Exploitation of Gene Expression and Cancer Biomarkers in Paving the Path to Era of Personalized Medicine

The efficacy of chemotherapeutic drug combinations may be predicted by concordance of gene response to the single agents

Determining the expression of genes in response to different classes of chemotherapeutic drugs may allow for a better understanding as to which may be used effectively in combination. In the present study, the human colorectal cancer cell line HCT116 was cultured with equi-active concentrations of a series of anti-cancer agents. Gene expression profiles were then measured by whole-genome microarray. Although each drug induced a unique signature of gene expression in tumour cells, there were marked similarities between certain drugs, even in those from different classes. For example, the antimalarial agent artesunate and the platinum-containing alkylating agent, oxaliplatin, produced a very similar mRNA expression pattern in HCT116 cells with ~14,000 genes being affected by the two drugs in the same way. Furthermore, the overall correlation of gene responses between two agents could predict whether their use in combination would lead to a greater or lesser effect on cell number, determined experimentally, than predicted by single agent experiments. The results indicated that even when working through different mechanisms, combining drugs that initiate a similar transcriptional response may constitute the best option for determining drug-combination strategies for the treatment of cancer.

High expression of maternal embryonic leucine-zipper kinase (MELK) impacts clinical outcomes in patients with ovarian cancer and its inhibition suppresses ovarian cancer cells growth ex vivo

Objective

Maternal embryonic leucine zipper kinase (MELK) is receiving an attention as a therapeutic target in various types of cancers. In this study, we aimed to evaluate the prognostic significance of MELK expression in ovarian cancer using clinical samples, and assessed the efficacy of a small molecule MELK inhibitor, OTS167, using patient-derived ovarian cancer cells as well as cell lines.

Methods

Expression levels of MELK in 11 ovarian cancer cell lines were confirmed by western blotting. Inhibitory concentration of OTS167 was determined by colorimetric assay. MELK messenger RNA (mRNA) expression was evaluated in 228 ovarian cancer patients by quantitative polymerase chain reaction. Growth inhibition of OTS167 was also evaluated using freshly-isolated primary ovarian cancer cells including spheroid formation condition.

Results

MELK mRNA expression was significantly higher in ovarian cancer than in normal ovaries (p<0.001), and high MELK mRNA expression was observed in patients with advanced stage, positive ascites cytology and residual tumor size. Patients with high MELK mRNA expression showed shorter progression-free survival (p=0.001). Expression of MELK was also confirmed in 10 of 11 ovarian cancer cell lines tested, and the half maximal inhibitory concentration of MELK inhibitor, OTS167, ranged from 9.3 to 60 nM. Additionally, OTS167 showed significant growth inhibitory effect against patient-derived ovarian cancer cells, regardless of their tumor locations, histologic subtypes and stages.

Conclusions

We demonstrated MELK as both a prognostic marker and a therapeutic target for ovarian cancer using clinical ovarian cancer samples. MELK inhibition by OTS167 may be an effective approach to treat ovarian cancer patients.

Multiplex coherent anti-Stokes Raman scattering microspectroscopy detection of lipid droplets in cancer cells expressing TrkB

For many years, scientists have been looking for specific biomarkers associated with cancer cells for diagnosis purposes. These biomarkers mainly consist of proteins located at the cell surface (e.g. the TrkB receptor) whose activation is associated with specific metabolic modifications. Identification of these metabolic changes usually requires cell fixation and specific dye staining. MCARS microspectroscopy is a label-free, non-toxic, and minimally invasive method allowing to perform analyses of live cells and tissues. We used this method to follow the formation of lipid droplets in three colorectal cancer cell lines expressing TrkB. MCARS images of cells generated from signal integration of CH₂ stretching modes allow to discriminate between lipid accumulation in the endoplasmic reticulum and the formation of cytoplasmic lipid droplets. We found that the number of the latter was related to the TrkB expression level. This result was confirmed thanks to the creation of a HEK cell line which over-expresses TrkB. We demonstrated that BDNF-induced TrkB activation leads to the formation of cytoplasmic lipid droplets, which can be abolished by K252a, an inhibitor of TrkB. So, MCARS microspectroscopy proved useful in characterizing cancer cells displaying an aberrant lipid metabolism.

Scalable Signature-Based Molecular Diagnostics Through On-chip Biomarker Profiling Coupled with Machine Learning

Molecular diagnostics have traditionally relied on discrete biological substances as diagnostic markers. In recent years however, advances in on-chip biomarker screening technologies and data analytics have enabled signature-based diagnostics. Such diagnostics aim to utilize unique combinations of multiple biomarkers or diagnostic 'fingerprints' rather than discrete analyte measurements. This approach has shown to improve both diagnostic accuracy and diagnostic specificity. In this review, signature-based diagnostics enabled by microfluidic and micro-/nano- technologies will be reviewed with a focus on device design and data analysis pipelines and methodologies. With increasing amounts of data available from microfluidic biomarker screening, isolation, and detection platforms, advanced data handling and analytics approaches can be employed. Thus, current data analysis approaches including machine learning and recent advances with image processing, along with potential future directions will be explored. Lastly, the needs and gaps in current literature will be elucidated to inform future efforts towards development of molecular diagnostics and biomarker screening technologies.

Screening of autophagy genes as prognostic indicators for glioma patients

Although autophagy is reported to be involved in tumorigenesis and cancer progression, its correlation with the prognosis of glioma patients remains unclear. Thus, the aim of this study was to identify prognostic autophagy-related genes, analyze their correlation with clinicopathological features of glioma, and further construct a prognostic model for glioma patients. After 139 autophagy-related genes were obtained from the GeneCards database, their expression data in glioma patients were extracted from the Chinese Glioma Genome Atlas database. Univariate and multivariate COX regression analyses were performed to identify prognostic autophagy-related genes. Ten hub autophagy-related genes associated with prognosis were identified. The autophagy risk score (ARS) was only positively correlated with histopathology (P = 0.000) and World Health Organization grade (P = 0.000). Kaplan-Meier analysis showed that the overall survival of patients with a high ARS was significantly worse than that of patients with a low ARS (hazard ratio = 1.59, 95% confidence interval = 1.25-2.03, P = 0.0001). In addition, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses revealed several common biological processes and signaling pathways related to the 10 hub genes in glioblastoma. A prediction model was developed for glioma patients, which demonstrated high prediction efficiency on calibration. Moreover, the area under the receiver operating characteristic curve values for 1-, 3- and 5-year survival probabilities were 0.790, 0.861, and 0.853, respectively. In conclusion, we identified 10 autophagy-related genes that can serve as novel prognostic biomarkers for glioma patients. Our prediction model accurately predicted patient outcomes, and thus, may be a valuable tool in clinical practice.

Co-expression based cancer staging and application

A novel method is developed for predicting the stage of a cancer tissue based on the consistency level between the co-expression patterns in the given sample and samples in a specific stage. The basis for the prediction method is that cancer samples of the same stage share common functionalities as reflected by the co-expression patterns, which are distinct from samples in the other stages. Test results reveal that our prediction results are as good or potentially better than manually annotated stages by cancer pathologists. This new co-expression-based capability enables us to study how functionalities of cancer samples change as they evolve from early to the advanced stage. New and exciting results are discovered through such functional analyses, which offer new insights about what functions tend to be lost at what stage compared to the control tissues and similarly what new functions emerge as a cancer advances. To the best of our knowledge, this new capability represents the first computational method for accurately staging a cancer sample. The R source code used in this study is available at GitHub (https://github.com/yxchspring/CECS).

A Novel 4-Gene Score to Predict Survival, Distant Metastasis and Response to Neoadjuvant Therapy in Breast Cancer

We generated a 4-gene score with genes upregulated in LM2-4, a metastatic variant of MDA-MB-231 (DOK 4, HCCS, PGF, and SHCBP1) that was strongly associated with disease-free survival (DFS) in TCGA cohort (hazard ratio [HR]>1.2, p < 0.02). The 4-gene score correlated with overall survival of TCGA (HR = 1.44, p < 0.001), which was validated with DFS and disease-specific survival of METABRIC cohort. The 4-gene score was able to predict worse survival or clinically aggressive tumors, such as high Nottingham pathological grade and advanced cancer staging. High score was associated with worse survival in the hormonal receptor (HR)-positive/Her2-negative subtype. High score enriched cell proliferation-related gene sets in GSEA. The score was high in primary tumors that originated, in and metastasized to, brain and lung, and it predicted worse progression-free survival for metastatic tumors. Good tumor response to neoadjuvant chemotherapy or hormonal therapy was accompanied by score reduction. High scores were also predictive of response to neoadjuvant chemotherapy for HR-positive/Her2-negative subtype. High score tumors had increased expression of T cell exhaustion marker genes, suggesting that the score may also be a biomarker for immunotherapy response. Our novel 4-gene score with both prognostic and predictive values may, therefore, be clinically useful particularly in HR-positive breast cancer.

Identification of Platform-Independent Diagnostic Biomarker Panel for Hepatocellular Carcinoma Using Large-Scale Transcriptomics Data

The high mortality rate of hepatocellular carcinoma (HCC) is primarily due to its late diagnosis. In the past, numerous attempts have been made to design genetic biomarkers for the identification of HCC; unfortunately, most of the studies are based on small datasets obtained from a specific platform or lack reasonable validation performance on the external datasets. In order to identify a universal expression-based diagnostic biomarker panel for HCC that can be applicable across multiple platforms, we have employed large-scale transcriptomic profiling datasets containing a total of 2,316 HCC and 1,665 non-tumorous tissue samples. These samples were obtained from 30 studies generated by mainly four types of profiling techniques (Affymetrix, Illumina, Agilent, and High-throughput sequencing), which are implemented in a wide range of platforms. Firstly, we scrutinized overlapping 26 genes that are differentially expressed in numerous datasets. Subsequently, we identified a panel of three genes (FCN3, CLEC1B, and PRC1) as HCC biomarker using different feature selection techniques. Three-genes-based HCC biomarker identified HCC samples in training/validation datasets with an accuracy between 93 and 98%, Area Under Receiver Operating Characteristic curve (AUROC) in a range of 0.97 to 1.0. A reasonable performance, i.e., AUROC 0.91–0.96 achieved on validation dataset containing peripheral blood mononuclear cells, concurred their non-invasive utility. Furthermore, the prognostic potential of these genes was evaluated on TCGA-LIHC and GSE14520 cohorts using univariate survival analysis. This analysis revealed that these genes are prognostic indicators for various types of the survivals of HCC patients (e.g., Overall Survival, Progression-Free Survival, Disease-Free Survival). These genes significantly stratified high-risk and low-risk HCC patients (p-value <0.05). In conclusion, we identified a universal platform-independent three-genes-based biomarker that can predict HCC patients with high precision and also possess significant prognostic potential. Eventually, we developed a web server HCCpred based on the above study to facilitate scientific community (http://webs.iiitd.edu.in/raghava/hccpred/).

Joint Transcriptomic Analysis of Lung Cancer and Other Lung Diseases

Background: Epidemiological and clinical evidence points cancer comorbidity with pulmonary chronic disease. The acquisition of some hallmarks of cancer by cells affected with lung pathologies as a cell adaptive mechanism to a shear stress, suggests that could be associated with the establishment of tumoral processes. Objective: To propose a bioinformatic pipeline for the identification of all deregulated genes and the transcriptional regulators (TFs) that are coexpressed during lung cancer establishment, and therefore could be important for the acquisition of the hallmarks of cancer. Methods: Ten microarray datasets (six of lung cancer, four of lung diseases) comparing normal and diseases-related lung tissue were selected to identify hub differentiated expressed genes (DEGs) in common between lung pathologies and lung cancer, along with transcriptional regulators through the utilization of specialized libraries from R language. DAVID bioinformatics tool for gene enrichment analyses was used to identify genes with experimental evidence associated to tumoral processes and signaling pathways. Coexpression networks of DEGs and TFs in lung cancer establishment were created with Coexnet library, and a survival analysis of the main hub genes was made. Results: Two hundred ten DEGs were identified in common between lung cancer and other lung diseases related to the acquisition of tumoral characteristics, which are coexpressed in a lung cancer network with TFs, suggesting that could be related to the establishment of the tumoral pathology in lung. The comparison of the coexpression networks of lung cancer and other lung diseases allowed the identification of common connectivity patterns (CCPs) with DEGs and TFs correlated to important tumoral processes and signaling pathways, that haven´t been studied to experimentally validate their role in the early stages of lung cancer. Some of the TFs identified showed a correlation between its expression levels and the survival of lung cancer patients. Conclusion: Our findings indicate that lung diseases share genes with lung cancer which are coexpressed in lung cancer, and might be able to explain the epidemiological observations that point to direct and inverse comorbid associations between some chronic lung diseases and lung cancer and represent a complex transcriptomic scenario.

Diagnostic and prognostic value of the BEX family in lung adenocarcinoma

Previous studies have demonstrated that members of the brain-expressed X-linked (BEX) family participate in a wide range of biological functions in normal and tumor tissues. However, their role and clinical significance in lung adenocarcinoma (LUAD) remains unclear. The present study investigated The Cancer Genome Atlas data and revealed that the BEX family was downregulated in LUAD tissues compared with adjacent non-cancerous tissues. Additionally, analysis of LUAD cohorts from the Oncomine database revealed similar results. Furthermore, the expression of BEX members was significantly decreased in several LUAD cell lines compared with normal lung epithelial cells in vitro. The aforementioned data mining and in vitro results suggested that the BEX family may be involved in the development of LUAD. Furthermore, receiver operating characteristic curve analysis revealed that BEX members exhibited high sensitivity and specificity for the diagnosis of patients with LUAD. The low expression levels of BEX1, BEX4 and BEX5 were associated with certain pathologic features, particularly in advanced LUAD. Survival analysis demonstrated that BEX members, particularly BEX4, were involved in the prognosis of patients with LUAD at early and late clinical stages. The results obtained in the current study suggested that BEX members may serve as potential tumor biomarkers for the diagnosis and prognosis of patients with LUAD.

RNA sequencing for research and diagnostics in clinical oncology

Molecular diagnostics is becoming one of the major drivers of personalized oncology. With hundreds of different approved anticancer drugs and regimens of their administration, selecting the proper treatment for a patient is at least nontrivial task. This is especially sound for the cases of recurrent and metastatic cancers where the standard lines of therapy failed. Recent trials demonstrated that mutation assays have a strong limitation in personalized selection of therapeutics, consequently, most of the drugs cannot be ranked and only a small percentage of patients can benefit from the screening. Other approaches are, therefore, needed to address a problem of finding proper targeted therapies. The analysis of RNA expression (transcriptomic) profiles presents a reasonable solution because transcriptomics stands a few steps closer to tumor phenotype than the genome analysis. Several recent studies pioneered using transcriptomics for practical oncology and showed truly encouraging clinical results. The possibility of directly measuring of expression levels of molecular drugs' targets and profiling activation of the relevant molecular pathways enables personalized prioritizing for all types of molecular-targeted therapies. RNA sequencing is the most robust tool for the high throughput quantitative transcriptomics. Its use, potentials, and limitations for the clinical oncology will be reviewed here along with the technical aspects such as optimal types of biosamples, RNA sequencing profile normalization, quality controls and several levels of data analysis.

Precision Medicine in Non Communicable Diseases

Non-communicable diseases (NCDs) are the leading cause of death and disease burden globally, cardiovascular diseases (CVDs) account for the major part of death related to NCDs followed by different types of cancer, chronic obstructive pulmonary disease (COPD), and diabetes. As the World Health Organization (WHO) and the United Nations have announced a 25% reduction in mortality of NCDs by 2025, different communities need to adopt preventive strategies for achieving this goal. Personalized medicine approach as a predictive and preventive strategy aims for a better therapeutic goal to the patients to maximize benefits and reduce harms. The clinical benefits of this approach are already realized in cancer targeted therapy, and its impact on other conditions needs more studies in different societies. In this review, we essentially describe the concept of personalized (or precision) medicine in association with NCDs and the future of precision medicine in prediction, prevention, and personalized treatment.

Patient recruitment strategies for adaptive enrichment designs with time-to-event endpoints

Background

Adaptive enrichment designs for clinical trials have great potential for the development of targeted therapies. They enable researchers to stop the recruitment process for a certain population in mid-course based on an interim analysis. However, adaptive enrichment designs increase the total trial period owing to the stoppage in patient recruitment to make interim decisions. This is a major drawback; it results in delays in the submission of clinical trial reports and the appearance of drugs on the market. Here, we explore three types of patient recruitment strategy for the development of targeted therapies based on the adaptive enrichment design.

Methods

We consider recruitment methods which provide an option to continue recruiting patients from the overall population or only from the biomarker-positive population even during the interim decision period. A simulation study was performed to investigate the operating characteristics by comparing an adaptive enrichment design using the recruitment methods with a non-enriched design.

Results

The number of patients was similar for both recruitment methods. Nevertheless, the adaptive enrichment design was beneficial in settings in which the recruitment period is expected to be longer than the follow-up period. In these cases, the adaptive enrichment design with continued recruitment from the overall population or only from the biomarker-positive population even during the interim decision period conferred a major advantage, since the total trial period did not differ substantially from that of trials employing the non-enriched design. By contrast, the non-enriched design should be used in settings in which the follow-up period is expected to be longer than the recruitment period, since the total trial period was notably shorter than that of the adaptive enrichment design. Furthermore, the utmost care is needed when the distribution of patient recruitment is concave, i.e., when patient recruitment is slow during the early period, since the total trial period is extended.

Conclusions

Adaptive enrichment designs that entail continued recruitment methods are beneficial owing to the shorter total trial period than expected in settings in which the recruitment period is expected to be longer than the follow-up period and the biomarker-positive population is promising.

Multifunctional Hybrid Magnetic Microgel Synthesis for Immune-Based Isolation and Post-Isolation Culture of Tumor Cells

Circulating tumor cells are of utmost importance among various biomarkers in liquid biopsies as a prognosis indicator of metastasis as well as in chemotherapeutic monitoring. This study introduces an efficient tool composed of soft nano/hybrid immune microgels for magnetic isolation of targeted tumor cells. The development process involves the in situ synthesis of magnetic nanoparticles within the three-dimensional matrix of thermoresponsive microgels. Surface modification and anti-EpCAM conjugation are adjusted by changing the temperature, and a conjugation efficiency of around 70% is achieved by using a protein G linker. Anti-EpCAM-conjugated nano/hybrid magnetic microgels are used to isolate EpCAM-expressing breast adenocarcinoma MCF-7 cells from culture media and whole blood with an efficiency of 75 and 70%, respectively. Furthermore, we demonstrate the ability of the hybrid microgels to isolate cancer cells with a purity of 65% and culture the cells post-isolation for further drug studies. The multifunctional hybrid microcarriers reported in this work can be potentially used for continuous monitoring of cancers and in personalized medicine.

Economic Value of Pharmacogenetic Testing for Cancer Drugs with Clinically Relevant Drug-Gene Associations: A Systematic Literature Review

BACKGROUND

Pharmacogenetic testing can provide predictive insights about the efficacy and safety of drugs used in cancer treatment. Although many drug-gene associations have been reported in the literature, the strength of evidence supporting each association can vary significantly. Even among the subgroup of drugs classified by the PharmGKB database to have a high or moderate level of evidence, there is limited information regarding the economic value of pharmacogenetic testing.

OBJECTIVES

To: (a) summarize the available pharmacoeconomic evidence assessing the value of pharmacogenetic testing for cancer drugs with clinically relevant drug-gene associations; (b) determine the quality of the studies that contain this evidence; and (c) discuss the quality of this evidence with respect to the level of evidence of the drug-gene associations.

METHODS

The PharmGKB database was used to identify cancer drugs with clinically relevant drug-gene associations graded high (1A, 1B) or moderate (2A, 2B). A systematic literature review was conducted using these drugs. Ovid MEDLINE and Embase databases were searched to identify cost-effectiveness, cost-utility, or cost-minimization studies comparing pharmacogenetic testing to an alternative. Cost and effect values from every relevant comparison within the studies were extracted, and the incremental cost-effectiveness ratio (ICER) was either extracted or calculated for each comparison. Quality assessment was conducted for each study using the Quality of Health Economic Studies (QHES) instrument. Qualitative synthesis was used to summarize the data.

RESULTS

The search yielded 2,191 citations, of which 35 studies met the inclusion criteria. Pharmacoeconomic studies were available for the following drugs from the PharmGKB database: fluoropyrimidine, 6-mercaptopurine, irinotecan, carboplatin, cisplatin, erlotinib, gefitinib, cetuximab, panitumumab, and trastuzumab. The studies were conducted in Asia, Europe, Canada, the United States, and Mexico and reported cost-utility, cost-effectiveness, and cost-minimization outcomes. The mean QHES score was 80 (SD = 22) for the studies of drug-gene pairs with high (1A, 1B) and moderate (2A, 2B) levels of evidence (1A = 82, 1B = 93, 2A = 71, and 2B = 74). There was variation across studies in terms of reporting. 109 relevant comparisons were identified within the studies. Of those that reported cost per life-year or cost per quality-adjusted life-year (n = 58 comparisons), pharmacogenetic testing was dominant in 21% overall and 42%, 21%, 17%, and 5% of the comparisons in Asia, Europe, Canada, and the United States, respectively. Variability was observed in the ICER values regardless of geographic region or drug. Pharmacogenetic testing was cost saving in 17 of 19 cost-minimization comparisons and was favored most frequently when compared with genetically indiscriminate strategies containing the drug of interest.

CONCLUSIONS

There was mixed evidence regarding the value of pharmacogenetic testing to guide cancer treatment. For future pharmacogenomic-related economic studies, we recommend prioritizing clinically relevant drug-gene associations and greater adherence to available best practice guidelines for conducting and reporting economic evaluation studies.

DISCLOSURES

No outside funding supported this review. Part of Hussain's research time was supported by a Merit Review Award (I01 BX000545), Medical Research Service, U.S. Department of Veterans Affairs. Hussain also reports personal fees from Bristol-Myers Squibb, AstraZeneca, Novartis, Bayer HealthCare Pharmaceuticals, and France Foundation, outside the submitted work. Onukwugha reports grants from Pfizer and Bayer HealthCare Pharmaceuticals, along with advisory board fees from Novo Nordisk, outside the submitted work. Faruque, Neuberger, and Noh have nothing to disclose.

Identification of Prognostic Biomarker Signatures and Candidate Drugs in Colorectal Cancer: Insights from Systems Biology Analysis

Background and objectives: Colorectal cancer (CRC) is the second most common cause of cancer-related death in the world, but early diagnosis ameliorates the survival of CRC. This report aimed to identify molecular biomarker signatures in CRC. Materials and Methods: We analyzed two microarray datasets (GSE35279 and GSE21815) from the Gene Expression Omnibus (GEO) to identify mutual differentially expressed genes (DEGs). We integrated DEGs with protein–protein interaction and transcriptional/post-transcriptional regulatory networks to identify reporter signaling and regulatory molecules; utilized functional overrepresentation and pathway enrichment analyses to elucidate their roles in biological processes and molecular pathways; performed survival analyses to evaluate their prognostic performance; and applied drug repositioning analyses through Connectivity Map (CMap) and geneXpharma tools to hypothesize possible drug candidates targeting reporter molecules. Results: A total of 727 upregulated and 99 downregulated DEGs were detected. The PI3K/Akt signaling, Wnt signaling, extracellular matrix (ECM) interaction, and cell cycle were identified as significantly enriched pathways. Ten hub proteins (ADNP, CCND1, CD44, CDK4, CEBPB, CENPA, CENPH, CENPN, MYC, and RFC2), 10 transcription factors (ETS1, ESR1, GATA1, GATA2, GATA3, AR, YBX1, FOXP3, E2F4, and PRDM14) and two microRNAs (miRNAs) (miR-193b-3p and miR-615-3p) were detected as reporter molecules. The survival analyses through Kaplan–Meier curves indicated remarkable performance of reporter molecules in the estimation of survival probability in CRC patients. In addition, several drug candidates including anti-neoplastic and immunomodulating agents were repositioned. Conclusions: This study presents biomarker signatures at protein and RNA levels with prognostic capability in CRC. We think that the molecular signatures and candidate drugs presented in this study might be useful in future studies indenting the development of accurate diagnostic and/or prognostic biomarker screens and efficient therapeutic strategies in CRC.

Precision Medicine and Radiogenomics in Breast Cancer: New Approaches toward Diagnosis and Treatment

Precision medicine is medicine optimized to the genotypic and phenotypic characteristics of an individual and, when present, his or her disease. It has a host of targets, including genes and their transcripts, proteins, and metabolites. Studying precision medicine involves a systems biology approach that integrates mathematical modeling and biology genomics, transcriptomics, proteomics, and metabolomics. Moreover, precision medicine must consider not only the relatively static genetic codes of individuals, but also the dynamic and heterogeneous genetic codes of cancers. Thus, precision medicine relies not only on discovering identifiable targets for treatment and surveillance modification, but also on reliable, noninvasive methods of identifying changes in these targets over time. Imaging via radiomics and radiogenomics is poised for a central role. Radiomics, which extracts large volumes of quantitative data from digital images and amalgamates these together with clinical and patient data into searchable shared databases, potentiates radiogenomics, which is the combination of genetic and radiomic data. Radiogenomics may provide voxel-by-voxel genetic information for a complete, heterogeneous tumor or, in the setting of metastatic disease, set of tumors and thereby guide tailored therapy. Radiogenomics may also quantify lesion characteristics, to better differentiate between benign and malignant entities, and patient characteristics, to better stratify patients according to risk for disease, thereby allowing for more precise imaging and screening. This report provides an overview of precision medicine and discusses radiogenomics specifically in breast cancer. ^© RSNA, 2018.

Applications of RNA Indexes for Precision Oncology in Breast Cancer

Precision oncology aims to offer the most appropriate treatments to cancer patients mainly based on their individual genetic information. Genomics has provided numerous valuable data on driver mutations and risk loci; however, it remains a formidable challenge to transform these data into therapeutic agents. Transcriptomics describes the multifarious expression patterns of both mRNAs and non-coding RNAs (ncRNAs), which facilitates the deciphering of genomic codes. In this review, we take breast cancer as an example to demonstrate the applications of these rich RNA resources in precision medicine exploration. These include the use of mRNA profiles in triple-negative breast cancer (TNBC) subtyping to inform corresponding candidate targeted therapies; current advancements and achievements of high-throughput RNA interference (RNAi) screening technologies in breast cancer; and microRNAs as functional signatures for defining cell identities and regulating the biological activities of breast cancer cells. We summarize the benefits of transcriptomic analyses in breast cancer management and propose that unscrambling the core signaling networks of cancer may be an important task of multiple-omic data integration for precision oncology.

Is nonmetastatic cutaneous melanoma predictable through genomic biomarkers?

Cutaneous melanoma is a highly aggressive skin cancer whose treatment and prognosis are critically affected by the presence of metastasis. In this study, we address the following issue: which gene transcripts and what kind of interactions between them can allow to predict nonmetastatic from metastatic melanomas with a high level of accuracy? We carry out a meta-analysis on the first gene expression set of the Leeds melanoma cohort, as made available online on 11 May 2016 through the ArrayExpress platform with MicroArray Gene Expression number 4725. According to the authors, primary melanoma mRNA expression was measured in 204 tumours using an illumina DASL HT12 4 whole-genome array. The tumour transcripts were selected through a recently proposed predictive-based regression algorithm for gene-network selection. A set of 64 equivalent models, each including only two gene transcripts, were each sufficient to accurately classify primary tumours into metastatic and nonmetastatic melanomas. The sensitivity and specificity of the genomic-based models were, respectively, 4% (95% confidence interval: 0.11-21.95%) and 99% (95% confidence interval: 96.96-99.99%). The very high specificity coupled with a significantly large positive likelihood ratio leads to a conclusive increase in the likelihood of disease when these biomarkers are present in the primary tumour. In conjunction with other highly sensitive methods, this approach can aspire to be part of the future standard diagnosis methods for the screening of metastatic cutaneous melanoma. The small dimension of the selected transcripts models enables easy handling of large-scale genomic testing procedures. Moreover, some of the selected transcripts have an understandable link with what is known about cutaneous melanoma oncogenesis, opening a window on the molecular pathways underlying the metastatic process of this disease.

Prognostic and histogenetic roles of gene alteration and the expression of key potentially actionable targets in salivary duct carcinomas

The molecular characteristics of therapeutically-relevant targets and their clinicopathological implications in salivary duct carcinomas (SDCs) are poorly understood. We investigated the gene alterations and the immunoexpression of crucial oncogenic molecules in 151 SDCs. The mutation rates that were identified, in order of frequency, were as follows: TP53, 68%; PIK3CA, 18%; H-RAS, 16%; BRAF, 4%; and AKT1, 1.5%. PIK3CA/H-RAS/BRAF mutations were more common in de novo SDC than in SDC ex-pleomorphic adenoma. Furthermore, these mutations were mutually exclusive for HER2 overexpression/amplification. TP53 mutations were frequently detected in cases with the aberrant p53 expression, and TP53 missense and truncating mutations were associated with p53-extreme positivity and negativity, respectively. DISH analysis revealed no cases of EGFR amplification. The rates of PI3K, p-Akt, and p-mTOR positivity were 34%, 22%, and 66%, respectively; PTEN loss was observed in 47% of the cases. These expressions were correlated according to the signaling axis. Cases with PI3K negativity and PTEN loss appeared to show a lower expression of androgen receptor. In the multivariate analysis, patients with SDC harboring TP53 truncating mutations showed shorter progression-free survival. Conversely, p-Akt positivity was associated with a favorable outcome. This study might provide information that leads to advances in personized therapy for SDC.

Background, current role, and potential applications of radiogenomics

With the genomic revolution in the early 1990s, medical research has been driven to study the basis of human disease on a genomic level and to devise precise cancer therapies tailored to the specific genetic makeup of a tumor. To match novel therapeutic concepts conceived in the era of precision medicine, diagnostic tests must be equally sufficient, multilayered, and complex to identify the relevant genetic alterations that render cancers susceptible to treatment. With significant advances in training and medical imaging techniques, image analysis and the development of high-throughput methods to extract and correlate multiple imaging parameters with genomic data, a new direction in medical research has emerged. This novel approach has been termed radiogenomics. Radiogenomics aims to correlate imaging characteristics (ie, the imaging phenotype) with gene expression patterns, gene mutations, and other genome-related characteristics and is designed to facilitate a deeper understanding of tumor biology and capture the intrinsic tumor heterogeneity. Ultimately, the goal of radiogenomics is to develop imaging biomarkers for outcome that incorporate both phenotypic and genotypic metrics. Due to the noninvasive nature of medical imaging and its ubiquitous use in clinical practice, the field of radiogenomics is rapidly evolving and initial results are encouraging. In this article, we briefly discuss the background and then summarize the current role and the potential of radiogenomics in brain, liver, prostate, gynecological, and breast tumors.

LEVEL OF EVIDENCE

5 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2017;47:604-620.