Network Analyses of Brain Tumor Patients' Multiomic Data Reveals Pharmacological Opportunities to Alter Cell State Transitions.

Glioblastoma Multiforme (GBM) remains a particularly difficult cancer to treat, and survival outcomes remain poor. In addition to the lack of dedicated drug discovery programs for GBM, extensive intratumor heterogeneity and epigenetic plasticity related to cell-state transitions are major roadblocks to successful drug therapy in GBM. To study these phenomenon, publicly available snRNAseq and bulk RNAseq data from patient samples were used to categorize cells from patients into four cell states (i.e. phenotypes), namely: (i) neural progenitor-like (NPC-like), (ii) oligodendrocyte progenitor-like (OPC-like), (iii) astrocyte- like (AC-like), and (iv) mesenchymal-like (MES-like). Patients were subsequently grouped into subpopulations based on which cell-state was the most dominant in their respective tumor. By incorporating phosphoproteomic measurements from the same patients, a protein-protein interaction network (PPIN) was constructed for each cell state. These four-cell state PPINs were pooled to form a single Boolean network that was used for in silico protein knockout simulations to investigate mechanisms that either promote or prevent cell state transitions. Simulation results were input into a boosted tree machine learning model which predicted the cell states or phenotypes of GBM patients from an independent public data source, the Glioma Longitudinal Analysis (GLASS) Consortium. Combining the simulation results and the machine learning predictions, we generated hypotheses for clinically relevant causal mechanisms of cell state transitions. For example, the transcription factor TFAP2A can be seen to promote a transition from the NPC-like to the MES-like state. Such protein nodes and the associated signaling pathways provide potential drug targets that can be further tested in vitro and support cell state-directed (CSD) therapy.

Network Analyses of Brain Tumor Patients' Multiomic Data Reveals Pharmacological Opportunities to Alter Cell State Transitions.

Glioblastoma Multiforme (GBM) remains a particularly difficult cancer to treat, and survival outcomes remain poor. In addition to the lack of dedicated drug discovery programs for GBM, extensive intratumor heterogeneity and epigenetic plasticity related to cell-state transitions are major roadblocks to successful drug therapy in GBM. To study these phenomenon, publicly available snRNAseq and bulk RNAseq data from patient samples were used to categorize cells from patients into four cell states (i.e. phenotypes), namely: (i) neural progenitor-like (NPC-like), (ii) oligodendrocyte progenitor-like (OPC-like), (iii) astrocyte-like (AC-like), and (iv) mesenchymal-like (MES-like). Patients were subsequently grouped into subpopulations based on which cell-state was the most dominant in their respective tumor. By incorporating phosphoproteomic measurements from the same patients, a protein-protein interaction network (PPIN) was constructed for each cell state. These four-cell state PPINs were pooled to form a single Boolean network that was used for in silico protein knockout simulations to investigate mechanisms that either promote or prevent cell state transitions. Simulation results were input into a boosted tree machine learning model which predicted the cell states or phenotypes of GBM patients from an independent public data source, the Glioma Longitudinal Analysis (GLASS) Consortium. Combining the simulation results and the machine learning predictions, we generated hypotheses for clinically relevant causal mechanisms of cell state transitions. For example, the transcription factor TFAP2A can be seen to promote a transition from the NPC-like to the MES-like state. Such protein nodes and the associated signaling pathways provide potential drug targets that can be further tested in vitro and support cell state-directed (CSD) therapy.

Pharmacokinetics of protein and peptide conjugates.

Protein and peptide conjugates have become an important component of therapeutic and diagnostic medicine. These conjugates are primarily designed to improve pharmacokinetics (PK) of those therapeutic or imaging agents, which do not possess optimal disposition characteristics. In this review we have summarized preclinical and clinical PK of diverse protein and peptide conjugates, and have showcased how different conjugation approaches are used to obtain the desired PK. We have classified the conjugates into peptide conjugates, non-targeted protein conjugates, and targeted protein conjugates, and have highlighted diagnostic and therapeutic applications of these conjugates. In general, peptide conjugates demonstrate very short half-life and rapid renal elimination, and they are mainly designed to achieve high contrast ratio for imaging agents or to deliver therapeutic agents at sites not reachable by bulky or non-targeted proteins. Conjugates made from non-targeted proteins like albumin are designed to increase the half-life of rapidly eliminating therapeutic or imaging agents, and improve their delivery to tissues like solid tumors and inflamed joints. Targeted protein conjugates are mainly developed from antibodies, antibody derivatives, or endogenous proteins, and they are designed to improve the contrast ratio of imaging agents or therapeutic index of therapeutic agents, by enhancing their delivery to the site-of-action.

Taxane resistance in castration-resistant prostate cancer: mechanisms and therapeutic strategies.

Despite its good initial response and significant survival benefit in patients with castration-resistant prostate cancer (CRPC), taxane therapy inevitably encounters drug resistance in all patients. Deep understandings of taxane resistant mechanisms can significantly facilitate the development of new therapeutic strategies to overcome taxane resistance and improve CRPC patient survival. Multiple pathways of resistance have been identified as potentially crucial areas of intervention. First, taxane resistant tumor cells typically have mutated microtubule binding sites, varying tubulin isotype expression, and upregulation of efflux transporters. These mechanisms contribute to reducing binding affinity and availability of taxanes. Second, taxane resistant tumors have increased stem cell like characteristics, indicating higher potential for further mutation in response to therapy. Third, the androgen receptor pathway is instrumental in the proliferation of CRPC and multiple hypotheses leading to this pathway reactivation have been reported. The connection of this pathway to the AKT pathway has received significant attention due to the upregulation of phosphorylated AKT in CRPC. This review highlights recent advances in elucidating taxane resistant mechanisms and summarizes potential therapeutic strategies for improved treatment of CRPC.