PKBOIN-12: A Bayesian Optimal Interval Phase I/II Design Incorporating Pharmacokinetics Outcomes to Find the Optimal Biological Dose.

Immunotherapies and targeted therapies have gained popularity due to their promising therapeutic effects across multiple treatment areas. The focus of early phase dose-finding clinical trials has shifted from finding the maximum tolerated dose (MTD) to identifying the optimal biological dose (OBD), which aims to balance the toxicity and efficacy outcomes, thus optimizing the risk-benefit trade-off. These trials often collect multiple pharmacokinetics (PK) outcomes to assess drug exposure, which has shown correlations with toxicity and efficacy outcomes but has not been utilized in the current dose-finding designs for OBD selection. Moreover, PK outcomes are usually available within days after initial treatment, much faster than toxicity and efficacy outcomes. To bridge this gap, we introduce the innovative model-assisted PKBOIN-12 design, which enhances BOIN12 by integrating PK information into both the dose-finding algorithm and the final OBD determination process. We further extend PKBOIN-12 to TITE-PKBOIN-12 to address the challenges of late-onset toxicity and efficacy outcomes. Simulation results demonstrate that PKBOIN-12 more effectively identifies the OBD and allocates a greater number of patients to it than BOIN12. Additionally, PKBOIN-12 decreases the probability of selecting inefficacious doses as the OBD by excluding those with low drug exposure. Comprehensive simulation studies and sensitivity analysis confirm the robustness of both PKBOIN-12 and TITE-PKBOIN-12 in various scenarios.

Bayesian dose escalation with overdose and underdose control utilizing all toxicities in Phase I/II clinical trials.

Escalation with overdose control (EWOC) is a commonly used Bayesian adaptive design, which controls overdosing risk while estimating maximum tolerated dose (MTD) in cancer Phase I clinical trials. In 2010, Chen and his colleagues proposed a novel toxicity scoring system to fully utilize patients' toxicity information by using a normalized equivalent toxicity score (NETS) in the range 0 to 1 instead of a binary indicator of dose limiting toxicity (DLT). Later in 2015, by adding underdosing control into EWOC, escalation with overdose and underdose control (EWOUC) design was proposed to guarantee patients the minimum therapeutic effect of drug in Phase I/II clinical trials. In this paper, the EWOUC-NETS design is developed by integrating the advantages of EWOUC and NETS in a Bayesian context. Moreover, both toxicity response and efficacy are treated as continuous variables to maximize trial efficiency. The dose escalation decision is based on the posterior distribution of both toxicity and efficacy outcomes, which are recursively updated with accumulated data. We compare the operation characteristics of EWOUC-NETS and existing methods through simulation studies under five scenarios. The study results show that EWOUC-NETS design treating toxicity and efficacy outcomes as continuous variables can increase accuracy in identifying the optimized utility dose (OUD) and provide better therapeutic effects.

Gaussian variational approximate inference for joint models of longitudinal biomarkers and a survival outcome.

The shared random effects joint model is one of the most widely used approaches to study the associations between longitudinal biomarkers and a survival outcome and make dynamic risk predictions using the longitudinally measured biomarkers. Various types of joint models have been developed under different settings in the past decades. One major limitation of joint models is that they could be computationally expensive for complex models where the number of the shared random effects is large. Moreover, the inferential accuracy of joint models could also be diminished for complex models due to approximation errors. However, complex models are frequently needed in practice, for example, when the longitudinal biomarkers have nonlinear trajectories over time or the number of longitudinal biomarkers of interest is large. In this article, we propose a novel Gaussian variational approximate inference approach for fitting joint models, which significantly improves computational efficiency while maintaining inferential accuracy. We conduct extensive simulation studies to evaluate the performance of our proposed method and compare it to existing methods. The performance of our proposed method is further demonstrated on a dataset of patients with primary biliary cirrhosis.

Exogenous recombinant N-acetylgalactosamine-4-sulfatase (Arylsulfatase B; ARSB) inhibits progression of B16F10 cutaneous melanomas and modulates cell signaling.

In the syngeneic, subcutaneous B16F10 mouse model of malignant melanoma, treatment with exogenous ARSB markedly reduced tumor size and extended survival. In vivo experiments showed that local treatment with exogenous N-acetylgalactosamine-4-sulfatase (Arylsulfatase B; ARSB) led to reduced tumor growth over time (p < 0.0001) and improved the probability of survival up to 21 days (p = 0.0391). Tumor tissue from the treated mice had lower chondroitin 4-sulfate (C4S) content and lower sulfotransferase activity. The free galectin-3 declined, and the SHP2 activity increased, due to altered binding with chondroitin 4-sulfate. These changes induced effects on transcription, which were mediated by Sp1, phospho-ERK1/2, and phospho-p38 MAPK. Reduced mRNA expression of chondroitin sulfate proteoglycan 4 (CSPG4), carbohydrate sulfotransferase 15 (N-acetylgalactosamine 4-sulfate 6-O-sulfotransferase), and matrix metalloproteinases 2 and 9 resulted. Experiments in the human melanoma cell line A375 demonstrated similar responses to exogenous ARSB as in the tumors, and inverse effects followed ARSB siRNA. ARSB, which removes the 4-sulfate group at the non-reducing end of C4S, acts as a tumor suppressor, and treatment with exogenous ARSB impacts on vital cell signaling and reduces the expression of critical genes associated with melanoma progression.

The Proteomic Landscape of Growth Factor Signaling Networks Associated with FAT1 Mutations in Head and Neck Cancers.

FAT1 is frequently mutated in head and neck squamous cell carcinoma (HNSCC), but the biological and clinical effects of FAT1 mutations in HNSCC remain to be fully elucidated. We investigated the landscape of altered protein and gene expression associated with FAT1 mutations and clinical outcomes of patients with HNSCC. FAT1 mutation was stratified with clinical information from The Cancer Genome Atlas HNSCC databases with more than 200 proteins or phosphorylated sites. FAT1 mutation was significantly more prevalent among HPV(-), female, and older patients and was enriched in oral, larynx, and hypopharynx primary tumors. FAT1 mutation was also significantly associated with lower FAT1 gene expression and increased protein expression of HER3_pY1289, IRS1, and CAVEOLIN1. From an independent International Cancer Genome Consortium dataset, FAT1 mutation in oral cancer co-occurred with top mutated genes TP53 and CASP8. Poorer overall survival or progression-free survival was observed in patients with FAT1 mutation or altered HER3_pY1289, IRS1, or CAVEOLIN1. Pathway analysis revealed dominant ERBB/neuregulin pathways linked to FAT1 mutations in HNSCC, and protein signature panels uncovered the heterogeneity of patient subgroups. Decreased pEGFR, pHER2, and pERK and upregulated pHER3 and HER3 proteins were observed in two FAT1 knockout HNSCC cell lines, supporting that FAT1 alterations lead to altered EGFR/ERBB signaling. In squamous cancers of the lung and cervix, a strong association of FAT1 and EGFR gene expressions was identified. Collectively, these results suggest that alteration of FAT1 appears to involve mostly HPV(-) HNSCC and may contribute to resistance to EGFR-targeted therapy. SIGNIFICANCE: Integrative bioinformatics and statistical analyses reveal a panel of genes and proteins associated with FAT1 mutation in HNSCC, providing important insights into prospective clinical investigations with targeted therapies.