Bioinformatics for precision medicine in oncology: principles and application to the SHIVA clinical trial

Lentinus edodes mycelium polysaccharide inhibits AGEs-induced HUVECs pyroptosis by regulating LncRNA MALAT1/miR-199b/mTOR axis and NLRP3/Caspase-1/GSDMD pathway

A novel Lentinus edodes mycelia polysaccharide (LMP) prepared in our laboratory has been identified to be effective in inhibiting the damage of islet β cells induced by glucose toxicity. However, whether it can effectively alleviate the pyroptosis of human umbilical vein endothelial cells (HUVECs) induced by advanced glycation end products (AGEs) remains unclear. Bioinformatics and cell biology techniques were used to explore the mechanism of LMP inhibiting AGEs-induced HUVECs damage. The results indicated that AGEs significantly increased the expression of LncRNA MALAT1, decreased cell viability to 79.67 %, increased intracellular ROS level to 248.19 % compared with the control group, which further led to cell membrane rupture. The release of LDH in cellular supernatant was increased to 149.42 %, and the rate of propidium iodide staining positive cells increased to 277.19 %, indicating the cell pyroptosis occurred. However, the above trend was effectively retrieved after the treatment with LMP. LMP effectively decreased the expression of LncRNA MALAT1 and mTOR, promoted the expression of miR-199b, inhibited AGEs-induced HUVECs pyroptosis by regulating the NLRP3/Caspase-1/GSDMD pathway. LncRNA MALAT1 might be a new target for LMP to inhibit AGEs-induced HUVECs pyroptosis. This study manifested the role of LMP in improving diabetes angiopathy and broadens the application of polysaccharide.

Cancer chemotherapy resistance: Mechanisms and recent breakthrough in targeted drug delivery

Conventional chemotherapy, one of the most widely used cancer treatment methods, has serious side effects, and usually results in cancer treatment failure. Drug resistance is one of the primary reasons for this failure. The most significant drawbacks of systemic chemotherapy are rapid clearance from the circulation, the drug's low concentration in the tumor site, and considerable adverse effects outside the tumor. Several ways have been developed to boost neoplasm treatment efficacy and overcome medication resistance. In recent years, targeted drug delivery has become an essential therapeutic application. As more mechanisms of tumor treatment resistance are discovered, nanoparticles (NPs) are designed to target these pathways. Therefore, understanding the limitations and challenges of this technology is critical for nanocarrier evaluation. Nano-drugs have been increasingly employed in medicine, incorporating therapeutic applications for more precise and effective tumor diagnosis, therapy, and targeting. Many benefits of NP-based drug delivery systems in cancer treatment have been proven, including good pharmacokinetics, tumor cell-specific targeting, decreased side effects, and lessened drug resistance. As more mechanisms of tumor treatment resistance are discovered, NPs are designed to target these pathways. At the moment, this innovative technology has the potential to bring fresh insights into cancer therapy. Therefore, understanding the limitations and challenges of this technology is critical for nanocarrier evaluation.

Actionability classification of variants of unknown significance correlates with functional effect

Genomically-informed therapy requires consideration of the functional impact of genomic alterations on protein expression and/or function. However, a substantial number of variants are of unknown significance (VUS). The MD Anderson Precision Oncology Decision Support (PODS) team developed an actionability classification scheme that categorizes VUS as either "Unknown" or "Potentially" actionable based on their location within functional domains and/or proximity to known oncogenic variants. We then compared PODS VUS actionability classification with results from a functional genomics platform consisting of mutant generation and cell viability assays. 106 (24%) of 438 VUS in 20 actionable genes were classified as oncogenic in functional assays. Variants categorized by PODS as Potentially actionable (N = 204) were more likely to be oncogenic than those categorized as Unknown (N = 230) (37% vs 13%, p = 4.08e-09). Our results demonstrate that rule-based actionability classification of VUS can identify patients more likely to have actionable variants for consideration with genomically-matched therapy.

CD44 is a potential immunotherapeutic target and affects macrophage infiltration leading to poor prognosis

CD44 plays a key role in the communication of CSCs with the microenvironment and the regulation of stem cell properties. UALCAN was used to analyze the expression of CD44 in bladder cancer (BLCA) and normal tissue. The UALCAN was utilized to analyze the prognostic value of CD44 in BLCA. The TIMER database was used to explore the relationship between CD44 and PD-L1; CD44 and tumor-infiltrating immune cells. The regulatory effect of CD44 on PD-L1 was verified by cell experiments in vitro. IHC confirmed the results of the bioinformatics analysis. GeneMania and Metascape were used to analyze protein-protein interaction (PPI) investigations and functional enrichment analysis. We found that BLCA patients with high CD44 expression had worse survival than those with low CD44 expression (P < 0.05). IHC and the TIMER database results showed that CD44 expression was positively correlated with PD-L1 expression (P < 0.05). At the cellular level, the expression of PD-L1 was significantly inhibited after CD44 expression was inhibited by siRNA. Immune infiltration analysis showed that CD44 expression levels in BLCA were significantly correlated with immune infiltration levels of different immune cells. IHC staining results further confirmed that the expression of CD44 in tumor cells was positively associated with the number of CD68⁺ macrophages and CD163⁺ macrophages (P < 0.05). Our results suggest that CD44 is a positive regulator of PD-L1 in BLCA and may be a key regulator of tumor macrophages infiltration and may be involved in M2 macrophage polarization. Our study provided new insights into the prognosis and immunotherapy of BLCA patients through macrophage infiltration and immune checkpoints.

Identification of Hub Diagnostic Biomarkers and Candidate Therapeutic Drugs in Heart Failure

Purpose

The objective of this study was to identify the potential regulatory mechanisms, diagnostic biomarkers, and therapeutic drugs for heart failure (HF).

Methods

Differentially expressed genes (DEGs) between HF and non-failing donors were screened from the GSE57345, GSE5406, and GSE3586 datasets. Database for Annotation Visualization and Integrated Discovery and Metascape were used for Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses respectively. The GSE57345 dataset was used for weighted gene co-expression network analysis (WGCNA). The intersecting hub genes from the DEGs and WGCNA were identified and verified with the GSE5406 and GSE3586 datasets. The diagnostic value of the hub genes was calculated through receiver operating characteristic analysis and net reclassification index (NRI). Gene set enrichment analysis (GSEA) was used to filter out the signaling pathways associated with the hub genes. SYBYL 2.1 was used for molecular docking of hub targets and potential HF drugs obtained from the connection map.

Results

Functional annotation of the DEGs showed enrichment of negative regulation of angiogenesis, endoplasmic reticulum stress response, and heart development. PTN, LUM, ISLR, and ASPN were identified as the hub genes of HF. GSEA showed that the key genes were related to the transforming growth factor-β (TGF-β) and Wnt signaling pathways. Sirolimus, LY-294002, and wortmannin have been confirmed as potential drugs for HF.

Conclusion

We identified new hub genes and candidate therapeutic drugs for HF, which are potential diagnostic, therapeutic and prognostic targets and warrant further investigation.

A computational method for prioritizing targeted therapies in precision oncology: performance analysis in the SHIVA01 trial

Precision oncology is currently based on pairing molecularly targeted agents (MTA) to predefined single driver genes or biomarkers. Each tumor harbors a combination of a large number of potential genetic alterations of multiple driver genes in a complex system that limits the potential of this approach. We have developed an artificial intelligence (AI)-assisted computational method, the digital drug-assignment (DDA) system, to prioritize potential MTAs for each cancer patient based on the complex individual molecular profile of their tumor. We analyzed the clinical benefit of the DDA system on the molecular and clinical outcome data of patients treated in the SHIVA01 precision oncology clinical trial with MTAs matched to individual genetic alterations or biomarkers of their tumor. We found that the DDA score assigned to MTAs was significantly higher in patients experiencing disease control than in patients with progressive disease (1523 versus 580, P = 0.037). The median PFS was also significantly longer in patients receiving MTAs with high (1000+ <) than with low (<0) DDA scores (3.95 versus 1.95 months, P = 0.044). Our results indicate that AI-based systems, like DDA, are promising new tools for oncologists to improve the clinical benefit of precision oncology.

Identification of Key Biomarkers and Immune Infiltration in Sciatic Nerve of Diabetic Neuropathy BKS-db/db Mice by Bioinformatics Analysis

Diabetic neuropathy (DN) is one of the chronic complications of diabetes which can cause severe harm to patients. In order to determine the key genes and pathways related to the pathogenesis of DN, we downloaded the microarray data set GSE27382 from Gene Expression Omnibus (GEO) and adopted bioinformatics methods for comprehensive analysis, including functional enrichment, construction of PPI networks, central genes screening, TFs-target interaction analysis, and evaluation of immune infiltration characteristics. Finally, we examined quantitative real- time PCR (qPCR) to validate the expression of hub genes. A total of 318 differentially expressed genes (DEGs) were identified, among which 125 upregulated DEGs were enriched in the mitotic nuclear division, extracellular region, immunoglobulin receptor binding, and p53 signaling pathway, while 193 downregulated DEGs were enriched in ion transport, membrane, synapse, sodium channel activity, and retrograde endocannabinoid signaling. GSEA plots showed that condensed nuclear chromosome kinetochore were the most significant enriched gene set positively correlated with the DN group. Importantly, we identified five central genes (Birc5, Bub1, Cdk1, Ccnb2, and Ccnb1), and KEGG pathway analysis showed that the five hub genes were focused on progesterone-mediated oocyte maturation, cell cycle, and p53 signaling pathway. The proportion of immune cells from DN tissue and normal group showed significant individual differences. In DN samples, T cells CD4 memory resting and dendritic cells resting accounted for a higher proportion, and macrophage M2 accounted for a lower proportion. In addition, all five central genes showed consistent correlation with immune cell infiltration levels. qPCR showed the same expression trend of five central genes as in our analysis. Our research identified key genes related to differential genes and immune infiltration related to the pathogenesis of DN and provided new diagnostic and potential therapeutic targets for DN.

VINYL: Variant prIoritizatioN by survivaL analysis

MOTIVATION

Clinical applications of genome re-sequencing technologies typically generate large amounts of data that need to be carefully annotated and interpreted to identify genetic variants potentially associated with pathological conditions. In this context, accurate and reproducible methods for the functional annotation and prioritization of genetic variants are of fundamental importance.

RESULTS

In this paper, we present VINYL, a flexible and fully automated system for the functional annotation and prioritization of genetic variants. Extensive analyses of both real and simulated datasets suggest that VINYL can identify clinically relevant genetic variants in a more accurate manner compared to equivalent state of the art methods, allowing a more rapid and effective prioritization of genetic variants in different experimental settings. As such we believe that VINYL can establish itself as a valuable tool to assist healthcare operators and researchers in clinical genomics investigations.

AVAILABILITY

VINYL is available at http://beaconlab.it/VINYL and https://github.com/matteo14c/VINYL.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Comprehensive analysis of prognostic gene signatures based on immune infiltration of ovarian cancer

BACKGROUND

Ovarian cancer (OV) is one of the most common malignant tumors of gynecology oncology. The lack of effective early diagnosis methods and treatment strategies result in a low five-year survival rate. Also, immunotherapy plays an important auxiliary role in the treatment of advanced OV patient, so it is of great significance to find out effective immune-related tumor markers for the diagnosis and treatment of OV.

METHODS

Based on the consensus clustering analysis of single-sample gene set enrichment analysis (ssGSEA) score transformed via The Cancer Genome Atlas (TCGA) mRNA profile, we obtained two groups with high and low levels of immune infiltration. Multiple machine learning methods were conducted to explore prognostic genes associated with immune infiltration. Simultaneously, the correlation between the expression of mark genes and immune cells components was explored.

RESULTS

A prognostic classifier including 5 genes (CXCL11, S1PR4, TNFRSF17, FPR1 and DHRS95) was established and its robust efficacy for predicting overall survival was validated via 1129 OV samples. Some significant variations of copy number on gene loci were found between two risk groups and it showed that patients with fine chemosensitivity has lower risk score than patient with poor chemosensitivity (P = 0.013). The high and low-risk groups showed significantly different distribution (P < 0.001) of five immune cells (Monocytes, Macrophages M1, Macrophages M2, T cells CD4 menory and T cells CD8).

CONCLUSION

The present study identified five prognostic genes associated with immune infiltration of OV, which may provide some potential clinical implications for OV treatment.

Construction of a 14-lncRNA risk score system predicting survival of children with acute myelocytic leukemia

Acute myelocytic leukemia (AML) is a frequent type of acute leukemia. The present study was performed to build a risk score system for the prognostic prediction of AML. AML RNA-sequencing data from samples from 111 children were downloaded from The Cancer Genome Atlas database. Using the DEseq and edgeR packages, the differentially expressed long non-coding RNAs (DE-lncRNAs) between bad and good prognosis groups were identified. A survival package was used to screen prognosis-associated lncRNAs and clinical factors. The optimal lncRNA combination was selected using the penalized package, and the risk-score system was built and evaluated. After the lncRNA-mRNA expression correlation network was constructed, the potential pathways involving the key lncRNAs were enriched using Gene Set Enrichment Analysis. Among the 61 DE-lncRNAs, 48 lncRNAs were significantly associated with prognosis. Relapse was an independent prognostic factor. The optimal 14-lncRNA risk score system was constructed. After 730 differentially expressed mRNAs were identified between the good and bad prognosis groups divided using a prognostic index, the lncRNA-mRNA expression correlation network was constructed. Enrichment analysis showed that semaphorin-3C [SEMA3C; regulated by probable leucine-tRNA ligase, mitochondrial (LARS2-AS1)] and secreted frizzled-related protein 5 [SFRP5; mediated by WASH complex subunit 5 (WASHC5)-antisense RNA 1 (AS1)] were involved in axon guidance and the Wnt signaling pathway, respectively. A 14-lncRNA (including paired box protein Pax8-AS1 and MYB AS1) risk-score system might be effective in predicting the prognosis of AML. Axon guidance (involving SEMA3C and LARS2-AS1) and the Wnt signaling pathway (involving SFRP5 and WASHC5-AS1) might be two important pathways affecting the prognosis of AML.

Molecular Profiling of Advanced Malignancies: A Community Oncology Network Experience and Review of Literature

Background: Many genomic alterations have been identified that are critical to the malignant phenotype. Some of these, termed “driver mutations,” are critical for tumor proliferation and progression. The landscape of targeted therapy has expanded as well. Next-generation sequencing (NGS) of tumors reveals cancer-related genomic alterations and provides therapeutic recommendations for specific targeted therapy. We analyzed our experience with FoundationOne, a validated NGS genomic profiling test, in a community oncology network.

Methods: NGS results from May 2014 to September 2016 from a community oncology network in Western Pennsylvania were analyzed. Medical records were reviewed for primary site, stage, biopsy site, time of testing, prior treatment, FDA-approved therapy in patient's and other tumor types and potential clinical trials based upon mutations detected. Two co-primary endpoints for this study were to determine the percentage of patients having mutations with a FDA-approved targeted agent and the percentage of patients in whom a treatment decision was made based on these NGS results.

Results: One Fifty-Seven NGS results were available for analysis. 82% patients had a mutation with a FDA-approved targeted agent available while 18% patients had no FDA-approved targeted agent for the mutation detected. Clinical trials were available for 93% cases. The NGS results were utilized in treatment decisions in 18% patients (n = 28) with, 7% (n = 11) initiating a targeted agent, 6% (n = 9) were on an appropriate targeted agent prior to testing and 5% (n = 8) being unable to start a targeted agent because of insurance denial, clinical deterioration or patient preference. 38% cases were tested early in the disease course (at diagnosis, during or shortly after first-line treatment) and 62% at progression.

Conclusions: NGS is a valuable tool to identify molecular targets for personalizing cancer care. From our experience, the actual number of patients starting a targeted agent based on NGS results is low but it provides substantial information in terms of providing additional treatment options, identifying resistance conferring mutations and facilitating clinical trial enrollment. Optimal time of testing, early or late in disease course, financial implications of testing and using targeted therapy and survival benefit of targeted therapy need further studies.

Precision medicine needs pioneering clinical bioinformaticians

Success in precision medicine depends on accessing high-quality genetic and molecular data from large, well-annotated patient cohorts that couple biological samples to comprehensive clinical data, which in conjunction can lead to effective therapies. From such a scenario emerges the need for a new professional profile, an expert bioinformatician with training in clinical areas who can make sense of multi-omics data to improve therapeutic interventions in patients, and the design of optimized basket trials. In this review, we first describe the main policies and international initiatives that focus on precision medicine. Secondly, we review the currently ongoing clinical trials in precision medicine, introducing the concept of 'precision bioinformatics', and we describe current pioneering bioinformatics efforts aimed at implementing tools and computational infrastructures for precision medicine in health institutions around the world. Thirdly, we discuss the challenges related to the clinical training of bioinformaticians, and the urgent need for computational specialists capable of assimilating medical terminologies and protocols to address real clinical questions. We also propose some skills required to carry out common tasks in clinical bioinformatics and some tips for emergent groups. Finally, we explore the future perspectives and the challenges faced by precision medicine bioinformatics.

Identification of Hub Genes in High-Grade Serous Ovarian Cancer Using Weighted Gene Co-Expression Network Analysis

Background

High-grade serous ovarian cancer (HGSOC) is the most malignant gynecologic tumor. This study reveals biomarkers related to HGSOC incidence and progression using the bioinformatics method.

Material/Methods

Five gene expression profiles were downloaded from GEO. Differentially-expressed genes (DEGs) in HGSOC and normal ovarian tissue samples were screened using limma and the function of DEGs was annotated by KEGG and GO analysis using clusterProfiler. A co-expression network utilizing the WGCNA package was established to define several hub genes from the key module. Furthermore, survival analysis was performed, followed by expression validation with datasets from TCGA and GTEx. Finally, we used single-gene GSEA to detect the function of prognostic hub genes.

Results

Out of the 1874 DEGs detected from 114 HGSOC versus 49 normal tissue samples, 956 were upregulated and 919 were downregulated. The functional annotation indicated that upregulated DEGs were mostly enriched in cell cycle, whereas the downregulated DEGs were enriched in the MAPK or Ras signaling pathway. Two modules significantly associated with HGSOC were excavated through WGCNA. After survival analysis and expression validation of hub genes, we found that 2 upregulated genes (MAD2L1 and PKD2) and 3 downregulated genes (DOCK5, FANCD2 and TBRG1) were positively correlated with HGSOC prognosis. GSEA for single-hub genes revealed that MAD2L1 and PKD2 were associated with proliferation, while DOCK5, FANCD2, and TBRG1 were associated with immune response.

Conclusions

We found that FANCD2, PKD2, TBRG1, and DOCK5 had prognostic value and could be used as potential biomarkers for HGSOC treatment.

Identification of biomarkers associated with Parkinson's disease by gene expression profiling studies and bioinformatics analysis

Parkinson's disease (PD) is associated with a selective loss of the neurons in the midbrain area called the substantia nigra pars compacta and the loss of projecting nerve fibers in the striatum. Predominant pathological hallmarks of PD are the degeneration of discrete neuronal populations and progressive accumulation of α-synuclein-containing intracytoplasmic inclusions called Lewy bodies and dystrophic Lewy neuritis. There is currently no therapy to terminate or delay the neurodegenerative process as the exact mechanisms underlying the pathogenesis of PD require further investigation. The identification and validation of novel biomarkers for the diagnosis of PD is a great challenge using contemporary approaches and optimizing sampling handling as well as interpretation using bioinformatics analysis. In this review, recent evidences associated with multi-omic data-sets and molecular mechanisms underlying PD are examined. A combined mapping of several transcriptional evidences could establish a patient-specific signature for early diagnose of PD though eligible systems biology tools, which can also help develop effective drug-based therapeutic approaches.

Metabolomics technology and bioinformatics for precision medicine

Precision medicine is rapidly emerging as a strategy to tailor medical treatment to a small group or even individual patients based on their genetics, environment and lifestyle. Precision medicine relies heavily on developments in systems biology and omics disciplines, including metabolomics. Combination of metabolomics with sophisticated bioinformatics analysis and mathematical modeling has an extreme power to provide a metabolic snapshot of the patient over the course of disease and treatment or classifying patients into subpopulations and subgroups requiring individual medical treatment. Although a powerful approach, metabolomics have certain limitations in technology and bioinformatics. We will review various aspects of metabolomics technology and bioinformatics, from data generation, bioinformatics analysis, data fusion and mathematical modeling to data management, in the context of precision medicine.

HTR1D, TIMP1, SERPINE1, MMP3 and CNR2 affect the survival of patients with colon adenocarcinoma

Colorectal cancer (CRC) is a tumor that derives from the rectum or colon, and colon adenocarcinoma (COAD) is the most common type of CRC. The present study was performed to identify genes that serve critical roles in the survival of patients with COAD. RNA-sequencing data of COAD was extracted from The Cancer Genome Atlas database, which included 480 tumor samples and 41 normal samples. Using the limma package, differential expression analysis was performed to identify the differentially expressed genes (DEGs). In addition, the potential functions and pathways for the identified DEGs were analyzed using the clusterProfiler package. After the samples were divided into high and low expression groups, survival analysis for the two groups was performed using the Kaplan-Meier model. Using Cytoscape software, a protein-protein interaction network was generated for the survival-associated genes. A total of 1,519 DEGs, including 568 upregulated genes and 951 downregulated genes, were identified in the COAD samples. Enrichment analysis suggested that the DEGs were implicated in numerous functional terms and pathways. Furthermore, 109 DEGs were identified to be survival-associated genes in COAD. According to the degrees of the network nodes, 5-hydroxytryptamine receptor 1D (HTR1D), TIMP metallopeptidase inhibitor 1 (TIMP1), serpin family E member 1 (SERPINE1), matrix metallopeptidase 3 (MMP3) and cannabinoid receptor 2 (CNR2) were key nodes, and the expression levels of these genes were analyzed in clinical samples of CRC. Therefore, the results of the present study suggest HTR1D, TIMP1, SERPINE1, MMP3 and CNR2 may affect the prognosis of patients with COAD.

SFTA1P, LINC00968, GATA6-AS1, TBX5-AS1, and FEZF1-AS1 are crucial long non-coding RNAs associated with the prognosis of lung squamous cell carcinoma

Lung cancer has high incidence and mortality rates, and lung squamous cell carcinoma (LUSC) is a common form of non-small-cell lung carcinoma (NSCLC). The aim of our study was to discover long non-coding RNAs (lncRNAs) associated with LUSC prognosis. RNA-sequencing data obtained from LUSC samples were extracted from The Cancer Genome Atlas database. Using the limma package, differentially expressed genes (DEGs; including differentially expressed lncRNA genes (DELs), coding genes (DECs), and other genes (DEOs)) between LUSC and control samples were analyzed. Using Kaplan-Meier survival analysis, prognosis-associated lncRNAs were further selected. Following the calculation of Pearson's correlation coefficients between DELs and other DEGs, the DEL-DEG co-expression network was visualized using Cytoscape software. Using the clusterProfiler package, potential functions for DECs co-expressed with DELs were predicted. There were 1,305 DEGs in LUSC samples, including 153 DELs, 1,109 DECs, and 43 DEOs. Based on survival analysis, 22 prognosis-associated lncRNAs (including surfactant associated 1, pseudogene (SFTA1P), long intergenic non-protein coding RNA 968 (LINC00968), GATA6 antisense RNA 1, (GATA6-AS1) TBX5 antisense RNA 1 (TBX5-AS1) and FEZF1 antisense RNA 1 (FEZF1-AS1)) in LUSC were selected from these DELs, and the associated abnormal expression levels were also verified in LUSC clinical samples. A DEL-DEG co-expression network was constructed, which involved 93 DELs. Co-expressed DECs were enriched for only 8 prognosis-associated DELs, including LINC00968, SFTA1P, and TBX5-AS1. Specifically, mitogen-activated protein kinase (MAPK) signaling pathway-associated genes were enriched in DECs co-expressed with LINC00968, SFTA1P, GATA6-AS1, TBX5-AS1 and FEZF1-AS1, which may be prognosis-associated lncRNAs in LUSC. In addition, LINC00968 may affect the outcome of patients with LUSC via the MAPK signaling pathway.

A 9‑lncRNA risk score system for predicting the prognosis of patients with hepatitis B virus‑positive hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer, and can be induced by hepatitis B virus (HBV) infection. The aim of the present study was to screen prognosis‑associated long noncoding RNAs (lncRNAs) and construct a risk score system for the disease. The RNA‑sequencing data of patients with HCC (including 100 HCC samples and 26 normal samples) were extracted from The Cancer Genome Atlas (TCGA) database. In addition, GSE55092, GSE19665 and GSE10186 datasets were downloaded from the Gene Expression Omnibus database. Combined with weighted gene co‑expression network analysis, the identification and functional annotation of stable modules was performed. Using the MetaDE package, the consensus differentially expressed RNAs (DE‑RNAs) were analyzed. To construct a risk score system, prognosis‑associated lncRNAs and the optimal lncRNA combination were separately analyzed by survival and penalized packages. Finally, pathway enrichment analysis for the nodes in an lncRNA‑mRNA network was conducted via Gene Set Enrichment Analysis. A total of four stable modules and 3,051 consensus DE‑RNAs were identified. The stable modules were significantly associated with the histological grades of HCC, tumor, node and metastasis stage, pathological stage, recurrence and exposure to radiation therapy. A 9‑lncRNA optimal combination [DiGeorge syndrome critical region gene 9, glucosidase, β, acid 3 (GBA3), HLA complex group 4, N‑acetyltransferase 8B, neighbor of breast cancer 1 gene 2, prostate androgen‑regulated transcript 1, ret finger protein like 1 antisense RNA 1, solute carrier family 22 member 18 antisense and T‑cell leukemia/lymphoma 6] was selected from the 14 prognosis‑associated lncRNAs, and was further supported by the validation dataset, GSE10186. The lncRNA‑mRNA co‑expression network revealed lncRNA GBA3 as a positive regulator of phosphoenolpyruvate carboxykinase 2, an important enzyme in the metabolic pathway of gluconeogenesis. A risk score system was established based on the optimal 9 lncRNAs, which may be valuable for predicting the prognosis of patients with HBV‑positive HCC and improving understanding of mechanisms associated with the pathogenesis of this disease. On the contrary, a larger, independent cohort of patients is required to further validate the risk‑score system.

Five-long non-coding RNA risk score system for the effective prediction of gastric cancer patient survival

The prognosis for patients with gastric cancer (GC) is usually poor, as the majority of patients have reached the advanced stages of disease at the point of diagnosis. Therefore, revealing the mechanisms of GC is necessary for the identification of key biomarkers and the development of effective targeted therapies. The present study aimed to identify long non-coding RNAs (lncRNAs) prominently expressed in patients with GC. The GC dataset (including 384 GC samples) was downloaded from The Cancer Genome Atlas database as the training set. A number of other GC datasets were obtained from the Gene Expression Omnibus database as validation sets. Following data processing, lncRNAs were annotated, followed by co-expression module analysis to identify stable modules, using the weighted gene co-expression network analysis (WGCNA) package. Prognosis-associated lncRNAs were screened using the ‘survival’ package. Following the selection of the optimal lncRNA combinations using the ‘penalized’ package, risk score systems were constructed and assessed. Consensus differentially-expressed RNAs (DE-RNAs) were screened using the MetaDE package, and an lncRNA-mRNA network was constructed. Additionally, pathway enrichment analysis was conducted for the network nodes using gene set enrichment analysis (GSEA). A total of seven modules (blue, brown, green, grey, red, turquoise and yellow) were obtained following WGCNA analysis, among which the green and turquoise modules were stable and associated with the histological grade of GC. A total of 12 prognosis-associated lncRNAs were identified in the two modules. Combined with the optimal lncRNA combinations, risk score systems were constructed. The risk score system based on the green module [including ITPK1 antisense RNA 1 (ITPK1-AS1), KCNQ1 downstream neighbor (KCNQ1DN), long intergenic non-protein coding RNA 167 (LINC00167), LINC00173 and LINC00307] was the more efficient at predicting risk compared with those based on the turquoise, or the green + turquoise modules. A total of 1,105 consensus DE-RNAs were identified; GSEA revealed that LINC00167, LINC00173 and LINC00307 had the same association directions with 4 pathways and the 32 genes involved in those pathways. In conclusion, a risk score system based on the green module may be applied to predict the survival of patients with GC. Furthermore, ITPK1-AS1, KCNQ1DN, LINC00167, LINC00173 and LINC00307 may serve as biomarkers for GC pathogenesis.

A random forest classifier predicts recurrence risk in patients with ovarian cancer

Ovarian cancer (OC) is associated with a poor prognosis due to difficulties in early detection. The aims of the present study were to construct a recurrence risk prediction model and to reveal important OC genes or pathways. RNA sequencing data was obtained for 307 OC samples, and the corresponding clinical data were downloaded from The Cancer Genome Atlas database. Additionally, two validation datasets, GSE44104 (20 recurrent and 40 non-recurrent OC samples) and GSE49997 (204 OC samples), were obtained from the Gene Expression Omnibus database. Differentially expressed genes were screened using the differential expression via distance synthesis algorithm, followed by gene ontology enrichment analysis and weighted gene coexpression network analysis (WGCNA). Furthermore, subnetwork analysis was conducted for the protein-protein interaction (PPI) network using the BioNet package. Finally, a random forest classifier was constructed based on the subnetwork nodes, and its reliability was validated using the GSE44104 and GSE49997 validation datasets. A total of 44 upregulated and 117 downregulated genes were identified in the recurrent samples. Enrichment analysis indicated that cytochrome P450 family 17 subfamily A member 1 (CYP17A1) was associated with ‘positive regulation of steroid hormone biosynthetic processes’. WGCNA identified turquoise and grey modules that were significantly correlated with status and prognosis. A significant PPI subnetwork containing 16 nodes was also identified, including: Transcription factor GATA-4; fibroblast growth factor 9; aromatase; 3β-hydroxysteroid dehydrogenase/δ5-4-isomerase type 2; corticosteroid 11β-dehydrogenase isozyme 1; CYP17A1; pituitary homeobox 2; left-right determination factor 1; homeobox protein ARX; estrogen receptor β; steroidogenic factor 1; forkhead box protein L2; myocardin; steroidogenic acute regulatory protein mitochondrial; vesicular inhibitory amino acid transporter; and twist-related protein 1. A random forest classifier was constructed using the subnetwork nodes as feature genes, which exhibited a 92% true positive rate when classifying recurrent and non-recurrent OC samples. The classifying efficiency of the random forest classifier was validated using the two other independent datasets. Overall, 44 upregulated and 117 downregulated genes associated with OC recurrence were identified. Furthermore, the 16 subnetwork node genes that were identified may be important molecules in OC recurrence.

Precision medicine in oncology: New practice models and roles for oncology pharmacists

PURPOSE

Three different precision medicine practice models developed by oncology pharmacists are described, including strategies for implementation and recommendations for educating the next generation of oncology pharmacy practitioners.

SUMMARY

Oncology is unique in that somatic mutations can both drive the development of a tumor and serve as a therapeutic target for treating the cancer. Precision medicine practice models are a forum through which interprofessional teams, including pharmacists, discuss tumor somatic mutations to guide patient-specific treatment. The University of Wisconsin, Indiana University, and Moffit Cancer Center have implemented precision medicine practice models developed and led by oncology pharmacists. Different practice models, including a clinic, a clinical consultation service, and a molecular tumor board (MTB), were adopted to enhance integration into health systems and payment structures. Although the practice models vary, commonalities of three models include leadership by the clinical pharmacist, specific therapeutic recommendations, procurement of medications for off-label use, and a research component. These three practice models function as interprofessional training sites for pharmacy and medical students and residents, providing an important training resource at these institutions. Key implementation strategies include interprofessional involvement, institutional support, integration into clinical workflow, and selection of model by payer mix.

CONCLUSION

MTBs are a pathway for clinical implementation of genomic medicine in oncology and are an emerging practice model for oncology pharmacists. Because pharmacists must be prepared to participate fully in contemporary practice, oncology pharmacy residents must be trained in genomic oncology, schools of pharmacy should expand precision medicine and genomics education, and opportunities for continuing education in precision medicine should be made available to practicing pharmacists.

Practical aspects of NGS-based pathways analysis for personalized cancer science and medicine

Nowadays, the personalized approach to health care and cancer care in particular is becoming more and more popular and is taking an important place in the translational medicine paradigm. In some cases, detection of the patient-specific individual mutations that point to a targeted therapy has already become a routine practice for clinical oncologists. Wider panels of genetic markers are also on the market which cover a greater number of possible oncogenes including those with lower reliability of resulting medical conclusions. In light of the large availability of high-throughput technologies, it is very tempting to use complete patient-specific New Generation Sequencing (NGS) or other "omics" data for cancer treatment guidance. However, there are still no gold standard methods and protocols to evaluate them. Here we will discuss the clinical utility of each of the data types and describe a systems biology approach adapted for single patient measurements. We will try to summarize the current state of the field focusing on the clinically relevant case-studies and practical aspects of data processing.

Development of Targeted Therapies Based on Gene Modification

With the advent of next-generation sequencing (NGS) and the demand for a personalized healthcare system, the fields of precision medicine and gene therapy are advancing in new directions. There is a push to identify genes that contribute to disease development, either alone or in conjunction with other genes or environmental factors, and then design targeted therapies based on this knowledge, rather than the traditional approach of treating generalized symptoms with pharmaceuticals in a one-size-fits-all manner. Identification of genes that contribute to disease pathogenesis and progression is critical for the maturation of the precision medicine field. Concomitant with a better understanding of disease pathology, precision medicine approaches can be adopted with greater confidence and are expected to lead to a new standard for clinical practice. In this chapter, we provide a brief introduction to precision medicine, discuss the importance of identifying genes and genetic variants that contribute to disease development and progression, offer examples of approaches that can be applied to treat specific diseases, and present some of the current challenges and limitations of precision medicine.

Implementation of a Precision Pathology Program Focused on Oncology-Based Prognostic and Predictive Outcomes

Personalized or precision medicine as a diagnostic and therapeutic paradigm was introduced some 10-15 years ago, with the advent of biomarker discovery as a mechanism for identifying prognostic and predictive attributes associated with treatment indication and outcome. While the concept is not new, the successful development and implementation of novel 'companion diagnostics', especially in oncology, continues to represent a significant challenge and is currently at the forefront of smart trial design and therapeutic choice. The ability to determine patient selection for a specific therapy has broad implications including better chances for a positive outcome, limited exposure to potentially toxic drugs and improved health economics. Importantly, a significant step in this paradigm is the role of predictive pathology or the accurate assessment of morphology at the microscopic level. In breast cancer, this has been most useful where histologic attributes such as the classification of tubular and cribriform carcinoma dictates surgery while neoadjuvant studies suggest that patients with lobular carcinoma are not likely to benefit from chemotherapy. The next level of 'personalized pathology' at the tissue-cellular level is the use of 'protein biomarker panels' to classify the disease process and ultimately drive tumor characterization and treatment. The following review article will focus on the evolution of predictive pathology from a subjective, 'opinion-based' approach to a quantitative science. In addition, we will discuss the individual components of the precise pathology platform including advanced image analysis, biomarker quantitation with mathematical modeling and the integration with fluid-based (i.e. blood, urine) analytics as drivers of next generation precise patient phenotyping.

PathOS: a decision support system for reporting high throughput sequencing of cancers in clinical diagnostic laboratories

Background

The increasing affordability of DNA sequencing has allowed it to be widely deployed in pathology laboratories. However, this has exposed many issues with the analysis and reporting of variants for clinical diagnostic use. Implementing a high-throughput sequencing (NGS) clinical reporting system requires a diverse combination of capabilities, statistical methods to identify variants, global variant databases, a validated bioinformatics pipeline, an auditable laboratory workflow, reproducible clinical assays and quality control monitoring throughout. These capabilities must be packaged in software that integrates the disparate components into a useable system.

Results

To meet these needs, we developed a web-based application, PathOS, which takes variant data from a patient sample through to a clinical report. PathOS has been used operationally in the Peter MacCallum Cancer Centre for two years for the analysis, curation and reporting of genetic tests for cancer patients, as well as the curation of large-scale research studies. PathOS has also been deployed in cloud environments allowing multiple institutions to use separate, secure and customisable instances of the system. Increasingly, the bottleneck of variant curation is limiting the adoption of clinical sequencing for molecular diagnostics. PathOS is focused on providing clinical variant curators and pathology laboratories with a decision support system needed for personalised medicine. While the genesis of PathOS has been within cancer molecular diagnostics, the system is applicable to NGS clinical reporting generally.

Conclusions

The widespread availability of genomic sequencers has highlighted the limited availability of software to support clinical decision-making in molecular pathology. PathOS is a system that has been developed and refined in a hospital laboratory context to meet the needs of clinical diagnostics. The software is available as a set of Docker images and source code at https://github.com/PapenfussLab/PathOS.

Electronic supplementary material

The online version of this article (doi:10.1186/s13073-017-0427-z) contains supplementary material, which is available to authorized users.

Mining the fecal proteome: from biomarkers to personalised medicine

INTRODUCTION

Fecal proteomics has gained increased prominence in recent years. It can provide insights into the diagnosis and surveillance of many bowel diseases by both identifying potential biomarkers in stool samples and helping identify disease-related pathways. Fecal proteomics has already shown its potential for the discovery and validation of biomarkers for colorectal cancer screening, and the analysis of fecal microbiota by MALDI-MS for the diagnosis of a range of bowel diseases is gaining clinical acceptance. Areas covered: Based on a comprehensive analysis of the current literature, we introduce the range of sensitive and specific proteomics methods which comprise the current 'Proteomics Toolbox', explain how the integration of fecal proteomics with data processing/bioinformatics has been used for the identification of potential biomarkers for both CRC and other gut-related pathologies and analysis of the fecal microbiome, outline some of the current fecal assays in current clinical practice and introduce the concept of personalised medicine which these technologies will help inform. Expert commentary: Integration of fecal proteomics with other proteomics and genomics strategies as well as bioinformatics is paving the way towards personalised medicine, which will bring with it improved global healthcare.

Toward Precision Healthcare: Context and Mathematical Challenges

Precision medicine refers to the idea of delivering the right treatment to the right patient at the right time, usually with a focus on a data-centered approach to this task. In this perspective piece, we use the term "precision healthcare" to describe the development of precision approaches that bridge from the individual to the population, taking advantage of individual-level data, but also taking the social context into account. These problems give rise to a broad spectrum of technical, scientific, policy, ethical and social challenges, and new mathematical techniques will be required to meet them. To ensure that the science underpinning "precision" is robust, interpretable and well-suited to meet the policy, ethical and social questions that such approaches raise, the mathematical methods for data analysis should be transparent, robust, and able to adapt to errors and uncertainties. In particular, precision methodologies should capture the complexity of data, yet produce tractable descriptions at the relevant resolution while preserving intelligibility and traceability, so that they can be used by practitioners to aid decision-making. Through several case studies in this domain of precision healthcare, we argue that this vision requires the development of new mathematical frameworks, both in modeling and in data analysis and interpretation.

Precision medicine in cancer: challenges and recommendations from an EU-funded cervical cancer biobanking study

Background:

Cervical cancer (CC) remains a leading cause of gynaecological cancer-related mortality worldwide. CC pathogenesis is triggered when human papillomavirus (HPV) inserts into the genome, resulting in tumour suppressor gene inactivation and oncogene activation. Collecting tumour and blood samples is critical for identifying these genetic alterations.

Methods:

BIO-RAIDs is the first prospective molecular profiling clinical study to include a substantial biobanking effort that used uniform high-quality standards and control of samples. In this European Union (EU)-funded study, we identified the challenges that were impeding the effective implementation of such a systematic and comprehensive biobanking effort.

Results:

The challenges included a lack of uniform international legal and ethical standards, complexities in clinical and molecular data management, and difficulties in determining the best technical platforms and data analysis techniques. Some difficulties were encountered by all investigators, while others affected only certain institutions, regions, or countries.

Conclusions:

The results of the BIO-RAIDs programme highlight the need to facilitate and standardise regulatory procedures, and we feel that there is also a need for international working groups that make recommendations to regulatory bodies, governmental funding agencies, and academic institutions to achieve a proficient biobanking programme throughout EU countries. This represents the first step in precision medicine.

Next-Generation Sequencing and Result Interpretation in Clinical Oncology: Challenges of Personalized Cancer Therapy

The tools of next-generation sequencing (NGS) technology, such as targeted sequencing of candidate cancer genes and whole-exome and -genome sequencing, coupled with encouraging clinical results based on the use of targeted therapeutics and biomarker-guided clinical trials, are fueling further technological advancements of NGS technology. However, NGS data interpretation is associated with challenges that must be overcome to promote the techniques' effective integration into clinical oncology practice. Specifically, sequencing of a patient's tumor often yields 30-65 somatic variants, but most of these variants are "passenger" mutations that are phenotypically neutral and thus not targetable. Therefore, NGS data must be interpreted by multidisciplinary decision-support teams to determine mutation actionability and identify potential "drivers," so that the treating physician can prioritize what clinical decisions can be pursued in order to provide cancer therapy that is personalized to the patient and his or her unique genome.

Big Data and machine learning in radiation oncology: State of the art and future prospects

Precision medicine relies on an increasing amount of heterogeneous data. Advances in radiation oncology, through the use of CT Scan, dosimetry and imaging performed before each fraction, have generated a considerable flow of data that needs to be integrated. In the same time, Electronic Health Records now provide phenotypic profiles of large cohorts of patients that could be correlated to this information. In this review, we describe methods that could be used to create integrative predictive models in radiation oncology. Potential uses of machine learning methods such as support vector machine, artificial neural networks, and deep learning are also discussed.

Treatment Algorithms Based on Tumor Molecular Profiling: The Essence of Precision Medicine Trials

With the advent of high-throughput molecular technologies, several precision medicine (PM) studies are currently ongoing that include molecular screening programs and PM clinical trials. Molecular profiling programs establish the molecular profile of patients' tumors with the aim to guide therapy based on identified molecular alterations. The aim of prospective PM clinical trials is to assess the clinical utility of tumor molecular profiling and to determine whether treatment selection based on molecular alterations produces superior outcomes compared with unselected treatment. These trials use treatment algorithms to assign patients to specific targeted therapies based on tumor molecular alterations. These algorithms should be governed by fixed rules to ensure standardization and reproducibility. Here, we summarize key molecular, biological, and technical criteria that, in our view, should be addressed when establishing treatment algorithms based on tumor molecular profiling for PM trials.

Molecular Dynamics: New Frontier in Personalized Medicine

The field of drug discovery has witnessed infinite development over the last decade with the demand for discovery of novel efficient lead compounds. Although the development of novel compounds in this field has seen large failure, a breakthrough in this area might be the establishment of personalized medicine. The trend of personalized medicine has shown stupendous growth being a hot topic after the successful completion of Human Genome Project and 1000 genomes pilot project. Genomic variant such as SNPs play a vital role with respect to inter individual's disease susceptibility and drug response. Hence, identification of such genetic variants has to be performed before administration of a drug. This process requires high-end techniques to understand the complexity of the molecules which might bring an insight to understand the compounds at their molecular level. To sustenance this, field of bioinformatics plays a crucial role in revealing the molecular mechanism of the mutation and thereby designing a drug for an individual in fast and affordable manner. High-end computational methods, such as molecular dynamics (MD) simulation has proved to be a constitutive approach to detecting the minor changes associated with an SNP for better understanding of the structural and functional relationship. The parameters used in molecular dynamic simulation elucidate different properties of a macromolecule, such as protein stability and flexibility. MD along with docking analysis can reveal the synergetic effect of an SNP in protein-ligand interaction and provides a foundation for designing a particular drug molecule for an individual. This compelling application of computational power and the advent of other technologies have paved a promising way toward personalized medicine. In this in-depth review, we tried to highlight the different wings of MD toward personalized medicine.

From prospective biobanking to precision medicine: BIO-RAIDs - an EU study protocol in cervical cancer

Background

Cervical cancer (CC) is -second to breast cancer- a dominant cause of gynecological cancer-related deaths worldwide. CC tumor biopsies and blood samples are of easy access and vital for the development of future precision medicine strategies.

Design

BIO-RAIDs is a prospective multicenter European study, presently recruiting patients in 6 EU countries. Tumor and liquid biopsies from patients with previously non-treated cervical cancer (stages IB2-IV) are collected at defined time points. Patients receive standard primary treatment according to the stage of their disease. 700 patients are planned to be enrolled. The main objectives are the discovery of -dominant molecular alterations, -signalling pathway activation, and -tumor micro-environment patterns that may predict response or resistance to treatment. An exhaustive molecular analysis is performed using 1° Next generation sequencing, 2° Reverse phase protein arrays and 3° Immuno-histochemistry.

Discussion

The clinical study BIO-RAIDs is activated in all planned countries, 170 patients have been recruited till now. This study will make an important contribution towards precision medicine treatments in cervical cancer. The results will support the development of clinical practice guidelines for cervical cancer patients to improve their prognosis and their quality of life.

Trial registration

Clinicaltrials.gov: NCT02428842, registered 10 February 2015.

The right drugs at the right time for the right patient: the MD Anderson precision oncology decision support platform

The development of resources for clinical interpretation of cancer-associated genetic alterations has significantly lagged behind the technical developments enabling their detection in a time- and cost-efficient manner. The lack of scientific and informatics decision support for oncologists can lead to no action being taken or suboptimal therapeutic choices being made, which could affect the clinical outcome of a patient as well as convoluting research findings from clinical trials. In this article, we describe the precision oncology decision support (PODS) platform developed within The Sheikh Khalifa Bin Zayed Al Nahyan Institute for Personalized Cancer Therapy (IPCT) at MD Anderson Cancer Center; the platform aims to bridge the gap between molecular alteration detection and identification of appropriate treatments.

A primer on precision medicine informatics

In this review, we describe key components of a computational infrastructure for a precision medicine program that is based on clinical-grade genomic sequencing. Specific aspects covered in this review include software components and hardware infrastructure, reporting, integration into Electronic Health Records for routine clinical use and regulatory aspects. We emphasize informatics components related to reproducibility and reliability in genomic testing, regulatory compliance, traceability and documentation of processes, integration into clinical workflows, privacy requirements, prioritization and interpretation of results to report based on clinical needs, rapidly evolving knowledge base of genomic alterations and clinical treatments and return of results in a timely and predictable fashion. We also seek to differentiate between the use of precision medicine in germline and cancer.

Evaluation and comparison of two commercially available targeted next-generation sequencing platforms to assist oncology decision making

Background

It is widely acknowledged that there is value in examining cancers for genomic aberrations via next-generation sequencing (NGS). How commercially available NGS platforms compare with each other, and the clinical utility of the reported actionable results, are not well known. During the course of the current study, the Foundation One (F1) test generated data on a combination of somatic mutations, insertion and deletion polymorphisms, chromosomal abnormalities, and deoxyribonucleic acid (DNA) copy number changes at ~250× coverage, while the Paradigm Cancer Diagnostic (PCDx) test generated the same type of data at >5,000× coverage, plus provided messenger RNA (mRNA) expression levels. We sought to compare and evaluate paired formalin-fixed paraffin-embedded tumor tissue using these two platforms.

Methods

Samples from patients with advanced solid tumors were submitted to both the F1 and PCDx vendors for NGS analysis. Turnaround time (TAT) was calculated. Biomarkers were considered clinically actionable if they had a published association with treatment response in humans and were assigned to the following categories: commercially available drug (CA), clinical trial drug (CT), or neither option (hereafter referred to as “None”).

Results

The demographics of the 21 unique patient tumor samples included ten men and eleven women, with a median age of 56 years. Due to insufficient archival tissue from the same collection period, in one case, we used samples from different collections. PCDx reported first results faster than F1 in 20 cases. When received at both vendors on the same day, PCDx reported first results for 14 of 15 cases, with a median TAT of 9 days earlier than F1 (P<0.0001). Categorization of CA compared to CT and none significantly favored PCDx (P=0.012).

Conclusion

In the current analysis, commercially available NGS platforms provided clinically relevant actionable targets (CA or CT) in 47%–67% of diverse cancer types. In the samples analyzed, PCDx significantly outperformed F1 in TAT, and had statistically significant higher clinically relevant actionable targets categorized as CA.

GeneMed: An Informatics Hub for the Coordination of Next-Generation Sequencing Studies that Support Precision Oncology Clinical Trials

We have developed an informatics system, GeneMed, for the National Cancer Institute (NCI) molecular profiling-based assignment of cancer therapy (MPACT) clinical trial (NCT01827384) being conducted in the National Institutes of Health (NIH) Clinical Center. This trial is one of the first to use a randomized design to examine whether assigning treatment based on genomic tumor screening can improve the rate and duration of response in patients with advanced solid tumors. An analytically validated next-generation sequencing (NGS) assay is applied to DNA from patients’ tumors to identify mutations in a panel of genes that are thought likely to affect the utility of targeted therapies available for use in the clinical trial. The patients are randomized to a treatment selected to target a somatic mutation in the tumor or with a control treatment. The GeneMed system streamlines the workflow of the clinical trial and serves as a communications hub among the sequencing lab, the treatment selection team, and clinical personnel. It automates the annotation of the genomic variants identified by sequencing, predicts the functional impact of mutations, identifies the actionable mutations, and facilitates quality control by the molecular characterization lab in the review of variants. The GeneMed system collects baseline information about the patients from the clinic team to determine eligibility for the panel of drugs available. The system performs randomized treatment assignments under the oversight of a supervising treatment selection team and generates a patient report containing detected genomic alterations. NCI is planning to expand the MPACT trial to multiple cancer centers soon. In summary, the GeneMed system has been proven to be an efficient and successful informatics hub for coordinating the reliable application of NGS to precision medicine studies.

Circulating tumor DNA as a non-invasive substitute to metastasis biopsy for tumor genotyping and personalized medicine in a prospective trial across all tumor types

Cell-free tumor DNA (ctDNA) has the potential to enable non-invasive diagnostic tests for personalized medicine in providing similar molecular information as that derived from invasive tumor biopsies. The histology-independent phase II SHIVA trial matches patients with targeted therapeutics based on previous screening of multiple somatic mutations using metastatic biopsies. To evaluate the utility of ctDNA in this trial, as an ancillary study we performed de novo detection of somatic mutations using plasma DNA compared to metastasis biopsies in 34 patients covering 18 different tumor types, scanning 46 genes and more than 6800 COSMIC mutations with a multiplexed next-generation sequencing panel. In 27 patients, 28 of 29 mutations identified in metastasis biopsies (97%) were detected in matched ctDNA. Among these 27 patients, one additional mutation was found in ctDNA only. In the seven other patients, mutation detection from metastasis biopsy failed due to inadequate biopsy material, but was successful in all plasma DNA samples providing three more potential actionable mutations. These results suggest that ctDNA analysis is a potential alternative and/or replacement to analyses using costly, harmful and lengthy tissue biopsies of metastasis, irrespective of cancer type and metastatic site, for multiplexed mutation detection in selecting personalized therapies based on the patient's tumor genetic content.

Circulating tumor DNA as a non-invasive substitute to metastasis biopsy for tumor genotyping and personalized medicine in a prospective trial across all tumor types

Cell-free tumor DNA (ctDNA) has the potential to enable non-invasive diagnostic tests for personalized medicine in providing similar molecular information as that derived from invasive tumor biopsies. The histology-independent phase II SHIVA trial matches patients with targeted therapeutics based on previous screening of multiple somatic mutations using metastatic biopsies. To evaluate the utility of ctDNA in this trial, as an ancillary study we performed de novo detection of somatic mutations using plasma DNA compared to metastasis biopsies in 34 patients covering 18 different tumor types, scanning 46 genes and more than 6800 COSMIC mutations with a multiplexed next-generation sequencing panel. In 27 patients, 28 of 29 mutations identified in metastasis biopsies (97%) were detected in matched ctDNA. Among these 27 patients, one additional mutation was found in ctDNA only. In the seven other patients, mutation detection from metastasis biopsy failed due to inadequate biopsy material, but was successful in all plasma DNA samples providing three more potential actionable mutations. These results suggest that ctDNA analysis is a potential alternative and/or replacement to analyses using costly, harmful and lengthy tissue biopsies of metastasis, irrespective of cancer type and metastatic site, for multiplexed mutation detection in selecting personalized therapies based on the patient's tumor genetic content.