvironment. Tumor cells must cross the blood-brain barrier and exploit brain-specific signaling and metabolic landscapes to establish metastases. Brain metastases display molecular and phenotypic programs distinct from other organs, reflecting pre-existing and acquired mutations during progression. We analyzed primary melanoma, metastatic melanoma, and metastatic brain tumors genomic datasets to identify mutated proteins and signaling pathways that are common to or distinct among these tumor types. Our comprehensive computational analyses uncovered key pathways and proteins associated with brain metastasis. Brain metastases exhibit unique mutational landscapes and convergent alterations in neurotrophin, MAPK, and PI3K/AKT signaling compared with primary and metastatic melanoma. Proteins especially frequent in brain metastases pathways support survival, proliferation, and adaptation in the brain microenvironment. Pharmacology-wise, rational strategies such as combining c-KIT and VEGFR inhibitors can suppress converging oncogenic pathways and mitigate resistance in advanced melanoma. Co-targeting neurotrophin receptors with MAPK pathway members, particularly in combination with BRAF inhibitors, appears a promising approach for treating melanoma brain metastases. Our findings underscore the molecular heterogeneity of melanoma brain metastases, shaped by interconnected signaling networks, and reveal therapeutic targets that may diverge from those in other metastatic sites.

bioinformatics↗

bioRxiv2024

Anticancer Target Combinations: Network-Informed Signaling-Based Approach to Discovery

While anticancer drug discovery has seen dramatic innovations and successes, sequential single therapies are time-limited by resistance, and combinatorial strategies have been lagging. The number of possible drug combinations is vast. To select drug combinations the oncologist requires knowledge of the optimal combination of proteins to co-target. Currently, combinations that the oncologist considers are primarily from empirical observations and clinical praxis. Our aim is to develop a signaling-based method to discover optimal proteins for the oncologist to co-target with drug combinations, and test it on available, patient-derived data. To temper the expected resistance to single drug regimen, we offer a concept-based stratified pipeline aimed at selecting co-targets for drug combinations. Our strategy is unique in its co-target selection being based on signaling pathways. This is significant since in cancer, drug resistance commonly bypasses blocked proteins by wielding alternative, or complementary, routes to execute cell proliferation. Our network-informed signaling-based approach harnesses advanced network concepts and metrics, and our compiled, tissue-specific co-existing mutations. Co-existing driver mutations are common in resistance. Thus, to mimic cancer and counter drug resistance scenarios, our pipeline seeks co-targets that when targeted by drug combinations, can shut off cancers modus operandi. That is, its parallel or complementary signaling pathways would be blocked. Rotating through combinations could further lessen emerging resistance. We applied it to patient-derived breast and colorectal ESR1|PIK3CA and BRAF|PIK3CA subnetworks. Consistently, in breast cancer, our results suggest co-targeting proteins from the ESR1|PIK3CA subnetwork with an alpelisib-LJM716 combination. In colorectal cancer, they co-target BRAF|PIK3CA with alpelisib, cetuximab, and encorafenib combination. Collectively, our pipelines results are promising, and validated by patient-based xenografts. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/617918v1_ufig1.gif" ALT="Figure 1"> View larger version (65K): org.highwire.dtl.DTLVardef@81dc11org.highwire.dtl.DTLVardef@197012dorg.highwire.dtl.DTLVardef@ce3853org.highwire.dtl.DTLVardef@d3ec3a_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2024

Co-occurring Mutations in Different Genes Can Fuel Oncogenic Signaling and Serve as Metastatic Tumor Markers

Interrogation of big genomic data and integration with large-scale protein-protein interaction networks and pathways, can provide deep patterns that are rare- yet can prompt dramatic phenotypic alterations and serve as clinical signatures. Mapping cancer-specific co-occurring mutation-pair signatures, in primary and metastatic tumors, is indispensable in precision oncology. The additivity of co-occurring driver mutations in different genes (in trans) can lead to powerful proliferation signals. Co-occurring rare in trans combinations can serve as metastasis markers; excluded combinations may indicate candidates for oncogene-induced senescence (OIS), a tumor-suppressive mechanism. Our statistical framework of the pan-cancer mutation profiles of [~]60,000 tumor sequences from the TCGA and AACR GENIE databases, identified 3424 statistically significant different double mutations in non-redundant pathways, that is, have different downstream targets that may promote specific cancers through single or multiple pathways. Our analysis indicates that they are mostly in primary tumors. We list actionable in trans mutations for 2385 metastatic tumors and provide co-occurrence trees of metastatic breast- cancer markers. This innovative work clarifies the mechanistic conceptual basis and establishes the first of its kind tool for identifying and predicting metastasis. Crucially, when coupled with their proliferative functions and pathways, and linked with drugs, it could provide an invaluable metastasis-targeting resource.

systems biology↗

bioRxiv2023

Neurodevelopmental disorders and cancer networks share pathways; but differ in mechanisms, signaling strength, and outcome

Neurodevelopmental disorders (NDDs) and cancer are connected, with immunity as their common factor. Their clinical presentations differ; however, individuals with NDDs are more likely to acquire cancer. Schizophrenia patients have [~]50% increased risk; autistic individuals also face an increased cancer likelihood. NDDs are associated with specific brain cell types at specific locations, emerging at certain developmental time windows during brain evolution. Their related mutations are germline; cancer mutations are sporadic, emerging during life. At the same time, NDDs and cancer share proteins, pathways, and mutations. Here we ask exactly which features they share, and how despite their commonality, they differ in outcomes. Our pioneering bioinformatics exploration of the mutations, reconstructed disease-specific networks, pathways, and transcriptome profiles of autism spectrum disorder (ASD) and cancers, points to elevated signal strength in pathways related to proliferation in cancer, and differentiation in ASD. Signaling strength, not the activating mutation, is the key factor in deciding cancer versus NDDs.

systems biology↗

bioRxiv2021

Discovery of Latent Drivers from Double Mutations in Pan-Cancer Data Reveal their Clinical Impact

Background Transforming patient-specific molecular data into clinical decisions is fundamental to personalized medicine. Despite massive advancements in cancer genomics, to date driver mutations whose frequencies are low, and their observable transformation potential is minor have escaped identification. Yet, when paired with other mutations in cis, such latent driver mutations can drive cancer. Here, we discover potential latent driver double mutations. Method We applied a statistical approach to identify significantly co-occurring mutations in the pan-cancer data of mutation profiles of [~]80,000 tumor sequences from the TCGA and AACR GENIE databases. The components of same gene doublets were assessed as potential latent drivers. We merged the analysis of the significant double mutations with drug response data of cell lines and patient derived xenografts (PDXs). This allowed us to link the potential impact of double mutations to clinical information and discover signatures for some cancer types. Results Our comprehensive statistical analysis identified 228 same gene double mutations of which 113 mutations are cataloged as latent drivers. Oncogenic activation of a protein can be through either single or multiple independent mechanisms of action. Combinations of a driver mutation with either a driver, a weak driver, or a strong latent driver have the potential of a single gene leading to a fully activated state and high drug response rate. Tumor suppressors require higher mutational load to coincide with double mutations compared to oncogenes which implies their relative robustness to losing their functions. Evaluation of the response of cell lines and patient-derived xenograft data to drug treatment indicate that in certain genes double mutations can increase oncogenic activity, hence a better drug response (e.g. in PIK3CA), or they can promote resistance to the drugs (e.g. in EGFR). Conclusion Our comprehensive analysis of same allele double mutations in cancer genome landscapes emphasizes that interrogation of big genomic data and integration with the results of large-scale small-molecule sensitivity data can provide deep patterns that are rare; but can still result in dramatic phenotypic alterations, and provide clinical signatures for some cancer types.

systems biology↗