Biology subjects

Fallahi-Sichani, M.

Publications and source records attributed to Fallahi-Sichani, M..

9 recordsLinked to original sources

bioRxiv2026

Reactivation of DRP1 plays a functional role in resistance to MEK inhibition in pancreatic cancer cells

BackgroundIn RAS-mutant tumors, ERK phosphorylates the mitochondrial fission GTPase DRP1 to promote mitochondrial fission. DRP1 activity is tumor-promoting in pancreatic and other RAS-driven cancers, but its role in therapeutic resistance is unknown. MethodsWe developed a panel of patient-derived pancreatic cancer cell lines resistant to the MEK inhibitor trametinib. We used immunofluorescence imaging, in vitro growth assays and orthotopic xenografts to determine the role of DRP1 in trametinib resistance. ResultsWe find that trametinib-resistant cells exhibit increased expression and phosphorylation of DRP1 compared to sensitive counterparts. Quantitative analysis of mitochondrial structure reveals that mitochondria in resistant cells are morphologically distinct and relatively smaller than sensitive cells treated with trametinib. Genetic and pharmacological inhibition of both c-Myc and CDK6 are sufficient to block DRP1 phosphorylation in resistant cells, suggesting that activation of a c-Myc-CDK6 signaling axis drives reactivation of mitochondrial fission in the absence of MAPK signaling. Importantly, deletion of DRP1 leads to either growth inhibition or re-sensitization to trametinib in resistant lines. ConclusionThese findings suggest DRP1 contributes to drug resistance, and that inhibition of mitochondrial fission might be a promising therapeutic strategy to combat resistance to MAPK and RAS inhibitors.

cancer biology↗

bioRxiv2026

Systematic multivariate analysis of chromatin complex dependencies reveals Set1C/COMPASS as a melanoma-enriched epigenetic vulnerability

Epigenetic dysregulation is a common feature of cancer. It creates vulnerabilities arising from an increased reliance on chromatin-based mechanisms that sustain malignant transcriptional states. While many chromatin regulators are broadly required for cellular viability, others function in a context-dependent manner across distinct oncogenic settings, tissue lineages, and differentiation states. Moreover, chromatin regulators often operate within multi-subunit complexes; thus, epigenetic vulnerabilities emerge from coordinated complex activities. Here, we integrate large-scale genetic dependency maps from human cancer cell lines with curated epigenetic complex annotations to perform a systematic, multivariate analysis of complex-level epigenetic dependencies across cancer lineages. Our analysis reveals that dependencies frequently cluster among functionally related chromatin complexes and that biologically related cancer types (e.g., hematologic malignancies) share similar dependency patterns, consistent with shared underlying epigenetic requirements. Focusing on melanoma, we identify multiple enriched epigenetic complex dependencies, including complexes previously associated with recurrent genetic alterations or melanocyte lineage regulation, as well as a previously unrecognized vulnerability involving the H3K4 methyltransferase complex Set1C/COMPASS. This dependency is not restricted to a specific melanoma differentiation state, but genetic depletion of CXXC1 (a critical complex-specific subunit) suggests that CXXC1-dependent melanoma cells require Set1C/COMPASS activity to maintain global H3K4 trimethylation (H3K4me3) and proliferation. Integrative modeling links Set1C/COMPASS dependency to MYC- and E2F-driven transcriptional programs, which are suppressed upon complex inhibition. Together, this work combines integrative, multivariate analysis of lineage-enriched epigenetic dependencies with genetic perturbation, transcriptional profiling, and single-cell analysis to uncover an enriched epigenetic dependency on Set1C/COMPASS in melanoma cells. Author SummaryCancer cells often rely on abnormal regulation of gene activity to support uncontrolled growth and survival. This regulation is controlled not only by genetic mutations, but also by epigenetic mechanisms, chemical and structural modifications to DNA and its associated proteins that determine which genes are turned on or off. Several therapies that target epigenetic regulators have shown promise, particularly in blood cancers. However, identifying which epigenetic mechanisms are most important in specific cancers remains challenging, especially because epigenetic regulators frequently work together as multi-protein complexes. In this study, we combine large-scale public datasets across many cancer lineages with computational modeling to systematically identify lineage-enriched epigenetic vulnerabilities. We found that certain epigenetic complexes are selectively important in specific cancer lineages. In melanoma, an aggressive skin cancer, we identified a previously unrecognized dependence on a protein complex that modifies chromatin at gene promoters. We show that disrupting this complex can impair gene programs that drive cell division and block cancer cell growth. Moreover, our study demonstrate how integrative computational approaches combined with experiments can uncover new targets for potential cancer therapy studies.

cancer biology↗

bioRxiv2026

Critical role of cell competition in gliomagenesis

ABSTRACTMalignant glioma is incurable. Using a mouse genetic mosaic system to generate sporadic Trp53,Nf1-null OPCs, we previously identified oligodendrocyte precursor cell (OPC) as a cell-of-origin of glioma. Here, we report that pre-malignant Trp53,Nf1-null OPCs outcompete wildtype counterparts during their expansion. Blocking competition by mutating/strengthening wildtype OPCs impeded both pre-malignant progression and malignant expansion of glioma. "In-tissue" phosphoproteomic profiling revealed an enrichment of phosphopeptides related to RNA splicing and protein translation at the peak of cell competition, suggesting that competitiveness may stem from unique protein species. Among candidates was mTORC1, whose pharmacological inhibition or genetic disruption resulted in a loss of competitiveness in our mouse model. Finally, analysis of patient biopsies and interrogating the role of individual gliomagenic mutations in OPC competition supported its relevance in human gliomas. Together, these findings identified the driving role of competitive interactions among OPCs in gliomagenesis, and suggest unconventional therapeutic strategies to target this process.

cancer biology↗

bioRxiv2025

Cell state plasticity emerging from co-regulated, competitive, and configurable interactions within the AP-1 network

AP-1 transcription factors have been implicated in cellular plasticity, differentiation-state heterogeneity, and phenotype switching in response to cancer therapies. Although AP-1 states, defined by combinatorial expression of AP-1 proteins, are heterogeneous within cell populations, only a subset of possible states is observed. How these states are constrained, why their distributions vary across cell populations, and what drives their phenotypically consequential transitions remain unclear. We develop a mechanistic ODE model of the AP-1 network, capturing dimerization-dependent, co-regulated, and competitive interactions. Calibrated to single-cell protein measurements across diverse melanoma populations and combined with statistical learning, the model reveals network features explaining population-specific AP-1 state distributions. These features correlate with MAPK signaling across tumor lines and individual cells. The model predicts and experiments validate adaptive AP-1 reconfiguration following MAPK inhibition, driving a dedifferentiated, therapy-resistant state that is attenuated through model-guided perturbations. These findings establish AP-1 as a configurable network and provide a quantitative framework for modulating AP-1 driven cell-state plasticity.

systems biology↗

bioRxiv2023

Multivariate modeling of metabolic state vulnerabilities across diverse cancer contexts reveals synthetically lethal associations

Targeting the distinct metabolic needs of tumor cells has recently emerged as a promising strategy for cancer therapy. The heterogeneous, context-dependent nature of cancer cell metabolism, however, poses challenges in identifying effective therapeutic interventions. Here, we utilize various unsupervised and supervised multivariate modeling approaches to systematically pinpoint recurrent metabolic states within hundreds of cancer cell lines, elucidate their association with tumor lineage and growth environments, and uncover vulnerabilities linked to their metabolic states across diverse genetic and tissue contexts. We validate key findings via analysis of data from patient-derived tumors and pharmacological screens, and by performing new genetic and pharmacological experiments. Our analysis uncovers new synthetically lethal associations between the tumor metabolic state (e.g., oxidative phosphorylation), driver mutations (e.g., loss of tumor suppressor PTEN), and actionable biological targets (e.g., mitochondrial electron transport chain). Investigating the mechanisms underlying these relationships can inform the development of more precise and context-specific, metabolism-targeted cancer therapies.

cancer biology↗

bioRxiv2022

Systematic analysis uncovers SYK dependency in NF1LoF melanoma cells

The loss of function (LoF) of NF1 is the third most frequent mutation that drives hyperactivated RAS and tumor growth in >10% of melanomas. NF1LoF melanoma cells, however, do not show consistent sensitivity to individual MEK, ERK, or PI3K/mTOR inhibitors. Here, we perform a targeted kinase inhibitor screen and identify a tool compound, named MTX-216, to be highly effective in blocking NF1LoF melanoma cells. Single-cell analysis links drug-induced cytotoxicity to effective co-suppression of proliferation marker Ki-67 and the ribosomal S6 phosphorylation, an integrator of multiple RAS-mediated signaling pathways. Using a combination of kinome selectivity assay, transcriptomic analysis, and genetic experiments, we find the anti-tumor efficacy of MTX-216 to be dependent on its ability to inhibit not only PI3K (its nominal target) but also SYK, and suppression of a group of genes that regulate mitochondrial electron transport chain and whose expression is associated with poor survival in NF1LoF melanoma patients. Furthermore, combinations of inhibitors targeting either MEK or PI3K/mTOR with an independent SYK kinase inhibitor or SYK knockdown show favorable effects. These studies provide a path to exploit SYK dependency to selectively block NF1LoF melanoma cells. Statement of significanceNF1LoF melanomas represent a subtype with hyperactivated RAS signaling, for which currently no targeted therapies are clinically available. Our systems pharmacology studies identify SYK as a new vulnerability in NF1LoF melanoma cells.

cancer biology↗

bioRxiv2021

AP-1 transcription factor network explains diverse patterns of cellular plasticity in melanoma

Cellular plasticity associated with fluctuations in transcriptional programs allows individual cells in a tumor to adopt heterogeneous differentiation states and switch phenotype during their adaptive responses to therapies. Despite increasing knowledge of such transcriptional programs, the molecular basis of cellular plasticity remains poorly understood. Here, we combine multiplexed transcriptional and protein measurements at population and single-cell levels with multivariate statistical modeling to show that the state of AP-1 transcription factor network plays a unifying role in explaining diverse patterns of plasticity in melanoma. We find that a regulated balance between AP-1 factors cJUN, JUND, FRA2, FRA1 and cFOS determines the intrinsic diversity of differentiation states and adaptive responses to MAPK inhibitors in melanoma cells. Perturbing this balance through genetic depletion of specific AP-1 proteins, or by MAPK inhibitors, shifts cellular heterogeneity in a predictable fashion. Thus, AP-1 may serve as a critical node for manipulating cellular plasticity with potential therapeutic implications.

systems biology↗

bioRxiv2020

Epigenetic modulation extends the oncogene addiction paradigm on the basis of tumor cell differentiation state

Hyperactivation of the MAPK signaling pathway motivates the clinical use of MAPK inhibitors for BRAF-mutant melanomas. Heterogeneity in differentiation state due to epigenetic plasticity, however, results in cell-to-cell variability in the state of MAPK dependency, diminishing the efficacy of MAPK inhibitors. To identify key regulators of such variability, we screened 276 epigenetic-modifying compounds, individually or combined with MAPK inhibitors, across genetically diverse and isogenic populations of melanoma cells. Following single-cell analysis and multivariate modeling, we identified three classes of epigenetic inhibitors that target distinct epigenetic states associated with either one of the lysine-specific histone demethylases KDM1A or KDM4B, or BET bromodomain proteins. While melanocytes remained insensitive, the anti-tumor efficacy of each inhibitor was predicted based on melanoma cells differentiation state and MAPK activity. Our systems pharmacology approach highlights a path toward identifying actionable epigenetic factors that extend the BRAF oncogene addiction paradigm on the basis of tumor cell differentiation state.

systems biology↗

bioRxiv2019

Phenotype-Based Probabilistic Analysis of Heterogeneous Responses to Cancer Drugs and Their Combination Efficacy

Cell-to-cell variability generates subpopulations of drug-tolerant cells that diminish the efficacy of cancer drugs. Efficacious combination therapies are thus needed to block drug-tolerant cells via minimizing the impact of heterogeneity. Probabilistic models such as Bliss independence are developed to evaluate drug interactions and their combination efficacy based on probabilities of specific actions mediated by drugs individually and in combination. In practice, however, these models are often applied to conventional dose-response curves in which a normalized parameter with a value between zero and one, generally referred to as fraction of cells affected (fa), is used to evaluate the efficacy of drugs and their combined interactions. We use basic probability theory, computer simulations, time-lapse live cell microscopy, and single-cell analysis to show that fa metrics may bias our assessment of drug efficacy and combination effectiveness. This bias may be corrected when dynamic probabilities of drug-induced phenotypic events, i.e. induction of cell death and inhibition of division, at a single-cell level are used as metrics to assess drug efficacy. Probabilistic phenotype metrics offer the following three benefits. First, in contrast to the commonly used fa metrics, they directly represent probabilities of drug action in a cell population. Therefore, they deconvolve differential degrees of drug effect on tumor cell killing versus inhibition of cell division, which may not be correlated for many drugs. Second, they increase the sensitivity of short-term drug response assays to cell-to-cell heterogeneities and the presence of drug-tolerant subpopulations. Third, their probabilistic nature allows them to be used directly in unbiased evaluation of synergistic efficacy in drug combinations using probabilistic models such as Bliss independence. Altogether, we envision that probabilistic analysis of single-cell phenotypes complements currently available assays via improving our understanding of heterogeneity in drug response, thereby facilitating the discovery of more efficacious combination therapies to block drug-tolerant cells.\n\nAuthor SummaryResistance to therapy due to tumor cell heterogeneity poses a major challenge to the use of cancer drugs. Cell-to-cell variability generates subpopulations of drug-tolerant cells that diminish therapeutic efficacy, even in populations of cells scored as highly sensitive based on drug potency. Overcoming such heterogeneity and blocking subpopulations of drug-tolerant cells motivate efforts toward identifying efficacious combination therapies. The success of these efforts depends on our ability to distinguish how heterogeneous populations of cells respond to individual drugs, and how these responses are influenced by combined drug interactions. In this paper, we propose mathematical and experimental frameworks to evaluate time-dependent drug interactions based on probabilistic metrics that quantify drug-induced tumor cell killing or inhibition of division at a single-cell level. These metrics can reveal heterogeneous drug responses and their changes with time and drug combinations. Thus, they have important implications for designing efficacious combination therapies, especially those designed to block or overcome drug-tolerant subpopulations of cancer cells.

systems biology↗