Biology subjects

Blakely, C. M.

Publications and source records attributed to Blakely, C. M..

9 recordsLinked to original sources

bioRxiv2025

Reversible therapeutic resistance in EGFR mutant lung cancer caused by RB1 loss induced lineage plasticity

Treatment with EGFR tyrosine kinase inhibitors (EGFR TKI) is standard of care for patients with lung cancer initiated by activating mutations in the EGFR gene. While EGFR TKI treatment is effective, virtually all patients progress on therapy. Loss of function mutations in the RB1 tumor suppressor gene are associated with poor EGFR TKI outcomes, but underlying mechanisms remain unclear. Analysis of genetically engineered experimental models and human clinical specimens described here demonstrates RB1 loss has minimal impact on initial cell cycle responses to EGFR TKI but markedly accelerates acquired resistance over time. RB1 loss promotes transcriptional plasticity that enables rare cells to spontaneously assume reversible, resistance-conferring lineage states. These findings highlight the importance of non-genetic adaptations in driving acquired EGFR TKI resistance, advance our understanding of how RB1 impacts cancer, and identify opportunities to improve EGFR TKI treatment outcomes by targeting reversible epigenetic resistance mechanisms.

cancer biology↗

bioRxiv2024

CDK4 and CDK6 upregulation promotes DNA replication stress, genomic instability and resistance to EGFR targeted therapy in lung cancer

Epidermal growth factor receptor (EGFR) mutant lung adenocarcinomas (LUAD) harbor a complex landscape of genetic co-alterations and potential oncogenic interactions. Among them are recurrent amplifications of the cell cycle regulatory genes CDK4 and CDK6, which have been clinically implicated in resistance to EGFR tyrosine kinase inhibitors (TKIs). However, the mechanisms by which CDK4/6 upregulation promotes therapy resistance remain poorly defined. Here, we demonstrate that CDK4 or CDK6 overexpression limits EGFR inhibitor-induced proliferative arrest, promoting continued cell cycle progression. This is accompanied by elevated replication stress, increased TPX2 expression, DNA damage leading to ATM activation, and ultimately genomic instability. Integrative transcriptomic and copy number analyses of EGFR-mutant LUAD tumors from both patients and preclinical models revealed that CDK4 or CDK6 amplification is associated with the upregulation of genes linked to tumor progression, including AGR2, ASNS, and STEAP1. CDK4 amplification was also highly correlated with gene expression changes associated with epithelial-to-mesenchymal transition (EMT) in a single-cell RNA sequencing dataset from patient biopsies. In preclinical models, co-treatment with CDK4/6 and EGFR inhibitors restored proliferative arrest, induced tumor cell apoptosis, and reduced replication stress, DNA damage, and genomic instability. Our findings uncover a mechanistic basis for EGFR inhibitor resistance of CDK4 and CDK6 amplified EGFR-mutant LUAD. They also provide a rationale for the biomarker-driven clinical development of combination EGFR and CDK4/6-targeted therapies for the treatment of a subset of EGFR-mutant LUAD patients.

cancer biology↗

bioRxiv2022

Predicting patient treatment response and resistance via single-cell transcriptomics of their tumors

Tailoring the best treatments to cancer patients is an important open challenge. Here, we build a precision oncology data science and software framework for PERsonalized single-Cell Expression-based Planning for Treatments In Oncology (PERCEPTION). Our approach capitalizes on recently published matched bulk and single-cell transcriptome profiles of large-scale cell-line drug screens to build treatment response models from patients single-cell (SC) tumor transcriptomics. First, we show that PERCEPTION successfully predicts the response to monotherapy and combination treatments in screens performed in cancer and patient-tumor-derived primary cells based on SC-expression profiles. Second, it successfully stratifies responders to combination therapy based on the patients tumors SC-expression in two very recent multiple myeloma and breast cancer clinical trials. Thirdly, it captures the development of clinical resistance to five standard tyrosine kinase inhibitors using tumor SC-expression profiles obtained during treatment in a lung cancer patients cohort. Notably, PERCEPTION outperforms state-of-the-art bulk expression-based predictors in all three clinical cohorts. In sum, this study provides a first-of-its-kind conceptual and computational method that is predictive of response to therapy in patients, based on the clonal SC gene expression of their tumors.

bioinformatics↗

bioRxiv2021

A focal adhesion kinase-YAP signaling axis drives drug tolerant persister cells and residual disease in lung cancer

Targeted therapy is effective in many tumor types including lung cancer, the leading cause of cancer mortality. Paradigm defining examples are targeted therapies directed against non-small cell lung cancer (NSCLC) subtypes with oncogenic alterations in EGFR, ALK and KRAS. The success of targeted therapy is limited by drug-tolerant tumor cells which withstand and adapt to treatment and comprise the residual disease state that is typical during treatment with clinical targeted therapies. Here, we integrate studies in patient-derived and immunocompetent lung cancer models and clinical specimens obtained from patients on targeted therapy to uncover a focal adhesion kinase (FAK)-YAP signaling axis that promotes residual disease during oncogenic EGFR-, ALK-, and KRAS-targeted therapies. FAK-YAP signaling inhibition combined with the primary targeted therapy suppressed residual drug-tolerant cells and enhanced tumor responses. This study unveils a FAK-YAP signaling module that promotes residual disease in lung cancer and mechanism-based therapeutic strategies to improve tumor response.

cancer biology↗

bioRxiv2020

Targeted cancer therapy induces APOBEC fuelling the evolution of drug resistance

Introductory paragraphThe clinical success of targeted cancer therapy is limited by drug resistance that renders cancers lethal in patients1-4. Human tumours can evolve therapy resistance by acquiring de novo genetic alterations and increased heterogeneity via mechanisms that remain incompletely understood1. Here, through parallel analysis of human clinical samples, tumour xenograft and cell line models and murine model systems, we uncover an unanticipated mechanism of therapy-induced adaptation that fuels the evolution of drug resistance. Targeted therapy directed against EGFR and ALK oncoproteins in lung cancer induced adaptations favoring apolipoprotein B mRNA-editing enzyme, catalytic polypeptide (APOBEC)-mediated genome mutagenesis. In human oncogenic EGFR-driven and ALK-driven lung cancers and preclinical models, EGFR or ALK inhibitor treatment induced the expression and DNA mutagenic activity of APOBEC3B via therapy-mediated activation of NF-{kappa}B signaling. Moreover, targeted therapy also mediated downregulation of certain DNA repair enzymes such as UNG2, which normally counteracts APOBEC-catalyzed DNA deamination events. In mutant EGFR-driven lung cancer mouse models, APOBEC3B was detrimental to tumour initiation and yet advantageous to tumour progression during EGFR targeted therapy, consistent with TRACERx data demonstrating subclonal enrichment of APOBEC-mediated mutagenesis. This study reveals how cancers adapt and drive genetic diversity in response to targeted therapy and identifies APOBEC deaminases as future targets for eliciting more durable clinical benefit to targeted cancer therapy.

cancer biology↗

bioRxiv2019

Heterogeneity and targeted therapy-induced adaptations in lung cancer revealed by longitudinal single-cell RNA sequencing

Lung cancer, the leading cause of cancer mortality, exhibits heterogeneity that enables adaptability, limits therapeutic success, and remains incompletely understood. Single-cell RNA sequencing (scRNAseq) of metastatic lung cancer was performed using 44 tumor biopsies obtained longitudinally from 27 patients before and during targeted therapy. Over 20,000 cancer and tumor microenvironment (TME) single-cell profiles exposed a rich and dynamic tumor ecosystem. scRNAseq of cancer cells illuminated targetable oncogenes beyond those detected clinically. Cancer cells surviving therapy as residual disease (RD) expressed an alveolar-regenerative cell signature suggesting a therapy-induced primitive cell state transition, whereas those present at on-therapy progressive disease (PD) upregulated kynurenine, plasminogen, and gap junction pathways. Active T-lymphocytes and decreased macrophages were present at RD and immunosuppressive cell states characterized PD. Biological features revealed by scRNAseq were biomarkers of clinical outcomes in independent cohorts. This study highlights how therapy-induced adaptation of the multi-cellular ecosystem of metastatic cancer shapes clinical outcomes.

cancer biology↗

bioRxiv2018

Co-occurring genetic alterations in the RAS pathway promote resistance to MET inhibitor treatment in non-small cell lung cancer with a MET exon 14 skipping mutation

PURPOSEWhile patients with advanced-stage non-small cell lung cancers (NSCLCs) harboring MET exon 14 skipping mutations (METex14) often benefit from MET tyrosine kinase inhibitor (TKI) treatment, clinical benefit is limited by primary and acquired drug resistance. The molecular basis for this resistance remains incompletely understood.\n\nMETHODSTargeted sequencing analysis was performed on cell-free circulating tumor DNA obtained from 289 patients with advanced-stage METex14-mutated NSCLC.\n\nRESULTSProminent co-occurring RAS-MAPK pathway gene alterations (e.g. in KRAS, NF1) were detected in NSCLCs with METex14 skipping alterations as compared to EGFR-mutated NSCLCs. There was an association between decreased MET TKI treatment response and RAS-MAPK pathway co-occurring alterations. In a preclinical model expressing a canonical METex14 mutation, KRAS overexpression or NF1 downregulation hyperactivated MAPK signaling to promote MET TKI resistance. This resistance was overcome by co-treatment with crizotinib and the MEK inhibitor trametinib.\n\nCONCLUSIONOur study provides a genomic landscape of co-occurring alterations in advanced-stage METex14-mutated NSCLC and suggests a potential combination therapy strategy targeting MAPK pathway signaling to enhance clinical outcomes.

cancer biology↗

bioRxiv2017

Evolution and clinical impact of genetic epistasis within EGFR-mutant lung cancers

Introductory paragraph Introductory paragraph Main text Methods Author Contributions Author Information References The current understanding of tumorigenesis is largely centered on a monogenic driver oncogene model. This paradigm is incompatible with the prevailing clinical experience in most solid malignancies: monotherapy with a drug directed against an individual oncogenic driver typically results in incomplete clinical responses and eventual tumor progression1-7. By profiling the somatic genetic alterations present in over 2,000 cases of lung cancer, the leading cause of cancer mortality worldwide8,9, we show that combinations of functional genetic alterations, i.e. genetic collectives dominate the landscape of ...

cancer biology↗

bioRxiv2016

Novel computational method for predicting polytherapy switching strategies to overcome tumor heterogeneity and evolution

1The success of targeted cancer therapy is limited by drug resistance that can result from tumor genetic heterogeneity. The current approach to address resistance typically involves initiating a new treatment after clinical/radiographic disease progression, ultimately resulting in futility in most patients. Towards a potential alternative solution, we developed a novel computational framework that uses human cancer profiling data to systematically identify dynamic, pre-emptive, and sometimes non-intuitive treatment strategies that can better control tumors in real-time. By studying lung adenocarcinoma clinical specimens and preclinical models, our computational analyses revealed that the best anti-cancer strategies addressed existing resistant subpopulations as they emerged dynamically during treatment. In some cases, the best computed treatment strategy used unconventional therapy switching while the bulk tumor was responding, a prediction we confirmed in vitro. The new framework presented here could guide the principled implementation of dynamic molecular monitoring and treatment strategies to improve cancer control.

cancer biology↗