Characterization of newly established Pralatrexate-resistant cell lines and the mechanisms of resistance

Therapeutic challenges in peripheral T-cell lymphoma

Peripheral T-cell lymphoma (PTCL) is a rare and heterogeneous group of hematological malignancies. Compared to our knowledge of B-cell tumors, our understanding of T-cell leukemia and lymphoma remains less advanced, and a significant number of patients are diagnosed with advanced stages of the disease. Unfortunately, the development of drug resistance in tumors leads to relapsed or refractory peripheral T-Cell Lymphomas (r/r PTCL), resulting in highly unsatisfactory treatment outcomes for these patients. This review provides an overview of potential mechanisms contributing to PTCL treatment resistance, encompassing aspects such as tumor heterogeneity, tumor microenvironment, and abnormal signaling pathways in PTCL development. The existing drugs aimed at overcoming PTCL resistance and their potential resistance mechanisms are also discussed. Furthermore, a summary of ongoing clinical trials related to PTCL is presented, with the aim of aiding clinicians in making informed treatment decisions.

Multi-CpG linear regression models to accurately predict paclitaxel and docetaxel activity in cancer cell lines

The microtubule-targeting paclitaxel (PTX) and docetaxel (DTX) are widely used chemotherapeutic agents. However, the dysregulation of apoptotic processes, microtubule-binding proteins, and multi-drug resistance efflux and influx proteins can alter the efficacy of taxane drugs. In this review, we have created multi-CpG linear regression models to predict the activities of PTX and DTX drugs through the integration of publicly available pharmacological and genome-wide molecular profiling datasets generated using hundreds of cancer cell lines of diverse tissue of origin. Our findings indicate that linear regression models based on CpG methylation levels can predict PTX and DTX activities (log-fold change in viability relative to DMSO) with high precision. For example, a 287-CpG model predicts PTX activity at R² of 0.985 among 399 cell lines. Just as precise (R²=0.996) is a 342-CpG model for predicting DTX activity in 390 cell lines. However, our predictive models, which employ a combination of mRNA expression and mutation as input variables, are less accurate compared to the CpG-based models. While a 290 mRNA/mutation model was able to predict PTX activity with R² of 0.830 (for 546 cell lines), a 236 mRNA/mutation model could calculate DTX activity at R² of 0.751 (for 531 cell lines). The CpG-based models restricted to lung cancer cell lines were also highly predictive (R²≥0.980) for PTX (74 CpGs, 88 cell lines) and DTX (58 CpGs, 83 cell lines). The underlying molecular biology behind taxane activity/resistance is evident in these models. Indeed, many of the genes represented in PTX or DTX CpG-based models have functionalities related to apoptosis (e.g., ACIN1, TP73, TNFRSF10B, DNASE1, DFFB, CREB1, BNIP3), and mitosis/microtubules (e.g., MAD1L1, ANAPC2, EML4, PARP3, CCT6A, JAKMIP1). Also represented are genes involved in epigenetic regulation (HDAC4, DNMT3B, and histone demethylases KDM4B, KDM4C, KDM2B, and KDM7A), and those that have never been previously linked to taxane activity (DIP2C, PTPRN2, TTC23, SHANK2). In summary, it is possible to accurately predict taxane activity in cell lines based entirely on methylation at multiple CpG sites.