Adaptive promising zone design for cancer immunotherapy with heterogeneous delayed treatment effect.

Indirect mechanisms of cancer immunotherapies result in delayed treatment effects that vary among patients. Consequently, the use of the log-rank test in trial design and analysis can lead to significant power loss and pose additional challenges for interim decisions in adaptive designs. In this paper, we describe patients' survival using a piecewise proportional hazard model with random lag time and propose an adaptive promising zone design for cancer immunotherapy with heterogeneous delayed effects. We provide solutions for calculating conditional power and adjusting the critical value for the log-rank test with interim data. We divide the sample space into three zones - unfavourable, promising, and favourable -based on re-estimations of the survival parameters, the log-rank test statistic at the interim analysis, and the initial and maximum sample sizes. If the interim results fall into the promising zone, the sample size is increased; otherwise, it remains unchanged. We show through simulations that our proposed approach has greater overall power than the fixed sample design and similar power to the matched group sequential trial. Furthermore, we confirm that critical value adjustment effectively controls the type I error rate inflation. Finally, we provide recommendations on the implementation of our proposed method in cancer immunotherapy trials.

Integrated Bulk and Single-cell RNA Sequencing Data Constructs and Validates a Prognostic Model for Non-small Cell Lung Cancer.

Background: Most of the current research on prognostic model construction for non-small cell lung cancer (NSCLC) only involves in bulk RNA-seq data without integration of single-cell RNA-seq (scRNA-seq) data. Besides, most of the prognostic models are constructed by predictive genes, ignoring other predictive variables such as clinical features. Methods: We obtained scRNA-seq data from GEO database and bulk RNA-seq data from TCGA database. We construct a prognostic model through the Least Absolute Shrinkage and Selection Operator (LASSO) and Cox regression. Furthermore, we performed ESTIMATE, CIBERSORT, immune checkpoint-related analyses and compared drug sensitivity using pRRophetic method judged by IC50 between different risk groups. Results: 14 tumor-related genes were extracted for model construction. The AUC for 1-, 3-, and 5 years overall survival prediction in TCGA and three validation cohorts were almost higher than 0.65, some of which were even higher than 0.7, even 0.8. Besides, calibration curves suggested no departure between model prediction and perfect fit. Additionally, immune-related and drug sensitivity results revealed potential targets and strategies for treatment, which can provide clinical guidance. Conclusion: We integrated traditional bulk RNA-seq and scRNA-seq data, along with predictive clinical features to develop a prognostic model for patients with NSCLC. According to the constructed model, patients in different groups can follow precise and individual therapeutic schedules based on immune characteristics as well as drug sensitivity.

AKT2 deficiency alleviates doxorubicin-induced cardiac injury via alleviating oxidative stress in cardiomyocytes.

Doxorubicin (DOX), a widely used chemotherapy agent in cancer treatment, encounters limitations in clinical efficacy due to associated cardiotoxicity. This study aims to explore the role of AKT serine/threonine kinase 2 (AKT2) in mitigating DOX-induced oxidative stress within the heart through both intracellular and extracellular signaling pathways. Utilizing Akt2 knockout (KO) and Nrf2 KO murine models, alongside neonatal rat cardiomyocytes (NRCMs), we systematically investigate the impact of AKT2 deficiency on DOX-induced cardiac injury. Our findings reveal that DOX administration induces significant oxidative stress, a primary contributor to cardiac injury. Importantly, Akt2 deficiency exhibits a protective effect by alleviating DOX-induced oxidative stress. Mechanistically, Akt2 deficiency facilitates nuclear translocation of NRF2, thereby suppressing intracellular oxidative stress by promoting the expression of antioxidant genes. Furthermore, We also observed that AKT2 inhibition facilitates superoxide dismutase 2 (SOD2) expression both inside macrophages and SOD2 secretion to the extracellular matrix, which is involved in lowering oxidative stress in cardiomyocytes upon DOX stimulation. The present study underscores the important role of AKT2 in mitigating DOX-induced oxidative stress through both intracellular and extracellular signaling pathways. Additionally, our findings propose promising therapeutic strategies for addressing DOX-induced cardiomyopathy in clinic.

Adaptive Bayesian information borrowing methods for finding and optimizing subgroup-specific doses.

In precision oncology, integrating multiple cancer patient subgroups into a single master protocol allows for the simultaneous assessment of treatment effects in these subgroups and promotes the sharing of information between them, ultimately reducing sample sizes and costs and enhancing scientific validity. However, the safety and efficacy of these therapies may vary across different subgroups, resulting in heterogeneous outcomes. Therefore, identifying subgroup-specific optimal doses in early-phase clinical trials is crucial for the development of future trials. In this article, we review various innovative Bayesian information-borrowing strategies that aim to determine and optimize subgroup-specific doses. Specifically, we discuss Bayesian hierarchical modeling, Bayesian clustering, Bayesian model averaging or selection, pairwise borrowing, and other relevant approaches. By employing these Bayesian information-borrowing methods, investigators can gain a better understanding of the intricate relationships between dose, toxicity, and efficacy in each subgroup. This increased understanding significantly improves the chances of identifying an optimal dose tailored to each specific subgroup. Furthermore, we present several practical recommendations to guide the design of future early-phase oncology trials involving multiple subgroups when using the Bayesian information-borrowing methods.

Cardiac injury activates STING signaling via upregulating SIRT6 in macrophages after myocardial infarction.

AIMS: This work sought to investigate the mechanism underlying the STING signaling pathway during myocardial infarction (MI), and explore the involvement and the role of SIRT6 in the process. MAIN METHODS: Mice underwent the surgery of permanent left anterior descending (LAD) artery constriction. Primary cardiomyocytes (CMs) and fibroblasts were subjected to hypoxia to mimic MI in vitro. STING expression was assessed in the infarct heart, and the effect of STING inhibition on cardiac fibrosis was explored. This study also evaluated the regulatory effect of STING by SIRT6 in macrophages. KEY FINDINGS: STING protein was increased in the infarct heart tissue, highlighting its involvement in the post-MI inflammatory response. Hypoxia-induced death of CMs and fibroblasts contributed to the upregulation of STING in macrophages, establishing the involvement of STING in the intercellular signaling during MI. Inhibition of STING resulted in a significant reduction of cardiac fibrosis at day 14 after MI. Additionally, this study identified SIRT6 as a key regulator of STING via influencing its acetylation and ubiquitination in macrophages, providing novel insights into the posttranscriptional modification and expression of STING at the acute phase after myocardial infarction. SIGNIFICANCE: This work shows the key role of SIRT6/STING signaling in the pathogenesis of cardiac injury after MI, suggesting that targeting this regulatory pathway could be a promising strategy to attenuate cardiac fibrosis after MI.

Enhancing phase I dose-finding trials design through dynamic borrowing information and handling late-onset toxicity.

Introduction: In recent years, there has been a growing trend among regulatory agencies to consider the use of historical controls in clinical trials as a means of improving the efficiency of trial design. In this paper, to enhance the statistical operating characteristic of Phase I dose-finding trials, we propose a novel model-assisted design method named "MEM-Keyboard". Methods: The proposed design is based on the multisource exchangeability models (MEMs) that allows for dynamic borrowing of information from multiple supplemental data sources, including historical trial data, to inform the dose-escalation process. Furthermore, with the frequent occurrence of delayed toxicity in novel anti-cancer drugs, we extended our proposed method to handle late-onset toxicity by incorporating historical data. This extended method is referred to as "MEM-TITE-Keyboard" and aims to improve the efficiency of early clinical trials. Results: Simulation studies have indicated that the proposed methods can improve the probability of correctly selecting the maximum tolerated dose (MTD) with an acceptable level of risk, compared to designs that do not account for information borrowing and late-onset toxicity. Discussion: The MEM-Keyboard and MEM-TITE-Keyboard, easy to implement in practice, provide a useful tool for identifying MTD and accelerating drug development.

MIDAS-2: an enhanced Bayesian platform design for immunotherapy combinations with subgroup efficacy exploration.

Although immunotherapy combinations have revolutionised cancer treatment, the rapid screening of effective and optimal therapies from large numbers of candidate combinations, as well as exploring subgroup efficacy, remains challenging. This necessitates innovative, integrated, and efficient trial designs. In this study, we extend the MIDAS design to include subgroup exploration and propose an enhanced Bayesian information borrowing platform design called MIDAS-2. MIDAS-2 enables quick and continuous screening of promising combination strategies and exploration of their subgroup effects within a unified platform design framework. We use a regression model to characterize the efficacy pattern in subgroups. Information borrowing is applied through Bayesian hierarchical modelling to improve trial efficiency considering the limited sample size in subgroups. Time trend calibration is also employed to avoid potential baseline drifts. Simulation results demonstrate that MIDAS-2 yields high probabilities for identifying the effective drug combinations as well as promising subgroups, facilitating appropriate selection of the best treatments for each subgroup. The proposed design is robust against small time trend drifts, and the type I error is successfully controlled after calibration when a large drift is expected. Overall, MIDAS-2 provides an adaptive drug screening and subgroup exploring framework to accelerate immunotherapy development in an efficient, accurate, and integrated fashion.

Silencing of genes by promoter hypermethylation shapes tumor microenvironment and resistance to immunotherapy in clear-cell renal cell carcinomas.

The efficacy of immune checkpoint inhibitors varies in clear-cell renal cell carcinoma (ccRCC), with notable primary resistance among patients. Here, we integrate epigenetic (DNA methylation) and transcriptome data to identify a ccRCC subtype characterized by cancer-specific promoter hypermethylation and epigenetic silencing of Polycomb targets. We develop and validate an index of methylation-based epigenetic silencing (iMES) that predicts primary resistance to immune checkpoint inhibition (ICI) in the BIONIKK trial. High iMES is associated with VEGF pathway silencing, endothelial cell depletion, immune activation/suppression, EZH2 activation, BAP1/SETD2 deficiency, and resistance to ICI. Combination therapy with hypomethylating agents or tyrosine kinase inhibitors may benefit patients with high iMES. Intriguingly, tumors with low iMES exhibit increased endothelial cells and improved ICI response, suggesting the importance of angiogenesis in ICI treatment. We also develop a transcriptome-based analogous system for extended applicability of iMES. Our study underscores the interplay between epigenetic alterations and tumor microenvironment in determining immunotherapy response.

Delineating the interplay between oncogenic pathways and immunity in anaplastic Wilms tumors.

Wilms tumors are highly curable in up to 90% of cases with a combination of surgery and radio-chemotherapy, but treatment-resistant types such as diffuse anaplastic Wilms tumors pose significant therapeutic challenges. Our multi-omics profiling unveils a distinct desert-like diffuse anaplastic Wilms tumor subtype marked by immune/stromal cell depletion, TP53 alterations, and cGAS-STING pathway downregulation, accounting for one-third of all diffuse anaplastic cases. This subtype, also characterized by reduced CD8 and CD3 infiltration and active oncogenic pathways involving histone deacetylase and DNA repair, correlates with poor clinical outcomes. These oncogenic pathways are found to be conserved in anaplastic Wilms tumor cell models. We identify histone deacetylase and/or WEE1 inhibitors as potential therapeutic vulnerabilities in these tumors, which might also restore tumor immunogenicity and potentially enhance the effects of immunotherapy. These insights offer a foundation for predicting outcomes and personalizing treatment strategies for aggressive pediatric Wilms tumors, tailored to individual immunological landscapes.

Development and validation of radiomics nomograms for preoperative prediction of characteristics in non-small cell lung cancer and circulating tumor cells.

To develop and validate 3 radiomics nomograms for preoperative prediction of pathological and progression diagnosis in non-small cell lung cancer (NSCLC) as well as circulating tumor cells (CTCs). A total of 224 and 134 patients diagnosed with NSCLC were respectively gathered in 2018 and 2019 in this study. There were totally 1197 radiomics features that were extracted and quantified from the images produced by computed tomography. Then we selected the radiomics features with predictive value by least absolute shrinkage and selection operator and combined them into radiomics signature. Logistic regression models were built using radiomics signature as the only predictor, which were then converted to nomograms for individualized predictions. Finally, the performance of the nomograms was assessed on both cohorts. Additionally, immunohistochemical correlation analysis was also performed. As for discrimination, the area under the curve of pathological diagnosis nomogram and progression diagnosis nomogram in NSCLC were both higher than 90% in the training cohort and higher than 80% in the validation cohort. The performance of the CTC-diagnosis nomogram was somehow unexpected where the area under the curve were range from 60% to 70% in both cohorts. As for calibration, nonsignificant statistics (P > .05) yielded by Hosmer-Lemeshow tests suggested no departure between model prediction and perfect fit. Additionally, decision curve analyses demonstrated the clinically usefulness of the nomograms. We developed radiomics-based nomograms for pathological, progression and CTC diagnosis prediction in NSCLC respectively. Nomograms for pathological and progression diagnosis were demonstrated well-performed to facilitate the individualized preoperative prediction, while the nomogram for CTC-diagnosis prediction needed improvement.

Local continual reassessment methods for dose finding and optimization in drug-combination trials.

Due to the limited sample size and large dose exploration space, obtaining a desirable dose combination is a challenging task in the early development of combination treatments for cancer patients. Most existing designs for optimizing the dose combination are model-based, requiring significant efforts to elicit parameters or prior distributions. Model-based designs also rely on intensive model calibration and may yield unstable performance in the case of model misspecification or sparse data. We propose to employ local, underparameterized models for dose exploration to reduce the hurdle of model calibration and enhance the design robustness. Building upon the framework of the partial ordering continual reassessment method, we develop local data-based continual reassessment method designs for identifying the maximum tolerated dose combination, using toxicity only, and the optimal biological dose combination, using both toxicity and efficacy, respectively. The local data-based continual reassessment method designs only model the local data from neighboring dose combinations. Therefore, they are flexible in estimating the local space and circumventing unstable characterization of the entire dose-exploration surface. Our simulation studies show that our approach has competitive performance compared to widely used methods for finding maximum tolerated dose combination, and it has advantages over existing model-based methods for optimizing optimal biological dose combination.

A Unique Gene Signature Predicting Recurrence Free Survival in Stage IA Lung Adenocarcinoma.

Objective: Resected stage IA lung adenocarcinoma (LUAD) has a reported 5-year recurrence free survival (RFS) of 63-81%. A unique gene signature stratifying patients with early stage LUAD as high or low-risk of recurrence would be valuable. Methods: GEO datasets combining European and North American LUAD patients (n=684) were filtered for stage IA (n=105) to develop a robust signature for recurrence (RFSscore). Univariate Cox proportional hazard regression model was used to assess associations of gene expression with RFS and OS. Leveraging a bootstrap approach of these identified upregulated genes allowed construction of a model which was evaluated by Area Under the Received Operating Characteristics. The optimal signature has RFSscore calculated via a linear combination of expression of selected genes weighted by the corresponding Cox regression derived coefficients. Log-rank analysis calculated RFS and OS. Results were validated using the LUAD TCGA transcriptomic NGS based dataset. Results: Rigorous bioinformatic analysis identified a signature of 4 genes: KNSTRN, PAFAH1B3, MIF, CHEK1. Kaplan-Meier analysis of stage IA LUAD with this signature resulted in 5-year RFS for low-risk of 90% compared to 53% for high-risk (HR 6.55, 95%CI 2.65-16.18, p-value <0.001), confirming the robustness of the gene signature with its clinical significance. Validation of the signature using TCGA dataset resulted in an AUC of 0.797 and 5-year RFS for low and high-risk stage IA patients being 91% and 67%, respectively (HR 3.44, 95%CI 1.16-10.23, p-value=0.044). Conclusions: This 4 gene signature stratifies European and North American patients with pathologically confirmed stage IA LUAD into low and high-risk groups for OS and more importantly RFS.

Integration of scRNA-seq and bulk RNA-seq constructs a stemness-related signature for predicting prognosis and immunotherapy responses in hepatocellular carcinoma.

PURPOSE: Cancer stem cells are associated with unfavorable prognosis in hepatocellular carcinoma (HCC). However, existing stemness-related biomarkers and prognostic models are limited. METHODS: The stemness-related signatures were derived from taking the union of the results obtained by performing WGCNA and CytoTRACE analysis at the bulk RNA-seq and scRNA-seq levels, respectively. Univariate Cox regression and the LASSO were applied for filtering prognosis-related signatures and selecting variables. Finally, ten gene signatures were identified to construct the prognostic model. We evaluated the differences in survival, genomic alternation, biological processes, and degree of immune cell infiltration in the high- and low-risk groups. pRRophetic and Tumor Immune Dysfunction and Exclusion (TIDE) algorithms were utilized to predict chemosensitivity and immunotherapy response. Human Protein Atlas (HPA) database was used to evaluate the protein expressions. RESULTS: A stemness-related prognostic model was constructed with ten genes including YBX1, CYB5R3, CDC20, RAMP3, LDHA, MTHFS, PTRH2, SRPRB, GNA14, and CLEC3B. Kaplan-Meier and ROC curve analyses showed that the high-risk group had a worse prognosis and the AUC of the model in four datasets was greater than 0.64. Multivariate Cox regression analyses verified that the model was an independent prognostic indicator in predicting overall survival, and a nomogram was then built for clinical utility in predicting the prognosis of HCC. Additionally, chemotherapy drug sensitivity and immunotherapy response analyses revealed that the high-risk group exhibited a higher likelihood of benefiting from these treatments. CONCLUSION: The novel stemness-related prognostic model is a promising biomarker for estimating overall survival in HCC.

A Bayesian phase I-II clinical trial design to find the biological optimal dose on drug combination.

In recent years, combined therapy shows expected treatment effect as they increase dose intensity, work on multiple targets and benefit more patients for antitumor treatment. However, dose -finding designs for combined therapy face a number of challenges. Therefore, under the framework of phase I-II, we propose a two-stage dose -finding design to identify the biologically optimal dose combination (BODC), defined as the one with the maximum posterior mean utility under acceptable safety. We model the probabilities of toxicity and efficacy by using linear logistic regression models and conduct Bayesian model selection (BMS) procedure to define the most likely pattern of dose-response surface. The BMS can adaptively select the most suitable model during the trial, making the results robust. We investigated the operating characteristics of the proposed design through simulation studies under various practical scenarios and showed that the proposed design is robust and performed well.

Racial Disparities in MiT Family Translocation Renal Cell Carcinoma.

Racial disparities have been documented in the biology and outcome of certain renal cell carcinomas (RCCs) among Black patients. However, little is known about racial differences in MiT family translocation RCC (TRCC). To investigate this issue, we performed a case-control study using data from The Cancer Genome Atlas (TCGA) and the Chinese OrigiMed2020 cohort. A total of 676 patients with RCC (14 Asian, 113 Black, and 525 White) were identified in TCGA, and TRCC was defined as RCC with TFE3/TFEB translocation or TFEB amplification, leading to 21 patients with TRCC (2 Asian, 8 Black, 10 White, and 1 unknown). Asian (2 of 14 [14.3%] vs 10 of 525 [1.9%]; P = .036) and Black (8 of 113 [7.1%] vs 1.9%; P = .007) patients with RCC showed significantly higher prevalence of TRCC compared with White patients with RCC. The overall mortality rate of TRCC was slightly higher in Asian and Black patients compared with White patients (HR: 6.05, P = .069). OrigiMed2020 Chinese patients with RCC had a significantly higher proportion of TRCC with TFE3 fusions than TCGA White patients with RCC (13 of 250 [5.2%] vs 7 of 525 [1.3%]; P = .003). Black patients with TRCC were more likely to exhibit the proliferative subtype than White patients (6 of 8 [75%] vs 2 of 9 [22.2%]; P = .057) for those who had RNA-seq profiles. We present evidence of higher prevalence of TRCC in Asian and Black patients with RCC compared with White patients and show that these tumors in Asian and Black patients have distinct transcriptional signatures and are associated with poor outcomes.

Immunogenicity and safety of heterologous immunisation with Ad5-nCOV in healthy adults aged 60 years and older primed with an inactivated SARS-CoV-2 vaccine (CoronaVac): a phase 4, randomised, observer-blind, non-inferiority trial.

Background: People over 60 have been found to develop less protection after two doses of inactivated COVID-19 vaccines than younger people. Heterologous immunisation could potentially induce more robust immune responses compared to homologous immunisation. We aimed to assess the immunogenicity and safety of a heterologous immunisation with an adenovirus type 5-vectored vaccine (Ad5-nCOV, Convidecia) among elderly who were primed with an inactivated vaccine (CoronaVac) previously. Methods: We did a randomised, observer-blinded, non-inferiority trial in healthy adults aged 60 years and older in Lianshui County (Jiangsu, China) between August 26, 2021 and May 15, 2022. 199 eligible participants who had received two doses of CoronaVac in the past 3-6 months were randomised (1:1) to receive a third dose of Convidecia (group A, n = 99) or CoronaVac (group B, n = 100), while 100 participants primed with one dose of CoronaVac in the past 1-2 months were randomised equally to receive a second dose of Convidecia (group C, n = 50) or CoronaVac (group D, n = 50). Participants and investigators were masked to the vaccine received. Primary outcomes were the geometric mean titers (GMTs) of neutralising antibodies against live SARS-CoV-2 virus 14 days after boosting and 28-day adverse reactions. This study was registered with ClinicalTrials.govNCT04952727. Findings: A heterologous third dose of Convidecia resulted in a 6.2-fold (GMTs: 286.4 vs 48.2), 6.3-fold (45.9 vs 7.3) and 7.5-fold (32.9 vs 4.4) increase in neutralising antibodies against SARS-CoV-2 wild-type, delta (B.1.617.2) and omicron (BA.1.1) 14 days post boosting, respectively, compared with the homologous boost. The heterologous booster with Convidecia induced significantly higher neutralsing activities, with up to 91% inhibition in binding of Spike to ACE2 for BA.4 and BA.5 variants, compared with 35% inhibition induced by three doses of CoronaVac. For participants primed with one dose of CoronaVac, a heterologous dose of Convidecia induced higher neutralising antibodies against wild-type than two doses of CoronaVac (GMTs: 70.9 vs 9.3, p < 0.0001), but not for that against variants of concern (GMTs against delta: 5.0 vs 4.0, p = 0.4876; GMTs against omicron: 4.8 vs 3.7, p = 0.4707). Adverse reactions were reported by 8 (8.1%) participants in group A and 4 (4.0%) in group B (p ＞ 0.05), and 8 (16.0%) in group C and 1 (2.0%) in group D (p = 0.031). Interpretation: In elderly individuals primed with two doses of CoronaVac, the heterologous immunisation with Convidecia induced strong antibodies against SARS-CoV-2 wildtype and variants of concern, which could be an alternative regimen for enhancing protection in this vulnerable population. Funding: National Natural Science Foundation of China, Jiangsu Provincial Key Research and Development Program, and Jiangsu Science Fund for Distinguished Young Scholars Program.

A comparative study of adaptive trial designs for dose optimization.

Recently, the US Food and Drug Administration Oncology Center of Excellence initiated Project Optimus to reform the dose optimization and dose selection paradigm in oncology drug development. The agency pointed out that the current paradigm for dose selection-based on the maximum tolerated dose (MTD)-is not sufficient for molecularly targeted therapies and immunotherapies, for which efficacy may not increase after the dose reaches a certain level. In these cases, it is more appropriate to identify the optimal biological dose (OBD) that optimizes the risk-benefit tradeoff of the drug. Project Optimus has spurred tremendous interest and urgent need for guidance on designing dose optimization trials. In this article, we review several representative dose optimization designs, including model-based and model-assisted designs, and compare their operating characteristics based on 10,000 randomly generated scenarios with various dose-toxicity and dose-efficacy curves and some fixed representative scenarios. The results show that, compared with model-based designs, model-assisted methods have advantages of easy-to-implement, robustness, and high accuracy to identify OBD. Some guidance is provided to help biostatisticians and clinicians to choose appropriate dose optimization methods in practice.

IBIS: identify biomarker-based subgroups with a Bayesian enrichment design for targeted combination therapy.

BACKGROUND: Combination therapies directed at multiple targets have potentially improved treatment effects for cancer patients. Compared to monotherapy, targeted combination therapy leads to an increasing number of subgroups and complicated biomarker-based efficacy profiles, making it more difficult for efficacy evaluation in clinical trials. Therefore, it is necessary to develop innovative clinical trial designs to explore the efficacy of targeted combination therapy in different subgroups and identify patients who are more likely to benefit from the investigational combination therapy. METHODS: We propose a statistical tool called 'IBIS' to Identify BIomarker-based Subgroups and apply it to the enrichment design framework. The IBIS contains three main elements: subgroup division, efficacy evaluation and subgroup identification. We first enumerate all possible subgroup divisions based on biomarker levels. Then, Jensen-Shannon divergence is used to distinguish high-efficacy and low-efficacy subgroups, and Bayesian hierarchical model (BHM) is employed to borrow information within these two subsets for efficacy evaluation. Regarding subgroup identification, a hypothesis testing framework based on Bayes factors is constructed. This framework also plays a key role in go/no-go decisions and enriching specific population. Simulation studies are conducted to evaluate the proposed method. RESULTS: The accuracy and precision of IBIS could reach a desired level in terms of estimation performance. In regard to subgroup identification and population enrichment, the proposed IBIS has superior and robust characteristics compared with traditional methods. An example of how to obtain design parameters for an adaptive enrichment design under the IBIS framework is also provided. CONCLUSIONS: IBIS has the potential to be a useful tool for biomarker-based subgroup identification and population enrichment in clinical trials of targeted combination therapy.

TIMEAS, a promising method for the stratification of testicular germ cell tumor patients with distinct immune microenvironment, clinical outcome and sensitivity to frontline therapies.

PURPOSE: With the heterogeneous genetic background, prognosis prediction and therapeutic targets for testicular germ cell tumors (TGCTs) are still unclear. We defined the tumor immune microenvironment activation status (TIMEAS). METHODS: We collected a total of 314 TGCT patients from four cohorts, including a 48-case microarray. A nonnegative matrix factorization algorithm was applied to identify the "immune factor", derived the top 150 weighted genes to divide patients into immune and non-immune classes, and further separated the immune class into activated and exhausted subgroups by nearest template prediction. Tumor mutant burden, gene mutation, and copy number alteration were compared with our recently developed package "MOVICS". A random forest algorithm was performed to establish a prediction model with fewer genes. Immunohistochemistry staining was performed to identify TIMEAS in the microarray. RESULTS: We constructed the TIMEAS in the TCGA-TGCT cohort and further validated it in the GSE3218 and GSE99420 cohorts. The immune class contained the activated status of T-lymphocytes, B-lymphocytes, and macrophages, while Treg cells and the WNT/TGFß signature were more activated in the immune-suppressed subgroup. Patients in the immune-exhausted subgroup had the worst prognosis, and 22.9% of patients in the immune-activated subgroup had KRAS mutations, which might stimulate the response of the immune system and lead to a favorable prognosis. The immune-exhausted group benefited more from chemotherapy, while the immune-activated subgroup responded well to anti-PD-1/PD-L1 therapy. FSCN1 was validated as the target of the immune-exhausted microenvironment by immunohistochemistry. CONCLUSION: TIMEAS classification can separate TGCT patients; patients in the immune-activated subgroup could benefit more from anti-PD-L1 immunotherapy, and those in the immune-exhausted subgroup are more suitable for chemotherapy.