NCI Cancer Research Data Commons: Lessons Learned and Future State.

More than ever, scientific progress in cancer research hinges on our ability to combine datasets and extract meaningful interpretations to better understand diseases and ultimately inform the development of better treatments and diagnostic tools. To enable the successful sharing and use of big data, the NCI developed the Cancer Research Data Commons (CRDC), providing access to a large, comprehensive, and expanding collection of cancer data. The CRDC is a cloud-based data science infrastructure that eliminates the need for researchers to download and store large-scale datasets by allowing them to perform analysis where data reside. Over the past 10 years, the CRDC has made significant progress in providing access to data and tools along with training and outreach to support the cancer research community. In this review, we provide an overview of the history and the impact of the CRDC to date, lessons learned, and future plans to further promote data sharing, accessibility, interoperability, and reuse. See related articles by Brady et al., p. 1384, Wang et al., p. 1388, and Pot et al., p. 1396.

Real-World Effectiveness of Lung Cancer Screening Using Deep Learning-Based Counterfactual Prediction.

The benefits and harms of lung cancer screening (LCS) for patients in the real-world clinical setting have been argued. Recently, discriminative prediction modeling of lung cancer with stratified risk factors has been developed to investigate the real-world effectiveness of LCS from observational data. However, most of these studies were conducted at the population level that only measured the difference in the average outcome between groups. In this study, we built counterfactual prediction models for lung cancer risk and mortality and examined for individual patients whether LCS as a hypothetical intervention reduces lung cancer risk and subsequent mortality. We investigated traditional and deep learning (DL)-based causal methods that provide individualized treatment effect (ITE) at the patient level and evaluated them with a cohort from the OneFlorida+ Clinical Research Consortium. We further discussed and demonstrated that the ITE estimation model can be used to personalize clinical decision support for a broader population.

Favorable Immunotherapy Plus Tyrosine Kinase Inhibition Outcome of Renal Cell Carcinoma Patients with Low CDK5 Expression.

PURPOSE: Immunotherapy (IO) plus tyrosine kinase inhibitor (TKI) has become the first-line treatment for advanced renal cell carcinoma, despite the lack of prognostic biomarkers. Cyclin-dependent kinase 5 (CDK5) affects the tumor microenvironment, which may influence the efficacy of TKI+IO. MATERIALS AND METHODS: Two cohorts from our center (Zhongshan Metastatic Renal Cell Carcinoma [ZS-MRCC] cohort, Zhongshan High-risk Localized Renal Cell Carcinoma [ZS-HRRCC] cohort) and one cohort from a clinical trial (JAVELIN-101) were enrolled. The expression of CDK5 of each sample was determined by RNA sequencing. Immune infiltration and T cell function were evaluated by flow cytometry and immunohistochemistry. Response and progression-free survival (PFS) were set as primary endpoints. RESULTS: Patients of low CDK5 expression showed higher objective response rate (60.0% vs. 23.3%) and longer PFS in both cohorts (ZS-MRCC cohort, p=0.014; JAVELIN-101 cohort, p=0.040). CDK5 expression was enhanced in non-responders (p < 0.05). In the ZS-HRRCC cohort, CDK5 was associated with decreased tumor-infiltrating CD8+ T cells, which was proved by immunohistochemistry (p < 0.05) and flow cytometry (Spearman's ρ=-0.49, p < 0.001). In the high CDK5 subgroup, CD8+ T cells revealed a dysfunction phenotype with decreased granzyme B, and more regulatory T cells were identified. A predictive score was further constructed by random forest, involving CDK5 and T cell exhaustion features. The RFscore was also validated in both cohorts. By utilizing the model, more patients might be distinguished from the overall cohort. Additionally, only in the low RFscore did TKI+IO outperform TKI monotherapy. CONCLUSION: High-CDK5 expression was associated with immunosuppression and TKI+IO resistance. RFscore based on CDK5 may be utilized as a biomarker to determine the optimal treatment strategy.

Cancer incidence after asthma diagnosis: Evidence from a large clinical research network in the United States.

BACKGROUND: Prior studies on the association between asthma and cancer show inconsistent results. This study aimed to generate additional evidence on the association between asthma and cancer, both overall, and by cancer type, in the United States. METHOD: We conducted a retrospective cohort study using 2012-2020 electronic health records and claims data in the OneFlorida+ clinical research network. Our study population included a cohort of adult patients with asthma (n = 90,021) and a matching cohort of adult patients without asthma (n = 270,063). We built Cox proportional hazards models to examine the association between asthma diagnosis and subsequent cancer risk. RESULTS: Our results showed that asthma patients were more likely to develop cancer compared to patients without asthma in multivariable analysis (hazard ratio [HR] = 1.36, 99% confidence interval [CI] = 1.29-1.44). Elevated cancer risk was observed in asthma patients without (HR = 1.60; 99% CI: 1.50-1.71) or with (HR = 1.11; 99% CI: 1.03-1.21) inhaled steroid use. However, in analyses of specific cancer types, cancer risk was elevated for nine of 13 cancers in asthma patients without inhaled steroid use but only for two of 13 cancers in asthma patients with inhaled steroid use, suggesting a protective effect of inhaled steroid use on cancer. CONCLUSION: This is the first study to report a positive association between asthma and overall cancer risk in the US population. More in-depth studies using real-word data are needed to further explore the causal mechanisms of asthma on cancer risk.

Unfavorable immunotherapy plus tyrosine kinase inhibition outcome of metastatic renal cell carcinoma after radical nephrectomy with increased ADAM9 expression.

Immunotherapy plus tyrosine kinase inhibitor (IO-TKI) has become the standard first-line therapy for advanced renal cell carcinoma (RCC). However, the modest response rate of IO-TKI therapy and the absence of biomarkers limited the selection of treatment strategies for RCC patients. There were three cohorts enrolled: two from our facility (ZS-MRCC and ZS-HRRCC) and one from a clinical study (JAVELIN-101). By RNA sequencing, the expression of ADAM9 in each sample was measured. By flow cytometry and immunohistochemistry, immune infiltration and T cell function were examined. Primary outcomes were established as treatment response and progression-free survival (PFS). Patients with low-ADAM9 expression had a higher objective response rate (56.5% vs 13.6%, P = 0.01) and longer PFS in both cohorts. In the ZS-HRRCC cohort, the expression of ADAM9 was associated with increased tumor-infiltrating T cells, which was proved by immunohistochemistry (P < 0.05) and flow cytometry (Spearman's ρ = 0.42, P < 0.001). In the high-ADAM9 group, CD8+ and CD4+ T cells revealed an exhausted phenotype with decreased GZMB (Spearman's ρ = - 0.31, P = 0.05, and Spearman's ρ = - 0.49, P < 0.001, respectively), and fewer Macrophages were identified. A predictive RFscore was further constructed by random forest approach, involving ADAM9 and immunologic genes. Only in the subgroup with the lower RFscore did IO-TKI outperform TKI monotherapy. High-ADAM9 expression was associated with immunosuppression and IO-TKI resistance. Expression of ADAM9 was also associated with the exhaustion and dysfunction of T cells. ADAM9-based RFscore has the potential to be used as a biomarker to distinguish the optimal patient treatment methods between IO-TKI and TKI monotherapy.

Simulating Colorectal Cancer Trials Using Real-World Data.

PURPOSE: Using real-world data (RWD)-based trial simulation approach, we aim to simulate colorectal cancer (CRC) trials and examine both effectiveness and safety end points in different simulation scenarios. METHODS: We identified five phase III trials comparing new treatment regimens with an US Food and Drug Administration-approved first-line treatment in patients with metastatic CRC (ie, fluorouracil, leucovorin, and irinotecan) as the standard-of-care (SOC) control arm. Using Electronic Health Record-derived data from the OneFlorida network, we defined the study populations and outcome measures using the protocols from the original trials. Our design scenarios were (1) simulation of the SOC fluorouracil, leucovorin, and irinotecan arm and (2) comparative effectiveness research (CER) simulation of the control and experimental arms. For each scenario, we adjusted for random assignment, sampling, and dropout. We used overall survival (OS) and severe adverse events (SAEs) to measure effectiveness and safety. RESULTS: We conducted CER simulations for two trials, and SOC simulations for three trials. The effect sizes of our simulated trials were stable across all simulation runs. Compared with the original trials, we observed longer OS and higher mean number of SAEs in both CER and SOC simulation. In the two CER simulations, hazard ratios associated with death from simulations were similar to that reported in the original trials. Consistent with the original trials, we found higher risk ratios of SAEs in the experiment arm, suggesting potentially higher toxicities from the new treatment regimen. We also observed similar SAE rates across all simulations compared with the original trials. CONCLUSION: In this study, we simulated five CRC trials, and tested two simulation scenarios with several different configurations demonstrated that our simulations can robustly generate effectiveness and safety outcomes comparable with the original trials using real-world data.

Comprehensive Analysis of the Potential Immune-Related Biomarker ATG101 that Regulates Apoptosis of Cholangiocarcinoma Cells After Photodynamic Therapy.

Autophagy related gene 101 (ATG101) plays a significant role in the occurrence and development of tumours by responding to stress. Our research aims to illustrate the correlation between the expression of ATG101 and tumor prognosis and its potential role and mechanism in tumor immunity and photodynamic therapy (PDT). First, integrated analysis of The Cancer Genome Atlas and Genotype-Tissue Expression portals were used to analyse the expression of ATG101. Then, Kaplan-Meier curves was applied in cholangiocarcinoma (CHOL) and liver hepatocellular carcinoma (LIHC) datasets for survival analysis. Next, the relationship between ATG101 expression and six immune cells, the immune microenvironment and immune checkpoints was analysed. Besides, the relationship between the expression of ATG101 and methyltransferase. GSEA was used to study the function and the related transcript factors of ATG101 in CHOL and LIHC. The effect of PDT on ATG101 was verified by microarray, qPCR and western blot. Then the effect of ATG101 and its regulatory factors on apoptosis were verified by siRNA, lentivirus transfection and Chip-qPCR. Comprehensive analysis showed that ATG101 was overexpressed in different tumours. Kaplan-Meier curves found that ATG101 was associated with poor prognosis in tumours (including CHOL and LIHC). We found that ATG101 can be used as a target and prognostic marker of tumour immunotherapy for different tumours. We also found that ATG101 regulates DNA methylation. GSEA analysis showed that ATG101 may play a critical role in CHOL and LIHC. Subsequent validation tests confirmed that the up-regulated ATG101 after PDT treatment is not conducive to the occurrence of apoptosis of cholangiocarcinoma cells. The high expression of ATG101 may be induced by the early stress gene EGR2. Our study highlights the significance of ATG101 in the study of tumour immunity and photodynamic therapy from a pan-cancer perspective.

An efficient and accurate distributed learning algorithm for modeling multi-site zero-inflated count outcomes.

Clinical research networks (CRNs), made up of multiple healthcare systems each with patient data from several care sites, are beneficial for studying rare outcomes and increasing generalizability of results. While CRNs encourage sharing aggregate data across healthcare systems, individual systems within CRNs often cannot share patient-level data due to privacy regulations, prohibiting multi-site regression which requires an analyst to access all individual patient data pooled together. Meta-analysis is commonly used to model data stored at multiple institutions within a CRN but can result in biased estimation, most notably in rare-event contexts. We present a communication-efficient, privacy-preserving algorithm for modeling multi-site zero-inflated count outcomes within a CRN. Our method, a one-shot distributed algorithm for performing hurdle regression (ODAH), models zero-inflated count data stored in multiple sites without sharing patient-level data across sites, resulting in estimates closely approximating those that would be obtained in a pooled patient-level data analysis. We evaluate our method through extensive simulations and two real-world data applications using electronic health records: examining risk factors associated with pediatric avoidable hospitalization and modeling serious adverse event frequency associated with a colorectal cancer therapy. In simulations, ODAH produced bias less than 0.1% across all settings explored while meta-analysis estimates exhibited bias up to 12.7%, with meta-analysis performing worst in settings with high zero-inflation or low event rates. Across both applied analyses, ODAH estimates had less than 10% bias for 18 of 20 coefficients estimated, while meta-analysis estimates exhibited substantially higher bias. Relative to existing methods for distributed data analysis, ODAH offers a highly accurate, computationally efficient method for modeling multi-site zero-inflated count data.

Validation of Real-World Data-based Endpoint Measures of Cancer Treatment Outcomes.

Recently, there has been a growing interest in using real-world data (RWD) to generate real-world evidence that complements clinical trials. To quantify treatment effects, it is important to develop meaningful RWD-based endpoints. In cancer trials, two real-world endpoints are of particular interest: real-world overall survival (rwOS) and real-world time to next treatment (rwTTNT). In this work, we identified ways to calculate these real-world endpoints with structured electronic health record (EHR) data and validate these endpoints against the gold-standard measurements of these endpoints derived from linked EHR and tumor registry (TR) data. In addition, we examined and reported data quality issues, especially inconsistencies between the EHR and TR data. Using a survival model, we show that the presence of next treatment was not significantly associated with rwOS, but patients who had longer rwTTNT had longer rwOS, validating the use of rwTTNT as a real-world surrogate marker for measuring cancer endpoints.

Trends in Gene Expression Profiling for Prostate Cancer Risk Assessment: A Systematic Review.

OBJECTIVES: The aim of the study is to review biotechnology advances in gene expression profiling on prostate cancer (PCa), focusing on experimental platform development and gene discovery, in relation to different study designs and outcomes in order to understand how they can be exploited to improve PCa diagnosis and clinical management. METHODS: We conducted a systematic literature review on gene expression profiling studies through PubMed/MEDLINE and Web of Science between 2000 and 2016. Tissue biopsy and clinical gene profiling studies with different outcomes (e.g., recurrence, survival) were included. RESULTS: Over 3,000 papers were screened and 137 full-text articles were selected. In terms of technology used, microarray is still the most popular technique, increasing from 50 to 70% between 2010 and 2015, but there has been a rise in the number of studies using RNA sequencing (13% in 2015). Sample sizes have increased, as well as the number of genes that can be screened all at once, but we have also observed more focused targeting in more recent studies. Qualitative analysis on the specific genes found associated with PCa risk or clinical outcomes revealed a large variety of gene candidates, with a few consistent cross-studies. CONCLUSIONS: The last 15 years of research in gene expression in PCa have brought a large volume of data and information that has been decoded only in part, but advancements in high-throughput sequencing technology are increasing the amount of data that can be generated. The variety of findings warrants the execution of both validation studies and meta-analyses. Genetic biomarkers have tremendous potential for early diagnosis of PCa and, if coupled with other diagnostics (e.g., imaging), can effectively be used to concretize less-invasive, personalized prediction of PCa risk and progression.

Feeder-independent derivation of induced-pluripotent stem cells from peripheral blood endothelial progenitor cells.

Induced-pluripotent stem cells (iPSCs) are a potential alternative cell source in regenerative medicine, which includes the use of differentiated iPSCs for cell therapies to treat coronary artery and/or peripheral arterial diseases. Late-outgrowth endothelial progenitor cells (late-EPCs) are a unique primary cell present in peripheral blood that exhibit high proliferative capacity, are being used in a wide variety of clinical trials, and have the ability to differentiate into mature endothelial cells. The objective of this study was to reprogram peripheral blood-derived late-EPCs to a pluripotent state under feeder-free and defined culture conditions. Late-EPCs that were retrovirally transduced with OCT4, SOX2, KLF4, c-MYC, and iPSC colonies were derived in feeder-free and defined media conditions. EPC-iPSCs expressed pluripotent markers, were capable of differentiating to cells from all three germ-layers, and retained a normal karyotype. Transcriptome analyses demonstrated that EPC-iPSCs exhibit a global gene expression profile similar to human embryonic stem cells (hESCs). We have generated iPSCs from late-EPCs under feeder-free conditions. Thus, peripheral blood-derived late-outgrowth EPCs represent an alternative cell source for generating iPSCs.