Biology subjects

Khiabanian, H.

Publications and source records attributed to Khiabanian, H..

12 recordsLinked to original sources

bioRxiv2025

Monoallelic expression characterizes a distinct molecular and clinical group of breast tumors

In diploid cells, allelic imbalance occurs when gene alleles are expressed at different levels. To investigate the allelic imbalance landscape in tumor samples, we developed Interval-Based Allelic Imbalance Detection (IB-Aid), a quantitative framework that uses interval arithmetic to robustly distinguish monoallelic from biallelic gene expression by computing confidence intervals based on sequencing measurement uncertainty. We applied this approach to The Cancer Genome Atlas Breast Invasive Carcinoma tumor samples and through unsupervised gene enrichment analyses, identified a group of patients with a distinct monoallelic gene expression signature. Notably, these patients were enriched in Black/African American patients and had tumors which had not been previously classified into any established molecular subtype. Clinically, these tumors were associated with poor overall survival, with survival outcomes comparable to the aggressive basal subtype. These findings suggest a potential link between allelic imbalance and breast cancer development and point to genetic and epigenetic mechanisms that drive allelic imbalance as novel biomarkers for prognosis and design of targeted treatment strategies.

cancer biology↗

bioRxiv2025

Dedifferentiation-Driven Oncogenic Stemness Promotes Tumor-Sustaining Adaptability in the Intestinal Epithelium

Intestinal tumorigenesis can occur via two distinct routes: bottom-up tumorigenesis occurs from mutations sustained in the Lgr5 stem cells, whereas top-down tumorigenesis is driven by dedifferentiation of epithelial cells near the intestinal lumen. While sporadic human colon adenomas exhibit features of top-down tumorigenesis, their biological determinants remain elusive. Here, using a Smad4 loss-of-function and {beta}-catenin gain-of-function (Smad4LOF:{beta}-cateninGOF) mouse model, we demonstrate that dedifferentiation-derived oncogenic stem cells sustain tumorigenesis more effectively than endogenous mutant stem cells harboring the mutation. The dedifferentiating villi epithelial cells showed early expression of CD44 and Lgr5, supporting oncogenic stemness. Aberrant Notch signaling in the villi epithelium was also detected at the onset of dedifferentiation, suggesting its contribution to dedifferentiation. Single-cell RNA sequencing revealed a distinct population of dedifferentiation-derived stem cells enriched for proliferative, metabolic, and mouse embryonic stem cell-like gene signatures, consistent with enhanced plasticity and tumorigenic potential. These mutant cells exhibited growth factor independence, indicating a capacity for niche-independent proliferation and metabolic adaptation to sustain tumor growth. These findings identify dedifferentiation-driven stemness, aberrant Notch activation, and metabolic plasticity as cooperative mechanisms that promote top-down intestinal tumorigenesis. This study provides insight into how oncogenic dedifferentiation contributes to tumor heterogeneity and persistence and has implications for therapeutic resistance in colorectal cancer.

cancer biology↗

bioRxiv2025

Guiding clustering and annotation in single-cell RNA sequencing using the average overlap metric

Defining cell types using unsupervised clustering algorithms based on transcriptional similarity is a powerful application of single-cell RNA sequencing. A single clustering resolution may not yield clusters that represent both broad, well-defined populations and smaller subpopulations simultaneously. Therefore, when cell identities are not known prior to sequencing, robust comparison and annotation of inferred de novo clusters remains a challenge. In this work, we define the distance between single-cell clusters by proposing the use of the average overlap metric to compare ranked lists of differentially expressed genes in a top-weighted manner. We first benchmark our approach in a truth-known dataset comprised of highly similar yet distinct T-cell populations and show that evaluating clusters with average overlap results in a consistent, precise, and biologically meaningful recapitulation of true cell identities. We then apply our approach to data of unsorted mouse thymocytes and characterize stages of T-cell development in the thymus, including minor populations of double-negative (CD4-CD8-) T-cells that are notoriously difficult to confidently detect in unsorted single-cell data. We demonstrate that measuring cluster similarity with average overlap of marker gene rankings enables robust, reproducible characterization of single cells and clarifies biological interpretation of their underlying identities in highly homogeneous populations.

bioinformatics↗

bioRxiv2023

Therapeutic targeting of ACLY in T-ALL in vivo

T-cell Acute Lymphoblastic Leukemia (T-ALL) is a hematological malignancy in need of novel therapeutic approaches. Here, we identify the ATP-citrate lyase ACLY as overexpressed and as a novel therapeutic target in T-ALL. To test the effects of ACLY in leukemia progression, we developed an isogenic model of NOTCH1-induced Acly conditional knockout leukemia. Importantly, we observed intrinsic antileukemic effects upon loss of ACLY, which further synergized with NOTCH1 inhibition in vivo. Metabolomic profiling upon ACLY loss revealed a metabolic crisis with reduced acetyl-CoA levels, as well as a decreased oxygen consumption rate. Gene expression profiling analyses showed that the transcriptional signature of ACLY loss very significantly correlates with the signature of MYC loss in vivo. Mechanistically, the decrease in acetyl-CoA led to reduced H3K27ac levels in Myc, resulting in transcriptional downregulation of Myc and drastically reduced MYC protein levels. Interestingly, our analyses also revealed a reciprocal relationship whereby ACLY itself is a direct transcriptional target of MYC, thus establishing a feedforward loop that is important for leukemia progression. Overall, our results identified a relevant ACLY-MYC axis and unveiled ACLY as a novel promising target for T-ALL treatment.

cancer biology↗

bioRxiv2023

Feature selection followed by a residuals-based normalization simplifies and improves single-cell gene expression analysis

Normalization is a crucial step in the analysis of single-cell RNA-sequencing (scRNA-seq) counts data. Its principal objectives are to reduce the systematic biases primarily introduced through technical sources and to transform the data to make it more amenable for application of established statistical frameworks. In the standard workflows, normalization is followed by feature selection to identify highly variable genes (HVGs) that capture most of the biologically meaningful variation across the cells. Here, we make the case for a revised workflow by proposing a simple feature selection method and showing that we can perform feature selection before normalization by relying on observed counts. We highlight that the feature selection step can be used to not only select HVGs but to also identify stable genes. We further propose a novel variance stabilization transformation inclusive residuals-based normalization method that in fact relies on the stable genes to inform the reduction of systematic biases. We demonstrate significant improvements in downstream clustering analyses through the application of our proposed methods on biological truth-known as well as simulated counts datasets. We have implemented this novel workflow for analyzing high-throughput scRNA-seq data in an R package called Piccolo.

bioinformatics↗

bioRxiv2022

Computational structure prediction methods enable the systematic identification of oncogenic mutations

Oncogenic mutations are associated with the activation of key pathways necessary for the initiation, progression and treatment-evasion of tumors. While large genomic studies provide the opportunity of identifying these mutations, the vast majority of variants have unclear functional roles presenting a challenge for the use of genomic studies in the clinical/therapeutic setting. Recent developments in predicting protein structures enable the systematic large-scale characterization of structures providing a link from genomic data to functional impact. Here, we observed that most oncogenic mutations tend to occur in protein regions that undergo conformation changes in the presence of the activating mutation or when interacting with a protein partner. By combining evolutionary information and protein structure prediction, we introduce the Evolutionary and Structure (ES) score, a computational approach that enables the systematic identification of hotspot somatic mutations in cancer. The predicted sites tend to occur in Short Linear Motifs and protein-protein interfaces. We test the use of ES-scores in genomic studies in pediatric leukemias that easily recapitulates the main mechanisms of resistance to targeted and chemotherapy drugs. To experimentally test the functional role of the predictions, we performed saturated mutagenesis in NT5C2, a protein commonly mutated in relapsed pediatric lymphocytic leukemias. The approach was able to capture both commonly mutated sites and identify previously uncharacterized functionally relevant regions that are not frequently mutated in these cancers. This work shows that the characterization of protein structures provides a link between large genomic studies, with mostly variants of unknown significance, to functional systematic characterization, prioritizing variants of interest in the therapeutic setting and informing on their possible mechanisms of action.

cancer biology↗

bioRxiv2022

A therapeutically targetable NOTCH1-SIRT1-KAT7 axis in T-cell Leukemia

T-cell Acute Lymphoblastic Leukemia (T-ALL) is a NOTCH1-driven disease in need of novel therapies. Here, we identify a NOTCH1-SIRT1-KAT7 link as a therapeutic vulnerability in T-ALL, in which SIRT1 is overexpressed downstream of a novel NOTCH1-bound enhancer. SIRT1 loss impairs leukemia generation, while SIRT1 overexpression accelerates leukemia and confers resistance to NOTCH1 inhibition in a deacetylase-dependent manner. Moreover, secondary SIRT1 loss extends survival and synergizes with NOTCH1 inhibition. Global acetyl-proteomics upon SIRT1 loss uncovered hyperacetylation of KAT7 and BRD1, subunits of a histone acetyltransferase complex targeting H4K12. Metabolic and gene expression profiling revealed a metabolic crisis together with a transcriptional signature resembling KAT7 deletion. Consistently, SIRT1 loss resulted in reduced H4K12ac, and overexpression of a non-acetylatable KAT7 mutant partly rescued SIRT1 loss-induced proliferation defects. The newly unveiled NOTCH1-SIRT1-KAT7 axis uncovers novel therapeutic targets in T-ALL and reveals a circular feedback mechanism balancing deacetylase/acetyltransferase activation with potentially broad relevance in cancer. Statement of significanceWe identified a novel axis in T-ALL whereby NOTCH1 activates SIRT1 through an enhancer region, and SIRT1 deacetylates and activates KAT7. Targeting SIRT1 shows antileukemic effects, partly mediated by KAT7 inactivation. Our results identify novel therapeutic targets and uncover a rheostat mechanism between deacetylase/acetyltransferase activities with potentially broader cancer relevance.

cancer biology↗

bioRxiv2021

Germline testing data validate inferences of mutational status for variants detected from tumor-only sequencing

Structured AbstractO_ST_ABSBackgroundC_ST_ABSPathogenic germline variants (PGV) in cancer susceptibility genes are usually identified in cancer patients through germline testing of DNA from blood or saliva: their detection can impact patient treatment options and potential risk reduction strategies for relatives. PGV can also be identified, in tumor sequencing assays, often performed without matched normal specimens. It is then critical to determine whether detected variants are somatic or germline. Here, we evaluate the clinical utility of computational inference of mutational status in tumor-only sequencing compared to germline testing results. Patients and MethodsTumor-only sequencing data from 1,608 patients were retrospectively analyzed to infer germline-versus-somatic status of variants using an information-theoretic, gene-independent approach. Loss of heterozygosity (LOH) was also determined. The predicted mutational models were compared to clinical germline testing results. Statistical measures were computed to evaluate performance. ResultsTumor-only sequencing detected 3,988 variants across 70 cancer susceptibility genes for which germline testing data were available. Our analysis imputed germline-versus-somatic status for >75% of all detected variants, with a sensitivity of 65%, specificity of 88%, and overall accuracy of 86% for pathogenic variants. False omission rate was 3%, signifying minimal error in misclassifying true PGV. A higher portion of PGV in known hereditary tumor suppressors were found to be retained with LOH in the tumor specimens (72%) compared to variants of uncertain significance (58%). ConclusionsTumor-only sequencing provides sufficient power to distinguish germline and somatic variants and infer LOH. Although accurate detection of PGV from tumor-only data is possible, analyzing sequencing data in the context of specimens tumor cell content allows systematic exclusion of somatic variants, and suggests a balance between type 1 and 2 errors for identification of patients with candidate PGV for standard germline testing. Our approach, implemented in a user-friendly bioinformatics application, facilities objective analysis of tumor-only data in clinical settings. HighlightsO_LIMost pathogenic germline variants in cancer predisposition genes can be identified by analyzing tumor-only sequencing data. C_LIO_LIInformation-theoretic gene-independent analysis of common sequencing data accurately infers germline vs. somatic status. C_LIO_LIA reasonable statistical balance can be established between sensitivity and specificity demonstrating clinical utility. C_LIO_LIPathogenic germline variants are more often detected with loss of heterozygosity vs. germline variants of uncertain significance. C_LI

bioinformatics↗

bioRxiv2019

All-FIT: Allele-Frequency-based Imputation of Tumor Purity from High-Depth Sequencing Data

MotivationClinical sequencing aims to identify somatic mutations in cancer cells for accurate diagnosis and treatment. However, most widely used clinical assays lack patient-matched control DNA and additional analysis is needed to distinguish somatic and unfiltered germline variants. Such computational analyses require accurate assessment of tumor cell content in individual specimens. Histological estimates often do not corroborate with results from computational methods that are primarily designed for normal-tumor matched data and can be confounded by genomic heterogeneity and presence of sub-clonal mutations.\n\nMethodsAll-FIT is an iterative weighted least square method to estimate specimen tumor purity based on the allele frequencies of variants detected in high-depth, targeted, clinical sequencing data.\n\nResultsUsing simulated and clinical data, we demonstrate All-FITs accuracy and improved performance against leading computational approaches, highlighting the importance of interpreting purity estimates based on expected biology of tumors.\n\nAvailability and ImplementationFreely available at http://software.khiabanian-lab.org.

bioinformatics↗

bioRxiv2018

TuBA: Tunable Biclustering Algorithm Reveals Clinically Relevant Tumor Transcriptional Profiles in Breast Cancer

BackgroundTraditional clustering approaches for gene expression data are not well adapted to address the complexity and heterogeneity of tumors, where small sets of genes may be aberrantly co-expressed in specific subsets of tumors. Biclustering algorithms that perform local clustering on subsets of genes and conditions help address this problem. We propose a graph-based Tunable Biclustering Algorithm (TuBA) based on a novel pairwise proximity measure, examining the relationship of samples at the extremes of genes expression profiles to identify similarly altered signatures. ResultsTuBAs predictions are consistent in 3,940 Breast Invasive Carcinoma (BRCA) samples from three independent sources, employing different technologies for measuring gene expression (RNASeq and Microarray). Over 60% of biclusters identified independently in each dataset had significant agreement in their gene sets, as well as similar clinical implications. About 50% of biclusters were enriched in the ER-/HER2- (or basal-like) subtype, while more than 50% were associated with transcriptionally active copy number changes. Biclusters representing gene co-expression patterns in stromal tissue were also identified in tumor specimens. ConclusionTuBA offers a simple biclustering method that can identify biologically relevant gene co-expression signatures not captured by traditional unsupervised clustering approaches. It complements biclustering approaches that are designed to identify constant or coherent submatrices in gene expression datasets, and outperforms them in identifying a multitude of altered transcriptional profiles that are associated with observed genomic heterogeneity of diseased states in breast cancer, both within and across tumor subtypes, a promising step in understanding disease heterogeneity, and a necessary first step in individualized therapy.

systems biology↗

bioRxiv2017

MERIT: a Mutation Error Rate Identification Toolkit for Ultra-deep Sequencing Applications

Rapid progress in high-throughput sequencing (HTS) has enabled the molecular characterization of mutational landscapes in heterogeneous populations and has improved our understanding of clonal evolution processes. Analyzing the sensitivity of detecting genomic mutations in HTS requires comprehensive profiling of sequencing artifacts. To this end, we introduce MERIT, designed for in-depth quantification of erroneous substitutions and small insertions and deletions, specifically for ultra-deep applications. MERIT incorporates an all-inclusive variant caller and considers genomic context, including the nucleotides immediately at 5' and 3', thereby establishing error rates for 96 possible substitutions as well as four singlebase and 16 double-base indels. We apply MERIT to ultra-deep sequencing data (1,300,000x) and show a significant relationship between error rates and genomic contexts. We devise an in silico approach to determine the optimal sequencing depth, where errors occur at rates similar to those of true mutations. Finally, we assess nucleotide-incorporation fidelity of four high-fidelity DNA polymerases in clinically relevant loci, and demonstrate how fixed detection thresholds may result in substantial false positive as well as false negative calls.

bioinformatics↗

bioRxiv2017

Highly sensitive detection of small variants in multi-sample ultra-deep tumor sequencing

One cause of cancer mortality is tumor evolution to therapy-resistant disease. First line therapy often targets the dominant clone, and drug resistance can emerges from preexisting clones that gain fitness through therapy-induced natural selection. Such mutations may be identified using targeted sequencing assays by analysis of noise in high-depth data. Here, we develop a comprehensive, unbiased model for sequencing error background. We find that noise in sufficiently deep DNA sequencing data can be approximated by aggregating negative binomial distributions. Mutations with frequencies above noise may have prognostic value. We evaluate our model with simulated exponentially expanded populations as well as data from cell line and patient sample dilution experiments, demonstrating its utility in prognosticating tumor progression. Our results may have the potential to identify significant mutations that can cause recurrence. These results are relevant in the pretreatment clinical setting to determine appropriate therapy and prepare for potential recurrence pretreatment.

bioinformatics↗