NF-κB protein expression associates with (18)F-FDG PET tumor uptake in non-small cell lung cancer: a radiogenomics validation study to understand tumor metabolism

Prediction of the differences in tumor mutation burden between primary and metastatic lesions by radiogenomics

Tumor mutational burden (TMB) is gaining attention as a biomarker for responses to immune checkpoint inhibitors in cancer patients. In this study, we evaluated the status of TMB in primary and liver metastatic lesions in patients with colorectal cancer (CRC). In addition, the status of TMB in primary and liver metastatic lesions was inferred by radiogenomics on the basis of computed tomography (CT) images. The study population included 24 CRC patients with liver metastases. DNA was extracted from primary and liver metastatic lesions obtained from the patients and TMB values were evaluated by next‐generation sequencing. The TMB value was considered high when it equaled to or exceeded 10/100 Mb. Radiogenomic analysis of TMB was performed by machine learning using CT images and the construction of prediction models. In 7 out of 24 patients (29.2%), the TMB status differed between the primary and liver metastatic lesions. Radiogenomic analysis was performed to predict whether TMB status was high or low. The maximum values for the area under the receiver operating characteristic curve were 0.732 and 0.812 for primary CRC and CRC with liver metastasis, respectively. The sensitivity, specificity, and accuracy of the constructed models for TMB status discordance were 0.857, 0.600, and 0.682, respectively. Our results suggested that accurate inference of the TMB status is possible using radiogenomics. Therefore, radiogenomics could facilitate the diagnosis, treatment, and prognosis of patients with CRC in the clinical setting.

Update on Molecular Imaging and Precision Medicine in Lung Cancer

Precision medicine integrates molecular pathobiology, genetic make-up, and clinical manifestations of disease in order to classify patients into subgroups for the purposes of predicting treatment response and suggesting outcome. By identifying those patients who are most likely to benefit from a given therapy, interventions can be tailored to avoid the expense and toxicity of futile treatment. Ultimately, the goal is to offer the right treatment, to the right patient, at the right time. Lung cancer is a heterogeneous disease both functionally and morphologically. Further, over time, clonal proliferations of cells may evolve, becoming resistant to specific therapies. PET is a sensitive imaging technique with an important role in the precision medicine algorithm of lung cancer patients. It provides anatomo-functional insight during diagnosis, staging, and restaging of the disease. It is a prognostic biomarker in lung cancer patients that characterizes tumoral heterogeneity, helps predict early response to therapy, and may direct the selection of appropriate treatment.

A highly expressed mRNA signature for predicting survival in patients with stage I/II non-small-cell lung cancer after operation

There is an urgent need to identify novel biomarkers that predict the prognosis of patients with NSCLC. In this study,we aim to find out mRNA signature closely related to the prognosis of NSCLC by new algorithm of bioinformatics. Identification of highly expressed mRNA in stage I/II patients with NSCLC was performed with the “Limma” package of R software. Survival analysis of patients with different mRNA expression levels was subsequently calculated by Cox regression analysis, and a multi-RNA signature was obtained by using the training set. Kaplan–Meier estimator, log-rank test and receiver operating characteristic (ROC) curves were used to analyse the predictive ability of the multi-RNA signature. RT-PCR used to verify the expression of the multi-RNA signature, and Westernblot used to verify the expression of proteins related to the multi-RNA signature. We identified fifteen survival-related mRNAs in the training set and classified the patients as high risk or low risk. NSCLC patients with low risk scores had longer disease-free survival than patients with high risk scores. The fifteen-mRNA signature was an independent prognostic factor, as shown by the ROC curve. ROC curve also showed that the combined model of the fifteen-mRNA signature and tumour stage had higher precision than stage alone. The expression of fifteen mRNAs and related proteins were higher in stage II NSCLC than in stage I NSCLC. Multi-gene expression profiles provide a moderate prognostic tool for NSCLC patients with stage I/II disease.

Targeting NF-κB mediated cell signaling pathway and inflammatory mediators by 1,2-diazole in A549 cells in vitro

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1,2-Diazole suppresses TNF-α induced MMP-2 expression.

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1,2-Diazole abrogate NF-κB activation and regulate cytokines.

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It exhibits potent in vitro anticancer effect against A549 cells.

Lung cancer is the leading cause of cancer deaths globally. The objective of this study was to investigate the effect of 1,2-diazole (pyrazole) as an anti-cancer drug on human non-small cell lung carcinoma A549 cells. We attempt to examine the expression level of pro-inflammatory proteins such as TNF-α, NF-κB-p65, MMP-2 and E-Cadherin which are commonly associated with an inflammatory response in epithelial cells and apoptosis in A549 cells. The LPS-induced cytokines and inflammatory mediators include TNF-α, IL-6, iNOS and COX-2 levels in A549 cells and the effect of pyrazole was studied. The present study reveals that, pyrazole inhibits A549 cells by suppressing TNF-α induced MMP-2 expression, thereby inhibiting the nuclear translocation of NF-κB-p65. Pyrazole significantly up-regulate the E-cadherin level and down-regulated MMP-2 expression that could probably preventing A549 cancer cells to invade. The study further substantiated the anti-cancer property of pyrazole by regulating the above mentioned level of LPS-induced cytokines and inflammatory mediators. The observations of the present study open a possibility for the development of an effective therapeutic agent that targets inflammatory and signaling pathway mediators to challenge human non-small cell lung carcinoma.

The Era of Radiogenomics in Precision Medicine: An Emerging Approach to Support Diagnosis, Treatment Decisions, and Prognostication in Oncology

With the rapid development of new technologies, including artificial intelligence and genome sequencing, radiogenomics has emerged as a state-of-the-art science in the field of individualized medicine. Radiogenomics combines a large volume of quantitative data extracted from medical images with individual genomic phenotypes and constructs a prediction model through deep learning to stratify patients, guide therapeutic strategies, and evaluate clinical outcomes. Recent studies of various types of tumors demonstrate the predictive value of radiogenomics. And some of the issues in the radiogenomic analysis and the solutions from prior works are presented. Although the workflow criteria and international agreed guidelines for statistical methods need to be confirmed, radiogenomics represents a repeatable and cost-effective approach for the detection of continuous changes and is a promising surrogate for invasive interventions. Therefore, radiogenomics could facilitate computer-aided diagnosis, treatment, and prediction of the prognosis in patients with tumors in the routine clinical setting. Here, we summarize the integrated process of radiogenomics and introduce the crucial strategies and statistical algorithms involved in current studies.

A multi-scale integrated analysis identifies KRT8 as a pan-cancer early biomarker

An early biomarker would transform our ability to screen and treat patients with cancer. The large amount of multi-scale molecular data in public repositories from various cancers provide unprecedented opportunities to find such a biomarker. However, despite identification of numerous molecular biomarkers using these public data, fewer than 1% have proven robust enough to translate into clinical practice. One of the most important factors affecting the successful translation to clinical practice is lack of real-world patient population heterogeneity in the discovery process. Almost all biomarker studies analyze only a single cohort of patients with the same cancer using a single modality. Recent studies in other diseases have demonstrated the advantage of leveraging biological and technical heterogeneity across multiple independent cohorts to identify robust disease biomarkers. Here we analyzed 17149 samples from patients with one of 23 cancers that were profiled using either DNA methylation, bulk and single-cell gene expression, or protein expression in tumor and serum. First, we analyzed DNA methylation profiles of 9855 samples across 23 cancers from The Cancer Genome Atlas (TCGA). We then examined the gene expression profile of the most significantly hypomethylated gene, KRT8, in 6781 samples from 57 independent microarray datasets from NCBI GEO. KRT8 was significantly over-expressed across cancers except colon cancer (summary effect size=1.05; p < 0.0001). Further, single-cell RNAseq analysis of 7447 single cells from lung tumors showed that genes that significantly correlated with KRT8 (p < 0.05) were involved in p53-related pathways. Immunohistochemistry in tumor biopsies from 294 patients with lung cancer showed that high protein expression of KRT8 is a prognostic marker of poor survival (HR = 1.73, p = 0.01). Finally, detectable KRT8 in serum as measured by ELISA distinguished patients with pancreatic cancer from healthy controls with an AUROC=0.94. In summary, our analysis demonstrates that KRT8 is (1) differentially expressed in several cancers across all molecular modalities and (2) may be useful as a biomarker to identify patients that should be further tested for cancer.

Artificial intelligence and hybrid imaging: the best match for personalized medicine in oncology

Artificial intelligence (AI) refers to a field of computer science aimed to perform tasks typically requiring human intelligence. Currently, AI is recognized in the broader technology radar within the five key technologies which emerge for their wide-ranging applications and impact in communities, companies, business, and value chain framework alike. However, AI in medical imaging is at an early phase of development, and there are still hurdles to take related to reliability, user confidence, and adoption. The present narrative review aimed to provide an overview on AI-based approaches (distributed learning, statistical learning, computer-aided diagnosis and detection systems, fully automated image analysis tool, natural language processing) in oncological hybrid medical imaging with respect to clinical tasks (detection, contouring and segmentation, prediction of histology and tumor stage, prediction of mutational status and molecular therapies targets, prediction of treatment response, and outcome). Particularly, AI-based approaches have been briefly described according to their purpose and, finally lung cancer-being one of the most extensively malignancy studied by hybrid medical imaging-has been used as illustrative scenario. Finally, we discussed clinical challenges and open issues including ethics, validation strategies, effective data-sharing methods, regulatory hurdles, educational resources, and strategy to facilitate the interaction among different stakeholders. Some of the major changes in medical imaging will come from the application of AI to workflow and protocols, eventually resulting in improved patient management and quality of life. Overall, several time-consuming tasks could be automatized. Machine learning algorithms and neural networks will permit sophisticated analysis resulting not only in major improvements in disease characterization through imaging, but also in the integration of multiple-omics data (i.e., derived from pathology, genomic, proteomics, and demographics) for multi-dimensional disease featuring. Nevertheless, to accelerate the transition of the theory to practice a sustainable development plan considering the multi-dimensional interactions between professionals, technology, industry, markets, policy, culture, and civil society directed by a mindset which will allow talents to thrive is necessary.

Artificial intelligence applications for oncological positron emission tomography imaging

Positron emission tomography (PET), a functional and dynamic molecular imaging technique, is generally used to reveal tumors' biological behavior. Radiomics allows a high-throughput extraction of multiple features from images with artificial intelligence (AI) approaches and develops rapidly worldwide. Quantitative and objective features of medical images have been explored to recognize reliable biomarkers, with the development of PET radiomics. This paper will review the current clinical exploration of PET-based classical machine learning and deep learning methods, including disease diagnosis, the prediction of histological subtype, gene mutation status, tumor metastasis, tumor relapse, therapeutic side effects, therapeutic intervention and evaluation of prognosis. The applications of AI in oncology will be mainly discussed. The image-guided biopsy or surgery assisted by PET-based AI will be introduced as well. This paper aims to present the applications and methods of AI for PET imaging, which may offer important details for further clinical studies. Relevant precautions are put forward and future research directions are suggested.

A Role for FDG PET Radiomics in Personalized Medicine?

Radiomics describes the extraction of multiple features from medical images, including molecular imaging modalities, that with bioinformatic approaches, provide additional clinically relevant information that may be invisible to the human eye. This information may complement standard radiological interpretation with data that may better characterize a disease or that may provide predictive or prognostic information. Progressing from predefined image features, often describing heterogeneity of voxel intensities within a volume of interest, there is increasing use of machine learning to classify disease characteristics and deep learning methods based on artificial neural networks that can learn features without a priori definition and without the need for preprocessing of images. There have been advances in standardization and harmonization of methods to a level that should support multicenter studies. However, in this relatively early phase of research in the field, there are limited aspects that have been adopted into routine practice. Most of the reports in the molecular imaging field describe radiomic approaches in cancer using 18F-fluorodeoxyglucose positron emission tomography (18F-FDG-PET). In this review, we will describe radiomics in molecular imaging and summarize the pertinent literature in lung cancer where reports are most prevalent and mature.

The Fire Within: NF-κB Involvement in Non-Small Cell Lung Cancer

Thirty-four years since its discovery, NF-κB remains a transcription factor with great potential for cancer therapy. However, NF-κB-targeted therapies have yet to find a way to be clinically translatable. Here, we focus exclusively on the role of NF-κB in non-small cell lung cancer (NSCLC) and discuss its contributing effect on cancer hallmarks such as inflammation, proliferation, survival, apoptosis, angiogenesis, epithelial-mesenchymal transition, metastasis, stemness, metabolism, and therapy resistance. In addition, we present our current knowledge of the clinical significance of NF-κB and its involvement in the treatment of patients with NSCLC with chemotherapy, targeted therapies, and immunotherapy.

18F-fluorodeoxyglucose positron emission tomography/computed tomography-based radiomic features for prediction of epidermal growth factor receptor mutation status and prognosis in patients with lung adenocarcinoma

Background

To investigate whether radiomic features from (¹⁸F)-fluorodeoxyglucose positron emission tomography/computed tomography [(¹⁸F)-FDG PET/CT] can predict epidermal growth factor receptor (EGFR) mutation status and prognosis in patients with lung adenocarcinoma.

Methods

One hundred and seventy-four consecutive patients with lung adenocarcinoma underwent (¹⁸F)-FDG PET/CT and EGFR gene testing were retrospectively analyzed. Radiomic features combined with clinicopathological factors to construct a random forest (RF) model to identify EGFR mutation status. The mutant/wild-type model was trained on a training group (n=139) and validated in an independent validation group (n=35). The second RF classifier predicting the 19/21 mutation site was also built and evaluated in an EGFR mutation subset (training group, n=80; validation group, n=25). Radiomic score and 5 clinicopathological factors were integrated into a multivariate Cox proportional hazard (CPH) model for predicting overall survival (OS). AUC (the area under the receiver characteristic curve) and C-index were calculated to evaluate the model’s performance.

Results

Of 174 patients, 109 (62.6%) harbored EGFR mutations, 21L858R was the most common mutation type [55.9% (61/109)]. The mutant/wild-type model was identified in the training (AUC, 0.77) and validation (AUC, 0.71) groups. The 19/21 mutation site model had an AUC of 0.82 and 0.73 in the training and validation groups, respectively. The C-index of the CPH model was 0.757. The survival time between targeted therapy and chemotherapy for patients with EGFR mutations was significantly different (P=0.03).

Conclusions

Radiomic features based on (¹⁸F)-FDG PET/CT combined with clinicopathological factors could reflect genetic differences and predict EGFR mutation type and prognosis.

What can artificial intelligence teach us about the molecular mechanisms underlying disease?

While molecular imaging with positron emission tomography or single-photon emission computed tomography already reports on tumour molecular mechanisms on a macroscopic scale, there is increasing evidence that there are multiple additional features within medical images that can further improve tumour characterization, treatment prediction and prognostication. Early reports have already revealed the power of radiomics to personalize and improve patient management and outcomes. What remains unclear is how these additional metrics relate to underlying molecular mechanisms of disease. Furthermore, the ability to deal with increasingly large amounts of data from medical images and beyond in a rapid, reproducible and transparent manner is essential for future clinical practice. Here, artificial intelligence (AI) may have an impact. AI encompasses a broad range of 'intelligent' functions performed by computers, including language processing, knowledge representation, problem solving and planning. While rule-based algorithms, e.g. computer-aided diagnosis, have been in use for medical imaging since the 1990s, the resurgent interest in AI is related to improvements in computing power and advances in machine learning (ML). In this review we consider why molecular and cellular processes are of interest and which processes have already been exposed to AI and ML methods as reported in the literature. Non-small-cell lung cancer is used as an exemplar and the focus of this review as the most common tumour type in which AI and ML approaches have been tested and to illustrate some of the concepts.

Association of metabolic and genetic heterogeneity in head and neck squamous cell carcinoma with prognostic implications: integration of FDG PET and genomic analysis

Purpose

The linkage between the genetic and phenotypic heterogeneity of the tumor has not been thoroughly evaluated. Herein, we investigated how the genetic and metabolic heterogeneity features of the tumor are associated with each other in head and neck squamous cell carcinoma (HNSC). We further assessed the prognostic significance of those features.

Methods

The mutant-allele tumor heterogeneity (MATH) score (n = 508), a genetic heterogeneity feature, and tumor glycolysis feature (GlycoS) (n = 503) were obtained from the HNSC dataset in the cancer genome atlas (TCGA). We identified matching patients (n = 33) who underwent 18F-fluorodeoxyglucose positron emission tomography (FDG PET) from the cancer imaging archive (TCIA) and obtained the following information from the primary tumor: metabolic, metabolic-volumetric, and metabolic heterogeneity features. The association between the genetic and metabolic features and their prognostic values were assessed.

Results

Tumor metabolic heterogeneity and metabolic-volumetric features showed a mild degree of association with MATH (n = 25, ρ = 0.4~0.5, P < 0.05 for all features). The patients with higher FDG PET features and MATH died sooner. Combination of MATH and tumor metabolic heterogeneity features showed a better stratification of prognosis than MATH. Also, higher MATH and GlycoS were associated with significantly worse overall survival (n = 499, P = 0.002 and 0.0001 for MATH and GlycoS, respectively). Furthermore, both MATH and GlycoS independently predicted overall survival after adjusting for clinicopathologic features and the other (P = 0.015 and 0.006, respectively).

Conclusion

Both tumor metabolic heterogeneity and metabolic-volumetric features assessed by FDG PET showed a mild degree of association with genetic heterogeneity in HNSC. Both metabolic and genetic heterogeneity features were predictive of survival and there was an additive prognostic value when the metabolic and genetic heterogeneity features were combined. Also, MATH and GlycoS were independent prognostic factors in HNSC; they can be used for precise prognostication once validated.

IRF8 induces senescence of lung cancer cells to exert its tumor suppressive function

Lung cancer is the leading cause of cancer-related deaths worldwide. However, tumor suppressor genes remain to be systemically determined for lung cancer. Here we report interferon regulatory factor 8 (IRF8), a member of the IRF family of transcription factors, as a potent lung tumor suppressor gene. Expression of IRF8 is frequently diminished in lung tumoral tissues and is associated with prognosis of non-small cell lung cancer (NSCLC) patients. Ectopic expression of IRF8 suppresses the NSCLC cells proliferation in vitro and tumorigenic potential in vivo. More importantly, forced expression of IRF8 through infection of recombinant virus inhibits lung tumorigenesis in genetically engineered mouse model (GEMM). Mechanistically, IRF8 inhibits AKT signaling and promotes accumulation of P27 protein, which results in senescence of lung cancer cells. Ectopic expression of IRF8 in tumor cells leads to regression of lung cancer tumor nodules in a xenograft tumor model. Our data, therefore, solidly shows IRF8 to be a lung cancer suppressor gene and may denote an opportunity for therapeutic intervention of NSCLC.

Radiomic signature: a non-invasive biomarker for discriminating invasive and non-invasive cases of lung adenocarcinoma

Purpose

We aimed to assess the classification performance of a computed tomography (CT)-based radiomic signature for discriminating invasive and non-invasive lung adenocarcinoma.

Patients and Methods

A total of 192 patients (training cohort, n=116; validation cohort, n=76) with pathologically confirmed lung adenocarcinoma were retrospectively enrolled in the present study. Radiomic features were extracted from preoperative unenhanced chest CT images to build a radiomic signature. Predictive performance of the radiomic signature were evaluated using an intra-cross validation cohort. Diagnostic performance of the radiomic signature was assessed via receiver operating characteristic (ROC) analysis.

Results

The radiomic signature consisted of 14 selected features and demonstrated good discrimination performance between invasive and non-invasive adenocarcinoma. The area under the ROC curve (AUC) for the training cohort was 0.83 (sensitivity, 0.84 ; specificity, 0.78; accuracy, 0.82), while that for the validation cohort was 0.77 (sensitivity, 0.94; specificity, 0.52 ; accuracy, 0.82).

Conclusion

The CT-based radiomic signature exhibited good classification performance for discriminating invasive and non-invasive lung adenocarcinoma, and may represent a valuable biomarker for determining therapeutic strategies in this patient population.

Diffusion-weighted MRI and 18F-FDG PET correlation with immunity in early radiotherapy response in BNL hepatocellular carcinoma mouse model: timeline validation

PURPOSE

Imaging probes/biomarkers that are correlated with molecular or microenvironmental alterations in tumors have been used not only in diagnosing cancer but also in assessing the efficacy of cancer treatment. We evaluated the early response of hepatocellular carcinoma (HCC) to radiation treatment using T2-weighted magnetic resonance imaging (MRI), diffusion-weighted (DW) MRI, and ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) positron emission tomography (PET).

METHODS

Orthotopic HCC tumors were established in the right liver lobe of Balb/c mice. Mice were longitudinally scanned using T2-weighted/DW MRI and ¹⁸F-FDG PET 1 day before and on days 1, 3, 6, 9 and 13 after irradiation with 15 Gy to the right liver lobe to determine tumor size, apparent diffusion coefficient (ADC) value, and maximum standardized uptake value. Immunohistochemical (IHC) staining was performed to validate the tumor microenvironment.

RESULTS

Irradiation markedly retarded tumor growth in the orthotopic HCC model and led to increaes in ADC values as early as on day 1 after irradiation. Irradiation also resulted in increases in ¹⁸F-FDG uptake on day 1 that were sustained until the end of the observation period. IHC staining revealed a decrease in the number of proliferative cells and a continuous macrophage influx into irradiated tumors, which dramatically altered the tumor microenvironment. Lastly, in vitro coculture of HCC cells and macrophages led to interaction between the cells and enhanced the cellular uptake of ¹⁸F-FDG.

CONCLUSION

ADC values and ¹⁸F-FDG uptake measured using DW MRI and ¹⁸F-FDG PET serve as potential biomarkers for early assessment of HCC tumor responses to radiation therapy.

Osteopontin promotes cancer cell drug resistance, invasion, and lactate production and is associated with poor outcome of patients with advanced non-small-cell lung cancer

Background

Osteopontin (OPN), a member of the small integrin binding ligand N-linked glycoprotein family, has been analyzed in numerous types of human malignancy.

Purpose

The present study detected the expression levels of OPN and evaluated its role in tumor progression in patients with non-small cell lung cancer (NSCLC).

Patients and methods

OPN expression levels were detected using immunohistochemistry in 101 NSCLC tumors. The mRNA and protein levels have significant difference between advanced NSCLC and stage I/II NSCLC. The drug resistance, invasive ability and lactate production of NSCLC cancer cell lines (A549 and SK-MES-1) were detected in cancer cells with the disturbance of OPN.

Results

Immunostaining indicated that OPN was primarily expressed in the cytoplasm of NSCLC cells. Moreover, OPN correlates with NSCLC clinical traits. The results demonstrated that OPN expression levels significantly correlated with cancer differentiation, distant metastasis and the efficacy of platinum-based treatment. Notably, the results identified OPN expression levels as a potential factor for predicting the response of cells to first-line platinum-based chemotherapy using multivariate analysis, as well as predicting cancer differentiation and distant metastasis. Additionally, the abrogation of OPN levels reduced lactate production in NSCLC cells and occurred along side with the downregulation of lactate dehydrogenase A (LDHA).

Conclusion

The results of the current study suggest that OPN may be able to predict poor prognosis and cisplatin resistance in patients.

Lung Cancer Radiogenomics: The Increasing Value of Imaging in Personalized Management of Lung Cancer Patients

Radiogenomics provide a large-scale data analytical framework that aims to understand the broad multiscale relationships between the complex information encoded in medical images (including computational, quantitative, and semantic image features) and their underlying clinical, therapeutic, and biological associations. As such it is a powerful and increasingly important tool for both clinicians and researchers involved in the imaging, evaluation, understanding, and management of lung cancers. Herein we provide an overview of the growing field of lung cancer radiogenomics and its applications.

Positron Emission Tomography 18F-Fluorodeoxyglucose Uptake Correlates with KRAS and EMT Gene Signatures in Operable Esophageal Adenocarcinoma

BACKGROUND

18F-fluorodeoxyglucose positron emission tomography is an imaging modality critical to the diagnosis and staging of esophageal cancer. Despite this, the genetic abnormalities associated with increased 18F-fluorodeoxyglucose (FDG)-maximum standardized uptake value (SUVmax) have not been previously explored in esophageal adenocarcinoma.

MATERIALS AND METHODS

Treatment-naïve patients, for whom frozen tissue and 18F-fluorodeoxyglucose positron emission tomography data were available, undergoing esophagectomy from 2003 to 2012, were identified. Primary tumor FDG-uptake (SUVmax) was quantified as low (<5), moderate, or high (>10). Genome-wide expression analyses (e.g., microarray) were used to examine gene expression differences associated with FDG-uptake.

RESULTS

Eighteen patients with stored positron emission tomography data and tissue were reviewed. Overall survival was similar between patients with high (n = 9) and low (n = 6) FDG-uptake tumors (P = 0.71). Differences in gene expression between tumors with high and low FDG-uptake included enriched expression of various matrix metalloproteinases, extracellular-matrix components, oncogenic signaling members, and PD-L1 (fold-change>2.0, P < 0.05) among the high-FDG tumors. Glycolytic gene expression and pathway involvement were similar between the high- and low-FDG tumor subsets (P = 0.126). Gene ontology analysis of the most differentially expressed genes demonstrated significant upregulation of gene sets associated with extracellular matrix organization and vascular development (P < 0.005). Gene set enrichment analysis further demonstrated associations between FDG-uptake intensity and canonical oncogenic processes, including hypoxia, angiogenesis, KRAS signaling, and epithelial-to-mesenchymal transition (P < 0.001). Interestingly, KRAS expression did not predict worse survival in a larger cohort (n = 104) of esophageal adenocarcinomas (P = 0.64).

CONCLUSIONS

These results suggest that elevated FDG-uptake is associated with a variety of oncogenic alterations in operable esophageal adenocarcinoma. These pathways present potential therapeutic targets among tumors exhibiting high FDG-uptake.

18F-FDG PET intensity correlates with a hypoxic gene signature and other oncogenic abnormalities in operable non-small cell lung cancer

BACKGROUND

18F-fluorodeoxyglucose positron emission tomography (FDG-PET) is critical for staging non-small-cell lung cancer (NSCLC). While PET intensity carries prognostic significance, the genetic abnormalities associated with increased intensity remain unspecified.

METHODS

NSCLC samples (N = 34) from 1999 to 2011 for which PET data were available were identified from a prospectively collected tumor bank. PET intensity was classified as mild, moderate, or intense based on SUVmax measurement or radiology report. Associations between genome-wide expression (RNAseq) and PET intensity were determined. Associations with overall survival were then validated in two external NSCLC cohorts.

RESULTS

Overall survival was significantly worse in patients with PET-intense (N = 11) versus mild (N = 10) tumors (p = 0.039). Glycolytic gene expression patterns were markedly similar between intense and mild tumors. Gene ontology analysis demonstrated significant enhancement of cell-cycle and proliferative processes in FDG-intense tumors (p<0.001). Gene set enrichment analysis (GSEA) suggested associations between PET-intensity and canonical oncogenic signaling pathways including MYC, NF-κB, and HIF-1. Using an external cohort of 25 tumors with PET and genomic profiling data, common genes and gene sets were validated for additional study (P<0.05). Of these common gene sets, 20% were associated with hypoxia or HIF-1 signaling. While HIF-1 expression did not correlate with poor survival in the NSCLC validation cohort (N = 442), established targets of hypoxia signaling (PLAUR, ADM, CA9) were significantly associated with poor overall survival.

CONCLUSIONS

PET-intensity is associated with a variety of oncogenic alterations in operable NSCLC. Adjuvant targeting of these pathways may improve survival among patients with PET-intense tumors.

Prognostic significance of NF-κB expression in non-small cell lung cancer: A meta-analysis

Nuclear factor kappa B (NF-κB), a key nuclear transcription factor, is associated with prognosis in a variety of human cancers. However, the clinical value of NF-κB in non-small cell lung cancer (NSCLC) is still controversial. Therefore, the aim of this meta-analysis was to obtain an accurate evaluation of the relationship between NF-κB expression and survival prognosis of NSCLC patients based on published articles. PubMed, EMBASE and Web of Science databases were systematically searched for potential articles. A total of 1159 patients from 7 eligible studies comparing prognostic significance of NF-κB expression levels in NSCLC were included in our meta-analysis. I² statistic and P value were performed to evaluate heterogeneity. The results of analysis were presented as hazard ratio (HR) or odds ratios (OR) with 95% confidence interval (95% CI). Subgroup analysis based on ethnicity of NSCLC patients and NF-kB cellular localization within cancer cells were conducted to illustrate the potential discrepancy. Significant heterogeneity was considered at I²>50% and P<0.05, and random-effects model was used. The combined results indicated that higher NF-κB expression was associated with shorter overall survival (OS) of NSCLC patients (HR = 2.78, 95% CI = 1.51–5.12, P = 0.001). Moreover, NF-κB expression was closely associated with tumor stage (HR = 0.32, 95% CI = 0.18–0.57, P<0.0001), lymph node metastasis (HR = 0.56, 95% CI = 0.38–0.83, P = 0.004) and 5-year OS for NSCLC patients (OR = 1.83, 95% CI = 1.02–3.31, P = 0.04). We conclude that NF-κB expression may be a potential unfavorable prognostic marker for NSCLC patients.

GFPT2-Expressing Cancer-Associated Fibroblasts Mediate Metabolic Reprogramming in Human Lung Adenocarcinoma

Metabolic reprogramming of the tumor microenvironment is recognized as a cancer hallmark. To identify new molecular processes associated with tumor metabolism, we analyzed the transcriptome of bulk and flow-sorted human primary non-small cell lung cancer (NSCLC) together with ¹⁸FDG-PET scans, which provide a clinical measure of glucose uptake. Tumors with higher glucose uptake were functionally enriched for molecular processes associated with invasion in adenocarcinoma and cell growth in squamous cell carcinoma (SCC). Next, we identified genes correlated to glucose uptake that were predominately overexpressed in a single cell-type comprising the tumor microenvironment. For SCC, most of these genes were expressed by malignant cells, whereas in adenocarcinoma, they were predominately expressed by stromal cells, particularly cancer-associated fibroblasts (CAF). Among these adenocarcinoma genes correlated to glucose uptake, we focused on glutamine-fructose-6-phosphate transaminase 2 (GFPT2), which codes for the glutamine-fructose-6-phosphate aminotransferase 2 (GFAT2), a rate-limiting enzyme of the hexosamine biosynthesis pathway (HBP), which is responsible for glycosylation. GFPT2 was predictive of glucose uptake independent of GLUT1, the primary glucose transporter, and was prognostically significant at both gene and protein level. We confirmed that normal fibroblasts transformed to CAF-like cells, following TGFβ treatment, upregulated HBP genes, including GFPT2, with less change in genes driving glycolysis, pentose phosphate pathway, and TCA cycle. Our work provides new evidence of histology-specific tumor stromal properties associated with glucose uptake in NSCLC and identifies GFPT2 as a critical regulator of tumor metabolic reprogramming in adenocarcinoma.Significance: These findings implicate the hexosamine biosynthesis pathway as a potential new therapeutic target in lung adenocarcinoma. Cancer Res; 78(13); 3445-57. ©2018 AACR.

Non-invasive tumor genotyping using radiogenomic biomarkers, a systematic review and oncology-wide pathway analysis

With targeted treatments playing an increasing role in oncology, the need arises for fast non-invasive genotyping in clinical practice. Radiogenomics is a rapidly evolving field of research aimed at identifying imaging biomarkers useful for non-invasive genotyping. Radiogenomic genotyping has the advantage that it can capture tumor heterogeneity, can be performed repeatedly for treatment monitoring, and can be performed in malignancies for which biopsy is not available. In this systematic review of 187 included articles, we compiled a database of radiogenomic associations and unraveled networks of imaging groups and gene pathways oncology-wide. Results indicated that ill-defined tumor margins and tumor heterogeneity can potentially be used as imaging biomarkers for 1p/19q codeletion in glioma, relevant for prognosis and disease profiling. In non-small cell lung cancer, FDG-PET uptake and CT-ground-glass-opacity features were associated with treatment-informing traits including EGFR-mutations and ALK-rearrangements. Oncology-wide gene pathway analysis revealed an association between contrast enhancement (imaging) and the targetable VEGF-signalling pathway. Although the need of independent validation remains a concern, radiogenomic biomarkers showed potential for prognosis prediction and targeted treatment selection. Quantitative imaging enhanced the potential of multiparametric radiogenomic models. A wealth of data has been compiled for guiding future research towards robust non-invasive genomic profiling.

c-Myc targeted regulators of cell metabolism in a transgenic mouse model of papillary lung adenocarcinoma

c-Myc's role in pulmonary cancer metabolism is uncertain. We therefore investigated c-Myc activity in papillary lung adenocarcinomas (PLAC). Genomics revealed 90 significantly regulated genes (> 3-fold) coding for cell growth, DNA metabolism, RNA processing and ribosomal biogenesis and bioinformatics defined c-Myc binding sites (TFBS) at > 95% of up-regulated genes. EMSA assays at 33 novel TFBS evidenced DNA binding activity and ChIP-seq data retrieved from public repositories confirmed these to be c-Myc bound. Dual-luciferase gene reporter assays developed for RNA-Terminal-Phosphate-Cyclase-Like-1(RCL1), Ribosomal-Protein-SA(RPSA), Nucleophosmin/Nucleoplasmin-3(NPM3) and Hexokinase-1(HK1) confirmed c-Myc functional relevance and ChIP assays with HEK293T cells over-expressing ectopic c-Myc demonstrated enriched c-Myc occupancy at predicted TFBS for RCL1, NPM3, HK1 and RPSA. Note, c-Myc recruitment on chromatin was comparable to the positive controls CCND2 and CDK4. Computational analyses defined master regulators (MR), i.e. heterogeneous nuclear ribonucleoprotein A1, nucleolin, the apurinic/apyrimidinic endonuclease 1, triosephosphate-isomerase 1, folate transporter (SLC19A1) and nucleophosmin to influence activity of up to 90% of PLAC-regulated genes. Their expression was induced by 3-, 3-, 6-, 3-, 11- and 7-fold, respectively. STRING analysis confirmed protein-protein-interactions of regulated genes and Western immunoblotting of fatty acid synthase, serine hydroxyl-methyltransferase 1, arginine 1 and hexokinase 2 showed tumor specific induction. Published knock down studies confirmed these proteins to induce apoptosis by disrupting neoplastic lipogenesis, by endorsing uracil accumulation and by suppressing arginine metabolism and glucose-derived ribonucleotide biosynthesis. Finally, translational research demonstrated high expression of MR and of 47 PLAC up-regulated genes to be associated with poor survival in lung adenocarcinoma patients (HR 3.2 p < 0.001) thus, providing a rationale for molecular targeted therapies in PLACs.

Co-expression of NF-κB-p65 and phosphorylated NF-κB-p105 is associated with poor prognosis in surgically resectable non-small cell lung cancer

Nuclear factor‐kappa B (NF‐κB) as a prognostic marker remains unclear in non‐small cell lung cancer (NSCLC). Here, we studied NF‐κB‐p65 (p65) expression and phosphorylated NF‐κB‐p105 (p‐p105) expression in NSCLC and correlated the finding with overall survival (OS) and clinicopathological features. A total of 186 archival samples from patients with surgically resectable NSCLC were probed with p65 and p‐p105 (Ser 932). The p65‐positive expression and p‐p105‐positive expression were defined as distinct nuclear p65 and cytoplasmic p‐p105 labelling in at least 1% of tumour cells, respectively. The positive staining of p65 alone, p‐p105 alone and co‐expression of p65 and p‐p105 were observed in 61 (32.8%), 90 (48.4%) and 35 (18.8%) patients, respectively. Co‐expression of p65 and p‐p105 but not of either p65 or p‐p105 alone was associated with a poor prognosis. Patients with co‐expression of p65 and p‐p105 had a shorter OS than others, median OS 26.5 months versus 64.1 months, HR 1.85 (95% CI: 1.18–2.91), P = 0.007. There was no statistically significant association between clinicopathological characteristics and either p65 or p‐p105 alone or co‐expression of p65 and p‐p105. This indicates that co‐expression of p65 and p‐p105 was a poor prognostic factor, and pathologic studies of NF‐κB expression could include multiple pathway components in NSCLC.

Radiogenomic Analysis of Oncological Data: A Technical Survey

In the last few years, biomedical research has been boosted by the technological development of analytical instrumentation generating a large volume of data. Such information has increased in complexity from basic (i.e., blood samples) to extensive sets encompassing many aspects of a subject phenotype, and now rapidly extending into genetic and, more recently, radiomic information. Radiogenomics integrates both aspects, investigating the relationship between imaging features and gene expression. From a methodological point of view, radiogenomics takes advantage of non-conventional data analysis techniques that reveal meaningful information for decision-support in cancer diagnosis and treatment. This survey is aimed to review the state-of-the-art techniques employed in radiomics and genomics with special focus on analysis methods based on molecular and multimodal probes. The impact of single and combined techniques will be discussed in light of their suitability in correlation and predictive studies of specific oncologic diseases.

Radiogenomic evaluation of lung cancer - Are there imaging characteristics associated with lung adenocarcinomas harboring BRAF mutations?

INTRODUCTION

We studied computed tomography (CT) features associated with BRAF mutated lung cancer.

MATERIALS AND METHODS

CT features of BRAF mutated lung cancers were compared to stage matched lesions without BRAF mutation.

RESULTS

47 (25%) patients with BRAF mutation and 141 (75%) without BRAF mutation were included. BRAF lesions were most frequently solid 37 (84%), spiculated 22 (50%), and peripheral 37 (84%). No feature of the primary tumor was significantly different between BRAF and non-BRAF groups. BRAF patients were more likely than KRAS patients to have pleural metastases [5 (11%) vs 0 (0%), p=0.045].

CONCLUSION

No feature of the primary tumor differentiates BRAF lesions from non-BRAF lesions.

Molecular Imaging and Precision Medicine in Lung Cancer

Precision medicine allows tailoring of preventive or therapeutic interventions to avoid the expense and toxicity of futile treatment given to those who will not respond. Lung cancer is a heterogeneous disease functionally and morphologically. PET is a sensitive molecular imaging technique with a major role in the precision medicine algorithm of patients with lung cancer. It contributes to the precision medicine of lung neoplasia by interrogating tumor heterogeneity throughout the body. It provides anatomofunctional insight during diagnosis, staging, and restaging of the disease. It is a biomarker of tumoral heterogeneity that helps direct selection of the most appropriate treatment, the prediction of early response to cytotoxic and cytostatic therapies, and is a prognostic biomarker in patients with lung cancer.

Applications and limitations of radiomics

Radiomics is an emerging field in quantitative imaging that uses advanced imaging features to objectively and quantitatively describe tumour phenotypes. Radiomic features have recently drawn considerable interest due to its potential predictive power for treatment outcomes and cancer genetics, which may have important applications in personalized medicine. In this technical review, we describe applications and challenges of the radiomic field. We will review radiomic application areas and technical issues, as well as proper practices for the designs of radiomic studies.

Inferring genome-wide functional modulatory network: a case study on NF-κB/RelA transcription factor

How different pathways lead to the activation of a specific transcription factor (TF) with specific effects is not fully understood. We model context-specific transcriptional regulation as a modulatory network: triplets composed of a TF, target gene, and modulator. Modulators usually affect the activity of a specific TF at the posttranscriptional level in a target gene-specific action mode. This action may be classified as enhancement, attenuation, or inversion of either activation or inhibition. As a case study, we inferred, from a large collection of expression profiles, all potential modulations of NF-κB/RelA. The predicted modulators include many proteins previously not reported as physically binding to RelA but with relevant functions, such as RNA processing, cell cycle, mitochondrion, ubiquitin-dependent proteolysis, and chromatin modification. Modulators from different processes exert specific prevalent action modes on distinct pathways. Modulators from noncoding RNA, RNA-binding proteins, TFs, and kinases modulate the NF-κB/RelA activity with specific action modes consistent with their molecular functions and modulation level. The modulatory networks of NF-κB/RelA in the context epithelial-mesenchymal transition (EMT) and burn injury have different modulators, including those involved in extracellular matrix (FBN1), cytoskeletal regulation (ACTN1), and metastasis-associated lung adenocarcinoma transcript 1 (MALAT1), a long intergenic nonprotein coding RNA, and tumor suppression (FOXP1) for EMT, and TXNIP, GAPDH, PKM2, IFIT5, LDHA, NID1, and TPP1 for burn injury.

Breast MRI radiogenomics: Current status and research implications

Breast magnetic resonance imaging (MRI) radiogenomics is an emerging area of research that has the potential to directly influence clinical practice. Clinical MRI scanners today are capable of providing excellent temporal and spatial resolution, which allows extraction of numerous imaging features via human extraction approaches or complex computer vision algorithms. Meanwhile, advances in breast cancer genetics research has resulted in the identification of promising genes associated with cancer outcomes. In addition, validated genomic signatures have been developed that allow categorization of breast cancers into distinct molecular subtypes as well as predict the risk of cancer recurrence and response to therapy. Current radiogenomics research has been directed towards exploratory analysis of individual genes, understanding tumor biology, and developing imaging surrogates to genetic analysis with the long-term goal of developing a meaningful tool for clinical care. The background of breast MRI radiogenomics research, image feature extraction techniques, approaches to radiogenomics research, and promising areas of investigation are reviewed. J. Magn. Reson. Imaging 2016;43:1269-1278.

NF-κB Expression and Outcomes in Solid Tumors: A Systematic Review and Meta-Analysis

Nuclear factor-kappaB (NF-κB) is a key inflammatory transcription factor expressed frequently in tumors. Numerous studies have investigated the correlation between NF-κB expression and prognosis in solid tumors, but the conclusions are still in contradiction. Here, we conduct a meta-analysis to explore the overall association of NF-κB overexpression and survival in human solid tumors. Pubmed and EBSCO databases were searched for studies evaluating expression of NF-κB (as measured by immunohistochemistry) and overall survival (OS) and disease-free survival (DFS) in solid tumors. Published data were extracted and computed into odds ratios (ORs) for death at 3, 5, and 10 years. Data were pooled using the Mantel-Haenszel random-effect model. All statistical tests were two-sided. Forty-four studies with a total of 4418 patients were included in this meta-analysis. NF-κB overexpression was associated with worse OS at 3 years (OR = 3.40, 95% confidence interval [CI]  = 2.41-4.79, P < 0.00001), 5 years (OR = 2.72, 95% CI = 1.92-3.85, P < 0.00001), and 10 years (OR = 2.63, 95% CI =  .34-5.16, P = 0.005) of solid tumors. Results for 3- and 5-year DFS were similar. NF-κB expression was associated with poor 3-year OS in both Tumor, Lymph Node, Metastasis stage I-II (OR = 9.11, 95% CI = 2.90-28.68, P = 0.0002) and III-IV (OR = 2.59, 95% CI = 1.61-4.15, P < 0.0001). There is no correlation between cellular localization of NF-kB overexpression and OS of solid tumors. Among the tumor types, NF-κB was associated with worse 3 year-OS of colorectal cancer (OR = 2.70, 95% CI = 1.64-4.46, P < 0.0001), esophageal carcinoma (OR = 6.00, 95% CI = 3.29-10.94, P < 0.0001) and worse 5 year-OS of colorectal cancer (OR = 2.72, 95% CI = 1.92-3.85, P < 0.00001), esophageal carcinoma (OR = 5.96, 95% CI = 3.48-10.18, P = 0.03), and nonsmall cell lung cancer (OR = 1.69, 95% CI = 1.20-2.38, P = 0.002). Expression of NF-κB is associated with worse survival in most solid tumors irrespective of NF-κB localization.

From genotype to phenotype: Are there imaging characteristics associated with lung adenocarcinomas harboring RET and ROS1 rearrangements?

INTRODUCTION

Recurrent gene rearrangements are important drivers of oncogenesis in non-small cell lung cancers. RET and ROS1 rearrangements are each found in 1-2% of lung adenocarcinomas and represent distinct molecular subsets. This study assessed the computed tomography (CT) imaging features of patients with RET- and ROS1-rearranged lung cancers.

METHODS

Eligible patients included pathologically-confirmed lung adenocarcinomas of any stage with a RET or ROS1 rearrangement via fluorescence in-situ hybridization or next-generation sequencing, and available pre-treatment baseline imaging for review. A cohort of EGFR-mutant lung cancers was identified as a control group. CT features assessed included location, consistency, contour, presence of cavitation, and calcification of the primary tumor. Presence of an effusion, lung metastases, adenopathy and extrathoracic disease were recorded. The Wilcoxon rank-sum/Kruskal-Wallis and Fisher's exact tests were used to compare features between groups.

RESULTS

73 patients with lung adenocarcinomas were identified: 17 (23%) with ROS1 fusions, 25 (34%) with RET fusions and 31 (43%) with EGFR mutations. ROS1-rearranged lung cancers were more likely to present as peripheral tumors in comparison to EGFR-mutant lung cancers (32% vs. 65%, p=0.04). RET-rearranged lung cancers did not significantly differ from EGFR-mutant lung cancers radiographically. The consistency of the primary lesion for RET and ROS fusions and EGFR mutations were most frequently solid and spiculated.

CONCLUSIONS

Lung adenocarcinomas with RET and ROS1 fusions share many radiographic features and those with ROS1 fusions are more likely to present as peripheral lesions in comparison to EGFR-mutant lung cancers.

The Regulation and Function of Lactate Dehydrogenase A: Therapeutic Potential in Brain Tumor

There are over 120 types of brain tumor and approximately 45% of primary brain tumors are gliomas, of which glioblastoma multiforme (GBM) is the most common and aggressive with a median survival rate of 14 months. Despite progress in our knowledge, current therapies are unable to effectively combat primary brain tumors and patient survival remains poor. Tumor metabolism is important to consider in therapeutic approaches and is the focus of numerous research investigations. Lactate dehydrogenase A (LDHA) is a cytosolic enzyme, predominantly involved in anaerobic and aerobic glycolysis (the Warburg effect); however, it has multiple additional functions in non‐neoplastic and neoplastic tissues, which are not commonly known or discussed. This review summarizes what is currently known about the function of LDHA and identifies areas that would benefit from further exploration. The current knowledge of the role of LDHA in the brain and its potential as a therapeutic target for brain tumors will also be highlighted. The Warburg effect appears to be universal in tumors, including primary brain tumors, and LDHA (because of its involvement with this process) has been identified as a potential therapeutic target. Currently, there are, however, no suitable LDHA inhibitors available for tumor therapies in the clinic.

LATS2 inhibits the activity of NF-κ B signaling by disrupting the interaction between TAK1 and IKKβ

NF-κB signaling plays very important role in the tumorigenesis of nonsmall cell lung cancer (NSCLC). However, the molecular mechanisms for the dysregulation of NF-κB signaling in NSCLC have not been fully understood. In the previous reports, we have showed that large tumor suppressor gene 2 (LATS2) inhibited NF-κB signaling in NSCLC cells, whereas the details for the mechanism remain unknown. Here, we reported that LATS2 is a suppressor of tumor necrosis factor (TNF-α)-induced NF-κB signaling by inhibiting the interaction between TAK1 and IKKβ. Overexpression of LATS2 largely blocked TNF-α-induced NF-κB activation and IκBα degradation, whereas knockdown of LATS2 showed the opposing results. Mechanistically, we identified that LATS2 interacted with IKKβ and blocked the interaction between IKKβ and TAK1. Our results indicate that LATS2 functions as a pivotal negative regulator in TNF-α-induced activation of NF-κB via disrupting the interaction of TAK1 with IKKβ.

Co-expression of β-arrestin1 and NF-кB is associated with cancer progression and poor prognosis in lung adenocarcinoma

β-arrestin1 and NF-κB have been demonstrated to be associated with tumorigenesis, tumor progression, and metastasis. Thus far, there is nevertheless little study about these two molecules in lung adenocarcinoma. The aim of this study was to investigate the correlation between β-arrestin1 and NF-κB expression and the clinicopathological characteristics in lung adenocarcinoma. A total of 115 surgically resected lung adenocarcinoma patients were recruited for the study. Expression of β-arrestin1 and p65 were detected by immunohistochemistry (IHC) in lung adenocarcinoma tissue samples. Nuclear expression of β-arrestin1 and p65 were observed in 39.1 % (45/115) and 46.1 % (53/115) cases of lung adenocarcinoma, respectively. And high expression of β-arrestin1 had negative prognostic impact for overall survival (OS) and disease-free survival (DFS) (p = 0.034 and p = 0.033). In addition, overexpression of p65 indicated a significantly poor OS and DFS than those of lower-expression (p = 0.038 and p = 0.041). Furthermore, co-expression of nuclear β-arrestin1 and p65 correlated with poorer OS and DFS in lung adenocarcinoma patients. Multivariate analysis using the Cox regression model confirmed that co-expression of nuclear β-arrestin1 and p65 was an independent prognostic factor for tumor progression (p = 0.008). In conclusion, these data indicated that co-expression of nuclear β-arrestin1 and p65 was a novel predictor for worse prognosis in patients with lung adenocarcinoma.

Medical imaging in personalised medicine: a white paper of the research committee of the European Society of Radiology (ESR)

The future of medicine lies in early diagnosis and individually tailored treatments, a concept that has been designated 'personalised medicine' (PM), which aims to deliver the right treatment to the right patient at the right time. Medical imaging has always been personalised and is fundamental to almost all aspects of PM. It is instrumental in solving clinical differential diagnoses. Imaging procedures are tailored to the clinical problem and patient characteristics. Screening for preclinical disease is done with imaging. Stratification based on imaging biomarkers can help identify individuals suited for preventive intervention. Treatment decisions are based on the in vivo visualisation of the location and extent of an abnormality, as well as the loco-regional physiological, biochemical and biological processes using structural and molecular imaging. Image-guided biopsy provides relevant tissue specimens for genetic/molecular characterisation. In addition, radiogenomics relate imaging biomarkers to these genetic and molecular features. Furthermore, imaging is essential to patient-tailored therapy planning, therapy monitoring and follow-up of disease, as well as targeting non-invasive or minimally invasive treatments, especially with the rise of theranostics. Radiologists need to be prepared for this new paradigm as it will mean changes in training, clinical practice and in research. Key Points • Medical imaging is a key component in personalised medicine • Personalised prevention will rely on image-based screening programmes • Anatomical, functional and molecular imaging biomarkers affect decisions on the type and intensity of treatment • Treatment response assessment with imaging will improve personalised treatment • Image-based invasive intervention integrates personalised diagnosis and personalised treatment.

Radiogenomic imaging-linking diagnostic imaging and molecular diagnostics

Radiogenomic imaging refers to the correlation between cancer imaging features and gene expression and is one of the most promising areas within science and medicine. High-throughput biological techniques have reshaped the perspective of biomedical research allowing for fast and efficient assessment of the entire molecular topography of a cell’s physiology providing new insights into human cancers. The use of non-invasive imaging tools for gene expression profiling of solid tumors could serve as a means for linking specific imaging features with specific gene expression patterns thereby allowing for more accurate diagnosis and prognosis and obviating the need for high-risk invasive biopsy procedures. This review focuses on the medical imaging part as one of the main drivers for the development of radiogenomic imaging.