Biology subjects

Zucchetto, A.

Publications and source records attributed to Zucchetto, A..

4 recordsLinked to original sources

bioRxiv2025

IL-16 production is a mechanism of resistance to BTK inhibitors and R-CHOP in lymphomas

Introducing Brutons tyrosine kinase (BTK) inhibitors has significantly improved outcomes for patients with B-cell malignancies and autoimmune disorders. However, resistance, either primary or acquired, remains a major clinical challenge. To better understand the underlying resistance mechanisms to BTK inhibitors, we established an ibrutinib-resistant model from a patient-derived splenic marginal zone lymphoma (MZL) cell line (VL51) through prolonged drug exposure. Resistant cells exhibited a 15-fold increase in ibrutinibs IC50, along with distinct morphological changes, mitochondrial activation, and cross-resistance to covalent, non-covalent BTK inhibitors and BTK degraders. Integrated transcriptomic, epigenomic, and proteomic analyses identified overexpression and secretion of IL-16 as a key feature of resistance, driven by chromatin remodeling and activation of the FLI1 transcription factor. IL-16 conferred ibrutinib resistance via CD9-mediated activation of the NF-{kappa}B and AKT signaling pathways and was found to be elevated in the serum of ibrutinib-refractory CLL patients. Functional studies showed that targeting the IL-16/CD9 axis using neutralizing antibodies or CD9-binding peptides restored sensitivity to BTK inhibitors and R-CHOP chemotherapy in MZL, mantle cell lymphoma, and diffuse large B-cell lymphoma models. These findings reveal a novel, targetable resistance mechanism with potential therapeutic implications for overcoming BTK inhibitor resistance in B-cell lymphomas.

cancer biology↗

bioRxiv2024

Model-based Bayesian inference of cancer dynamics from heterogenous longitudinal data

The kinetic parameters of cancer population dynamics are critical for developing reliable predictors of tumour growth patterns, extracting metrics for patient stratification and creating algorithms that can forecast clinically significant events. Here, we introduce a model-based Bayesian framework that leverages longitudinal phenotypic (e.g., tumour volume, cell counts) or genotypic (e.g., mutation frequency) data to infer critical parameters of tumour progression within a single patient. Our models uses population genetics to estimate probability distributions for tumour growth rates, initiation and extinction times, pinpointing abrupt shifts in tumour dynamics due to treatment response and revealing associations between drug resistance and pre-existing cancer cell populations. We apply our framework to address pivotal clinical questions across three major cancer types. In colorectal cancer, we use tumour markers data to identify extensive pre-existing RAS-linked resistance to cetuximab. In lung cancer, we use somatic mutation frequencies in citculating tumour DNA to determine prognostic growth rates and develop a test for monitoring minimal residual disease. In chronic lymphocytic leukaemia, we use white blood cell counts to stratify patients by growth patterns and predict time to treatment, advancing adaptive monitoring strategies.

cancer biology↗

bioRxiv2023

ERBB4-mediated signaling is a mediator of resistance to BTK and PI3K inhibitors in B cell lymphoid neoplasms

BTK and PI3K inhibitors are among the drugs approved for the treatment of patients with lymphoid neoplasms. Although active, their ability to lead as single agents to long-lasting complete remission is rather limited especially in the lymphoma setting. This indicates that tumor cells often develop resistance to the drugs. Here, we show that the overexpression of ERBB4 and its ligands represents a modality for B cell neoplastic cells to bypass the anti-tumor activity of BTK and PI3K inhibitors and that targeted pharmacological interventions can restore sensitivity to the small molecules. We started from a marginal zone lymphoma (MZL) cell line, Karpas-1718, kept under prolonged exposure to the PI3K{delta} inhibitor idelalisib until acquisition of resistance, or with no drug. Cells underwent transcriptome, miRNA and methylation profiling, whole exome sequencing, and pharmacological screening which led to the identification of the overexpression of ERBB4 and its ligands HBEGF and NRG2 in the resistant cells. Cellular and genetic experiments demonstrated the involvement of this axis in blocking the anti-tumor activity of various BTK and PI3K inhibitors, currently used in the clinical setting. Addition of recombinant HBEGF induced resistance to BTK and PI3K inhibitors in parental cells but also in additional lymphoma models. Combination with the ERBB inhibitor lapatinib was beneficial in resistant cells and in other lymphoma models already expressing the identified resistance factors. Multi-omics analysis underlined that an epigenetic reprogramming affected the expression of the resistance-related factors, and pretreatment with demethylating agents or EZH2 inhibitors overcame the resistance. Resistance factors were shown to be expressed in clinical samples, further extending the findings of the study. In conclusions, we identified a novel ERBB4-driven mechanism of resistance to BTK and PI3K inhibitors and treatments that appear to overcome it. Key pointsO_LIA mechanism of secondary resistance to the PI3K{delta} and BTK inhibitors in B cell neoplasms driven by secreted factors. C_LIO_LIResistance can be reverted by targeting ERBB signaling. C_LI

cancer biology↗

bioRxiv2022

Proteogenomics refines the molecular classification of chronic lymphocytic leukemia

Cancer heterogeneity at the proteome level may explain differences in therapy response and prognosis beyond the currently established genomic and transcriptomic based diagnostics. The relevance of proteomics for disease classifications remains to be established in clinically heterogeneous cancer entities such as chronic lymphocytic leukemia (CLL). Here, we characterized the proteome and transcriptome in-depth alongside genetic and ex-vivo drug response profiling in a clinically well annotated CLL discovery cohort (n= 68). Unsupervised clustering of the proteome data revealed six subgroups. Five of these proteomic groups were associated with genetic features, while one group was only detectable at the proteome level. This new group was characterized by accelerated disease progression, high spliceosomal protein abundances associated with aberrant splicing, and low B cell receptor signaling protein abundances (ASB-CLL). We developed classifiers to identify ASB-CLL based on its characteristic proteome or splicing signature in two independent cohorts (n= 165, n= 169) and confirmed that ASB-CLL comprises about 20 % of CLL patients. The inferior overall survival observed in ASB-CLL was independent of both TP53- and IGHV mutation status. Our multi-omics analysis refines the classification of CLL and highlights the potential of proteomics to improve cancer patient stratification beyond genetic and transcriptomic profiling. Single sentence summaryWe performed the largest proteogenomic analysis of CLL, linked proteomic profiles to clinical outcomes, and discovered a new poor outcome subgroup (ASB-CLL).

cancer biology↗