An adaptive gBOIN design with shrinkage boundaries for phase I dose-finding trials

Background With the emergence of molecularly targeted agents and immunotherapies, the landscape of phase I trials in oncology has been changed. Though these new therapeutic agents are very likely induce multiple low- or moderate-grade toxicities instead of DLT, most of the existing phase I trial designs account for the binary toxicity outcomes. Motivated by a pediatric phase I trial of solid tumor with a continuous outcome, we propose an adaptive generalized Bayesian optimal interval design with shrinkage boundaries, gBOINS, which can account for continuous, toxicity grades endpoints and regard the conventional binary endpoint as a special case. Result The proposed gBOINS design enjoys convergence properties, e.g., the induced interval shrinks to the toxicity target and the recommended dose converges to the true maximum tolerated dose with increased sample size. Conclusion The proposed gBOINS design is transparent and simple to implement. We show that the gBOINS design has the desirable finite property of coherence and large-sample property of consistency. Numerical studies show that the proposed gBOINS design yields good performance and is comparable with or superior to the competing design.

A computational method for prioritizing targeted therapies in precision oncology: performance analysis in the SHIVA01 trial

Precision oncology is currently based on pairing molecularly targeted agents (MTA) to predefined single driver genes or biomarkers. Each tumor harbors a combination of a large number of potential genetic alterations of multiple driver genes in a complex system that limits the potential of this approach. We have developed an artificial intelligence (AI)-assisted computational method, the digital drug-assignment (DDA) system, to prioritize potential MTAs for each cancer patient based on the complex individual molecular profile of their tumor. We analyzed the clinical benefit of the DDA system on the molecular and clinical outcome data of patients treated in the SHIVA01 precision oncology clinical trial with MTAs matched to individual genetic alterations or biomarkers of their tumor. We found that the DDA score assigned to MTAs was significantly higher in patients experiencing disease control than in patients with progressive disease (1523 versus 580, P = 0.037). The median PFS was also significantly longer in patients receiving MTAs with high (1000+ <) than with low (<0) DDA scores (3.95 versus 1.95 months, P = 0.044). Our results indicate that AI-based systems, like DDA, are promising new tools for oncologists to improve the clinical benefit of precision oncology.

Zinc Administration and Improved Serum Markers of Hepatic Fibrosis in Patients with Autoimmune Hepatitis

Aim: The aim of the present study is to investigate the effect of long-term zinc supplementation, which is important for the activation of various enzymes that contribute to antioxidant and antifibrotic activities, on the improvement of serum fibrotic markers in patients with autoimmune hepatitis (AIH). Methods: A total of 38 patients with AIH under regular treatment at our hospital who provided their consent for being treated with polaprezinc (75 mg twice daily) were included and classified into 2 groups: the patients with zinc elevation (n = 27) and the patients without zinc elevation (n = 11). Serum biomarker of fibrosis, protein expression levels of matrix metalloproteinases (MMPs), and their inhibitors (TIMPs) were evaluated. Results: A significant difference was found between the variability of serum procollagen type Ⅲ and collagen type Ⅳ-7S between the 2 groups before and after zinc administration for more than 24 months (p = 0.043 and p = 0.049). In the patients with zinc elevation, no significant changes were found in collagenase (MMP-1 and MMP-13) before and after zinc administration, whereas a significant increase in the expression of gelatinase (MMP-2 and MMP-9) was found after administration (p = 0.021 and p = 0.005). As for the relative ratio of MMPs to TIMPs, only MMP-9 to TIMP-1 showed a significant increase (p = 0.004). Conclusions: Long-term treatment with polaprezinc has been demonstrated to safely improve serum fibrosis indices through increases in MMP-2/-9 and MMP-9/TIMP-1 and is expected to be well combined with direct antifibrotic therapies such as molecularly targeted agents.

Molecularly profiled trials: toward a framework of actions for the “nil actionables”

The sequencing of tumour or blood samples is increasingly used to stratify patients into clinical trials of molecularly targeted agents, and this approach has frequently demonstrated clinical benefit for those who are deemed eligible. But what of those who have no clear and evident molecular driver? What of those deemed to have “nil actionable” mutations? How might we deliver better therapeutic opportunities for those left behind in the clamour toward stratified therapeutics? And what significant learnings lie hidden in the data we amass but do not interrogate and understand? This Perspective article suggests a holistic approach to the future treatment of such patients, and sets a framework through which significant additional patient benefit might be achieved. In order to deliver upon this framework, it encourages and invites the clinical community to engage more enthusiastically and share learnings with colleagues in the early drug discovery community, in order to deliver a step change in patient care.

A Note on the Required Sample Size of Model-Based Dose-Finding Methods for Molecularly Targeted Agents

Random forest has proven to be a successful machine learning method, but it also can be time-consuming for handling large datasets, especially for doing iterative tasks. Machine learning iterative imputation methods have been well accepted by researchers for imputing missing data, but such methods can be more time-consuming than standard imputation methods. To overcome this drawback, different parallel computing strategies have been proposed but their impact on imputation results and subsequent statistical analyses are relatively unknown. Newly proposed random forest implementations, such as ranger and randomForestSRC, have provided alternatives for easier parallelization, but their validity for doing iterative imputation are still unclear. Using random-forest imputation algorithm missForest as an example, this study examines two parallelized methods using newly proposed random forest implementations in comparison with the two parallel strategies (variable-wise distributed computation and model-wise distributed computation) using language-level parallelization from the software package. Results from the simulation experiments showed that the parallel strategies could influence both the imputation process and the final imputation results differently. Different parallel strategies can improve computational speed to a variable extent, and based on simulations, ranger can provide performance boost for datasets of different sizes with reasonable accuracy. Specifically, even though different strategies can produce similar normalized root mean squared prediction errors, the variable-wise distributed strategy led to additional biases when estimating the mean and inter-correlation of the covariates and their regression coefficients. And parallelization by randomForestSRC can lead to changes in both prediction errors and estimates.

Stereotactic Radiosurgery and Targeted Therapies for Brain Metastases from Solid Cancers

Perpetual advances in the diagnostic tools, local plus systemic cancer treatments, and ensued lengthened survival times led to striking increments in the incidence rates of brain metastases (BMs), with a collective incidence range of 20-40% for all solid cancers. Stereotactic radiosurgery (SRS) and innovative molecularly targeted therapies are continuously gaining growing significance in the triumphant management of BMs, as the brain represents a sanctuary site for the vast majority of the conventional cytotoxic chemotherapies. In this scenario, the molecularly targeted agents appear to be an attractive alternative to traditional chemotherapeutics as they can modulate cancer metabolism and progression and exert synergism with radiation therapy. Therefore, the present paper intends to sum up the accessible proof on the consolidated utilization of SRS and molecularly targeted agents in the precise management of BMs from certain solid cancers, specifically the non-small-cell lung, breast, and renal-cell carcinomas, and malignant melanomas.

Identification of the Response-Related Biomarker of Bimonthly Hepatic Arterial Infusion Chemotherapy

Despite the availability of molecularly targeted agents for advanced hepatocellular carcinoma (aHCC), these are limited to compensated cirrhotic patients, and concerns about decreased hepatic functional reserve (HFR) and unknown adverse events, which may affect long-term survival, remain unaddressed. In this study, we enrolled 96 aHCC patients treated with bimonthly hepatic arterial infusion chemotherapy (B-HAIC) with cisplatin or sorafenib monotherapy (oral sorafenib 400 mg twice daily) not only to demonstrate its efficacy and significance but also to indicate preferable candidates by setting a response-related biomarker. Differences in treatment had no significant effect on overall survival (OS). The response rate in patients treated with B-HAIC was relatively higher than those treated with sorafenib. HFR was well maintained over the treatment course with B-HAIC, while it was significantly impaired with sorafenib. By employing multivariate analysis, we found negative trends between progression-free survival (PFS) periods and serum levels of alpha fetoprotein as well as des-gamma-carboxy prothrombin (DCP). In addition, a logistic regression analysis of the relationship between serum DCP levels and PFS periods over 420 days (14 months) showed that the PFS periods of patients with higher DCP was significantly shorter than those of patients with lower DCP (p = 0.02). Subsequently, the present study demonstrated the efficacy and safety of B-HAIC and identified a predictor of unpreferable patients. Based on these results, B-HAIC might be an alternative treatment after the implementation of new molecularly targeted therapies.

Designing Dose-Finding Phase I Clinical Trials: Top 10 Questions That Should Be Discussed With Your Statistician

In recent years, the landscape in clinical trial development has changed to involve many molecularly targeted agents, immunotherapies, or radiotherapy, as a single agent or in combination. Given their different mechanisms of action and lengths of administration, these agents have different toxicity profiles, which has resulted in numerous challenges when applying traditional designs such as the 3 + 3 design in dose-finding clinical trials. Novel methods have been proposed to address these design challenges such as combinations of therapies or late-onset toxicities. However, their design and implementation require close collaboration between clinicians and statisticians to ensure that the appropriate design is selected to address the aims of the study and that the design assumptions are pertinent to the study drug. The goal of this paper is to provide guidelines for appropriate questions that should be considered early in the design stage to facilitate the interactions between clinical and statistical teams and to improve the design of dose-finding clinical trials for novel anticancer agents.

New Drugs for Recurrent or Metastatic Nasopharyngeal Cancer

Chemotherapy has been the backbone for the treatment of recurrent or metastatic nasopharyngeal carcinoma (RMNPC), which remains an incurable disease. Currently the most active area of therapeutic investigations in RMNPC is in immunotherapy, especially after the results of five anti-programmed death-1 (anti-PD-1) antibodies, i.e. pembrolizumab, nivolumab, camrelizumab, toripalimab and tislelizumab, have demonstrated monotherapy objective response rates of 21%–43%. Combinations using anti-PD1/L1 antibodies as backbone to evaluate their additivity or synergy with cytotoxic chemotherapy, molecularly targeted agents, or other immuno-oncology compounds are actively being developed. Besides immune checkpoint blockade, additional ways to modulate the host immune system, such as Epstein-Barr virus (EBV)-directed vaccination against viral antigens (such as EBNA1, LMP1, LMP2) with dendritic cells or peptides, adoptive cell transfer of autologous or HLA-matched allogeneic EBV-specific cytotoxic T lymphocytes, CAR or TCR T-cell therapy, personalized cancer vaccines and oncolytic viruses are being explored. Finally, novel molecularly targeted agents that have entered human testing in RMNPC include apatinib and anlotinib (antiangiogenic agents), MAK683 (an embryonic ectoderm development or EED protein inhibitor), among others. This review provides an update of ongoing clinical trials evaluating these new compounds in RMNPC.

The Lung Microbiome: A Central Mediator of Host Inflammation and Metabolism in Lung Cancer Patients?

Lung cancer is the leading cause of cancer-related death. Over the past 5–10 years lung cancer outcomes have significantly improved in part due to better treatment options including immunotherapy and molecularly targeted agents. Unfortunately, the majority of lung cancer patients do not enjoy durable responses to these new treatments. Seminal research demonstrated the importance of the gut microbiome in dictating responses to immunotherapy in melanoma patients. However, little is known regarding how other sites of microbiota in the human body affect tumorigenesis and treatment responses. The lungs were traditionally thought to be a sterile environment; however, recent research demonstrated that the lung contains its own dynamic microbiota that can influence disease and pathophysiology. Few studies have explored the role of the lung microbiome in lung cancer biology. In this review article, we discuss the links between the lung microbiota and cancer, with particular focus on immune responses, metabolism and strategies to target the lung microbiome for cancer prevention.