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Biology subjects

Facciotto, C.

Publications and source records attributed to Facciotto, C..

2 recordsLinked to original sources

Epigenetic inhibitors sensitize DLBCL cells to rituximab and doxorubicin

Primary therapy for diffuse large B-cell lymphoma (DLBCL) is an immunochemotherapy regimen comprising rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP). While R-CHOP cures 60% of the patients with DLBCL, those who do not respond or relapse have dismal prognosis, and effective treatment strategies are needed. Due to the plastic nature of the epigenome and its fundamental role in regulating cell identity, we hypothesized that reprogramming the epigenome can overcome resistance to R-CHOP. We developed a novel drug screening protocol to identify epigenetic modifiers that sensitize DLBCL cell lines to immunochemotherapy. Of the herein tested 60 epigenetic compounds, we identified several histone deacetylase (HDAC) and histone methyltransferase (HMT) inhibitors that acted synergistically with immunochemotherapy. We show that sensitization through HDAC and HMT inhibitors is achieved by dysregulating homologous recombination, a central DNA repair pathway, as well as by disrupting the cell cycle and affecting the apoptotic pathway. Epigenetic inhibitors are well-tolerated, which together with our findings support their use in combination with immunochemotherapy in patients with primary refractory and relapsed DLBCL.

cancer biology↗

Identifying differentially methylated sites in samples with varying tumor purity

DNA methylation aberrations are common in many cancer types. A major challenge hindering comparison of patient-derived samples is that they comprise of heterogeneous collection of cancer and microenvironment cells. We present a computational method that allows comparing cancer methylomes in two or more heterogeneous tumor samples featuring differing, unknown fraction of cancer cells. The method is unique in that it allows comparison also in the absence of normal cell control samples and without prior tumor purity estimates, as these are often unavailable or unreliable in clinical samples. We use simulations and next-generation methylome, RNA, and whole-genome sequencing data from two cancer types to demonstrate that the method is accurate and outperforms alternatives. The results show that our method adapts well to various cancer types and to a wide range of tumor content, and works robustly without a control or with controls derived from various sources. The method is freely available at https://bitbucket.org/anthakki/dmml.

bioinformatics↗