Comparative Transcriptome Profiling Reveals Coding and Noncoding RNA Differences in NSCLC from African Americans and European Americans

Prognostic predictive modeling of non-small cell lung cancer associated with cadmium-related pathogenic genes

Persistent exposure to low-dose of cadmium is strongly linked to both the development and prognosis of non-small cell lung cancer (NSCLC), yet the precise molecular mechanism behind this relationship remains uncertain. In this study, cadmium-related pathogenic genes (CRPGs) in NSCLC were identified via differential expression analysis. NSCLC patient clusters related to CRPGs were constructed through univariate Cox and K-means clustering algorithms. Multivariate Cox and least absolute shrinkage and selection operator (LASSO) regression analyses were employed to determine the prognosis. Sixteen CRPGs showed a significant association with NSCLC. We found biological and prognostic differences between patients in clusters A and B. A predictive prognostic risk model for NSCLC revealed that FAM83H, MSMO1, and SNAI1 are central. Hence, the 3 hub genes were named. To further elucidate the role of CRPGs in NSCLC, A549 cells were exposed to CdCl2. The mRNA and protein expression levels of the 3 hub genes and cell invasion were detected. Moreover, 10 μM CdCl2 may increase the protein expression of 3 hub genes and enhance the invasive ability of A549 cells. This risk model may have established a theoretical foundation for investigating the mechanisms, treatment, and prognosis of NSCLC.

Machine-learning and scRNA-Seq-based diagnostic and prognostic models illustrating survival and therapy response of lung adenocarcinoma

Lung cancer is a major cause accounting for cancer-related mortalities, with lung adenocarcinoma (LUAD) being the most prevalent subtype. Given the high clinical and cellular heterogeneities of LUAD, accurate diagnosis and prognosis are crucial to avoid overdiagnosis and overtreatment. Taking full advantage of scRNA-Seq data to resolve the tumor heterogeneities, we explored the overall landscape of LUAD microenvironment. Utilizing the stage-specific tumor cell markers, we have developed highly accurate diagnostic and prognostic models with elevated sensitivity and specificity. The diagnostic model, developed through random forest algorithms with a thirteen-gene signature, achieved an accuracy of 96.4% and an AUC of 0.993. These metrics were further demonstrated by benchmarking with available models and scoring systems in independent cohorts. Concurrently, the prognostic model, formulated via Cox regression with a six-gene signature, effectively predicted overall survival, with elevated risk scores associated with increased fractions of cancer-associated fibroblasts, and higher likelihood of immune escape and T-cell exclusion. Subsequently, two nomograms were developed to predict survival and drug responses, facilitating their integration into clinical practice. Overall, this study underscores the potential of our models for efficient, rapid, and cost-effective diagnosis and prognosis of LUAD, adaptable to multiple expression profiling platforms and quantification methods.

Differences in systemic immune parameters in individuals with lung cancer according to race

Introduction

Racial and ethnic disparities in the presentation and outcomes of lung cancer are widely known. To evaluate potential factors contributing to these observations, we measured systemic immune parameters in Black and White patients with lung cancer.

Methods

Patients scheduled to receive cancer immunotherapy were enrolled in a multi-institutional prospective biospecimen collection registry. Clinical and demographic information were obtained from electronic medical records. Pre-treatment peripheral blood samples were collected and analyzed for cytokines using a multiplex panel and for immune cell populations using mass cytometry. Differences between Black and White patients were determined and corrected for multiple comparisons.

Results

A total of 187 patients with non-small cell lung cancer (Black, 19; White, 168) were included in the analysis. There were no significant differences in baseline characteristics between Black and White patients. Compared to White patients, Black patients had significantly lower levels of CCL23 and CCL27, and significantly higher levels of CCL8, CXCL1, CCL26, CCL25, CCL1, IL-1 b, CXCL16, and IFN-γ (all P <0.05, FDR<0.1). Black patients also exhibited greater populations of non-classical CD16+ monocytes, NKT-like cells, CD4+ cells, CD38+ monocytes, and CD57+ gamma delta T cells (all P <0.05).

Conclusions

Black and White patients with lung cancer exhibit several differences in immune parameters, with Black patients exhibiting greater levels of numerous pro-inflammatory cytokines and cell populations. The etiology and clinical significance of these differences warrant further evaluation.

Differential Non-Coding RNA Profiles for Lung Cancer Early Detection in African and White Americans

Introduction

Lung cancer leads in cancer-related deaths. Disparities are observed in lung cancer rates, with African Americans (AAs) experiencing disproportionately higher incidence and mortality compared to other ethnic groups. Non-coding RNAs (ncRNAs) play crucial roles in lung tumorigenesis. Our objective was to identify ncRNA biomarkers associated with the racial disparity in lung cancer.

Methods

Using droplet digital PCR, we examined 93 lung-cancer-associated ncRNAs in the plasma and sputum samples from AA and White American (WA) participants, which included 118 patients and 92 cancer-free smokers. Subsequently, we validated our results with a separate cohort comprising 56 cases and 72 controls.

Results

In the AA population, plasma showed differential expression of ten ncRNAs, while sputum revealed four ncRNAs when comparing lung cancer patients to the control group. In the WA population, the plasma displayed eleven ncRNAs, and the sputum had five ncRNAs showing differential expression between the lung cancer patients and the control group. For AAs, we identified a three-ncRNA panel (plasma miRs-147b, 324-3p, 422a) diagnosing lung cancer in AAs with 86% sensitivity and 89% specificity. For WAs, a four-ncRNA panel was developed, comprising sputum miR-34a-5p and plasma miRs-103-3p, 126-3p, 205-5p, achieving 88% sensitivity and 87% specificity. These panels remained effective across different stages and histological types of lung tumors and were validated in the independent cohort.

Conclusions

The ethnicity-related ncRNA signatures have promise as biomarkers to address the racial disparity in lung cancer.

Identification of potential regulatory mechanisms and therapeutic targets for lung cancer

Lung cancer poses a significant health threat globally, especially in regions like India, with 5-year survival rates remain alarmingly low. Our study aimed to uncover key markers for effective treatment and early detection. We identified specific genes related to lung cancer using the BioXpress database and delved into their roles through DAVID enrichment analysis. By employing network theory, we explored the intricate interactions within lung cancer networks, identifying ASPM and MKI67 as crucial regulator genes. Predictions of microRNA and transcription factor interactions provided additional insights. Examining gene expression patterns using GEPIA and KM Plotter revealed the clinical relevance of these key genes. In our pursuit of targeted therapies, Drug Bank pointed to methotrexate as a potential drug for the identified key regulator genes. Confirming this, molecular docking studies through Swiss Dock showed promising binding interactions. To ensure stability, we conducted molecular dynamics simulations using the AMBER 16 suite. In summary, our study pinpoints ASPM and MKI67 as vital regulators in lung cancer networks. The identification of hub genes and functional pathways enhances our understanding of molecular processes, offering potential therapeutic targets. Importantly, methotrexate emerged as a promising drug candidate, supported by robust docking and simulation studies. These findings lay a solid foundation for further experimental validations and hold promise for advancing personalized therapeutic strategies in lung cancer.Communicated by Ramaswamy H. Sarma.

A Systems Biology Approach to Understand the Racial Disparities in Colorectal Cancer

Racial disparities between Black/African Americans (AA) and White patients in colorectal cancer are an ever-growing area of concern. Black/AA show the highest incidence and have the highest mortality among major U.S. racial groups. There is no definite cause other than possible sociodemographic, socioeconomic, education, nutrition, delivery of healthcare, screening, and cultural factors. A primary limitation in this field is the lack of and small sample size of Black/AA studies. Thus, this study aimed to investigate whether differences in gene expression contribute to this ongoing unanswered racial disparity issue. In this study, we examined transcriptomic data of Black/AA and White patient cohorts using a bioinformatic and systems biology approach. We performed a Kaplan-Meier overall survival analysis between both patient cohorts across critical colorectal cancer signal transduction networks (STN), to determine the differences in significant genes across each cohort. Other bioinformatic analyses performed included PROGENy (pathway responsive genes for activity inference), RNA sequencing differential expression using DESeq2, multivariable-adjusted regression, and other associated Kaplan-Meier analyses. These analyses identified novel prognostic genes independent from each cohort, 176 differentially expressed genes, and specific patient cohort STN survival associations. Despite the overarching limitation, the results revealed several novel differences in gene expression between the colorectal cancer Black/AA and White patient cohorts, which allows one to dive deeper into and understand the behavior on a systems level of what could be driving this racial difference across colorectal cancer. Concretely, this information can guide precision medicine approaches tailored specifically for colorectal cancer racial disparities.

SIGNIFICANCE

The purpose of this work is to investigate the racial disparities in colorectal cancer between Black/AA and White patient cohorts using a systems biology and bioinformatic approach. Our study investigates the underlying biology of each patient cohort. Concretely, the findings of this study include disparity-associated genes and pathways, which provide a tangible starting point to guide precision medicine approaches tailored specifically for colorectal cancer racial disparities.

Safety and efficacy of durvalumab after concurrent chemoradiation in Black patients with locally advanced non-small cell lung cancer

BACKGROUND

The PACIFIC trial established consolidative durvalumab after concurrent chemoradiation as standard-of-care in patients with stage III or unresectable non-small cell lung cancer (NSCLC). Black patients, however, comprised just 2% (n = 14) of randomized patients in this trial, warranting real-world evaluation of the PACIFIC regimen in these patients.

METHODS

This single-institution, multi-site study included 105 patients with unresectable stage II/III NSCLC treated with concurrent chemoradiation followed by durvalumab between 2017 and 2021. Overall survival (OS), progression-free survival (PFS), and grade ≥3 pneumonitis-free survival (PNFS) were compared between Black and non-Black patients using Kaplan-Meier and Cox regression analyses.

RESULTS

A total of 105 patients with a median follow-up of 22.8 months (interquartile range, 11.3-37.3 months) were identified for analysis, including 57 Black (54.3%) and 48 (45.7%) non-Black patients. The mean radiation prescription dose was higher among Black patients (61.5 ± 2.9 Gy vs. 60.5 ± 1.9 Gy; p = .031), but other treatment characteristics were balanced between groups. The median OS (not-reached vs. 39.7 months; p = .379) and PFS (31.6 months vs. 19.3 months; p = .332) were not statistically different between groups. Eight (14.0%) Black patients discontinued durvalumab due to toxicity compared to 13 (27.1%) non-Black patients (p = .096). The grade ≥3 pneumonitis rate was similar between Black and non-Black patients (12.3% vs. 12.5%; p = .973), and there was no significant difference in time to grade ≥3 PNFS (p = .904). Three (5.3%) Black patients and one (2.1%) non-Black patient developed grade 5 pneumonitis.

CONCLUSIONS

The efficacy and tolerability of consolidative durvalumab after chemoradiation appears to be comparable between Black and non-Black patients.

Methods for Using Race and Ethnicity in Prediction Models for Lung Cancer Screening Eligibility

Importance

Using race and ethnicity in clinical prediction models can reduce or inadvertently increase racial and ethnic disparities in medical decisions.

Objective

To compare eligibility for lung cancer screening in a contemporary representative US population by refitting the life-years gained from screening-computed tomography (LYFS-CT) model to exclude race and ethnicity vs a counterfactual eligibility approach that recalculates life expectancy for racial and ethnic minority individuals using the same covariates but substitutes White race and uses the higher predicted life expectancy, ensuring that historically underserved groups are not penalized.

Design, Setting, and Participants

The 2 submodels composing LYFS-CT NoRace were refit and externally validated without race and ethnicity: the lung cancer death submodel in participants of a large clinical trial (recruited 1993-2001; followed up until December 31, 2009) who ever smoked (n = 39 180) and the all-cause mortality submodel in the National Health Interview Survey (NHIS) 1997-2001 participants aged 40 to 80 years who ever smoked (n = 74 842, followed up until December 31, 2006). Screening eligibility was examined in NHIS 2015-2018 participants aged 50 to 80 years who ever smoked. Data were analyzed from June 2021 to September 2022.

Exposure

Including and removing race and ethnicity (African American, Asian American, Hispanic American, White) in each LYFS-CT submodel.

Main Outcomes and Measures

By race and ethnicity: calibration of the LYFS-CT NoRace model and the counterfactual approach (ratio of expected to observed [E/O] outcomes), US individuals eligible for screening, predicted days of life gained from screening by LYFS-CT.

Results

The NHIS 2015-2018 included 25 601 individuals aged 50 to 80 years who ever smoked (2769 African American, 649 Asian American, 1855 Hispanic American, and 20 328 White individuals). Removing race and ethnicity from the submodels underestimated lung cancer death risk (expected/observed [E/O], 0.72; 95% CI, 0.52-1.00) and all-cause mortality (E/O, 0.90; 95% CI, 0.86-0.94) in African American individuals. It also overestimated mortality in Hispanic American (E/O, 1.08, 95% CI, 1.00-1.16) and Asian American individuals (E/O, 1.14, 95% CI, 1.01-1.30). Consequently, the LYFS-CT NoRace model increased Hispanic American and Asian American eligibility by 108% and 73%, respectively, while reducing African American eligibility by 39%. Using LYFS-CT with the counterfactual all-cause mortality model better maintained calibration across groups and increased African American eligibility by 13% without reducing eligibility for Hispanic American and Asian American individuals.

Conclusions and Relevance

In this study, removing race and ethnicity miscalibrated LYFS-CT submodels and substantially reduced African American eligibility for lung cancer screening. Under counterfactual eligibility, no one became ineligible, and African American eligibility increased, demonstrating the potential for maintaining model accuracy while reducing disparities.

Mild exposure to fine particulate matter promotes angiogenesis in non-small cell lung carcinoma

Fine particulate matter (PM2.5) is associated with public health problems worldwide. Especially, PM2.5 induces epigenetic and microenvironmental changes in lung cancer. Angiogenesis is important for the development and growth of cancer and is mediated by angiogenic factors, including vascular endothelial growth factor. However, the effects of mild PM2.5 exposure on angiogenesis in lung cancer remain unclear. In this study, we examined angiogenic effects using relatively lower concentrations of PM2.5 than in other studies and found that PM2.5 increased angiogenic activities in both endothelial cells and non-small cell lung carcinoma cells. PM2.5 also promoted the growth and angiogenesis of lung cancer via the induction of hypoxia-inducible factor-1α (HIF-1α) in a xenograft mouse tumor model. Angiogenic factors, including vascular endothelial growth factor (VEGF), were highly expressed in lung cancer patients in countries with high PM2.5 levels in the atmosphere, and high expression of VEGF in lung cancer patients lowered the survival rate. Collectively, these results provide new insight into the mechanisms by which mild exposure to PM2.5 is involved in HIF-1α-mediated angiogenesis in lung cancer patients.

Impact of Race on Outcomes of Advanced Stage Non-Small Cell Lung Cancer Patients Receiving Immunotherapy

BACKGROUND

The impact of race in advanced stage non-small cell lung cancer (NSCLC) patients treated with immune checkpoint inhibitors (ICIs) is conflicting. Our study sought to examine racial disparities in time to treatment initiation (TTI), overall survival (OS), and progression-free survival (PFS) using a population that was almost equally black and white.

METHODS

This was a retrospective cohort study of stage IV NSCLC patients > 18 years receiving immunotherapy at our center between 2014 and 2021. Kaplan-Meier curves and the multivariate Cox proportional hazards model determined the predictors of OS and PFS. Analyses were undertaken using IBM PSAW (SPSS v.28).

RESULTS

Out of 194 patients who met the inclusion criteria, 42.3% were black (n = 82). In the multivariate analysis, there was no difference in PFS (HR: 0.96; 95% CI: 0.66,1.40; p = 0.846) or OS (HR: 0.99; 95% CI: 0.66, 1.48; p = 0.966). No difference in treatment selection was observed between white and black patients (p = 0.363), nor was there a difference observed in median time to overall treatment initiation (p = 0.201).

CONCLUSIONS

No difference was observed in OS and PFS in black and white patients. Black patients' reception of timelier immunotherapy was an unanticipated finding. Future studies are necessary to better understand how race impacts patient outcomes.

Toxicogenomics scoring system: TGSS, a novel integrated risk assessment model for chemical carcinogenicity prediction

BACKGROUND

Given the increasing exposure of humans to environmental chemicals and the limitations of conventional toxicity test, there is an urgent need to develop next-generation risk assessment methods.

OBJECTIVES

This study aims to establish a novel computational system named Toxicogenomics Scoring System (TGSS) to predict the carcinogenicity of chemicals coupling chemical-gene interactions with multiple cancer transcriptomic datasets.

METHODS

Chemical-related gene signatures were derived from chemical-gene interaction data from the Comparative Toxicogenomics Database (CTD). For each cancer type in TCGA, genes were ranked by their effects on tumorigenesis, which is based on the differential expression between tumor and normal samples. Next, we developed carcinogenicity scores (C-scores) using pre-ranked GSEA to quantify the correlation between chemical-related gene signatures and ranked gene lists. Then we established TGSS by systematically evaluating the C-scores in multiple chemical-tumor pairs. Furthermore, we examined the performance of our approach by ROC curves or prognostic analyses in TCGA and multiple independent cancer cohorts.

RESULTS

Forty-six environmental chemicals were finally included in the study. C-score was calculated for each chemical-tumor pair. The C-scores of IARC Group 3 chemicals were significantly lower than those of chemicals in Group 1 (P-value = 0.02) and Group 2 (P-values = 7.49 ×10^-5). ROC curves analysis indicated that C-score could distinguish "high-risk chemicals" from the other compounds (AUC = 0.67) with a specificity and sensitivity of 0.86 and 0.57. The results of survival analysis were also in line with the assessed carcinogenicity in TGSS for the chemicals in Group 1. Finally, consistent results were further validated in independent cancer cohorts.

CONCLUSION

TGSS highlighted the great potential of integrating chemical-gene interactions with gene-cancer relationships to predict the carcinogenic risk of chemicals, which would be valuable for systems toxicology.

Investigation of racial differences in survival from non-small cell lung cancer with immunotherapy use: A Texas study

Background

The use of immunotherapy is associated with improved survival among patients with Non-Small Cell Lung Cancer (NSCLC) and has gained widespread use in its management. However, there is limited information on whether the survival benefits associated with immunotherapy differ among races and ethnicities.

Objective

This study aimed to investigate racial differences in survival amongst patients with NSCLC who received immunotherapy as the first-line treatment in Texas.

Methods

Patients with NSCLC who received immunotherapy between October 2015 to December 2018 were identified from the Texas Cancer Registry (TCR). Disease-specific survival was evaluated and compared among patients across racial/ethnic categories using the Kaplan-Meier survival analysis, log-rank test, and a multivariable Cox proportional hazard regression model following an inverse probability treatment weighting (IPTW) propensity score analysis.

Results

A total of 1453 patients were included in the analysis. Median survival (in months) was longest among Asians (34, 95% CI: 15-Not Estimable), followed by African Americans (AAs) (23, 95% CI: 15-34), Hispanics (22, 95% CI: 16-26), and Whites (19, 95% CI: 17-22). The adjusted regression estimates had no statistically significant differences in survival among AAs (aHR = 0.97; 95% CI = 0.78-1.20; P =0.77) and Hispanics (aHR = 0.96; 95% CI = 0.77-1.19, P =0.73) when compared to White patients. Asians on the other hand, had 40% reduction in mortality risk compared to Whites (aHR = 0.60; 95% CI = 0.39-0.94, P = 0.03).

Conclusions

Our study indicated that African Americans and Hispanics do not have poorer survival compared to White patients when receiving immunotherapy as first-line treatment. Asians however had longer survival compared to Whites. Our findings suggest that existing racial disparity in NSCLC survival might be mitigated with the use of immunotherapy and should be considered in providing care to these minority groups.

Immune infiltration patterns and identification of new diagnostic biomarkers GDF10, NCKAP5, and RTKN2 in non-small cell lung cancer

This study aimed to identify potential biomarkers for non-small cell lung cancer (NSCLC) and analyze the role of immune cell infiltration in NSCLC. R software was used to screen differentially expressed genes (DEGs) from NSCLC datasets obtained from the Gene Expression Omnibus (GEO) database, and functional correlation analysis was performed. The machine learning algorithms were used to screen the potential biomarkers of NSCLC. The diagnostic values were assessed through receiver operating characteristic (ROC) curves. The protein and mRNA expression levels of potential biomarkers were verified based on the Human Protein Atlas (HPA) database and qRT-PCR. CIBERSORT was used to evaluate the infiltration of immune cells in NSCLC tissues, and the correlation between potential biomarkers and infiltrated immune cell was analyzed. Finally, specific siRNAs were utilized to reduce the GDF10, NCKAP5, and RTKN2 expression in A549 and H1975 cells. The proliferation ability of A549 and H1975 cells was detected by MTT assay. A total of 848 upregulated DEGs and 1308 downregulated DEGs were identified. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses showed that the DEGs were mainly related to cell division. Disease ontology (DO) enrichment analysis showed that the diseases with these DEGs were mainly lung diseases, including NSCLC. In addition,three potential biomarkers were identified: GDF10, NCKAP5, and RTKN2. Immune cell infiltration analysis showed that resting NK cells, activated dendritic cells, and Tregs may be involved in the pathogenesis of NSCLC. Meanwhile, GDF10, NCKAP5, and RTKN2 were negatively correlated with Tregs and naïve B cells but were positively correlated with activated dendritic cells and resting NK cells. Immunohistochemical staining showed that the expression of GDF10, NCKAP5, and RTKN2 in the lung tissue of patients with NSCLC was lower than that of normal lung tissue. qRT-PCR also confirmed that the mRNA expression of three biomarkers in NSCLC cell lines A549 and H1975 were significantly lower than those in human normal lung epithelial cells BEAS-2B. An MTT assay showed that GDF10, NCKAP5, and RTKN2 knockdown significantly promoted the proliferation of A549 and H1975 cells. The in vitro experiments showed that GDF10, NCKAP5, and RTKN2 played the inhibitory effects on NSCLC cell lines proliferation. Hence, GDF10, NCKAP5, and RTKN2 can be used as diagnostic biomarkers for NSCLC.

Racial Disparities in Clinical Outcomes on Investigator-Initiated Breast Cancer Clinical Trials at an Urban Medical Center

BACKGROUND

Black women are 40% more likely to die of breast cancer compared to White women. Inadequate representation of Black patients in clinical trials may contribute to health care inequity. We aimed to assess breast cancer clinical outcomes in Non-Hispanic Black (Black) versus Non-Hispanic White (White) women with metastatic breast cancer (MBC) enrolled on investigator-initiated clinical trials at Winship Cancer Institute at Emory University, given the significant number of patients from underrepresented minority groups seen at Winship.

MATERIALS AND METHODS

Black and White women with MBC on investigator-initiated trials at Emory between 2009 and 2019 were retrospectively evaluated. Univariate analyses and multiple logistic regression models were used to assess clinical response and treatment toxicities. Differences in overall survival between groups was assessed using quantile analysis.

RESULTS

Sixty-two women with MBC were included (66% White vs. 34% Black). Black patients had less clinical benefit from the trial therapy as only 57% had partial response or stable disease as best response compared to 78% of White women (P = .09). Quantile analysis showed significant difference in mean survival between Whites and Blacks by the end of follow up (64 vs. 38 months). There were no significant differences in toxicities between groups.

CONCLUSION

Participation rates of Black women with MBC on investigator-initiated clinical trials at an urban cancer center were higher compared to key national trials. Black women had worse treatment response and survival. These results reinforce the need for assessment of tumor differences by ancestry and continued improvement in minority representation on clinical trials.

Autoencoder Networks Decipher the Association between Lung Cancer and Alzheimer’s Disease

Lung cancer is the most common malignancy and is responsible for the largest cancer-related mortality worldwide. Alzheimer’s disease is a degenerative neurological disease that burdens healthcare worldwide. While the two diseases are distinct, several transcriptomic studies have demonstrated they are linked. However, no concordant conclusion on how they are associated has been drawn. Since these studies utilized conventional bioinformatics methods, such as the differentially expressed gene (DEG) analysis, it is naturally expected that the proportion of DEGs having either the same or inverse directions in lung cancer and Alzheimer’s disease is substantial. This raises the inconsistency. Therefore, a novel bioinformatics method capable of determining the direction of association is desirable. In this study, the moderated t-tests were first used to identify DEGs that are shared by the two diseases. For the shared DEGs, separate autoencoder (AE) networks were trained to extract a one-dimensional representation (pseudogene) for each disease. Based on these pseudogenes, the association direction between lung cancer and Alzheimer’s disease was inferred. AE networks based on 266 shared DEGs revealed a comorbidity relationship between Alzheimer’s disease and lung cancer. Specifically, Spearman’s correlation coefficient between the predicted values using the two AE networks for the Alzheimer’s disease test set was 0.825 and for the lung cancer test set was 0.316. Novel bioinformatics methods such as an AE network may help decipher how distinct diseases are associated by providing the refined representations of dysregulated genes.

Glutathione peroxidase 2 overexpression promotes malignant progression and cisplatin resistance of KRAS‑mutated lung cancer cells

Kirsten rat sarcoma viral oncogene homolog (KRAS) aberrations frequently occur in patients with lung cancer. Oncogenic KRAS is characterized by excessive reactive oxygen species (ROS) accumulation, thus, ROS detoxification may contribute to KRAS‑driven lung tumorigenesis. In the present study, the influence of glutathione peroxidase 2 (GPX2) on malignant progression and cisplatin resistance of KRAS‑driven lung cancer was explored. The RNA sequencing data from TCGA lung cancer samples and GEO database were downloaded and analyzed. The effects of GPX2 on KRAS‑driven lung tumorigenesis were evaluated by western blotting, cell viability assay, soft agar assay, Transwell assay, tumor xenograft model, flow cytometry, BrdU incorporation assay, transcriptome RNA sequencing, luciferase reporter assay and RNA immunoprecipitation. In the present study, GPX2 was upregulated in patients with non‑small cell lung carcinoma (NSCLC), and positively correlated with poor overall survival. Ectopic GPX2 expression facilitated malignant progression of KRAS^G12C‑transformed BEAS‑2B cells. Moreover, GPX2 overexpression promoted growth, migration, invasion, tumor xenograft growth and cisplatin resistance of KRAS‑mutated NSCLC cells, while GPX2 knockdown exhibited the opposite effects. GPX2 overexpression reduced ROS accumulation and increased matrix metalloproteinase‑1 (MMP1) expression in KRAS‑mutated NSCLC cells. In addition, GPX2 was directly targeted by miR‑325‑3p, while MMP1 knockdown or miR‑325‑3p overexpression partially abrogated the effects of GPX2 in NSCLC cells. In conclusion, the results indicated that GPX2 facilitated malignant progression and cisplatin resistance of KRAS‑driven lung cancer, and inhibition of GPX2 may be a feasible strategy for lung cancer treatment, particularly in patients with active KRAS mutations.

[Cuproptosis-related immune gene signature predicts clinical benefits from anti-PD-1/PD-L1 therapy in non-small-cell lung cancer

Non-small-cell lung cancer (NSCLC) remains the major cause of cancer-related death. Immune checkpoint inhibition has become the cornerstone treatment for NSCLC. Cuproptosis is a newly identified form of cell death relying on mitochondrial respiration that might play a role in shaping tumor immune microenvironment (TIME). The clinical significance of cuproptosis-related genes (CRGs) remains unclear and warrant investigation. The current study extracted RNA sequencing profiles and corresponding clinical information from six aggregated datasets from the Gene Expression Omnibus (GEO) repository as the training set, and from The Cancer Genome Atlas (TCGA) database as the testing set. Cuproptosis-related immune genes (CRIMGs) were obtained through coexpression analysis, univariate Cox regression analysis, and LASSO analysis for overall survival (OS) association analysis. Consensus clustering was employed to divide the subjects into clusters. Stepwise multivariate Cox regression was used to establish the prognostic CRIMG_score from the CRIMGs. A 17-gene prediction signature was established that informed patients' OS both in the training and testing datasets (p < 0.001). The predictive value of the signature in terms of immunotherapeutic responses was assessed in two publicly available NSCLC immunotherapy datasets (POPLAR and OAK studies) and an internal dataset from Sun Yat-sen University Cancer Center (ORIENT-11 study). Patients in the high-risk group displayed worse survival, a characteristic suppressive tumor immune microenvironment, and low immunotherapeutic benefits compared to those in the low-risk group. Collectively, the CRIMG_score established herein could serve as a promising indicator of prognosis and immunotherapeutic response in patients with NSCLC.

Tumor microenvironment-associated lactate metabolism regulates the prognosis and precise checkpoint immunotherapy outcomes of patients with lung adenocarcinoma

BACKGROUND

Despite the wide clinical application of checkpoint inhibitor immunotherapy in lung adenocarcinoma, its limited benefit to patients remains puzzling to researchers. One of the mechanisms of immunotherapy resistance may be the dysregulation of lactate metabolism in the immunosuppressive tumor microenvironment (TME), which can inhibit dendritic cell maturation and prevent T-cell invasion into tumors. However, the key genes related to lactate metabolism and their influence on the immunotherapeutic effects in lung adenocarcinoma have not yet been investigated in depth.

METHODS

In this study, we first surveyed the dysregulated expression of genes related to lactate metabolism in lung adenocarcinoma and then characterized their biological functions. Using machine learning methods, we constructed a lactate-associated gene signature in The Cancer Genome Atlas cohort and validated its effectiveness in predicting the prognosis and immunotherapy outcomes of patients in the Gene Expression Omnibus cohorts.

RESULTS

A 7-gene signature based on the metabolomics related to lactate metabolism was found to be associated with multiple important clinical features of cancer and was an independent prognostic factor.

CONCLUSIONS

These results suggest that rather than being simply a metabolic byproduct of glycolysis, lactate in the TME can affect immunotherapy outcomes. Therefore, the mechanism underlying this effect of lactate is worthy of further study.

Upregulation of nuclear division cycle 80 contributes to therapeutic resistance via the promotion of autophagy-related protein-7-dependent autophagy in lung cancer

Lung cancer remains the leading cause of malignant mortality worldwide. Hence, the discovery of novel targets that can improve therapeutic effects in lung cancer patients is an urgent need. In this study, we screened differentially expressed genes using isobaric tags for relative and absolute quantitation (iTRAQ) analysis and datasets from the cancer genome atlas database, and found that nuclear division cycle 80 (NDC80) might act as a novel prognostic indicator of lung cancer. The expression of NDC80 was significantly increased in lung cancer tissues, as compared to normal tissues, and high expression levels of NDC80 were correlated with unfavorable survival rates. Furthermore, an in vitro analysis showed that the stable knockdown of NDC80 decreased the cell viability and increased therapeutic sensitivity in two lung cancer cell lines, A549-IRR and H1246-IRR. Moreover, gene set enrichment analysis results showed that NDC80 was enriched in autophagy-related pathways. The downregulation of NDC80 inhibited the formation of autophagosomes, and reduced the expression of autophagy-related proteins such as LC3II, Beclin-1, and p62 in lung cancer cells. To further clarify the role of NDC80 as a downstream regulator of autophagy, we validated autophagic mediators through iTRAQ analysis and real-time polymerase chain reaction arrays. Autophagy-related protein7 (ATG7) was observed to be downregulated after the knockdown of NDC80 in lung cancer cells. Immunohistochemistry assay results revealed that both NDC80 and ATG7 were upregulated in an array of lung adenocarcinoma samples, compared to normal tissues, and the expression of NDC80 was identified to be positively associated with the levels of ATG7. Our findings suggest that NDC80 promotes the development of lung cancer by regulating autophagy, and might serve as a potential target for increasing the therapeutic sensitivity of lung cancer.

Patients with Lung Cancer of Different Racial Backgrounds Harbor Distinct Immune Cell Profiles

Tumors accumulated with infiltrated immune cells (hot tumors) have a higher response rate to immune checkpoint blockade, when compared with those with minimal T-cell infiltration (cold tumors). We report here that patients with lung cancer with different racial backgrounds harbored distinct immune cell profiles in the tumor microenvironment. Compared with African Americans (AA), Caucasian Americans (CA) exhibited increased immune cell infiltration and vasculature, and increased survival. Changes of survival and immune profile were most pronounced among active smokers and nonsmokers, compared with former smokers and total patients. Neighborhood analysis showed that immune cells accumulated around cancer cells in CAs but not AAs. Our findings reveal intrinsic biological differences between AA and CA patients with lung cancer, suggesting that treatment plans should be tailored for patients with different racial backgrounds.

Significance

We report biological racial differences among patients with lung cancer where Caucasians present a hot tumor microenvironment compared with cold tumor in AAs. Treatment plans should be customized to maximize therapeutic outcomes.

Action for Increasing Diversity, Market Access, and Capacity in Oncology Registration Trials—Is Africa the Answer? Report From a Satellite Session of the Accelerating Anti-Cancer Agent Development and Validation Workshop

Patients of African ancestry are not well-represented in cancer clinical trials despite bearing a disproportionate share of mortality both in United States and Africa. We describe key stakeholder perspectives and priorities related to bringing early-stage cancer clinical trials to Africa and outline essential action steps. Increasing Diversity, Market Access, and Capacity in Oncology Registration Trials—Is Africa the Answer? satellite session was organized at 2021 Accelerating Anti-Cancer Agent Development and Validation Workshop. Panelists included representatives of African Organization for Research and Training in Cancer, Uganda Cancer Institute, Uganda Women's Cancer Support Organization, BIO Ventures for Global Health, Bill & Melinda Gates Foundation, the US Food and Drug Administration, Nigeria's National Agency for Food and Drug Administration and Control, Bayer, and Genentech, with moderators from ASCO and American Cancer Society. Key discussion themes and resulting action steps were agreed upon by all participants. Panelists agreed that increasing diversity in cancer clinical trials by including African patients is key to ensuring novel drugs are safe and effective across populations. They underscored the importance of equity in clinical trial access for patients in Africa. Panelists discussed their values related to access and barriers to opening clinical trials in Africa and described innovative solutions from their work aimed at overcoming these obstacles. Multisectoral collaboration efforts that allow leveraging of limited resources and result in sustainable capacity building and mutually beneficial long-term partnerships were discussed as key to outlined action steps. The panel discussion resulted in valuable insights about key stakeholder values and priorities related to bringing early-stage clinical trials to Africa, as well as specific actions for each stakeholder group.

Promoting Best Practice in Cancer Care in Sub Saharan Africa

Promoting best practice in the management of a cancer patient is rooted in the application of new knowledge derived through various sources including population science, laboratory advances, and translational research. Ultimately, the impact of these advances depends on their application at the patient's bedside. A close collaboration between the oncologist and the pathologist is critical in underwriting progress in the management of the cancer patient. Recent advancements have shown that more granular characteristics of the tumor and the microenvironment are defining determinants when it comes to disease course and overall outcome. Whereas, histologic features and basic immunohistochemical characterization were previously adequate to define the tumor and establish treatment recommendation, the growing capability of the pathologist to provide molecular characterization of the tumor and its microenvironment, as well as, the availability of novel therapeutic agents have revolutionized cancer treatment paradigms and improved patient-outcomes and survival. While such capacity and capability appear readily available in most developed high-income countries (HIC), it will take a concerted and collaborative effort of all stakeholders to pave the way in the same stride in the low and middle-income countries (LMIC), which bear a disproportionate burden of human illness and cancers. Patients in the LMIC present with disease at advanced stage and often display characteristics unlike those encountered in the developed world. To keep stride and avoid the disenfranchisement of patients in the LMIC will require greater participation of LMIC patients on the global clinical trial platform, and a more equitable and affordable sharing of diagnostic and therapeutic capabilities between the developed and developing world. Key to the success of this progress and improvement of patient outcomes in the developing world is the close collaboration between the oncologist and the pathologist in this new era of precision and personalized medicine.

Cardiotoxicity risk factors with immune checkpoint inhibitors

Background

Checkpoint-inhibitor immunotherapies have had a profound effect in the treatment of cancer by inhibiting down-regulation of T-cell response to malignancy. The cardiotoxic potential of these agents was first described in murine models and, more recently, in numerous clinical case reports of pericarditis, myocarditis, pericardial effusion, cardiomyopathy, and new arrhythmias. The objective of our study was to determine the frequency of and associated risk factors for cardiotoxic events in patients treated with immune checkpoint inhibitors.

Methods

Medical records of patients who underwent immunotherapy with durvalumab, ipilimumab, nivolumab, and pembrolizumab at Wake Forest Baptist Health were reviewed. We collected retrospective data regarding sex, cancer type, age, and cardiovascular disease risk factors and medications. We aimed to identify new diagnoses of heart failure, atrial fibrillation, ventricular fibrillation/tachycardia, myocarditis, and pericarditis after therapy onset. To assess the relationship between CVD risk factors and the number of cardiac events, a multivariate model was applied using generalized linear regression. Incidence rate ratios were calculated for every covariate along with the adjusted P-value. We applied a multivariate model using logistic regression to assess the relationship between CVD risk factors and mortality. Odds ratios were calculated for every covariate along with the adjusted P-value. Adjusted P-values were calculated using multivariable regression adjusting for other covariates.

Results

Review of 538 medical records revealed the following events: 3 ventricular fibrillation/tachycardia, 12 pericarditis, 11 atrial fibrillation with rapid ventricular rate, 0 myocarditis, 8 heart failure. Significant risk factors included female gender, African American race, and tobacco use with IRR 3.34 (95% CI 1.421, 7.849; P = 0.006), IRR 3.39 (95% CI 1.141, 10.055; P = 0.028), and IRR 4.21 (95% CI 1.289, 13.763; P = 0.017) respectively.

Conclusions

Our study revealed 34 significant events, most frequent being pericarditis (2.2%) and atrial fibrillation (2.0%) with strongest risk factors being female gender, African American race, and tobacco use. Patients who meet this demographic, particularly those with planned pembrolizumab treatment, may benefit from early referral to a cardio-oncologist. Further investigation is warranted on the relationship between CTLA-4 and PD-L1 expression and cardiac adverse events with ICIs, particularly for these subpopulations.

CDK1 and HSP90AA1 Appear as the Novel Regulatory Genes in Non-Small Cell Lung Cancer: A Bioinformatics Approach

Lung cancer is one of the most invasive cancers affecting over a million of the population. Non-small cell lung cancer (NSCLC) constitutes up to 85% of all lung cancer cases, and therefore, it is essential to identify predictive biomarkers of NSCLC for therapeutic purposes. Here we use a network theoretical approach to investigate the complex behavior of the NSCLC gene-regulatory interactions. We have used eight NSCLC microarray datasets GSE19188, GSE118370, GSE10072, GSE101929, GSE7670, GSE33532, GSE31547, and GSE31210 and meta-analyzed them to find differentially expressed genes (DEGs) and further constructed a protein–protein interaction (PPI) network. We analyzed its topological properties and identified significant modules of the PPI network using cytoscape network analyzer and MCODE plug-in. From the PPI network, top ten genes of each of the six topological properties like closeness centrality, maximal clique centrality (MCC), Maximum Neighborhood Component (MNC), radiality, EPC (Edge Percolated Component) and bottleneck were considered for key regulator identification. We further compared them with top ten hub genes (those with the highest degrees) to find key regulator (KR) genes. We found that two genes, CDK1 and HSP90AA1, were common in the analysis suggesting a significant regulatory role of CDK1 and HSP90AA1 in non-small cell lung cancer. Our study using a network theoretical approach, as a summary, suggests CDK1 and HSP90AA1 as key regulator genes in complex NSCLC network.

TKI resistant-based prognostic immune related gene signature in LUAD, in which FSCN1 contributes to tumor progression

Drug resistance reflects the evolution of tumors, which is the main cause of recurrence and death. Currently, EGFR-TKI treatment is the first-line therapy for lung adenocarcinoma (LUAD) patients. Although EGFR-TKI achieved good effects at the beginning, most of the LUAD patients eventually acquired resistance. Therefore, it's urgently need to develop a strong criterion for identifying these patients who may benefit from additional therapy. In this study, we established a three TKI resistant-related gene signature (DDIT4, OAS3, FSCN1), and determined that's an accuracy, independent and specific prognostic model for LUAD patients. Patients categorized as high-risk by this signature showed more sensitive to chemotherapy, and exhibited higher expression of common immune checkpoints such as PD-L1/B3H7/PD-L2/IDO1. Moreover, these patients were characterized by increased infiltration of M0 macrophage and activated memory CD4⁺ T cells. The expression and prognostic values of DDIT4, FSCN1 and OAS3 were further confirmed in clinical data. In addition, experimental data showed that FSCN1 promoted LUAD development via PI3K/AKT signaling. In conclusion, this signature is highly predictive of prognostic in LUAD patients, and may serve as a powerful prediction tool for LUAD patients to further choose chemo- and immunotherapies.

Race and ethnic group dependent space radiation cancer risk predictions

Future space missions by national space agencies and private industry, including space tourism, will include a diverse makeup of crewmembers with extensive variability in age, sex, and race or ethnic groups. The relative risk (RR) model is used to transfer epidemiology data between populations to estimate radiation risks. In the RR model cancer risk is assumed to be proportional to background cancer rates and limited by other causes of death, which are dependent on genetic, environmental and dietary factors that are population dependent. Here we apply the NSCR-2020 model to make the first predictions of age dependent space radiation cancer risks for several U.S. populations, which includes Asian-Pacific Islanders (API), Black, Hispanic (white and black), and White (non-Hispanic) populations. Results suggest that male API and Hispanic populations have the overall lowest cancer risks, while White females have the highest risk. Blacks have similar total cancer rates than Whites, however their reduced life expectancy leads to modestly lower lifetime radiation risks compared to Whites. There are diverse tissue specific cancer risk ranking across sex and race, which include sex specific organ risks, female’s having larger lung, stomach, and urinary-bladder radiation risks, and male’s having larger colon and brain risks.

Disparities in Lung Cancer: miRNA Isoform Characterization in Lung Adenocarcinoma

Despite the development of targeted therapeutics, immunotherapy, and strategies for early detection, lung cancer carries a high mortality. Further, significant racial disparities in outcomes exist for which the molecular drivers have yet to be fully elucidated. The growing field of Epitranscriptomics has introduced a new layer of complexity to the molecular pathogenesis of cancer. RNA modifications can occur in coding and non-coding RNAs, such as miRNAs, possibly altering their gene regulatory function. The potential role for such modifications as clinically informative biomarkers remains largely unknown. Here, we concurrently profiled canonical miRNAs, shifted isomiRs (templated and non-templated), and miRNAs with single-point modification events (RNA and DNA) in White American (W) and Black or African American (B/AA) lung adenocarcinoma (LUAD) patients. We found that while most deregulated miRNA isoforms were similar in W and B/AA LUAD tissues compared to normal adjacent tissues, there was a subgroup of isoforms with deregulation according to race. We specifically investigated an edited miRNA, miR-151a-3p with an A-to-I editing event at position 3, to determine how its altered expression may be associated with activation of divergent biological pathways between W and B/AA LUAD patients. Finally, we identified distinct race-specific miRNA isoforms that correlated with prognosis for both Ws and B/AAs. Our results suggested that concurrently profiling canonical and non-canonical miRNAs may have potential as a strategy for identifying additional distinct biological pathways and biomarkers in lung cancer.

Social Disparities in Thoracic Surgery Database Research: Implications and Impact

A complex relationship exists between health care disparities and large databases among the thoracic surgical patient population. Using the example of thoracic malignancies, the ability of investigations leveraging large databases and novel analytical approaches to highlight disparate access to care and discordant outcomes following treatment is illustrated. Large, widely used databases may not be representative of the thoracic surgical patient population as a whole, and caution must be used when interpreting and generalizing results gleaned from such database analyses. Ensuring appropriate representation of all relevant patient subgroups in research databases will improve external generalizability and scientific validity of future investigations.

Undertreatment of Pancreatic Cancer: The Intersection of Bias, Biology, and Geography

Pancreatic cancer is the third leading cause of cancer deaths in the United States. Black patients with pancreatic cancer experience higher incidence and increased mortality. Although racial biologic differences exist, socioeconomic status, insurance type, physician bias, and patient beliefs contribute to the disparities in outcomes observed among patients who are Black, indigenous, and people of color.

HSD17B6 downregulation predicts poor prognosis and drives tumor progression via activating Akt signaling pathway in lung adenocarcinoma

Lung adenocarcinoma is one of the most frequent tumor subtypes, involving changes in a variety of oncogenes and tumor suppressor genes. Hydroxysteroid 17-Beta Dehydrogenase 6 (HSD17B6) could synthetize dihydrotestosterone, abnormal levels of which are associated with progression of multiple tumors. Previously, we showed that HSD17B6 inhibits malignant progression of hepatocellular carcinoma. However, the mechanisms underlying inhibiting tumor development by HSD17B6 are not clear. Moreover, its role in lung adenocarcinoma (LUAD) is yet unknown. Here, we investigated its expression profile and biological functions in LUAD. Analysis of data from the LUAD datasets of TCGA, CPTAC, Oncomine, and GEO revealed that HSD17B6 mRNA and protein expression was frequently lower in LUAD than in non-neoplastic lung tissues, and its low expression correlated significantly with advanced tumor stage, large tumor size, poor tumor differentiation, high tumor grade, smoking, and poor prognosis in LUAD. In addition, its expression was negatively regulated by miR-31-5p in LUAD. HSD17B6 suppressed LUAD cell proliferation, migration, invasion, epithelial-mesenchymal transition (EMT), and radioresistance. Furthermore, HSD17B6 overexpression in LUAD cell lines enhanced PTEN expression and inhibited AKT phosphorylation, inactivating downstream oncogenes like GSK3β, β-catenin, and Cyclin-D independent of dihydrotestosterone, revealing an underlying antitumor mechanism of HSD17B6 in LUAD. Our findings indicate that HSD17B6 may function as a tumor suppressor in LUAD and could be a promising prognostic indicator for LUAD patients, especially for those receiving radiotherapy.

Identification of Key Biomarkers and Pathways in Small-Cell Lung Cancer Using Biological Analysis

Background

Small-cell lung cancer (SCLC) is a major cause of carcinoma-related deaths worldwide. The aim of this study was to identify the key biomarkers and pathways in SCLC using biological analysis.

Methods

Key genes involved in the development of SCLC were identified by downloading three datasets from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were screened using the GEO2R online analyzer; for the functional annotation and pathway enrichment analysis of genes, Funrich software was used. Construction of protein-to-protein interaction (PPI) networks was accomplished using the Search Tool for the Retrieval of Interacting Genes (STRING), and network visualization and module identification were performed using Cytoscape.

Results

A total of 268 DEGs were ultimately obtained. The enriched functions and pathways of the upregulated DEGs included cell cycle, mitotic, and DNA replication, and the downregulated DEGs were enriched in epithelial-to-mesenchymal transition, serotonin degradation, and noradrenaline. Analysis of significant modules demonstrated that the upregulated genes are primarily concentrated in functions related to cell cycle and DNA replication. Kaplan-Meier analysis of hub genes revealed that they may promote the carcinogenesis and progression of SCLC. The result of ONCOMINE demonstrated that these 10 hub genes were significantly overexpressed in SCLC compared with normal samples.

Conclusion

Identification of the molecular functions and signaling pathways of participating DEGs can deepen the current understanding of the molecular mechanisms of SCLC. The knowledge gained from this work may contribute to the development of treatment options and improve the prognosis of SCLC in the future.

A comprehensive map of alternative polyadenylation in African American and European American lung cancer patients

Deciphering the post-transcriptional mechanisms (PTM) regulating gene expression is critical to understand the dynamics underlying transcriptomic regulation in cancer. Alternative polyadenylation (APA)-regulation of mRNA 3'UTR length by alternating poly(A) site usage-is a key PTM mechanism whose comprehensive analysis in cancer remains an important open challenge. Here we use a method and analysis pipeline that sequences 3'end-enriched RNA directly to overcome the saturation limitation of traditional 5'-3' based sequencing. We comprehensively map the APA landscape in lung cancer in a cohort of 98 tumor/non-involved tissues derived from European American and African American patients. We identify a global shortening of 3'UTR transcripts in lung cancer, with notable functional implications on the expression of both coding and noncoding genes. We find that APA of non-coding RNA transcripts (long non-coding RNAs and microRNAs) is a recurrent event in lung cancer and discover that the selection of alternative polyA sites is a form of non-coding RNA expression control. Our results indicate that mRNA transcripts from EAs are two times more likely than AAs to undergo APA in lung cancer. Taken together, our findings comprehensively map and identify the important functional role of alternative polyadenylation in determining transcriptomic heterogeneity in lung cancer.

Operational Strategies for Clinical Trials in Africa

PURPOSE

In a dramatic reversal of longstanding trends, cancer now kills more Africans than malaria. Despite Africa’s growing cancer burden, individuals of African descent, notably those residing in Africa, remain drastically under-represented in cancer clinical trials. Two recent summits—the 1st All Africa Clinical Trial Summit and the Operational Strategy for Clinical Trials in Nigeria Summit—convened experts from governments, the private sector, universities, and professional societies to define the barriers to Africa’s participation in multicenter clinical studies and the strategies to eliminate those impedances.

METHODS

The discussions held during the two clinical trial summits were condensed into a set of 10 recommendations covering five broad categories (funding, regulation, capacity building, Africa-centric approach, and patient engagement). In this article, four programs are presented as examples of how the summits’ recommendations can be put into practice to improve Africa’s ability to attract clinical trials, in particular, cancer clinical trials.

RESULTS

These example programs all leveraged a multilateral, Africa-driven approach to building Africa’s clinical trial capacity, increasing visibility of Africa’s current clinical trial capabilities and priorities, improving regulatory infrastructure and enforcement on the continent, and optimizing patient and clinician engagement strategies.

CONCLUSION

The four programs are anticipated to catalyze the involvement of more African health care sites in cancer clinical trials, enroll a greater number of African patients with cancer in those trials, and, ultimately, reverse Africa’s growing cancer incidence and mortality rates. Each program acts as a blueprint for organizations—whether government, academic, or industry—seeking to address the summits’ recommendations and increase Africa’s contributions to and active participation in clinical research.

Race as a Contributor to Stromal Modulation of Tumor Progression

Stromal cells play crucial roles in tumor development and are increasingly attractive targets for therapy. There are considerable racial disparities in the incidence and progression of many tumors, reflecting both environmental exposure and genetic differences existing between races. Tumorigenesis and tumor progression are linked to both the propensity to suffer an initiating event and the host response to such an event once it occurs, contributing to incidence and outcomes. In this review, we focused on racial disparities in the tumor microenvironment (TME) of different cancers as potential modulators of growth, metastasis, and response to treatment. Several studies suggest that the TME in AA has a distinct tumor biology and may facilitate both early onset and aggressive tumor growth while inhibiting anti-tumorigenic properties. The TME of AA patients often exhibits an immunosuppressive microenvironment with a substantial enrichment of immune inflammatory pathways and genes. As a result, AA patients can potentially benefit more from treatment strategies that modulate the immune system. Focusing on TME components for diagnostic and therapeutic purposes to address racial disparities is a promising area of investigation. Future basic and clinical research studies on personalized cancer diagnosis and treatment should acknowledge the significance of TME in racial disparities.

ITGA2, LAMB3, and LAMC2 may be the potential therapeutic targets in pancreatic ductal adenocarcinoma: an integrated bioinformatics analysis

Pancreatic ductal adenocarcinoma (PDAC) is the most common form of pancreatic cancer with an abysmal prognosis rate over the last few decades. Early diagnosis and prevention could effectively combat this malignancy. Therefore, it is crucial to discover potential biomarkers to identify asymptomatic premalignant or early malignant tumors of PDAC. Gene expression analysis is a powerful technique to identify candidate biomarkers involved in disease progression. In the present study, five independent gene expression datasets, including 321 PDAC tissues and 208 adjacent non-cancerous tissue samples, were subjected to statistical and bioinformatics analysis. A total of 20 differentially expressed genes (DEGs) were identified in PDAC tissues compared to non-cancerous tissue samples. Gene ontology and pathway enrichment analysis showed that DEGs were mainly enriched in extracellular matrix (ECM), cell adhesion, ECM-receptor interaction, and focal adhesion signaling. The protein-protein interaction network was constructed, and the hub genes were evaluated. Collagen type XII alpha 1 chain (COL12A1), fibronectin 1 (FN1), integrin subunit alpha 2 (ITGA2), laminin subunit beta 3 (LAMB3), laminin subunit gamma 2 (LAMC2), thrombospondin 2 (THBS2), and versican (VCAN) were identified as hub genes. The correlation analysis revealed that identified hub genes were significantly interconnected. Wherein COL12A1, FN1, ITGA2, LAMB3, LAMC2, and THBS2 were significantly associated with PDAC pathological stages. The Kaplan-Meier survival plots revealed that ITGA2, LAMB3, and LAMC2 expression were inversely correlated with a prolonged patient survival period. Furthermore, the Human Protein Atlas database was used to validate the expression and cellular origins of hub genes encoded proteins. The protein expression of hub genes was higher in pancreatic cancer tissue than in normal pancreatic tissue samples, wherein ITGA2, LAMB3, and LAMC2 were exclusively expressed in pancreatic cancer cells. Pancreatic cancer cell-specific expression of these three proteins may play pleiotropic roles in cancer progression. Our results collectively suggest that ITGA2, LAMB3, and LAMC2 could provide deep insights into pancreatic carcinogenesis molecular mechanisms and provide attractive therapeutic targets.

GEO Data Mining Identifies OLR1 as a Potential Biomarker in NSCLC Immunotherapy

Non-small cell lung cancer (NSCLC) is the most common type of lung cancer. The tumor immune microenvironment (TME) in NSCLC is closely correlated to tumor initiation, progression, and prognosis. TME failure impedes the generation of an effective antitumor immune response. In this study, we attempted to explore TME and identify a potential biomarker for NSCLC immunotherapy. 48 potential immune-related genes were identified from 11 eligible Gene Expression Omnibus (GEO) data sets. We applied the CIBERSORT computational approach to quantify bulk gene expression profiles and thereby infer the proportions of 22 subsets of tumor-infiltrating immune cells (TICs); 16 kinds of TICs showed differential distributions between the tumor and control tissue samples. Multiple linear regression analysis was used to determine the correlation between TICs and 48 potential immune-related genes. Nine differential immune-related genes showed statistical significance. We analyzed the influence of nine differential immune-related genes on NSCLC immunotherapy, and OLR1 exhibited the strongest correlation with four well-recognized biomarkers (PD-L1, CD8A, GZMB, and NOS2) of immunotherapy. Differential expression of OLR1 showed its considerable potential to divide TICs distribution, as determined by non-linear dimensionality reduction analysis. In immunotherapy prediction analysis with the comparatively reliable tool TIDE, patients with higher OLR1 expression were predicted to have better immunotherapy outcomes, and OLR1 expression was potentially highly correlated with PD-L1 expression, the average of CD8A and CD8B, IFNG, and Merck18 expression, T cell dysfunction and exclusion potential, and other significant immunotherapy predictors. These findings contribute to the current understanding of TME with immunotherapy. OLR1 also shows potential as a predictor or a regulator in NSCLC immunotherapy.

Bioinformatics analysis of the circRNA-miRNA-mRNA network for non-small cell lung cancer

OBJECTIVE

Non-small cell lung cancer (NSCLC) accounts for approximately 80% of all lung cancers, but its pathogenesis has not been fully elucidated. Therefore, it is valuable to explore the pathogenesis of NSCLC to improve diagnosis and identify novel treatment biomarkers.

METHODS

Circular (circ)RNA, micro (mi)RNA, and gene expression datasets of NSCLC were analyzed to identify those that were differentially expressed between tumor and healthy tissues. Common genes were found and pathway enrichment analyses were performed. Survival analysis was used to identify hub genes, and their level of methylation and association with immune cell infiltration were analyzed. Finally, an NSCLC circRNA-miRNA-mRNA network was constructed.

RESULTS

Eight miRNAs and 211 common genes were identified. Gene ontology and Kyoto Encyclopedia of Genes and Genomes analyses revealed that cell projection morphogenesis, blood vessel morphogenesis, muscle cell proliferation, and synapse organization were enriched. Ten hub genes were found, of which the expression of DTL and RRM2 was significantly related to NSCLC patient prognosis. Significant methylation changes and immune cell infiltration correlations with DTL and RRM2 were also detected.

CONCLUSIONS

hsa_circ_0001947/hsa-miR-637/RRM2 and hsa_circ_0072305/hsa-miR-127-5p/DTL networks were constructed, and identified molecules may be involved in the occurrence and development of NSCLC.

Immune-Based Cancer Treatment: Addressing Disparities in Access and Outcomes

Avoidable differences in the care and outcomes of patients with cancer (i.e., cancer care disparities) emerge or worsen with discoveries of new, more effective approaches to cancer diagnosis and treatment. The rapidly expanding use of immunotherapy for many different cancers across the spectrum from late to early stages has, predictably, been followed by emerging evidence of disparities in access to these highly effective but expensive treatments. The danger that these new treatments will further widen preexisting cancer care and outcome disparities requires urgent corrective intervention. Using a multilevel etiologic framework that categorizes the targets of intervention at the individual, provider, health care system, and social policy levels, we discuss options for a comprehensive approach to prevent and, where necessary, eliminate disparities in access to the clinical trials that are defining the optimal use of immunotherapy for cancer, as well as its safe use in routine care among appropriately diverse populations. We make the case that, contrary to the traditional focus on the individual level in descriptive reports of health care disparities, there is sequentially greater leverage at the provider, health care system, and social policy levels to overcome the challenge of cancer care and outcomes disparities, including access to immunotherapy. We also cite examples of effective government-sponsored and policy-level interventions, such as the National Cancer Institute Minority-Underserved Community Oncology Research Program and the Affordable Care Act, that have expanded clinical trial access and access to high-quality cancer care in general.

Knowledge-Guided "Community Network" Analysis Reveals the Functional Modules and Candidate Targets in Non-Small-Cell Lung Cancer

Non-small-cell lung cancer (NSCLC) represents a heterogeneous group of malignancies that are the leading cause of cancer-related death worldwide. Although many NSCLC-related genes and pathways have been identified, there remains an urgent need to mechanistically understand how these genes and pathways drive NSCLC. Here, we propose a knowledge-guided and network-based integration method, called the node and edge Prioritization-based Community Analysis, to identify functional modules and their candidate targets in NSCLC. The protein–protein interaction network was prioritized by performing a random walk with restart algorithm based on NSCLC seed genes and the integrating edge weights, and then a “community network” was constructed by combining Girvan–Newman and Label Propagation algorithms. This systems biology analysis revealed that the CCNB1-mediated network in the largest community provides a modular biomarker, the second community serves as a drug regulatory module, and the two are connected by some contextual signaling motifs. Moreover, integrating structural information into the signaling network suggested novel protein–protein interactions with therapeutic significance, such as interactions between GNG11 and CXCR2, CXCL3, and PPBP. This study provides new mechanistic insights into the landscape of cellular functions in the context of modular networks and will help in developing therapeutic targets for NSCLC.

A Call to Action: Dismantling Racial Injustices in Preclinical Research and Clinical Care of Black Patients Living with Small Cell Lung Cancer

Small cell lung cancer (SCLC) is an aggressive disease with dismal survival rates and limited therapeutic options. SCLC development is strongly associated with exposure to tobacco carcinogens. However, additional genetic and environmental risk factors that contribute to SCLC pathogenesis are beginning to emerge. Here, we specifically assess disparities pertaining to SCLC in Black populations. In contrast to non-small cell lung cancer, preliminary data suggest that Black individuals may actually be at a lower risk of developing SCLC relative to white individuals. This difference remains unexplained but urgently needs to be verified in larger data sets, because it could provide important new insights and approaches to understanding this recalcitrant tumor. Importantly, little biological information exists on SCLC in Black individuals, and few patient-derived preclinical SCLC models from diverse ancestries are available in the laboratory. Unfortunately, we note strikingly low numbers of Black participants in clinical trials testing new treatments for SCLC. Evidence further indicates that care for patients with SCLC may vary between communities with a large fraction of Black patients and those without. Together, these observations underscore the need to better investigate genetic, environmental, and socioeconomic factors associated with SCLC development, preclinical research, clinical care, and outcomes.

Biological Aspects of Cancer Health Disparities

Racial and ethnic disparities span the continuum of cancer care and are driven by a complex interplay among social, psychosocial, lifestyle, environmental, health system, and biological determinants of health. Research is needed to identify these determinants of cancer health disparities and to develop interventions to achieve cancer health equity. Herein, we focus on the overall burden of ancestry-related molecular alterations, the functional significance of the alterations in hallmarks of cancer, and the implications of the alterations for precision oncology and immuno-oncology. In conclusion, we reflect on the importance of estimating ancestry, improving diverse racial and ethnic participation in cancer clinical trials, and examining the intersection among determinants of cancer health disparities.

RNA splicing and aggregate gene expression differences in lung squamous cell carcinoma between patients of West African and European ancestry

OBJECTIVES

Despite disparities in lung cancer incidence and mortality, the molecular landscape of lung cancer in patients of African ancestry remains underexplored, and race-related differences in RNA splicing remain unexplored.

MATERIALS AND METHODS

We identified differentially spliced genes (DSGs) and differentially expressed genes (DEGs) in biobanked lung squamous cell carcinoma (LUSC) between patients of West African and European ancestry, using ancestral genotyping and Affymetrix Clariom D array. DSGs and DEGs were validated independently using the National Cancer Institute Genomic Data Commons. Associated biological processes, overlapping canonical pathways, enriched gene sets, and cancer relevance were identified using Gene Ontology Consortium, Ingenuity Pathway Analysis, Gene Set Enrichment Analysis, and CancerMine, respectively. Association with LUSC survival was conducted using The Cancer Genome Atlas.

RESULTS

4,829 DSGs and 267 DEGs were identified, including novel targets in NSCLC as well as genes identified previously to have relevance to NSCLC. RNA splicing events within 3 DSGs as well as 1 DEG were validated in the independent cohort. 853 DSGs and 29 DEGs have been implicated as potential drivers, oncogenes and/or tumor suppressor genes. Biological processes enriched among DSGs and DEGs included metabolic process, biological regulation, and multicellular organismal process and, among DSGs, ion transport. Overlapping canonical pathways among DSGs included neuronal signaling pathways and, among DEGs, cell metabolism involving biosynthesis. Gene sets enriched among DSGs included KRAS Signaling, UV Response, E2 F Targets, Glycolysis, and Coagulation. 355 RNA splicing events within DSGs and 18 DEGs show potential association with LUSC patient survival.

CONCLUSION

These DSGs and DEGs, which show potential biological and clinical relevance, could have the ability to drive novel biomarker and therapeutic development to mitigate LUSC disparities.

Cancer Omics in Africa: Present and Prospects

During the last century, cancer biology has been arguably one of the most investigated research fields. To gain deeper insight into cancer mechanisms, scientists have been attempting to integrate multi omics data in cancer research. Cancer genomics, transcriptomics, metabolomics, proteomics, and metagenomics are the main multi omics strategies used currently in the diagnosis, prognosis, treatment, and biomarker discovery in cancer. In this review, we describe the use of different multi omics strategies in cancer research in the African continent and discuss the main challenges facing the implementation of these approaches in African countries such as the lack of training programs in bioinformatics in general and omics strategies in particular and suggest paths to address deficiencies. As a way forward, we advocate for the establishment of an "African Cancer Genomics Consortium" to promote intracontinental collaborative projects and enhance engagement in research activities that address indigenous aspects for cancer precision medicine.

Whole-Exome Profiling of NSCLC Among African Americans

INTRODUCTION

Lung cancer incidence is higher among African Americans (AAs) compared with European Americans (EAs) in the United States, especially among men. Although significant progress has been made profiling the genomic makeup of lung cancer in EAs, AAs continue to be underrepresented. Our objective was to chart the genome-wide landscape of somatic mutations in lung cancer tumors from AAs.

METHODS

In this study, we used the whole-exome sequencing of 82 tumor and noninvolved tissue pairs from AAs. Patients were selected from an ongoing case-control study conducted by the National Cancer Institute and the University of Maryland.

RESULTS

Among all samples, we identified 178 significantly mutated genes (p < 0.05), five of which passed the threshold for false discovery rate (p < 0.1). In lung adenocarcinoma (LUAD) tumors, mutation rates in STK11 (p = 0.05) and RB1 (p = 0.008) were significantly higher in AA LUAD tumors (25% and 13%, respectively) compared with The Cancer Genome Atlas EA samples (14% and 4%, respectively). In squamous cell carcinomas, mutation rates in STK11 (p = 0.002) were significantly higher among AA (8%) than EA tumors from The Cancer Genome Atlas (1%). Integrated somatic mutation data with CIBERSORT (Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts) data analysis revealed LUAD tumors from AAs carrying STK11 mutations have decreased interferon signaling.

CONCLUSIONS

Although a considerable degree of the somatic mutation landscape is shared between EAs and AAs, discrete differences in mutation frequency in potentially important oncogenes and tumor suppressors exist. A better understanding of the molecular basis of lung cancer in AA patients and leveraging this information to guide clinical interventions may help reduce disparities.

Analysis of Heterogeneity in Survival Benefit of Immunotherapy in Oncology According to Patient Demographics and Performance Status: A Systematic Review and Meta-Analysis of Overall Survival Data

OBJECTIVES

Immunotherapy (IO) has become standard of care (SOC) for many advanced malignancies, although identifying patients likely to benefit remains difficult. We sought to assess whether demographic factors are associated with response to IO, compared with SOC systemic therapy, using stratified meta-analysis.

METHODS

A systematic review of MEDLINE, PubMed, Embase, and Scopus from inception to October 2, 2018. Randomized controlled trials comparing IO to SOC in patients with advanced solid organ malignancies were included if results were stratified by age, performance status (PS), or race, assessing overall survival (OS). Pooled hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated for each group using random-effects models independently.

RESULTS

We identified 21 eligible randomized controlled trials, including 20 stratified by age, 17 by PS, and 4 by race. Patients with PS 0 (HR, 0.74; 95% CI, 0.63-0.86) and PS≥1 (HR, 0.75; 95% CI, 0.68-0.83) had similar OS benefits from IO compared with SOC (P=0.80). There was no difference on the basis of patient race (white vs. nonwhite) (P=0.46). IO demonstrated an OS benefit for younger (below 65 y: HR, 0.73; 95% CI, 0.65-0.82) and older (65 y and above: HR, 0.79; 95% CI, 0.71-0.88) patients with no difference between age groups (P=0.27). Among prespecified subgroup analyses, there was significant effect modification in 2 subgroups: younger patients in the first-line setting (P=0.03) and those receiving anti-CTLA-4 drugs (P=0.05).

CONCLUSIONS

When examining OS using stratified meta-analysis, we did not demonstrate significant differences in IO efficacy according to patient age, PS or race, though data on race were sparse.

OSluca: An Interactive Web Server to Evaluate Prognostic Biomarkers for Lung Cancer

Lung cancer is the principal cause of leading cancer-related incidence and mortality in the world. Various studies have excavated the potential prognostic biomarkers for cancer patients based on gene expression profiles. However, most of these reported biomarkers lack independent validation in multiple cohorts. Herein, we collected 35 datasets with long-term follow-up clinical information from TCGA (2 cohorts), GEO (32 cohorts), and Roepman study (1 cohort), and developed a web server named OSluca (Online consensus Survival for Lung Cancer) to assess the prognostic value of genes in lung cancer. The input of OSluca is an official gene symbol, and the output web page of OSluca displays the survival analysis summary with a forest plot and a survival table from Cox proportional regression in each cohort and combined cohorts. To test the performance of OSluca, 104 previously reported prognostic biomarkers in lung carcinoma were evaluated in OSluca. In conclusion, OSluca is a highly valuable and interactive prognostic web server for lung cancer. It can be accessed at http:// bioinfo.henu.edu.cn/LUCA/LUCAList.jsp.

Genomic assessment distinguishes intrapulmonary metastases from synchronous primary lung cancers

Background

Multiple synchronous lung tumors (MSLT), particularly within a single lobe, represent a diagnostic and treatment challenge. While histologic assessment was once the only method to possibly distinguish multiple primary lung cancers, there is a growing interest in identifying unique genomic features or mutations to best characterize these processes.

Methods

In order to differentiate multiple primary lung malignancies from intrapulmonary metastases in patients with MSLT, we performed whole exome sequencing (WES) on 10 tumor samples from 4 patients with MSLT.

Results

Shared mutations between tumors from the same patient varied from 0-91%. Patient 3 shared no common mutations; however, in Patients 2 and 4, identical mutations were identified among all tumors from each patient, suggesting that the three tumors identified in Patient 3 represent separate primary lung cancers, while those of Patients 1, 2 and 4 signify hematogenous and lymphatic spread.

Conclusions

A high proportion of shared mutations between different lung tumors is likely indicative of intrapulmonary metastatic disease, while tumors with distinct genomic profiles likely represent multiple primary malignancies driven by distinct molecular events. Application of genomic profiling in the clinical setting may prove to be important to precise management of patients with MSLT.

Systems Biology Integration and Screening of Reliable Prognostic Markers to Create Synergies in the Control of Lung Cancer Patients

This study aims to achieve a clearer and stronger understanding of all the mechanisms involved in the occurrence as well as in the progression of lung cancer along with discovering trustworthy prognostic markers. We combined four gene expression profiles (GSE19188, GSE19804, GSE101929, and GSE18842) from the GEO database and screened the commonly differentially expressed genes (CDEGs). We performed differentially expressed group analysis on CDEGs, alteration and mutational analysis, and expression level verification of core differential genes. Systems biology discoveries in our examination are predictable with past reports. Curiously, our examination revealed that screened biomarker adjustments, for the most part, coexist in lung cancer. After screening 952 CDEGs, we found that the up-regulation of neuromedin U (NMU) and GTSE1 in the case of lung cancer is related to poor prognosis. On the other hand, FOS CDKN1C expression is associated with poor prognosis and is responsible for the down-regulation of CDKN1C and FOS. Changes in these qualities are on free pathways to lung cancer and are not usually of combined quality variety. Even though biomarkers were related to both survival occasions in our examination, it gives us another point of view while playing out the investigation of hereditary changes and clinical highlights employing information mining. Based on our results, we found potential and prospective clinical applications in GTSE1, NMU, FOS, and CDKN1C to act as prognostic markers in case of lung cancer.

Downregulation of hsa-microRNA-204-5p and identification of its potential regulatory network in non-small cell lung cancer: RT-qPCR, bioinformatic- and meta-analyses

Background

Pulmonary malignant neoplasms have a high worldwide morbidity and mortality, so the study of these malignancies using microRNAs (miRNAs) has attracted great interest and enthusiasm. The aim of this study was to determine the clinical effect of hsa-microRNA-204-5p (miR-204-5p) and its underlying molecular mechanisms in non-small cell lung cancer (NSCLC).

Methods

Expression of miR-204-5p was investigated by real-time quantitative PCR (RT-qPCR). After data mining from public online repositories, several integrative assessment methods, including receiver operating characteristic (ROC) curves, hazard ratios (HR) with 95% confidence intervals (95% CI), and comprehensive meta-analyses, were conducted to explore the expression and clinical utility of miR-204-5p. The potential objects regulated and controlled by miR-204-5p in the course of NSCLC were identified by estimated target prediction and analysis. The regulatory network of miR-204-5p, with its target genes and transcription factors (TFs), was structured from database evidence and literature references.

Results

The expression of miR-204-5p was downregulated in NSCLC, and the downtrend was related to gender, histological type, vascular invasion, tumor size, clinicopathologic grade and lymph node metastasis (P<0.05). MiR-204-5p was useful in prognosis, but was deemed unsuitable at present as an auxiliary diagnostic or prognostic risk factor for NSCLC due to the lack of statistical significance in meta-analyses and absence of large-scale investigations. Gene enrichment and annotation analyses identified miR-204-5p candidate targets that took part in various genetic activities and biological functions. The predicted TFs, like MAX, MYC, and RUNX1, interfered in regulatory networks involving miR-204-5p and its predicted hub genes, though a modulatory loop or axis of the miRNA-TF-gene that was out of range with shortage in database prediction, experimental proof and literature confirmation.

Conclusions

The frequently observed decrease in miR-204-5p was helpful for NSCLC diagnosis. The estimated target genes and TFs contributed to the anti-oncogene effects of miR-204-5p.