Predictive biomarkers in precision medicine and drug development against lung cancer

Mitochondrial creatine kinase 1 regulates the cell cycle in non-small cell lung cancer via activation of cyclin-dependent kinase 4

BACKGROUND

Non-small cell lung cancer (NSCLC) is the main type of the most common malignant tumor in the world. Previous studies have shown that the expression level of mitochondrial creatine kinase 1 (CKMT1) is abnormal in NSCLC, but the mechanism of its effect remains unclear. Therefore, in this study, we intend to clarify the potential mechanism of CKMT1 in NSCLC and provide the theoretical basis for the clinical application of CKMT1.

METHODS

The function of CKMT1 in NSCLC was identified by analyzing the GEO dataset and evaluating using in vitro and in vivo models. Protein mass spectrometry was used to find proteins interacting with CKMT1, and Co-immunoprecipitation (Co-IP) and GST-pull down experiments were used to verify the interaction between proteins. The immunofluorescence (IF) assay was used to explore the functional position of CKMT1 in cells. The effect of CKMT1 expression level on the efficacy of paclitaxel (TAX) in the treatment of NSCLC was analyzed by a combined TAX experiment in vivo and in vitro.

RESULTS

CKMT1 expression was increased in NSCLC and CKMT1 promoted the proliferation of NSCLC cells in vitro and in vivo. CKMT1 knockdown resulted in a significantly increased G0/G1 fraction and decreased S phase cell fraction, indicating G1 phase arrest. Mechanically, the cyclin-dependent kinase 4 (CDK4) was identified to interact with CKMT1, and the crucial binding areas were focused on the DH domain of CKMT1 and the N- and C-terminal of CDK4. A fraction of the CDK4 proteins colocalize and interact with the CKMT1 at mitochondria, the level of phosphorylated CDK4 was regulated by CKMT1. Hence, the decrease in CKMT1 expression level could increase the antitumor effect of G2/M cell cycle antagonist-TAX in NSCLC in vitro and in vivo.

CONCLUSIONS

CKMT1 could interact with CDK4 in mitochondria and regulate the phosphorylated level of CDK4, thus contributing to the proliferation and cell cycle transition of NSCLC cells. And CKMT1 could be a potential target to improve the sensitivity of chemotherapy based on TAX.

Transforming Diagnosis and Therapeutics Using Cancer Genomics

In past quarter of the century, much has been understood about the genetic variation and abnormal genes that activate cancer in humans. All the cancers somehow possess alterations in the DNA sequence of cancer cell's genome. In present, we are heading toward the era where it is possible to obtain complete genome of the cancer cells for their better diagnosis, categorization and to explore treatment options.

Genomic Alteration Spectrum of Non-Small Cell Lung Cancer Patients in East-China Characterized by Tumor Tissue DNA and Cell-Free DNA

Introduction

From an oncologic perspective, genetic detection is becoming a frontline clinical test, used to identify actionable alterations for targeted therapy, monitor molecular clonal tumor evolution, indicate disease progression and prognosis, and predict medication efficacy and resistance. From analysis of both tumor tissue and cell-free DNA from a large cohort of non-small cell lung cancer patients in East-China, we characterized the full spectrum of genomic alterations.

Methods

The study comprised 3000 unpaired samples including 1351 tumor tissue DNA (tDNA) and 1649 cell-free circulating tumor DNA (cfDNA) samples, from which 67 cancer-related genes were sequenced and the genetic alteration profiles were depicted. Integrative molecular analyses identified the frequently mutated genes, uncovered co-occurring somatic alterations, described the distribution of hotspot variants, analyzed the frequency of variant alleles, and notably distinguished actionable, novel variants.

Results

The most commonly affected genes were EGFR, TP53, KRAS, CDKN2A, and PIK3CA in both tDNA and cfDNA samples. EGFR and CTNNB1, PIK3CA and PTEN, ERBB2 and SMO were found to have frequent co-occurring alterations in tDNA samples, while EGFR and SMO, KRAS and PDGFRA, PIK3CA and KDR were in cfDNA samples. A large number of primary druggable variants were identified in tDNA samples, while numerous drug-resistance variants, rare actionable variants, and non-EGFR actionable variants were detected in cfDNA samples. Novel variants were enriched in KDR, KIT, TP53, ABL1, FGFR1 in tDNA samples while the majority of novel variants were distributed in PDGFRA, EGFR, KIT, ROS1, BRCA2 in cfDNA samples. Variant allele frequency in tDNA samples was significantly (P < 0.001) higher than that in cfDNA samples.

Conclusion

The results revealed considerable differences in the alteration characteristics between the two kinds of specimens. To date, this study represents the largest real-world investigation of its kind, derived from the largest number of patients in East-China. It reinforced and expanded the mechanism of molecular analysis of neoplastic genetic profiles.

Unbiased peptoid cell screen identifies a peptoid targeting newly appeared cell surface vimentin on tumor transformed early lung cancer cells

To identify potential new reagents and biomarkers for early lung cancer detection we combined the use of a novel preclinical isogenic model of human lung epithelial cells comparing non-malignant cells with those transformed to full malignancy using defined oncogenic changes and our on-bead two color (red and green stained cells) (OBTC) peptoid combinatorial screening methodology. The preclinical model used normal parent lung epithelial cells (HBEC3-KT, labeled with green dye) and isogenic fully malignant transformed derivatives (labeled with a red dye) via the sequential introduction of key genetic alterations of p53 knockdown, oncogenic KRAS and overexpression of cMYC (HBEC3^{p53, KRAS, cMYC}). Using the unbiased OBTC screening approach, we tested 100,000 different peptoids and identified only one (named JM3A) that bound to the surface of the HBEC3^{p53, KRAS, cMYC} cells (red cells) but not HBEC3-KT cells (green cells). Using the JM3A peptoid and proteomics, we identified the protein bound as vimentin using multiple validation approaches. These all confirmed the cell surface expression of vimentin (CSV) on transformed (HBEC3^{p53, KRAS, cMYC}) but not on untransformed (HBEC3-KT) cells. JM3A coupled with fluorophores was able to detect and stain cell surface vimentin on very early stage lung cancers but not normal lung epithelial cells in a fashion comparable to that using anti-vimentin antibodies. We conclude: using a combined isogenic preclinical model of lung cancer and two color screening of a large peptoid library, we have identified differential expression of cell surface vimentin (CSV) after malignant transformation of lung epithelial cells, and developed a new peptoid reagent (JM3A) for detection of CSV which works well in staining of early stage NSCLCs. This new, highly specific, easy to prepare, CSV detecting JM3A peptoid provides an important new reagent for identifying cancer cells in early stage tumors as well as a resource for detection and isolating of CSV expressing circulating tumor cells.

[18F]AlF-NOTA-FAPI-04 PET/CT uptake in metastatic lesions on PET/CT imaging might distinguish different pathological types of lung cancer

Purpose

Heterogeneity is found in the tumor microenvironment among different pathological types of tumors. Radionuclide-labeled fibroblast-activation-protein inhibitor (FAPI), as an important tracer for non-invasive imaging of the tumor microenvironment, can be used to evaluate the expression of FAP in cancer-associated fibroblasts, macrophages, and tumor cells. Our aim was to explore the ability of [¹⁸F]AlF-NOTA-FAPI-04 positron emission tomography (PET)/computed tomography (CT) to distinguish different types of lung cancer by evaluating the uptake of this tracer in primary and metastatic lesions.

Methods

We prospectively enrolled 61 patients with histopathologically proven primary lung cancer with metastases. PET/CT scanning was performed before any antitumor therapy and 1 h after injection of 235.10 ± 3.89 MBq of [¹⁸F]AlF-NOTA-FAPI-04. Maximum standard uptake values (SUV_max) were calculated for comparison among primary and metastatic lesions. Immunohistochemical staining for FAP was performed on tumor specimens.

Results

Sixty-one patients with adenocarcinoma (ADC, n = 30), squamous cell carcinoma (SCC, n = 17), and small cell lung cancer (SCLC, n = 14) were enrolled in this study, and 61 primary tumors and 199 metastases were evaluated. No difference in [¹⁸F]AlF-NOTA-FAPI-04 uptake was observed among primary ADC, SCC, and SCLC tumors (P = 0.198). Additionally, no difference in uptake was found between primary and metastatic lesions of lung cancer with the same pathological type (P > 0.05). However, uptake did differ among metastases of differing pathological types (P < 0.001). The SUV_max of metastatic lymph nodes was highest for SCC, followed by ADC and then SCLC (P < 0.001). The SUV_max of bone metastases also was highest for SCC, followed by ADC and SCLC (P < 0.05), but no difference was observed between ADC and SCLC. The SUV_max of metastases in other organs was higher in SCC cases than in ADC cases but did not differ between SCC and SCLC or ADC and SCLC. Bone metastases exhibited higher uptake than those of lymph nodes and other organs in SCC and ADC (P < 0.05) but not in SCLC. Positive correlations were found between FAPI uptake and FAP expression in surgical plus biopsy specimens (r = 0.439, P = 0.012) and surgical specimens (r = 0.938, P = 0.005).

Conclusion

[¹⁸F]AlF-NOTA-FAPI-04 PET/CT imaging revealed differences in FAP expression in metastases of lung cancer, with the highest expression specifically in bone metastases, and thus, may be valuable for distinguishing different pathological types of lung cancer.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00259-021-05638-z.

Globally ncRNAs Expression Profiling of TNBC and Screening of Functional lncRNA

One of the most well-known cancer subtypes worldwide is triple-negative breast cancer (TNBC) which has reduced prediction due to its antagonistic biotic actions and target's deficiency for the treatment. The current work aims to discover the countenance outlines and possible roles of lncRNAs in the TNBC via computational approaches. Long non-coding RNAs (lncRNAs) exert profound biological functions and are widely applied as prognostic features in cancer. We aim to identify a prognostic lncRNA signature for the TNBC. First, samples were filtered out with inadequate tumor purity and retrieved the lncRNA expression data stored in the TANRIC catalog. TNBC sufferers were divided into two prognostic classes which were dependent on their survival time (shorter or longer than 3 years). Random forest was utilized to select lncRNA features based on the lncRNAs differential expression between shorter and longer groups. The Stochastic gradient boosting method was used to construct the predictive model. As a whole, 353 lncRNAs were differentially transcribed amongst the shorter and longer groups. Using the recursive feature elimination, two lncRNAs were further selected. Trained by stochastic gradient boosting, we reached the highest accuracy of 69.69% and area under the curve of 0.6475. Our findings showed that the two-lncRNA signs can be proved as potential biomarkers for the prognostic grouping of TNBC's sufferers. Many lncRNAs remained dysregulated in TNBC, while most of them are likely play a role in cancer biology. Some of these lncRNAs were linked to TNBC's prediction, which makes them likely to be promising biomarkers.

Identification of a 4-lncRNA signature predicting prognosis of patients with non-small cell lung cancer: a multicenter study in China

Background

Previous findings have indicated that the tumor, nodes, and metastases (TNM) staging system is not sufficient to accurately predict survival outcomes in patients with non-small lung carcinoma (NSCLC). Thus, this study aims to identify a long non-coding RNA (lncRNA) signature for predicting survival in patients with NSCLC and to provide additional prognostic information to TNM staging system.

Methods

Patients with NSCLC were recruited from a hospital and divided into a discovery cohort (n = 194) and validation cohort (n = 172), and detected using a custom lncRNA microarray. Another 73 NSCLC cases obtained from a different hospital (an independent validation cohort) were examined with qRT-PCR. Differentially expressed lncRNAs were determined with the Significance Analysis of Microarrays program, from which lncRNAs associated with survival were identified using Cox regression in the discovery cohort. These prognostic lncRNAs were employed to construct a prognostic signature with a risk-score method. Then, the utility of the prognostic signature was confirmed using the validation cohort and the independent cohort.

Results

In the discovery cohort, we identified 305 lncRNAs that were differentially expressed between the NSCLC tissues and matched, adjacent normal lung tissues, of which 15 are associated with survival; a 4-lncRNA prognostic signature was identified from the 15 survival lncRNAs, which was significantly correlated with survivals of NSCLC patients. This signature was further validated in the validation cohort and independent validation cohort. Moreover, multivariate Cox analysis demonstrates that the 4-lncRNA signature is an independent survival predictor. Then we established a new risk-score model by combining 4-lncRNA signature and TNM staging stage. The receiver operating characteristics (ROC) curve indicates that the prognostic value of the combined model is significantly higher than that of the TNM stage alone, in all the cohorts.

Conclusions

In this study, we identified a 4-lncRNA signature that may be a powerful prognosis biomarker and can provide additional survival information to the TNM staging system.

Emerging role of phytochemicals in targeting predictive, prognostic, and diagnostic biomarkers of lung cancer

Lung-cancer is the foremost cause of cancer in humans worldwide, of which 80-85% cases are composed of non-small cell lung carcinoma. All treatment decisions depend on the pattern of biomarkers selection to enhance the response to the targeted therapies. Although advanced treatments are available for lung-cancer, the disease treatment remains not adequate. There are several synthetic chemotherapeutic agents available for the treatment of lung cancer. However, due to their toxic effect, survival rate is still 15-18%. Besides, medicinal plants are a huge reservoir of natural products that provide protective effects against lung cancer. Likewise, successful studies of potential phytochemicals in targeting lung-cancer biomarkers have created a novel paradigm for the discovery of potent drugs against lung-cancer. Hence, to defeat severe toxicity and resistance towards the synthetic drugs, detailed studies are required regarding the available phytochemicals and targets responsible for the treatment of lung-cancer. The present review provides a comprehensive information about the lung-cancer biomarkers under the classification of predictive, prognostic, and diagnostic type. Moreover, it discusses and enlists the phytochemicals with mode of action against different biomarkers, effective doses in in vitro, in vivo, and clinical studies, the limitations associated with usage of phytochemicals as a drug to prevent/cure lung-cancer and the latest techniques employed to overcome such issues.

The Combination of CD147 and MMP-9 Serum Levels Is Identified as Novel Chemotherapy Response Markers of Advanced Non-Small-Cell Lung Cancer

To evaluate the correlation between the changes in serum concentrations of cluster of differentiation-147 (scCD147) and chemotherapy outcome in patients with NSCLC and evaluate the combination of scCD147 with serum matrix metalloproteinase-9 (scMMP-9) levels in the prediction of chemotherapy response, eighty-two patients with advanced LC were enrolled. Newly diagnosed cases were treated with platinum-based chemotherapy. We measured scCD147 protein levels in LC cases by ELISA and used receiver operating characteristic (ROC) curves to analyze the results. Four time points were chosen to examine the association between the changes in scCD147 and chemotherapy outcome: before chemotherapy and 21 days after the start of the first, second, and fourth chemotherapy cycles. We assessed the combination of scCD147 and scMMP-9 serum levels in predicting the chemotherapy response. scCD147 was higher in LC cases than that in healthy volunteers (HVs). scCD147 was associated with distant metastases and TNM stage. scCD147 and scMMP-9 appeared to be independent predictive factors for chemotherapy outcomes after the first and second chemotherapy cycles for patients with NSCLC. Multivariable analysis also demonstrated that variations in scCD147 and scMMP-9 could be independent factors for monitoring chemotherapy outcome for patients with NSCLC. Furthermore, when scCD147 and scMMP-9 are combined into a new risk model, it has a markedly better prediction of chemotherapy outcomes than each protein alone. scCD147 and MMP-9 are potential predictive biomarkers for efficacy, and their combination significantly improves the predictive power for chemotherapy response in patients with NSCLC.

Robust Biomarker Screening Using Spares Learning Approach for Liver Cancer Prognosis

LncRNAs, miRNAs, mRNAs, methylation, and proteins exert profound biological functions and are widely applied as prognostic features in liver cancer. This study aims to identify prognostic biomarkers' signature for liver cancer. Samples with inadequate tumor purity were filtered out and the expression data from different resources were retrieved. The Spares learning approach was applied to select lncRNAs, miRNAs, mRNAs, methylation, and proteins' features based on their differentially expressed groups. The LASSO boosting technique was employed for the predictive model construction. A total of 200 lncRNAs, 200 miRNAs, 371 mRNAs, 371 methylations, and 184 proteins were observed to be differentially expressed. Five lncRNAs, 11 miRNAs, 30 mRNAs, 4 methylations, and 3 proteins were selected for further evaluation using the feature elimination technique. The highest accuracy of 89.32% is achieved as a result of training and learning by Spares learning methodology. Final outcomes revealed that 5 lncRNA, 11 miRNA, 30 mRNA, 4 methylation, and 3 protein signatures could be potential biomarkers for the prognosis of liver cancer patients.

Concordance of Treatment Recommendations for Metastatic Non-Small-Cell Lung Cancer Between Watson for Oncology System and Medical Team

Objective

The disease complexity of metastatic non-small-cell lung cancer (mNSCLC) makes it difficult for physicians to make clinical decisions efficiently and accurately. The Watson for Oncology (WFO) system of artificial intelligence might help physicians by providing fast and precise treatment regimens. This study measured the concordance of the medical treatment regimens of the WFO system and actual clinical regimens, with the aim of determining the suitability of WFO recommendations for Chinese patients with mNSCLC.

Methods

Retrospective data of mNSCLC patients were input to the WFO, which generated a treatment regimen (WFO regimen). The actual regimen was made by physicians in a medical team for patients (medical-team regimen). The factors influencing the consistency of the two treatment options were analyzed by univariate and multivariate analyses.

Results

The concordance rate was 85.16% between the WFO and medical-team regimens for mNSCLC patients. Logistic regression showed that the concordance differed significantly for various pathological types and gene mutations in two treatment regimens. Patients with adenocarcinoma had a lower rate of “recommended” regimen than those with squamous cell carcinoma. There was a statistically significant difference in EGFR-mutant patients for “not recommended” regimens with inconsistency rate of 18.75%. In conclusion, the WFO regimen has 85.16% consistency rate with medical-team regimen in our treatment center. The different pathological type and different gene mutation markedly influenced the agreement rate of the two treatment regimens.

Conclusion

WFO recommendations have high applicability to mNSCLC patients in our hospital. This study demonstrates that the valuable WFO system may assist the doctors better to determine the accurate and effective treatment regimens for mNSCLC patients in the Chinese medical setting.

Remaining challenges in predicting patient outcomes for diffuse large B-cell lymphoma

Introduction: Diffuse large B-cell lymphoma (DLBCL) is the most common non-Hodgkin lymphoma and is an aggressive malignancy with heterogeneous outcomes. Diverse methods for DLBCL outcomes assessment ranging from clinical to genomic have been developed with variable predictive and prognostic success.Areas covered: The authors provide an overview of the various methods currently used to estimate prognosis in DLBCL patients. Models incorporating cell of origin, genomic features, sociodemographic factors, treatment effectiveness measures, and machine learning are described.Expert opinion: The clinical and genetic heterogeneity of DLBCL presents distinct challenges in predicting response to therapy and overall prognosis. Successful integration of predictive and prognostic tools in clinical trials and in a standard clinical workflow for DLBCL will likely require a combination of methods incorporating clinical, sociodemographic, and molecular factors with the aid of machine learning and high-dimensional data analysis.

Cost burden of diffuse large B-cell lymphoma

Introduction: Diffuse large B-cell lymphoma (DLBCL) is the most common non-Hodgkin lymphoma and is a clinically heterogeneous disease. Treatment pathways for DLBCL are diverse and integrate established and novel therapies.Areas covered: We review the cost burden of DLBCL and the cost-effectiveness of DLBCL management including precision and cellular medicine. We utilized Medical Subject Heading (MeSH) terms and keywords to search the National Library of Medicine online MEDLINE database (PubMed) for articles related to cost, cost burden, and cost-of-illness of DLBCL and cost-effectiveness of DLBCL management strategies published in English as of June 2019.Expert commentary: Available and developing DLBCL therapies offer improved outcomes and often curative treatment at considerable financial expense, and the total cost burden for DLBCL management is substantial for patients and the healthcare system. In the era of personalized medicine, CAR T cells and targeted therapies provide exciting avenues for current and future DLBCL care and can further increase treatment cost. Determinations of cost and cost-effectiveness in DLBCL treatment pathways should continue to guide care providers and systems in identifying cost reduction strategies to provide appropriate therapies to the greatest number of patients in treating DLBCL.

Shear Wave Elastography of Breast Lesions: Quantitative Analysis of Elastic Heterogeneity Improves Diagnostic Performance

The aim of this study was to evaluate whether quantitative analysis of elastic heterogeneity (EH) could improve the diagnostic performance of shear wave elastography (SWE) in breast lesions. From August 2016 to August 2017, 280 patients were enrolled in this prospective study. All lesions were evaluated with the ultrasound Breast Imaging Reporting and Data System (BI-RADS) and SWE with Virtual Touch tissue imaging quantification. The shear wave velocity (SWV) of the three areas of highest stiffness and lowest stiffness within the lesions were measured to calculate the maximum SWV (SWV_max), mean SWV (SWV_mean) and EH. The EH was determined as the difference between the averaged highest SWV and lowest SWV. The diagnostic performance-including the area under the receiver operating characteristic curve (AUC) and the sensitivity and specificity of BI-RADS, EH, SWV_max and SWV_mean-were analyzed. The AUC of EH, SWV_max and SWV_mean were 0.963, 0.949 and 0.937, respectively. The sensitivity of EH was 93.75%, which was significantly higher than that of SWV_max (84.37%) and SWV_mean (84.37%) (p < 0.001); there was no significant difference in the specificity among EH, SWV_max and SWV_mean (p > 0.05). For category 4A lesions, EH predicted all the malignant lesions, while two cancers were misdiagnosed by SWV_max and SWV_mean, respectively. Quantitative analysis of EH can improve the sensitivity of SWE for the differential diagnosis of breast lesions without loss of specificity.

Matrix Metalloproteinase 11 Is a Potential Therapeutic Target in Lung Adenocarcinoma

Lung cancer is one of the leading causes of cancer-associated death, with the etiology largely unknown. The aim of this study was to identify key driver genes with therapeutic potentials in lung adenocarcinoma (LUAD). Transcriptome microarray data from four GEO datasets (GEO: GSE7670, GSE10072, GSE68465, and GSE43458) were jointly analyzed for differentially expressed genes (DEGs). Ontologic analysis showed that most of the upregulated DEGs enriched in collagen catabolic and fibril organization processes were regulated by matrix metalloproteinases (MMPs). Matrix metalloproteinase 11 (MMP11), the highest upregulated MMP family member in LUAD-transformed cells, acted in an autocrine manner and was significantly increased in sera of LUAD patients. MMP11 depletion severely impaired LUAD cell proliferation, migration, and invasion in vitro, in line with retarded tumor growth in xenograft models. Treatment of different human LUAD cell lines with anti-MMP11 antibody significantly retarded cell growth and migration. Administration of anti-MMP11 antibody at a dose of 1 μg/g body weight significantly suppressed tumor growth in xenograft models. These findings indicate that MMP11 is a key cancer driver gene in LUAD and is an appealing target for antibody therapy.

Immunotherapy for non-small cell lung cancers: biomarkers for predicting responses and strategies to overcome resistance

Recent breakthroughs in targeted therapy and immunotherapy have revolutionized the treatment of lung cancer, the leading cause of cancer-related deaths in the United States and worldwide. Here we provide an overview of recent progress in immune checkpoint blockade therapy for treatment of non-small cell lung cancer (NSCLC), and discuss biomarkers associated with the treatment responses, mechanisms underlying resistance and strategies to overcome resistance. The success of immune checkpoint blockade therapies is driven by immunogenicity of tumor cells, which is associated with mutation burden and neoantigen burden in cancers. Lymphocyte infiltration in cancer tissues and interferon-γ-induced PD-L1 expression in tumor microenvironments may serve as surrogate biomarkers for adaptive immune resistance and likelihood of responses to immune checkpoint blockade therapy. In contrast, weak immunogenicity of, and/or impaired antigen presentation in, tumor cells are primary causes of resistance to these therapies. Thus, approaches that increase immunogenicity of cancer cells and/or enhance immune cell recruitment to cancer sites will likely overcome resistance to immunotherapy.

Overexpression of amplified in breast cancer 1 (AIB1) gene promotes lung adenocarcinoma aggressiveness in vitro and in vivo by upregulating C-X-C motif chemokine receptor 4

Background

We previously found that overexpression of the gene known as amplified in breast cancer 1 (AIB1) was associated with lymph node metastasis and poor prognosis in patients with lung adenocarcinoma. However, the role of AIB1 in that malignancy remains unknown. The present study aimed to investigate the function of AIB1 in the process of lung adenocarcinoma cell metastasis.

Methods

A series of in vivo and in vitro assays were performed to elucidate the function of AIB1, while real-time PCR and Western blotting were utilized to identify the potential downstream targets of AIB1 in the process of lung adenocarcinoma metastasis. Rescue experiments and in vitro assays were performed to investigate whether the invasiveness of AIB1-induced lung adenocarcinoma was mediated by C-X-C motif chemokine receptor 4 (CXCR4).

Results

The ectopic overexpression of AIB1 in lung adenocarcinoma cells substantially enhanced cell migration and invasive abilities in vitro and tumor metastasis in vivo, whereas the depletion of AIB1 expression substantially inhibited lung adenocarcinoma cell migration and invasion. CXCR4 was identified as a potential downstream target of AIB1 in lung adenocarcinoma. The knockdown of AIB1 greatly reduced CXCR4 gene expression at both the transcription and protein levels, whereas the knockdown of CXCR4 in cells with AIB1 ectopic overexpression diminished AIB1-induced migration and invasion in vitro and tumor metastasis in vivo. Furthermore, we found a significant positive association between the expression of AIB1 and CXCR4 in lung adenocarcinoma patients (183 cases), and the co-overexpression of AIB1 and CXCR4 predicted the poorest prognosis.

Conclusions

These findings suggest that AIB1 promotes the aggressiveness of lung adenocarcinoma in vitro and in vivo by upregulating CXCR4 and that it might be usable as a novel prognostic marker and/or therapeutic target for this disease.

Electronic supplementary material

The online version of this article (10.1186/s40880-018-0320-1) contains supplementary material, which is available to authorized users.

Biomarkers: Delivering on the expectation of molecularly driven, quantitative health

Biomarkers are the pillars of precision medicine and are delivering on expectations of molecular, quantitative health. These features have made clinical decisions more precise and personalized, but require a high bar for validation. Biomarkers have improved health outcomes in a few areas such as cancer, pharmacogenetics, and safety. Burgeoning big data research infrastructure, the internet of things, and increased patient participation will accelerate discovery in the many areas that have not yet realized the full potential of biomarkers for precision health. Here we review themes of biomarker discovery, current implementations of biomarkers for precision health, and future opportunities and challenges for biomarker discovery. Impact statement Precision medicine evolved because of the understanding that human disease is molecularly driven and is highly variable across patients. This understanding has made biomarkers, a diverse class of biological measurements, more relevant for disease diagnosis, monitoring, and selection of treatment strategy. Biomarkers' impact on precision medicine can be seen in cancer, pharmacogenomics, and safety. The successes in these cases suggest many more applications for biomarkers and a greater impact for precision medicine across the spectrum of human disease. The authors assess the status of biomarker-guided medical practice by analyzing themes for biomarker discovery, reviewing the impact of these markers in the clinic, and highlight future and ongoing challenges for biomarker discovery. This work is timely and relevant, as the molecular, quantitative approach of precision medicine is spreading to many disease indications.

Co-clinical trials demonstrate predictive biomarkers for dovitinib, an FGFR inhibitor, in lung squamous cell carcinoma

Background

We conducted co-clinical trials in patient-derived xenograft (PDX) models to identify predictive biomarkers for the multikinase inhibitor dovitinib in lung squamous cell carcinoma (LSCC).

Methods

The PDX01-02 were established from LSCC patients enrolled in the phase II trial of dovitinib (NCT01861197) and PDX03-05 were established from LSCC patients receiving surgery. These five PDX tumors were subjected to in vivo test of dovitinib efficacy, whole exome sequencing and gene expression profiling.

Results

The PDX tumors recapitulate histopathological properties and maintain genomic characteristics of originating tumors. Concordant with clinical outcomes of the trial enrolled-LSCC patients, dovitinib produced substantial tumor regression in PDX-01 and PDX-05, whereas it resulted in tumor progression in PDX-02. PDX-03 and -04 also displayed poor antitumor efficacy to dovitinib. Mutational and genome-wide copy number profiles revealed no correlation between genomic alterations of FGFR1-3 and sensitivity to dovitinib. Of note, gene expression profiles revealed differentially expressed genes including FGF3 and FGF19 between PDX-01 and 05 and PDX-02-04. Pathway analysis identified two FGFR signaling-related gene sets, FGFR ligand binding/activation and SHC-mediated cascade pathway were substantially up-regulated in PDX-01 and 05, compared with PDX-02-04. The comparison of gene expression profiles between dovitinib-sensitive versus -resistant lung cancer cell lines in the Cancer Cell Line Encyclopedia database also found that transcriptional activation of 18 key signaling components in FGFR pathways can predict the sensitivity to dovitinib both in cell lines and PDX tumors. These results highlight FGFR pathway activation as a key molecular determinant for sensitivity to dovitinib.

Conclusions

FGFR gene expression signatures are predictors for the response to dovitinib in LSCC.

ARHGEF19 interacts with BRAF to activate MAPK signaling during the tumorigenesis of non-small cell lung cancer

Rho guanine nucleotide exchange factors (RhoGEFs) are proteins that activate Rho GTPases in response to extracellular stimuli and regulate various biologic processes. ARHGEF19, one of RhoGEFs, was reported to activate RhoA in the Wnt-PCP pathway controlling convergent extension in Xenopus gastrulation. The goal of our study was to identify the role and molecular mechanisms of ARHGEF19 in the tumorigenesis of non-small cell lung cancer (NSCLC). ARHGEF19 expression was significantly elevated in NSCLC tissues, and ARHGEF19 levels were significantly associated with lymph node status, distant metastasis and TNM stage; Patients with high ARHGEF19 levels had poor overall survival (OS) and progression-free survival (PFS). Our investigations revealed that ARHGEF19 overexpression promoted the cell proliferation, invasion and metastasis of lung cancer cells, whereas knockdown of this gene inhibited these processes. Mechanistically, ARHGEF19 activated the mitogen-activated protein kinase (MAPK) pathway in a RhoA-independent manner: ARHGEF19 interacted with BRAF and facilitated the phosphorylation of its downstream kinase MEK1/2; both the Dbl homology (DH) and Pleckstrin homology (PH) domains of ARHGEF19 were indispensable for the phosphorylation of MEK1/2. Furthermore, downregulation of miR-29b was likely responsible for the increased expression of ARHGEF19 in lung cancer tissues and, consequently, the abnormal activation of MAPK signaling. These findings suggest that ARHGEF19 upregulation, due to the low expression of miR-29 in NSCLC tissues, may play a crucial role in NSCLC tumorigenesis by activating MAPK signaling. ARHGEF19 could serve as a negative prognostic marker as well as a therapeutic target for NSCLC patients.

Overcoming resistance to anti-PD immunotherapy in a syngeneic mouse lung cancer model using locoregional virotherapy

Anti-PD-1 and anti-PD-L1 immunotherapy has provided a new therapeutic opportunity for treatment of advanced-stage non-small cell lung cancer (NSCLC). However, overall objective response rates are approximately 15%-25% in all NSCLC patients who receive anti-PD therapy. Therefore, strategies to overcome primary resistance to anti-PD immunotherapy are urgently needed. We hypothesized that the barrier to the success of anti-PD therapy in most NSCLC patients can be overcome by stimulating the lymphocyte infiltration at cancer sites through locoregional virotherapy. To this end, in this study, we determined combination effects of anti-PD immunotherapy and oncolytic adenoviral vector-mediated tumor necrosis factor-α-related apoptosis-inducing ligand (TRAIL) gene therapy (Ad/E1-TRAIL) or adenoviral-mediated TP53 (Ad/CMV-TP53) gene therapy in syngeneic mice bearing subcutaneous tumors derived from M109 lung cancer cells. Both anti-PD-1 and anti-PD-L1 antibodies failed to elicit obvious therapeutic effects in the M109 tumors. Intratumoral administration of Ad/E1-TRAIL or Ad/CMV-TP53 alone suppressed tumor growth in animals preexposed to an adenovector and bearing subcutaneous tumors derived from M109 cells. However, combining either anti-PD-1 or anti-PD-L1 antibody with these two adenoviral vectors elicited the strongest anticancer activity in mice with existing immunity to adenoviral vectors. Dramatically enhanced intratumoral immune response was detected in this group of combination therapy based on infiltrations of CD4+ and CD8+ lymphocytes and macrophages in tumors. Our results demonstrate that resistance to anti-PD-1 immunotherapy in syngeneic mouse lung cancer can be overcome by locoregional virotherapy.

Clinical multi-omics strategies for the effective cancer management

Cancer is a global health issue as a multi-factorial complex disease, and early detection and novel therapeutic strategies are required for more effective cancer management. With the development of systemic analytical -omics strategies, the therapeutic approach and study of the molecular mechanisms of carcinogenesis and cancer progression have moved from hypothesis-driven targeted investigations to data-driven untargeted investigations focusing on the integrated diagnosis, treatment, and prevention of cancer in individual patients. Predictive, preventive, and personalized medicine (PPPM) is a promising new approach to reduce the burden of cancer and facilitate more accurate prognosis, diagnosis, as well as effective treatment. Here we review the fundamentals of, and new developments in, -omics technologies, together with the key role of a variety of practical -omics strategies in PPPM for cancer treatment and diagnosis.

BIOLOGICAL SIGNIFICANCE

In this review, a comprehensive and critical overview of the systematic strategy for predictive, preventive, and personalized medicine (PPPM) for cancer disease was described in a view of cancer prognostic prediction, diagnostics, and prevention as well as cancer therapy and drug responses. We have discussed multi-dimensional data obtained from various resources and integration of multisciplinary -omics strategies with computational method which could contribute the more effective PPPM for cancer. This review has provided the novel insights of the current applications of each and combined -omics technologies, which showed their powerful potential for the establishment of PPPM for cancer.

Evaluation of K-ras and p53 expression in pancreatic adenocarcinoma using the cancer genome atlas

Genetic alterations in K-ras and p53 are thought to be critical in pancreatic cancer development and progression. However, K-ras and p53 expression in pancreatic adenocarcinoma have not been systematically examined in The Cancer Genome Atlas (TCGA) Data Portal. Information regarding K-ras and p53 alterations, mRNA expression data, and protein/protein phosphorylation abundance was retrieved from The Cancer Genome Atlas (TCGA) databases, and analyses were performed by the cBioPortal for Cancer Genomics. The mutual exclusivity analysis showed that events in K-ras and p53 were likely to co-occur in pancreatic adenocarcinoma (Log odds ratio = 1.599, P = 0.006). The graphical summary of the mutations showed that there were hotspots for protein activation. In the network analysis, no solid association between K-ras and p53 was observed in pancreatic adenocarcinoma. In the survival analysis, neither K-ras nor p53 were associated with both survival events. As in the data mining study in the TCGA databases, our study provides a new perspective to understand the genetic features of K-ras and p53 in pancreatic adenocarcinoma.

Multiplex digital colour-coded barcode technology on RNA extracted from routine cytological samples of patients with non-small cell lung cancer: pilot study

In the advanced stages of non-small cell lung cancer (NSCLC), molecular testing is often performed on archival cytological smears. The nCounter system (NanoString Technologies) is a new promising multiplex digital colour-coded barcode technology. However, its feasibility to evaluate the RNA expression of clinical relevant biomarkers on routine cytological smears is still uncertain. To this end, RNA was extracted from 12 NSCLC routine stained cytological smears, and nCounter analysis performed by using a 48-gene panel. Overall, 11/12 (92%) of the smears were adequate for the secondary analysis, fulfilling the quality check parameter analysis of nSolver software. This pilot study shows that RNA nCounter analysis is feasible on routine cytological smears preparing the field for the implementation of this technology in the routine setting.

Proportion and clinical features of never-smokers with non-small cell lung cancer

BACKGROUND

The proportion of never-smokers with non-small cell lung cancer (NSCLC) is increasing, but that in Korea has not been well addressed in a large population. We aimed to evaluate the proportion and clinical features of never-smokers with NSCLC in a large single institution.

METHODS

We analyzed clinical data of 1860 consecutive patients who were newly diagnosed with NSCLC between June 2011 and December 2014.

RESULTS

Of the 1860 NSCLC patients, 707 (38.0%) were never-smokers. The proportions of women (83.7% vs. 5.6%) and adenocarcinoma (89.8% vs. 44.9%) were higher among never-smokers than among ever-smokers. Significantly more never-smokers were diagnosed at a younger median age (65 vs. 68 years, P < 0.001) and earlier stage (stage I-II, 44.5% vs. 38.9%, P = 0.015) compared with ever-smokers. Epidermal growth factor receptor mutations (57.8% vs. 24.4%, P < 0.001) and anaplastic lymphoma kinase rearrangements (7.8% vs. 2.8%, P < 0.001) were more common in never-smokers, whereas Kirsten rat sarcoma viral oncogene homolog mutations (5.8% vs. 9.6%, P = 0.021) were less frequently encountered in never-smokers than in ever-smokers. Never-smokers showed longer survival after adjusting for the favorable effects of younger age, female sex, adenocarcinoma histology, better performance status, early stage disease, being asymptomatic at diagnosis, received antitumor treatment, and the presence of driver mutations (hazard ratio, 0.624; 95% confidence interval, 0.460-0.848; P = 0.003).

CONCLUSIONS

More than one-third of the Korean patients with NSCLC were never-smokers. NSCLC in never-smokers had different clinical characteristics and major driver mutations and resulted in longer overall survival compared with NSCLC in ever-smokers.

Global efforts in conquering lung cancer in China

Lung cancer, the most prevalent and deadly malignancy in the world, poses a particularly critical healthcare challenge to China due to the rapidly increasing new cases and the unique cancer genetics in Chinese patient population. Substantial progress has been made in molecular diagnosis and personalized treatment of the disease. The field is now moving towards multiple new directions to include (1) new generation of targeted agents such as epidermal growth factor receptor and anaplastic lymphoma kinase inhibitors to overcome resistance to their early generation counterparts; and (2) deeper understanding of tumor genetics of each individual patient and consequently the application of biomarkers to guide personalized treatment as well as novel drug development including combination therapy. The increasing capacity in innovative cancer drug research and development is supported by extensive collaboration within China and globally, and across academia and industry, to build up expertise and infrastructure in early-phase clinical testing of novel drugs. With these combined efforts, new and better medicines will be available for lung cancer patients in China in the near future.