Biology subjects

Nakken, S.

Publications and source records attributed to Nakken, S..

10 recordsLinked to original sources

bioRxiv2026

Immune-cell depleted diffuse large B-cell lymphomas have reduced expression of MHC class I

Immunotherapy has transformed treatment for many cancers. In the aggressive and genetically heterogeneous diffuse large B-cell lymphoma (DLBCL), CD19 CAR T-cell therapy is highly effective, whereas immune checkpoint blockade has shown limited benefit. Loss of MHC expression is a common mechanism to escape T-cell cytotoxicity, and loss of MHC class I (MHC-I) and II are frequent in DLBCL. We applied imaging mass cytometry to diagnostic biopsies from younger, high-risk DLBCL patients to map the tumor microenvironment (TME) spatial architecture in relation to tumor cell MHC expression, mutational status, transcriptomic and proteomic profiles. Neighborhood analyses identified four TME subtypes: immune-cell depleted and three immune-infiltrated types (mixed, CD4 T cell-rich, CD8 T-cell/macrophage-rich). Depleted cases had shorter overall survival (p = 0.033) and increased expression of proteins involved in DNA replication and proliferation markers compared to infiltrated cases. Tumor cell MHC-I expression was heterogeneous. Cases with low frequency of MHC-I-pos tumor cells were enriched for the depleted TME type. MHC-I-pos tumor cells were surrounded by CD4 and CD8 T cells and M1 macrophages, whereas MHC-I-neg tumor cells were closer to other MHC-I-neg tumor cells. These findings suggest that TME-based classification incorporating tumor cell MHC-I status may improve individualized immunotherapy selection.

cancer biology↗

bioRxiv2026

CANCAN: high-resolution copy number and mutation heterogeneity analysis of DNA sequence data for clinical applications

High-throughput DNA sequencing is central to precision oncology, yet robust and interpretable methods for integrated analysis of copy number alterations and somatic variants across sequencing platforms remain limited. We present CANCAN (Copy number integrative ANalysis in CANcer), a platform-agnostic computational framework for high-resolution analysis of allele-specific copy number and variant data. CANCAN integrates novel normalization and segmentation strategies and enables inference of tumor purity, ploidy, subclonality and mutation multiplicity, while providing statistical confidence estimates and transparent evaluation of alternative solutions. Benchmarking across whole-genome, whole-exome and targeted sequencing datasets from TCGA and the IMPRESS-Norway study demonstrates high concordance with established methods, with particularly strong performance on targeted sequencing data. CANCAN accurately estimates global genomic features, including purity and ploidy, even at reduced sequencing coverage, and shows comparable or improved agreement relative to existing tools. In addition, it provides detailed visualization of the genomic context of clinically relevant biomarkers, supporting diagnostic interpretation. CANCAN constitutes a reproducible and interpretable approach for integrated genomic analysis, addressing key methodological and practical challenges in clinical cancer genomics.

bioinformatics↗

bioRxiv2026

Interclonal cooperation and suppression shape early Ras-driven tumour growth

Cancer is generally thought to be caused by expansion of a single mutant cell. However, analyses of human early lesions show that tumours can originate from several genetically distinct cell populations1-5. How neighbouring mutant clones interact to shape tumourigenesis, and which driver genes mediate these effects is largely unexplored. Here, we use an in vivo mosaic Drosophila epithelial model to systematically test interclonal interactions during early Ras-driven tumourigenesis. We screened 88 recurrent RAS co-mutated driver genes in human carcinomas for their ability to modify Ras-clone growth when disrupted in neighbouring epithelial clones. This uncovered two opposing classes of interactions: Interclonal cooperativity, where neighbouring mutant clones promote the overgrowth of Ras mutant tumours, and interclonal suppression, in which neighbours restrain Ras tumours, unexpectedly improving host survival. The strongest suppressive modifiers included canonical cell competition regulators, including Myc, archipelago (ago/FBXW7), and taiman (tai/NCOA1-3). In contrast, the strongest cooperative modifiers were disruptions of XNP/ATRX and SWI/SNF chromatin remodelling subunits (including Osa/ARID1A, Bap170/ARID2, Polybromo/PBRM1, among others), which in neighbouring cells induce a wound-like inflammatory program and drive an anabolic, pro-growth state in Ras tumours. Notably, the disruption of SWI/SNF components cell autonomously within Ras tumours confers no cooperativity. We show that interclonal cooperative Ras tumour growth requires reactive oxidative species and prostaglandin synthesis in SWI/SNF-disrupted clones. Together, this study provides a catalogue and emerging principles of cooperative and suppressive interclonal interactions among recurrent RAS co-mutated drivers, extending the rules of oncogenic cooperation beyond cell intrinsic co-mutation.

cancer biology↗

bioRxiv2025

Perturbational fitness analysis of CRISPR screens uncovers information-theoretic relation between gene function and selection

Despite major advances in genetic screening technology, a formal approach for quantifying gene function remains underdeveloped, thereby limiting the utility of these techniques in deciphering the complex behavior of human cells. In this study, we leverage information theory with a perturbational analysis of replicator dynamics to characterize functional drivers of selection in pooled CRISPR screens. Our approach challenges established methods for CRISPR screen analysis, while offering additional insight into selection dynamics through the Kullback-Leibler divergence (DKL) and cumulants of the fitness distribution. By modeling fluctuations in gene-fitness effects as a linear response to environmental perturbations, we derive a geometric measure for genomic information content based on a second-order approximation of the DKL. Our analysis reveals that functional information--encoded (or shared) between genes--can be quantified by analyzing the directions corresponding to maximal conditional selection within the space of decomposed gene-environment interactions. This geometric representation offers several advantages for the functional analysis of the human genome and its network architecture. Moreover, by constraining the space to cell-type-specific fluctuations, we uncover developmental and tissue-specific functional signatures. These findings represent significant progress in the dynamic analysis of gene function and in the functional wiring of the human genome.

systems biology↗

bioRxiv2024

The RAB27A effector SYTL5 regulates mitophagy and mitochondrial metabolism

SYTL5 is a member of the Synaptotagmin-Like (SYTL) protein family that differs from the Synaptotagmin family by having a unique N-terminal Synaptotagmin homology domain that directly interacts with the small GTPase RAB27A. Several SYTL protein family members have been implicated in plasma membrane transport and exocytosis, but the specific function of SYTL5 remains unknown. We here show that SYTL5 is a RAB27A effector and that both proteins localise to mitochondria and vesicles containing mitochondrial material. Mitochondrial recruitment of SYTL5 depends on its interaction with functional RAB27A. We demonstrate that SYTL5-RAB27A positive vesicles containing mitochondrial material, autophagy proteins and LAMP1 form during hypoxia and that depletion of SYTL5 and RAB27A reduces mitophagy under hypoxia mimicking conditions, indicating a role for these proteins in mitophagy. Indeed, we find that SYTL5 interacts with proteins involved in vesicle-mediated transport and cellular response to stress and that its depletion compromises mitochondrial respiration and increases glucose uptake. Intriguingly, SYTL5 expression is significantly reduced in tumours of the adrenal gland, and correlates positively with survival for patients with adrenocortical carcinoma.

cell biology↗

bioRxiv2024

StarSignDNA: Signature tracing for accurate representation of mutational processes

All cell lineages accumulate mutations over time, increasing the probability that some lineages eventually become malignant. Many of the processes responsible for generating mutations are leaving a characteristic footprint in the genome that allows their presence to be detected. However, the mutational pattern in a tumour is usually the combined result of multiple mutational processes being at work simultaneously, and the problem of disentangling the different footprints and their relative impact then becomes a deconvolution problem. Several algorithms have been developed for this purpose, most of them involving a factorization of the mutation count matrix into two non-negative matrices, representing respectively the underlying mutational signatures and the relative weighting of (or exposure to) these signatures. Here, we introduce the StarSignDNA algorithm for mutational signature analysis, which offers efficient re-fitting and de novo mutational signature extraction. StarSignDNA is capable of deciphering well-differentiated signatures linked to known mutagenic mechanisms and suggesting clinically relevant predictions for a single patient. The package offers a command line-based interface and data visualization routines. Author summaryStarSignDNA is a novel algorithm for identifying mutational signatures from cancer sequencing data. It excels in low mutation count scenarios, balancing signature detection and true signature discovery. StarSignDNA improves prediction accuracy and biological validity by addressing challenges such as overfitting and underfitting, achieving optimal variable selection and shrinkage, and ensuring interpretability. In re-fitting, it reduces prediction variance and handles sparsity, resulting in more precise predictions. In de novo extraction, it improves the detection of challenging signatures and achieves better alignment between detected and known signatures through unsupervised cross-validation. Its unique features, including prediction confidence and customizable reference signatures, offer valuable insights for clinical single-sample analysis.

bioinformatics↗

bioRxiv2021

GRAMD1C promotes autophagy initiation and mitochondrial bioenergetics through regulation of ER-mitochondria cholesterol transport

During autophagy, cytosolic cargo is sequestered into double-membrane vesicles called autophagosomes. The origin and identity of the membranes that form the autophagosome remain to be fully characterized. Here, we investigated the role of cholesterol in starvation- induced autophagy and identify a role for the ER-localized cholesterol transport protein GRAMD1C in the regulation of autophagy and mitochondrial function. We demonstrate that cholesterol depletion leads to a rapid induction of autophagy, possibly caused by a corresponding increased abundance of curved autophagy membranes. We further show that GRAMD1C is a negative regulator of starvation-induced autophagy. Similar to its yeast orthologue, GRAMD1C is recruited to mitochondria through its GRAM domain. Additionally, we find that GRAMD1C depletion leads to increased mitochondrial cholesterol accumulation and mitochondrial oxidative phosphorylation. Finally, we demonstrate that expression of GRAM family genes is linked to clear cell renal carcinoma survival, highlighting the pathophysiological relevance of cholesterol transport proteins.

biochemistry↗

bioRxiv2019

Cancer Predisposition Sequencing Reporter (CPSR): a flexible variant report engine for germline screening in cancer

The value of high-throughput germline genetic testing is increasingly recognized in clinical cancer care. Disease-associated germline variants in cancer patients are important for risk management and surveillance, surgical decisions, and can also have major implications for treatment strategies since many are in DNA repair genes. With the increasing availability of high-throughput DNA sequencing in cancer clinics and research, there is thus a need to provide clinically oriented sequencing reports for germline variants and their potential therapeutic relevance on a per-patient basis. To meet this need we have developed the Cancer Predisposition Sequencing Reporter (CPSR), an open-source computational workflow that generates a structured report of germline variants identified in known cancer predisposition genes, highlighting markers of therapeutic, prognostic, and diagnostic relevance. A fully automated variant classification procedure based on more than 30 refined ACMG criteria represents an integral part of the workflow. Importantly, the set of cancer predisposition genes profiled in the report can be flexibly chosen from more than 40 virtual gene panels established by scientific experts, enabling customization of the report for different screening purposes and clinical contexts. The report can be configured to also list actionable secondary variant findings as recommended by ACMG, as well as the status of low-risk variants from genome-wide association studies in cancer. CPSR demonstrates superior sensitivity and comparable specificity for the detection of pathogenic variants when compared to existing algorithms. Technically, the tool is implemented in Python/R, and is freely available through Docker technology. Source code, documentation, example reports, and installation instructions are accessible via the project GitHub page: https://github.com/sigven/cpsr.

bioinformatics↗

bioRxiv2017

Sample-Index Misassignment Impacts Tumor Exome Sequencing

Sample pooling enabled by dedicated indexes is a common and cost-effective strategy used in high-throughput DNA sequencing. Index misassignment leading to cross-sample contamination has however been described as a general problem of sequencing instruments which utilize exclusion amplification. Using real-life data from multiple tumor sequencing projects, we demonstrate that co-multiplexed samples can induce artifactual calls closely resembling high-quality somatic variant calls, and argue that dual indexing is the most reliable countermeasure.

genomics↗

bioRxiv2017

Personal Cancer Genome Reporter: Variant Interpretation Report For Precision Oncology

SummaryIndividual tumor genomes pose a major challenge for clinical interpretation due to their unique sets of acquired mutations. There is a general scarcity of tools that can i) systematically interrogate cancer genomes in the context of diagnostic, prognostic, and therapeutic biomarkers, ii) prioritize and highlight the most important findings, and iii) present the results in a format accessible to clinical experts. We have developed a stand-alone, open-source software package for somatic variant annotation that integrates a comprehensive set of knowledge resources related to tumor biology and therapeutic biomarkers, both at the gene and variant level. Our application generates a tiered report that will aid the interpretation of individual cancer genomes in a clinical setting.\n\nAvailability and ImplementationThe software is implemented in Python/R, and is freely available through Docker technology. Documentation, example reports, and installation instructions are accessible via the project GitHub page: https://github.com/sigven/pcgr)\n\nContactsigven@ifi.uio.no

bioinformatics↗