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Kalender Atak, Z.

Publications and source records attributed to Kalender Atak, Z..

5 recordsLinked to original sources
bioRxiv2025

Pan-cancer analysis in the real-world setting uncovers immunogenomic drivers of acquired resistance post-immunotherapy

Immune checkpoint blockade (ICB) has revolutionised cancer therapy, yet resistance -- both primary and acquired -- remains a significant obstacle, affecting the majority of patients. Here, we leveraged a large-scale, real-world clinicogenomic dataset to systematically explore the molecular underpinnings of ICB resistance in the post-progression setting. Analysing over 5,000 pan-cancer patients with clinical and pre-/post-treatment genomic and transcriptomic data, we identify distinct immunogenomic drivers of acquired vs. primary ICB resistance. Post-ICB progression, acquired resistance showed extended survival compared to primary resistance across all cancer types. The acquired resistance clinical phenotype was paralleled by a universally immune-inflamed, albeit dysfunctional, tumour microenvironment (TME) at the onset of acquired resistance, with sustained or ICB-induced inflammatory and interferon responses. We confirm previously described mechanisms of acquired resistance, including B2M loss-of-function (LoF) in non-small cell lung cancer (NSCLC), and identify novel potential mediators, including LoF of TGFBR2 in NSCLC, CYLD in head and neck cancer, and RUNX1 in triple negative breast cancer. Further supporting their involvement in resistance, these acquired ICB alterations associated with immune-escaped TMEs, characterised by active immunomodulatory oncogenic signalling, hyperproliferation and invasiveness, or altered tumour metabolism. These findings emphasise the heterogeneity of molecular drivers of acquired resistance to ICB within and across cancers, and highlight the potential for personalised therapeutic interventions post-progression to improve patient outcomes.

cancer biology↗
bioRxiv2019

Single-cell gene regulatory network analysis reveals new melanoma cell states and transition trajectories during phenotype switching

Melanoma is notorious for its cellular heterogeneity, which is at least partly due to its ability to transition between alternate cell states. Similarly to EMT, melanoma cells with a melanocytic phenotype can switch to a mesenchymal-like phenotype. However, scattered emerging evidence indicates that additional, intermediate state(s) may exist. In order to search for such new melanoma states and decipher their underlying gene regulatory network (GRN), we extensively studied ten patient-derived melanoma cultures by single-cell RNA-seq of >39,000 cells. Although each culture exhibited a unique transcriptome, we identified shared gene regulatory networks that underlie the extreme melanocytic and mesenchymal cell states, as well as one (stable) intermediate state. The intermediate state was corroborated by a distinct open chromatin landscape and governed by the transcription factors EGR3, NFATC2, and RXRG. Single-cell migration assays established that this \"transition\" state exhibits an intermediate migratory phenotype. Through a dense time-series sampling of single cells and dynamic GRN inference, we unraveled the sequential and recurrent arrangement of transcriptional programs at play during phenotype switching that ultimately lead to the mesenchymal cell state. We provide the scRNA-Seq data with 39,263 melanoma cells on our SCope platform and the ATAC-seq data on a UCSC hub to jointly serve as a resource for the melanoma field. Together, this exhaustive analysis of melanoma cell state diversity indicates that additional states exists between the two extreme melanocytic and mesenchymal-like states. The GRN we identified may serve as a new putative target to prevent the switch to mesenchymal cell state and thereby, acquisition of metastatic and drug resistant potential.

genomics↗
bioRxiv2017

SCENIC: Single-Cell Regulatory Network Inference And Clustering

Single-cell RNA-seq allows building cell atlases of any given tissue and infer the dynamics of cellular state transitions during developmental or disease trajectories. Both the maintenance and transitions of cell states are encoded by regulatory programs in the genome sequence. However, this regulatory code has not yet been exploited to guide the identification of cellular states from single-cell RNA-seq data. Here we describe a computational resource, called SCENIC (Single Cell rEgulatory Network Inference and Clustering), for the simultaneous reconstruction of gene regulatory networks (GRNs) and the identification of stable cell states, using single-cell RNA-seq data. SCENIC outperforms existing approaches at the level of cell clustering and transcription factor identification. Importantly, we show that cell state identification based on GRNs is robust towards batch-effects and technical-biases. We applied SCENIC to a compendium of single-cell data from the mouse and human brain and demonstrate that the proper combinations of transcription factors, target genes, enhancers, and cell types can be identified. Moreover, we used SCENIC to map the cell state landscape in melanoma and identified a gene regulatory network underlying a proliferative melanoma state driven by MITF and STAT and a contrasting network controlling an invasive state governed by NFATC2 and NFIB. We further validated these predictions by showing that two transcription factors are predominantly expressed in early metastatic sentinel lymph nodes. In summary, SCENIC is the first method to analyze scRNA-seq data using a network-centric, rather than cell-centric approach. SCENIC is generic, easy to use, and flexible, and allows for the simultaneous tracing of genomic regulatory programs and the mapping of cellular identities emerging from these programs. Availability: SCENIC is available as an R workflow based on three new R/Bioconductor packages: GENIE3, RcisTarget and AUCell. As scalable alternative to GENIE3, we also provide GRNboost, paving the way towards the network analysis across millions of single cells.

bioinformatics↗
bioRxiv2017

Identification Of cis-Regulatory Mutations Generating De Novo Edges In Personalized Cancer Gene Regulatory Networks

The identification of functional non-coding mutations is a key challenge in the field of genomics, where whole-genome re-sequencing can swiftly generate a set of all genomic variants in a sample, such as a tumor biopsy. The size of the human regulatory landscape places a challenge on finding recurrent cis-regulatory mutations across samples of the same cancer type. Therefore, powerful computational approaches are required to sift through the tens of thousands of non-coding variants, to identify potentially functional variants that have an impact on the gene expression profile of the sample. Here we introduce an integrative analysis pipeline, called - cisTarget, to filter, annotate and prioritize non-coding variants based on their putative effect on the underlying 'personal' gene regulatory network. We first validate -cisTarget by re-analyzing three cases of oncogenic non-coding mutations, namely the TAL1 and LMO1 enhancer mutations in T-ALL, and the TERT promoter mutation in melanoma. Next, we re-sequenced the full genome of ten cancer cell lines of six different cancer types, and used matched transcriptome data and motif discovery to infer master regulators for each sample. We identified candidate functional non-coding mutations that generate de novo binding sites for these master regulators, and that result in the up-regulation of nearby oncogenic drivers. We finally validated the predictions using tertiary data including matched epigenome data. Our approach is generally applicable to re-sequenced cancer genomes, or other genomes, when a disease- or sample-specific gene signature is available for network inference. -cisTarget is available from http://mucistarget.aertslab.org.

genomics↗