Clinical tumor sequencing: opportunities and challenges for precision cancer medicine

Development of a generic decision guide for patients in oncology: a qualitative interview study

BACKGROUND

Many patients with cancer want to be involved in healthcare decisions. For adequate participation, awareness of one's own desires and preferences and sufficient knowledge about medical measures are indispensable. In order to support patient participation, a decision guide for patients with cancer was developed as part of a larger project called TARGET, which specifically aims to improve the care of patients with rare cancer.

METHODS

The development of the decision guide took place from 08.2022 to 03.2023. The decision guide is a single component of a complex intervention that aims to facilitate decision support in cancer care for patients. For the development, existing development and evaluation studies of Question Prompt Lists (QPLs) were identified through systematic literature searches in the MEDLINE via PubMed, PsycInfo, and CINAHL databases. The decision guide was pre-tested for feasibility, usability, completeness and acceptance with the target groups through guided individual interviews. Sociodemographic data were collected anonymously. An expert review was conducted. The verbatim transcribed interviews were analysed using content analysis according to Kuckartz with MAXQDA. The guide has been iteratively optimized based on the results.

RESULTS

A generic decision guide for patients with cancer for diagnostic or treatment decisions was developed in both PDF web-based formats, based on the Ottawa Personal Decision Guide. It was supplemented with decision-related questions from QPLs for patients with cancer. The pre-test comprised seven expert reviews of (psych)oncologists and experts in evidence-based health information and ten interviews with cancer patients (n = 7), family relatives (n = 2), and one caregiver. The results were coded into nine main categories. The results indicated a good feasibility, usability and acceptability of the guide. The tool was perceived as comprehensive and appropriate. Individual elements were identified as modifiable for better comprehensibility. The target audience appreciated the decision guide as a good support option.

CONCLUSION

The decision guide is potentially a useful support option for patients with cancer facing medical decisions in their further course of treatment. In the TARGET project, it will be made available to patients and can be supplemented with decision coaching. Further steps for implementation into healthcare structures are necessary.

CLINICAL TRIAL NUMBER

Not applicable.

Navigating the Collective: Nanoparticle-Assisted Identification of Leader Cancer Cells During Migration

Cancer-related deaths primarily occur due to metastasis, a process involving the migration and invasion of cancer cells. In most solid tumors, metastasis occurs through collective cell migration (CCM), guided by "cellular leaders". These leader cells generate forces through actomyosin-mediated protrusion and contractility. The cytoskeletal mechanisms employed by metastatic cells during the migration process closely resemble the use of the actin cytoskeleton in endocytosis. In our previous work, we revealed that tumor cells exhibiting high metastatic potential (MP) are more adept at encapsulating 100-200 nm nanoparticles than those with lower MP. The objective of this study was to investigate whether nanoparticle encapsulation could effectively differentiate leader tumor cells during their CCM. To achieve our objectives, we employed a two-dimensional CCM model grounded in the wound-healing ("scratch") assay, utilizing two breast cancer cell lines, MCF7 and MDA-MB-231, which display low and high migratory potential, respectively. We conducted calibration experiments to identify the "optimal time" at which cells exhibit peak speed during wound closure. Furthermore, we carried out experiments to assess nanoparticle uptake, calculating the colocalization coefficient, and employed phalloidin staining to analyze the anisotropy and orientation of actin filaments. The highest activity for low-MP cells was achieved at 2.6 h during the calibration experiments, whereas high-MP cells were maximally active at 3.9 h, resulting in 8% and 11% reductions in wound area, respectively. We observed a significant difference in encapsulation efficiency between leader and peripheral cells for both high-MP (p < 0.013) and low-MP (p < 0.02) cells. Moreover, leader cells demonstrated a considerably higher anisotropy coefficient (p < 0.029), indicating a more organized, directional structure of actin filaments compared to peripheral cells. Thus, nanoparticle encapsulation offers a groundbreaking approach to identifying the most aggressive and invasive leader cells during the CCM process in breast cancer. Detecting these cells is crucial for developing targeted therapies that can effectively curb metastasis and improve patient outcomes.

Health equity innovation in precision medicine: data stewardship and agency to expand representation in clinicogenomics

Most forms of clinical research examine a very minute cross section of the patient journey. Much of the knowledge and evidence base driving current genomic medicine practice entails blind spots arising from underrepresentation and lack of research participation in clinicogenomic databases. The flaws are perpetuated in AI models and clinical practice guidelines that reflect the lack of diversity in data being used. Participation in clinical research and biobanks is impeded in many populations due to a variety of factors that include knowledge, trust, healthcare access, administrative barriers, and technology gaps. A recent symposium brought industry, clinical, and research participants in clinicogenomics to discuss practical challenges and potential for new data sharing models that are patient centric and federated in nature and can address health disparities that might be perpetuated by lack of diversity in clinicogenomic research, biobanks, and datasets. Clinical data governance was recognized as a multiagent problem, and governance practices need to be more patient centric to address most barriers. Digital tools that preserve privacy, document provenance, and enable the management of data as intellectual property have great promise. Policy updates realigning and rationalizing clinical data governance practices are warranted.

The Evolving Role of Genomic Testing in Early Breast Cancer: Implications for Diagnosis, Prognosis, and Therapy

Multigene prognostic genomic assays have become indispensable in managing early breast cancer (EBC), offering crucial information for risk stratification and guiding adjuvant treatment strategies in conjunction with traditional clinicopathological parameters. The American Society of Clinical Oncology (ASCO) guidelines endorse these assays, though some clinical contexts still lack definitive recommendations. The dynamic landscape of EBC management demands further refinement and optimization of genomic assays to streamline their incorporation into clinical practice. The breast cancer community is poised at the brink of transformative advances in enhancing the clinical utility of genomic assays, aiming to significantly improve the precision and effectiveness of both diagnosis and treatment for women with EBC. This article methodically examines the testing methodologies, clinical validity and utility, costs, diagnostic frameworks, and methodologies of the established genomic tests, including the Oncotype Dx Breast Recurrence Score®, MammaPrint, Prosigna®, EndoPredict®, and Breast Cancer Index (BCI). Among these tests, Prosigna and EndoPredict® have at present been validated only on a prognostic level, while Oncotype Dx, MammaPrint, and BCI hold both a prognostic and predictive role. Oncologists and pathologists engaged in the management of EBC will find in this review a thorough comparison of available genomic assays, as well as strategies to optimize the utilization of the information derived from them.

Lightweight Low-Rank Adaptation Vision Transformer Framework for Cervical Cancer Detection and Cervix Type Classification

Cervical cancer is a major health concern worldwide, highlighting the urgent need for better early detection methods to improve outcomes for patients. In this study, we present a novel digital pathology classification approach that combines Low-Rank Adaptation (LoRA) with the Vision Transformer (ViT) model. This method is aimed at making cervix type classification more efficient through a deep learning classifier that does not require as much data. The key innovation is the use of LoRA, which allows for the effective training of the model with smaller datasets, making the most of the ability of ViT to represent visual information. This approach performs better than traditional Convolutional Neural Network (CNN) models, including Residual Networks (ResNets), especially when it comes to performance and the ability to generalize in situations where data are limited. Through thorough experiments and analysis on various dataset sizes, we found that our more streamlined classifier is highly accurate in spotting various cervical anomalies across several cases. This work advances the development of sophisticated computer-aided diagnostic systems, facilitating more rapid and accurate detection of cervical cancer, thereby significantly enhancing patient care outcomes.

The Genomics Organization for Academic Laboratories (GOAL): A vision for a genomics future for academic pathology

Innovative and self-sustaining clinical genomics laboratories specializing in cutting-edge oncology testing are critical to the success of academic pathology departments and resident and fellow education in molecular pathology. However, the pressures and challenges facing these laboratories are numerous, including the complexities of validating comprehensive cancer next-generation sequencing (NGS) panels, competition from commercial laboratories, and the reimbursement and regulatory hurdles inherent in high-complexity testing. Cross-institutional collaborations, including shared assay content and interpretative frameworks, are a valuable element to academic laboratory success. To address these and other needs, the Genomics Organization for Academic Laboratories (GOAL) was conceived in 2018, incorporated in 2020 and has grown to include 29 participating institutions in 2022. Here, we describe the mission of GOAL, its structure, and the outcomes and projects undertaken in its first years.

Multi-Gene Next-Generation Sequencing Panel for Analysis of BRCA1/BRCA2 and Homologous Recombination Repair Genes Alterations Metastatic Castration-Resistant Prostate Cancer

Despite significant therapeutic advances, metastatic CRPC (mCRPC) remains a lethal disease. Mutations in homologous recombination repair (HRR) genes are frequent in mCRPC, and tumors harboring these mutations are known to be sensitive to PARP inhibitors. The aim of this study was to verify the technical effectiveness of this panel in the analysis of mCRPC, the frequency and type of mutations in the BRCA1/BRCA2 genes, as well as in the homologous recombination repair (HRR) genes. A total of 50 mCRPC cases were analyzed using a multi-gene next-generation sequencing panel evaluating a total of 1360 amplicons in 24 HRR genes. Of the 50 cases, 23 specimens (46.0%) had an mCRPC harboring a pathogenic variant or a variant of uncertain significance (VUS), whereas in 27 mCRPCs (54.0%), no mutations were detected (wild-type tumors). BRCA2 was the most commonly mutated gene (14.0% of samples), followed by ATM (12.0%), and BRCA1 (6.0%). In conclusion, we have set up an NGS multi-gene panel that is capable of analyzing BRCA1/BRCA2 and HRR alterations in mCRPC. Moreover, our clinical algorithm is currently being used in clinical practice for the management of patients with mCRPC.

Artificial-intelligence-based molecular classification of diffuse gliomas using rapid, label-free optical imaging

Molecular classification has transformed the management of brain tumors by enabling more accurate prognostication and personalized treatment. However, timely molecular diagnostic testing for patients with brain tumors is limited, complicating surgical and adjuvant treatment and obstructing clinical trial enrollment. In this study, we developed DeepGlioma, a rapid (<90 seconds), artificial-intelligence-based diagnostic screening system to streamline the molecular diagnosis of diffuse gliomas. DeepGlioma is trained using a multimodal dataset that includes stimulated Raman histology (SRH); a rapid, label-free, non-consumptive, optical imaging method; and large-scale, public genomic data. In a prospective, multicenter, international testing cohort of patients with diffuse glioma (n = 153) who underwent real-time SRH imaging, we demonstrate that DeepGlioma can predict the molecular alterations used by the World Health Organization to define the adult-type diffuse glioma taxonomy (IDH mutation, 1p19q co-deletion and ATRX mutation), achieving a mean molecular classification accuracy of 93.3 ± 1.6%. Our results represent how artificial intelligence and optical histology can be used to provide a rapid and scalable adjunct to wet lab methods for the molecular screening of patients with diffuse glioma.

DNA and RNA Alterations Associated with Colorectal Peritoneal Metastases: A Systematic Review

BACKGROUND

As colorectal cancer (CRC) patients with peritoneal metastases (PM) have a poor prognosis, new treatment options are currently being investigated for CRC patients. Specific biomarkers in the primary tumor could serve as a prediction tool to estimate the risk of distant metastatic spread. This would help identify patients eligible for early treatment.

AIM

To give an overview of previously studied DNA and RNA alterations in the primary tumor correlated to colorectal PM and investigate which gene mutations should be further studied.

METHODS

A systematic review of all published studies reporting genomic analyses on the primary tissue of CRC tumors in relation to PM was undertaken according to PRISMA guidelines.

RESULTS

Overall, 32 studies with 18,906 patients were included. BRAF mutations were analyzed in 17 articles, of which 10 found a significant association with PM. For all other reported genes, no association with PM was found. Two analyses with broader cancer panels did not reveal any new biomarkers.

CONCLUSION

An association of specific biomarkers in the primary tumors of CRC patients with metastatic spread into peritoneum could not be proven. The role of BRAF mutations should be further investigated. In addition, studies searching for potential novel biomarkers are still required.

Combined Focused Next-Generation Sequencing Assays to Guide Precision Oncology in Solid Tumors: A Retrospective Analysis from an Institutional Molecular Tumor Board

BACKGROUND

Increasing knowledge of cancer biology and an expanding spectrum of molecularly targeted therapies provide the basis for precision oncology. Despite extensive gene diagnostics, previous reports indicate that less than 10% of patients benefit from this concept.

METHODS

We retrospectively analyzed all patients referred to our center's Molecular Tumor Board (MTB) from 2018 to 2021. Molecular testing by next-generation sequencing (NGS) included a 67-gene panel for the detection of short-sequence variants and copy-number alterations, a 53- or 137-gene fusion panel and an ultra-low-coverage whole-genome sequencing for the detection of additional copy-number alterations outside the panel's target regions. Immunohistochemistry for microsatellite instability and PD-L1 expression complemented NGS.

RESULTS

A total of 109 patients were referred to the MTB. In all, 78 patients received therapeutic proposals (70 based on NGS) and 33 were treated accordingly. Evaluable patients treated with MTB-recommended therapy (n = 30) had significantly longer progression-free survival than patients treated with other therapies (n = 17) (4.3 vs. 1.9 months, p = 0.0094). Seven patients treated with off-label regimens experienced major clinical benefits.

CONCLUSION

The combined focused sequencing assays detected targetable alterations in the majority of patients. Patient benefits appeared to lie in the same range as with large-scale sequencing approaches.

Unravelling the tumour genome: The evolutionary and clinical impacts of structural variants in tumourigenesis

Structural variants (SVs) represent a major source of aberration in tumour genomes. Given the diversity in the size and type of SVs present in tumours, the accurate detection and interpretation of SVs in tumours is challenging. New classes of complex structural events in tumours are discovered frequently, and the definitions of the genomic consequences of complex events are constantly being refined. Detailed analyses of short-read whole-genome sequencing (WGS) data from large tumour cohorts facilitate the interrogation of SVs at orders of magnitude greater scale and depth. However, the inherent technical limitations of short-read WGS prevent us from accurately detecting and investigating the impact of all the SVs present in tumours. The expanded use of long-read WGS will be critical for improving the accuracy of SV detection, and in fully resolving complex SV events, both of which are crucial for determining the impact of SVs on tumour progression and clinical outcome. Despite the present limitations, we demonstrate that SVs play an important role in tumourigenesis. In particular, SVs contribute significantly to late-stage tumour development and to intratumoural heterogeneity. The evolutionary trajectories of SVs represent a window into the clonal dynamics in tumours, a comprehensive understanding of which will be vital for influencing patient outcomes in the future. Recent findings have highlighted many clinical applications of SVs in cancer, from early detection to biomarkers for treatment response and prognosis. As the methods to detect and interpret SVs improve, elucidating the full breadth of the complex SV landscape and determining how these events modulate tumour evolution will improve our understanding of cancer biology and our ability to capitalise on the utility of SVs in the clinical management of cancer patients. © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

The History and Future of Basic and Translational Cell-Free DNA Research at a Glance

We discuss the early history of the structure of DNA and its involvement in gene structure as well as its mobility in and between cells and between tissues in the form of circulating cell-free DNA (cfDNA). This is followed by a view of the present status of the studies on cfDNA and clinical applications of circulating cell-free tumor DNA (ctDNA). The future developments and roles of ctDNA are also considered.

The Clinical Utility and Impact of Next Generation Sequencing in Gynecologic Cancers

Simple Summary

Cancer cells harbor many genetic abnormalities, but the key oncogenic pathways that lead to clinically evident cancer require driver mutations termed actionable mutations. These actionable mutations can be detected using genomic profiling or next-generation sequencing tests. This discovery has led to a tremendous change in treatment regimens from standard chemotherapy to targeted therapy where drugs are specifically targeted against these actionable mutations. Due to the cost-effectiveness and various testing platforms, utilization of these tests by oncologists has increased enormously, but the impact of targeted therapy based on these test results is still understudied. We aimed to identify the clinical utility rate of the tests and analyze the survival benefit for those receiving targeted therapy based on the test results of gynecologic cancer patients. Our findings showed high clinical utility of the tests used by gynecologic oncologists along with a significant survival benefit.

Abstract

Next generation sequencing (NGS) has facilitated the identification of molecularly targeted therapies. However, clinical utility is an emerging challenge. Our objective was to identify the clinical utility of NGS testing in gynecologic cancers. A retrospective review of clinico-pathologic data was performed on 299 gynecological cancers where NGS testing had been performed to identify (1) recognition of actionable targets for therapy, (2) whether the therapy changed based on the findings, and (3) the impact on survival. High grade serous carcinoma was the most common tumor (52.5%). The number of genetic alterations ranged from 0 to 25 with a mean of 2.8/case. The most altered genes were TP53, PIK3CA, BRCA1 and BRCA2. Among 299 patients, 100 had actionable alterations (79 received a targeted treatment (Group1), 29 did not receive treatment (Group 2), and there were no actionable alterations in 199 (Group3). The death rate in groups 1, 2 and 3 was 54.4%, 42.8% and 50.2%, with an average survival of 18.6, 6.6 and 10.8 months, respectively (p = 0.002). In summary, NGS testing for gynecologic cancers detected 33.4% of actionable alterations with a high clinical action rate. Along with the high clinical utility of NGS, testing also seemed to improve survival for patients who received targeted treatment.

Recommendations for Specimen and Therapy Selection in Colorectal Cancer

INTRODUCTION

Next-generation sequencing has emerged as a clinical tool for the identification of actionable mutations to triage advanced colorectal cancer patients for targeted therapies. The literature is conflicted as to whether primaries or their metastases should be selected for sequencing. Some authors suggest that either site may be sequenced, whereas others recommend sequencing the primary, the metastasis, or even both tumors. Here, we address this issue head on with a meta-analysis and provide for the first time a set of sensible recommendations to make this determination.

METHODS

From our own series, we include 43 tumors from 13 patients including 14 primaries, 10 regional lymph node metastases, 17 distant metastases, and two anastomotic recurrences sequenced using the 50 gene Ion AmpliSeq cancer NGS panel v2.

RESULTS

Based on our new cohort and a meta-analysis, we found that ~ 77% of patient-matched primary-metastatic pairs have identical alterations in these 50 cancer-associated genes.

CONCLUSIONS

Low tumor cellularity, tumor heterogeneity, clonal evolution, treatment status, sample quality, and/or size of the sequencing panel accounted for a proportion of the differential detection of mutations at primary and metastatic sites. The therapeutic implications of the most frequently discordant alterations (TP53, APC, PIK3CA, and SMAD4) are discussed. Our meta-analysis indicates that a subset of patients who fail initial therapy may benefit from sequencing of additional sites to identify new actionable genomic abnormalities not present in the initial analysis. Evidence-based recommendations are proposed.

Early-stage economic analysis of research-based comprehensive genomic sequencing for advanced cancer care

Genomic research is driving discovery for future population benefit. Limited evidence exists on immediate patient and health system impacts of research participation. This study uses real-world data and quasi-experimental matching to examine early-stage cost and health impacts of research-based genomic sequencing. British Columbia’s Personalized OncoGenomics (POG) single-arm program applies whole genome and transcriptome analysis (WGTA) to characterize genomic landscapes in advanced cancers. Our cohort includes POG patients enrolled between 2014 and 2015 and 1:1 genetic algorithm–matched usual care controls. We undertake a cost consequence analysis and estimate 1-year effects of WGTA on patient management, patient survival, and health system costs reported in 2015 Canadian dollars. WGTA costs are imputed and forecast using system of equations modeling. We use Kaplan-Meier survival analysis to explore survival differences and inverse probability of censoring weighted linear regression to estimate mean 1-year survival times and costs. Non-parametric bootstrapping simulates sampling distributions and enables scenario analysis, revealing drivers of incremental costs, survival, and net monetary benefit for assumed willingness to pay thresholds. We identified 230 POG patients and 230 matched controls for cohort inclusion. The mean period cost of research-funded WGTA was $26,211 (SD: $14,191). Sequencing costs declined rapidly, with WGTA forecasts hitting $13,741 in 2021. The incremental healthcare system effect (non-research expenditures) was $5203 (95% CI: 75, 10,424) compared to usual care. No overall survival differences were observed, but outcome heterogeneity was present. POG patients receiving WGTA-informed treatment experienced incremental survival gains of 2.49 months (95% CI: 1.32, 3.64). Future cost consequences became favorable as WGTA cost drivers declined and WGTA-informed treatment rates improved to 60%. Our study demonstrates the ability of real-world data to support evaluations of only-in-research health technologies. We identify situations where precision oncology research initiatives may produce survival benefit at a cost that is within healthcare systems’ willingness to pay. This economic evidence informs the early-stage healthcare impacts of precision oncology research.

Germline Testing Data Validate Inferences of Mutational Status for Variants Detected From Tumor-Only Sequencing

Pathogenic germline variants (PGVs) in cancer susceptibility genes are usually identified through germline testing of DNA from blood or saliva: their detection can affect treatment options and potential risk-reduction strategies for patient relatives. PGV can also be identified in tumor sequencing assays, which, when performed without patient-matched normal specimens, render determination of variants' germline or somatic origin critical.

METHODS

Tumor-only sequencing data from 1,608 patients were retrospectively analyzed to infer germline versus somatic status of variants using an information-theoretic, gene-independent approach. Loss of heterozygosity was also determined. Predicted mutational models were compared with clinical germline testing results. Statistical measures were computed to evaluate performance.

RESULTS

Tumor-only sequencing detected 3,988 variants across 70 cancer susceptibility genes for which germline testing data were available. We imputed germline versus somatic status for > 75% of all detected variants, with a sensitivity of 65%, specificity of 88%, and overall accuracy of 86% for pathogenic variants. False omission rate was 3%, signifying minimal error in misclassifying true PGV. A higher portion of PGV in known hereditary tumor suppressors were found to be retained with loss of heterozygosity in the tumor specimens (72%) compared with variants of uncertain significance (58%).

CONCLUSION

Analyzing tumor-only data in the context of specimens' tumor cell content allows precise, systematic exclusion of somatic variants and suggests a balance between type 1 and 2 errors for identification of patients with candidate PGV for standard germline testing. Although technical or systematic errors in measuring variant allele frequency could result in incorrect inference, misestimation of specimen purity could result in inferring somatic variants as germline in somatically mutated tumor suppressor genes. A user-friendly bioinformatics application facilitates objective analysis of tumor-only data in clinical settings.

Identification of a Four-Gene Signature With Prognostic Significance in Endometrial Cancer Using Weighted-Gene Correlation Network Analysis

Endometrial hyperplasia (EH) is a precursor for endometrial cancer (EC). However, biomarkers for the progression from EH to EC and standard prognostic biomarkers for EC have not been identified. In this study, we aimed to identify key genes with prognostic significance for the progression from EH to EC. Weighted-gene correlation network analysis (WGCNA) was used to identify hub genes utilizing microarray data (GSE106191) downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were identified from the Uterine Corpus Endometrial Carcinoma (UCEC) dataset of The Cancer Genome Atlas database. The Limma-Voom R package was applied to detect differentially expressed genes (DEGs; mRNAs) between cancer and normal samples. Genes with |log2 (fold change [FC])| > 1.0 and p < 0.05 were considered as DEGs. Univariate and multivariate Cox regression and survival analyses were performed to identify potential prognostic genes using hub genes overlapping in the two datasets. All analyses were conducted using R Bioconductor and related packages. Through WGCNA and overlapping genes in hub modules with DEGs in the UCEC dataset, we identified 42 hub genes. The results of the univariate and multivariate Cox regression analyses revealed that four hub genes, BUB1B, NDC80, TPX2, and TTK, were independently associated with the prognosis of EC (Hazard ratio [95% confidence interval]: 0.591 [0.382–0.912], p = 0.017; 0.605 [0.371–0.986], p = 0.044; 1.678 [1.132–2.488], p = 0.01; 2.428 [1.372–4.29], p = 0.02, respectively). A nomogram was established with a risk score calculated using the four genes’ coefficients in the multivariate analysis, and tumor grade and stage had a favorable predictive value for the prognosis of EC. The survival analysis showed that the high-risk group had an unfavorable prognosis compared with the low-risk group (p < 0.0001). The receiver operating characteristic curves also indicated that the risk model had a potential predictive value of prognosis with area under the curve 0.807 at 2 years, 0.783 at 3 years, and 0.786 at 5 years. We established a four-gene signature with prognostic significance in EC using WGCNA and established a nomogram to predict the prognosis of EC.

Genetic testing for homologous recombination repair (HRR) in metastatic castration-resistant prostate cancer (mCRPC): challenges and solutions

Patients with metastatic castration-resistant prostate cancer (mCRPC) have an average survival of only 13 months. Identification of novel predictive and actionable biomarkers in the homologous recombination repair (HRR) pathway in up to a quarter of patients with mCRPC has led to the approval of targeted therapies like poly-ADP ribose polymerase inhibitors (PARPi), with the potential to improve survival outcomes. The approval of PARPi has led to guideline bodies such as the National Comprehensive Cancer Network (NCCN) to actively recommend germline and or somatic HRR gene panel testing to identify patients who will benefit from PARPi. However, there are several challenges as genetic testing is still at an early stage especially in low- and middle-income countries, with cost and availability being major impediments. In addition, there are issues such as choice of optimal tissue for genetic testing, archival, storage, retrieval of tissue blocks, interpretation and classification of variants in the HRR pathway, and the need for pretest and post-test genetic counseling. This review provides insights into the HRR gene mutations prevalent in mCRPC and the challenges for a more widespread gene testing to identify actionable germline pathogenic variants and somatic mutations in the HRR pathway, and proposes a clinical algorithm to enhance the efficiency of the gene testing process.

Clinical cancer genomic profiling

Technological innovation and rapid reduction in sequencing costs have enabled the genomic profiling of hundreds of cancer-associated genes as a component of routine cancer care. Tumour genomic profiling can refine cancer subtype classification, identify which patients are most likely to benefit from systemic therapies and screen for germline variants that influence heritable cancer risk. Here, we discuss ongoing efforts to enhance the clinical utility of tumour genomic profiling by integrating tumour and germline analyses, characterizing allelic context and identifying mutational signatures that influence therapy response. We also discuss the potential clinical utility of more comprehensive whole-genome and whole-transcriptome sequencing and ultra-sensitive cell-free DNA profiling platforms, which allow for minimally invasive, serial analyses of tumour-derived DNA in blood.

Discovery through clinical sequencing in oncology

The molecular characterization of tumors now informs clinical cancer care for many patients. This advent of molecular oncology is driven by the expanding number of therapeutic biomarkers that can predict sensitivity to both approved and investigational agents. Beyond its role in driving clinical trial enrollments and guiding therapy in individual patients, large-scale clinical genomics in oncology also represents a rapidly expanding research resource for translational scientific discovery. Here, we review the progress, opportunities, and challenges of scientific and translational discovery from prospective clinical genomic screening programs now routinely conducted in cancer patients.

Medical oncologists' perspectives of the Veterans Affairs National Precision Oncology Program

BACKGROUND

To support the rising need for testing and to standardize tumor DNA sequencing practices within the U.S. Department of Veterans Affairs (VA)'s Veterans Health Administration (VHA), the National Precision Oncology Program (NPOP) was launched in 2016. We sought to assess oncologists' practices, concerns, and perceptions regarding Next-Generation Sequencing (NGS) and the NPOP.

MATERIALS AND METHODS

Using a purposive total sampling approach, oncologists who had previously ordered NGS for at least one tumor sample through the NPOP were invited to participate in semi-structured interviews. Questions assessed the following: expectations for the NPOP, procedural requirements, applicability of testing results, and the summative utility of the NPOP. Interviews were assessed using an open coding approach. Thematic analysis was conducted to evaluate the completed codebook. Themes were defined deductively by reviewing the direct responses to interview questions as well as inductively by identifying emerging patterns of data.

RESULTS

Of the 105 medical oncologists who were invited to participate, 20 (19%) were interviewed from 19 different VA medical centers in 14 states. Five recurrent themes were observed: (1) Educational Efforts Regarding Tumor DNA Sequencing Should be Undertaken, (2) Pathology Departments Share a Critical Role in Facilitating Test Completion, (3) Tumor DNA Sequencing via NGS Serves as the Most Comprehensive Testing Modality within Precision Oncology, (4) The Availability of the NPOP Has Expanded Options for Select Patients, and (5) The Completion of Tumor DNA Sequencing through the NPOP Could Help Improve Research Efforts within VHA Oncology Practices.

CONCLUSION

Medical oncologists believe that the availability of tumor DNA sequencing through the NPOP could potentially lead to an improvement in outcomes for veterans with metastatic solid tumors. Efforts should be directed toward improving oncologists' understanding of sequencing, strengthening collaborative relationships between oncologists and pathologists, and assessing the role of comprehensive NGS panels within the battery of precision tests.

The enduring importance of family health history in the era of genomic medicine and risk assessment

Improving disease risk prediction and tailoring preventive interventions to patient risk factors is one of the primary goals of precision medicine. Family health history is the traditional approach to quickly gather genetic and environmental data relevant to the patient. While the utility of family health history is well-documented, its utilization is variable, in part due to lack of patient and provider knowledge and incomplete or inaccurate data. With the advances and reduced costs of sequencing technologies, comprehensive sequencing tests can be performed as a risk assessment tool. We provide an overview of each of these risk assessment approaches, the benefits and limitations and implementation challenges.

All-FIT: allele-frequency-based imputation of tumor purity from high-depth sequencing data

SUMMARY

Clinical sequencing aims to identify somatic mutations in cancer cells for accurate diagnosis and treatment. However, most widely used clinical assays lack patient-matched control DNA and additional analysis is needed to distinguish somatic and unfiltered germline variants. Such computational analyses require accurate assessment of tumor cell content in individual specimens. Histological estimates often do not corroborate with results from computational methods that are primarily designed for normal-tumor matched data and can be confounded by genomic heterogeneity and presence of sub-clonal mutations. Allele-frequency-based imputation of tumor (All-FIT) is an iterative weighted least square method to estimate specimen tumor purity based on the allele frequencies of variants detected in high-depth, targeted, clinical sequencing data. Using simulated and clinical data, we demonstrate All-FIT's accuracy and improved performance against leading computational approaches, highlighting the importance of interpreting purity estimates based on expected biology of tumors.

AVAILABILITY AND IMPLEMENTATION

Freely available at http://software.khiabanian-lab.org.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Next-Generation Sequencing and the Clinical Oncology Workflow: Data Challenges, Proposed Solutions, and a Call to Action

PURPOSE

Next-generation sequencing (NGS) of tumor and germline DNA is foundational for precision oncology, with rapidly expanding diagnostic, prognostic, and therapeutic implications. Although few question the importance of NGS in modern oncology care, the process of gathering primary molecular data, integrating it into electronic health records, and optimally using it as part of a clinical workflow remains far from seamless. Numerous challenges persist around data standards and interoperability, and clinicians frequently face difficulties in managing the growing amount of genomic knowledge required to care for patients and keep up to date.

METHODS

This review provides a descriptive analysis of genomic data workflows for NGS data in clinical oncology and issues that arise from the inconsistent use of standards for sharing data across systems. Potential solutions are described.

RESULTS

NGS technology, especially for somatic genomics, is well established and widely used in routine patient care, quality measurement, and research. Available genomic knowledge bases play an evolving role in patient management but lack harmonization with one another. Questions about their provenance and timeliness of updating remain. Potentially useful standards for sharing genomic data, such as HL7 FHIR and mCODE, remain primarily in the research and/or development stage. Nonetheless, their impact will likely be seen as uptake increases across care settings and laboratories. The specific use case of ASCO CancerLinQ, as a clinicogenomic database, is discussed.

CONCLUSION

Because the electronic health records of today seem ill suited for managing genomic data, other solutions are required, including universal data standards and applications that use application programming interfaces, along with a commitment on the part of sequencing laboratories to consistently provide structured genomic data for clinical use.

Opportunities, resources, and techniques for implementing genomics in clinical care

Advances in technologies for assessing genomic variation and an increasing understanding of the effects of genomic variants on health and disease are driving the transition of genomics from the research laboratory into clinical care. Genomic medicine, or the use of an individual's genomic information as part of their clinical care, is increasingly gaining acceptance in routine practice, including in assessing disease risk in individuals and their families, diagnosing rare and undiagnosed diseases, and improving drug safety and efficacy. We describe the major types and measurement tools of genomic variation that are currently of clinical importance, review approaches to interpreting genomic sequence variants, identify publicly available tools and resources for genomic test interpretation, and discuss several key barriers in using genomic information in routine clinical practice.

The Development of a Personalised Training Framework: Implementation of Emerging Technologies for Performance

Over the last decade, there has been considerable interest in the individualisation of athlete training, including the use of genetic information, alongside more advanced data capture and analysis techniques. Here, we explore the evidence for, and practical use of, a number of these emerging technologies, including the measurement and quantification of epigenetic changes, microbiome analysis and the use of cell-free DNA, along with data mining and machine learning. In doing so, we develop a theoretical model for the use of these technologies in an elite sport setting, allowing the coach to better answer six key questions: (1) To what training will my athlete best respond? (2) How well is my athlete adapting to training? (3) When should I change the training stimulus (i.e., has the athlete reached their adaptive ceiling for this training modality)? (4) How long will it take for a certain adaptation to occur? (5) How well is my athlete tolerating the current training load? (6) What load can my athlete handle today? Special consideration is given to whether such an individualised training framework will outperform current methods as well as the challenges in implementing this approach.

Somatic Testing: Implications for Targeted Treatment

OBJECTIVE

To provide an overview of key considerations for somatic testing for the purpose of targeting cancer treatment.

DATA SOURCES

Literature; research reports.

CONCLUSION

Genomic testing of cancer cells to identify variants that drive the carcinogenic process is becoming common in clinical settings. Providers and patients need to weigh the potential benefits of testing with technologic and logistic issues.

IMPLICATIONS FOR NURSING PRACTICE

Testing is available for thousands of genomic variants to identify one or more to guide targeted treatment. Oncology nurses need to understand the benefits and limitations of participating in patient-centered implementation of this testing.

Current views on tumor mutational burden in patients with non-small cell lung cancer treated by immune checkpoint inhibitors

In the last few years, the treatment of patients with non-small cell lung cancer (NSCLC) has impressively benefitted from immunotherapy, in particular from the inhibition of immune checkpoints such as programmed cell death-1 (PD-1) and programmed cell death ligand-1 (PD-L1). However, despite the significant survival benefit for some patients with advanced NSCLC, the objective response rates (ORRs) remain relatively low no more than 20-30% with a large proportion of patients demonstrating primary resistance. Although the selection of NSCLC patients for the first-line treatment is currently guided by the expression of PD-L1 in tumor cells as detected by immunohistochemistry, this is not the case for the second-line setting. Moreover, the sensitivity and specificity of PD-L1 expression is modest which has prompted the search for additional predictive biomarkers. In this context, the assessment of the tumor mutational burden (TMB), defined as the total number of nonsynonymous mutations in the coding regions of genes, has recently emerged as an additional powerful biomarker to select patients for immunotherapy. The purpose of our review is to highlight the recent advances as well as the challenges and perspectives in the field of TMB and immunotherapy for patients with NSCLC.

Clinical Validation of Targeted Solid Tumor Profiling

Large-scale tumor profiling studies have generated massive amounts of data that have been instrumental for the detection of recurrent driver mutations in many tumor types. These driver mutations as well as the concurrent passenger mutations are now being used for a more accurate diagnosis of the tumor and prognosis for the patient. Moreover, therapeutic inhibitors toward specific mutations are already on the market and many clinical trials are ongoing to approve novel therapeutic drugs. The broad-range identification of these somatic mutations is key to this tailored personalized medicine approach, which preferentially has to be performed by a multigene multihotspot method such as massive parallel sequencing, also called next generation sequencing (NGS). The implementation of NGS in molecular diagnostics of tumor profiling however, requires a firm validation to minimize the occurrence of false positives and false negatives, thereby yielding highly accurate and robust clinical data.Here, we describe the different performance characteristics as well as quality metrics that should be analyzed for the robust diagnostic validation of tumor profiling in order to meet the requirements of international standards specific for medical laboratories, such as the ISO15189:2012 standard. These metrics include assays that assess the precision, limit of detection, accuracy, sensitivity, specificity, and robustness of the entire workflow from DNA enrichment up to the final report.

Gene Expression Detection Assay for Cancer Clinical Use

Cancer is a genetic disease where genetic variations cause abnormally functioning genes that appear to alter expression. Proteins, the final products of gene expression, determine the phenotypes and biological processes. Therefore, detecting gene expression levels can be used for cancer diagnosis, prognosis, and treatment prediction in a clinical setting. In this review, we investigated six gene expression assay systems (qRT-PCR, DNA microarray, nCounter, RNA-Seq, FISH, and tissue microarray) that are currently being used in clinical cancer studies. Some of these methods are also commonly used in a modified way; for example, detection of DNA content or protein expression. Herein, we discuss their principles, sample preparation, design, quantification and sensitivity, data analysis, time for sample preparation and processing, and cost. We also compared these methods according to their sample selection, particularly for the feasibility of using formalin-fixed paraffin-embedded (FFPE) samples, which are routinely archived for clinical cancer studies. We intend to provide a guideline for choosing an assay method with respect to its oncological applications in a clinical setting.

The value of cell-free DNA for molecular pathology

Over the past decade, advances in molecular biology and genomics techniques have revolutionized the diagnosis and treatment of cancer. The technological advances in tissue profiling have also been applied to the study of cell-free nucleic acids, an area of increasing interest for molecular pathology. Cell-free nucleic acids are released from tumour cells into the surrounding body fluids and can be assayed non-invasively. The repertoire of genomic alterations in circulating tumour DNA (ctDNA) is reflective of both primary tumours and distant metastatic sites, and ctDNA can be sampled multiple times, thereby overcoming the limitations of the analysis of single biopsies. Furthermore, ctDNA can be sampled regularly to monitor response to treatment, to define the evolution of the tumour genome, and to assess the acquisition of resistance and minimal residual disease. Recently, clinical ctDNA assays have been approved for guidance of therapy, which is an exciting first step in translating cell-free nucleic acid research tests into clinical use for oncology. In this review, we discuss the advantages of cell-free nucleic acids as analytes in different body fluids, including blood plasma, urine, and cerebrospinal fluid, and their clinical applications in solid tumours and haematological malignancies. We will also discuss practical considerations for clinical deployment, such as preanalytical factors and regulatory requirements. Copyright © 2018 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Versatile ion S5XL sequencer for targeted next generation sequencing of solid tumors in a clinical laboratory

BACKGROUND

Next generation sequencing based tumor tissue genotyping involves complex workflow and a relatively longer turnaround time. Semiconductor based next generation platforms varied from low throughput Ion PGM to high throughput Ion Proton and Ion S5XL sequencer. In this study, we compared Ion PGM and Ion Proton, with a new Ion S5XL NGS system for workflow scalability, analytical sensitivity and specificity, turnaround time and sequencing performance in a clinical laboratory.

METHODS

Eighteen solid tumor samples positive for various mutations as detected previously by Ion PGM and Ion Proton were selected for study. Libraries were prepared using DNA (range10-40ng) from micro-dissected formalin-fixed, paraffin-embedded (FFPE) specimens using the Ion Ampliseq Library Kit 2.0 for comprehensive cancer (CCP), oncomine comprehensive cancer (OCP) and cancer hotspot panel v2 (CHPv2) panel as per manufacturer's instructions. The CHPv2 were sequenced using Ion PGM whereas CCP and OCP were sequenced using Ion Proton respectively. All the three libraries were further sequenced individually (S540) or multiplexed (S530) using Ion S5XL. For S5XL, Ion chef was used to automate template preparation, enrichment of ion spheres and chip loading. Data analysis was performed using Torrent Suite 4.6 software on board S5XL and Ion Reporter. A limit of detection and reproducibility studies was performed using serially diluted DLD1 cell line.

RESULTS

A total of 241 variant calls (235 single nucleotide variants and 6 indels) expected in the studied cohort were successfully detected by S5XL with 100% and 97% concordance with Ion PGM and Proton, respectively. Sequencing run time was reduced from 4.5 to 2.5 hours with output range of 3-5 GB (S530) and 8-9.3Gb (S540). Data analysis time for the Ion S5XL is faster 1 h (S520), 2.5 h (S530) and 5 h (S540) chip, respectively as compared to the Ion PGM (3.5-5 h) and Ion Proton (8h). A limit detection of 5% allelic frequency was established along with high inter-run reproducibility.

CONCLUSION

Ion S5XL system simplified workflow in a clinical laboratory, was feasible for running smaller and larger panels on the same instrument, had a shorter turnaround time, and showed good concordance for variant calls with similar sensitivity and reproducibility as the Ion PGM and Proton.

The ins and outs of molecular pathology reporting

The raid evolution in molecular pathology resulting in an increasing complexity requires careful reporting. The need for standardisation is clearer than ever. While synoptic reporting was first used for reporting hereditary genetic diseases, it is becoming more frequent in pathology, especially molecular pathology reports too. The narrative approach is no longer feasible with the growing amount of essential data present on the report, although narrative components are still necessary for interpretation in molecular pathology. On the way towards standardisation of reports, guidelines can be a helpful tool. There are several guidelines that focus on reporting in the field of hereditary diseases, but it is not always feasible to extrapolate these to the reporting of somatic variants in molecular pathology. The rise of multi-gene testing causes challenges for the laboratories. In order to provide a continuous optimisation of the laboratory testing process, including reporting, external quality assessment is essential and has already proven to improve the quality of reports. In general, a clear and concise report for molecular pathology can be created by including elements deemed important by different guidelines, adapting the report to the process flows of the laboratory and integrating the report with the laboratory information management system and the patient record.

Next-Generation Sequencing in Oncology: Genetic Diagnosis, Risk Prediction and Cancer Classification

Next-generation sequencing (NGS) technology has expanded in the last decades with significant improvements in the reliability, sequencing chemistry, pipeline analyses, data interpretation and costs. Such advances make the use of NGS feasible in clinical practice today. This review describes the recent technological developments in NGS applied to the field of oncology. A number of clinical applications are reviewed, i.e., mutation detection in inherited cancer syndromes based on DNA-sequencing, detection of spliceogenic variants based on RNA-sequencing, DNA-sequencing to identify risk modifiers and application for pre-implantation genetic diagnosis, cancer somatic mutation analysis, pharmacogenetics and liquid biopsy. Conclusive remarks, clinical limitations, implications and ethical considerations that relate to the different applications are provided.

Applications of molecular testing in surgical pathology of the head and neck

Molecular testing in routine surgical pathology is becoming an important component of the workup of many different types of tumors. In fact, in some organ systems, guidelines now suggest that the standard of care is to obtain specific molecular panels for tumor classification and/or therapeutic planning. In the head and neck, clinically applicable molecular tests are not as abundant as in other organ systems. Most current head and neck biomarkers are utilized for diagnosis rather than as companion diagnostic tests to predict therapeutic response. As the number of potential molecular biomarker assays increases and cost pressures escalate, the pathologist must be able to navigate the molecular testing pathways. This review explores scenarios in which molecular testing might be beneficial and cost-effective in head and neck pathology.

Cancer Precision Medicine: From Cancer Screening to Drug Selection and Personalized Immunotherapy

With the accelerating progress in basic and clinical cancer research, a huge body of new discoveries and powerful technologies has allowed us to implement a 'Cancer Precision Medicine (CPM)' system for cancer patients. The CPM system covers a wide range of cancer management including cancer screening, monitoring of relapse/recurrence, selection/prediction of effective drugs/treatments, and personalized immunotherapy. In this system individual cancer patients expect to receive personalized care: an appropriate dose of the right drug at the right time. We here aim to summarize and discuss a possible workflow for precision medicine for cancer patients by reviewing recent booming technologies and treatments that have been used or will potentially be used in the CPM system.

Development of a RNA-Seq Based Prognostic Signature in Lung Adenocarcinoma

Background

Precision therapy for lung cancer will require comprehensive genomic testing to identify actionable targets as well as ascertain disease prognosis. RNA-seq is a robust platform that meets these requirements, but microarray-derived prognostic signatures are not optimal for RNA-seq data. Thus, we undertook the first prognostic analysis of lung adenocarcinoma RNA-seq data and generated a prognostic signature.

Methods

Lung adenocarcinoma RNA-seq and clinical data from The Cancer Genome Atlas (TCGA) were divided chronologically into training (n = 255) and validation (n = 157) cohorts. In the training cohort, prognostic association was assessed by univariate Cox analysis. A prognostic signature was built with stepwise multivariable Cox analysis. Outcomes by risk group, stage, and mutation status were analyzed with Kaplan-Meier and multivariable Cox analyses. All the statistical tests were two-sided.

Results

In the training cohort, 96 genes had prognostic association with P values of less than or equal to 1.00x10^-4, including five long noncoding RNAs (lncRNAs). Stepwise regression generated a four-gene signature, including one lncRNA. Signature high-risk cases had worse overall survival (OS) in the TCGA validation cohort (hazard ratio [HR] = 3.07, 95% confidence interval [CI] = 2.00 to 14.62) and a University of Michigan institutional cohort (n = 67; HR = 2.05, 95% CI = 1.18 to 4.55), and worse metastasis-free survival in the TCGA validation cohort (HR = 3.05, 95% CI = 2.31 to 13.37). The four-gene prognostic signature also statistically significantly stratified overall survival in important clinical subsets, including stage I (HR = 2.78, 95% CI = 1.91 to 11.13), EGFR wild-type (HR = 3.01, 95% CI = 1.73 to 14.98), and EGFR mutant (HR = 8.99, 95% CI = 62.23 to 141.44). The four-gene prognostic signature also stood out on top when compared with other prognostic signatures.

Conclusions

Here, we present the first RNA-seq prognostic signature for lung adenocarcinoma that can provide a powerful prognostic tool for precision oncology as part of an integrated RNA-seq clinical sequencing program.

Next generation sequencing in cancer: opportunities and challenges for precision cancer medicine

Over the past decade, testing the genes of patients and their specific cancer types has become standardized practice in medical oncology since somatic mutations, changes in gene expression and epigenetic modifications are all hallmarks of cancer. However, while cancer genetic assessment has been limited to single biomarkers to guide the use of therapies, improvements in nucleic acid sequencing technologies and implementation of different genome analysis tools have enabled clinicians to detect these genomic alterations and identify functional and disease-associated genomic variants. Next-generation sequencing (NGS) technologies have provided clues about therapeutic targets and genomic markers for novel clinical applications when standard therapy has failed. While Sanger sequencing, an accurate and sensitive approach, allows for the identification of potential novel variants, it is however limited by the single amplicon being interrogated. Similarly, quantitative and qualitative profiling of gene expression changes also represents a challenge for the cancer field. Both RT-PCR and microarrays are efficient approaches, but are limited to the genes present on the array or being assayed. This leaves vast swaths of the transcriptome, including non-coding RNAs and other features, unexplored. With the advent of the ability to collect and analyze genomic sequence data in a timely fashion and at an ever-decreasing cost, many of these limitations have been overcome and are being incorporated into cancer research and diagnostics giving patients and clinicians new hope for targeted and personalized treatment. Below we highlight the various applications of next-generation sequencing in precision cancer medicine.

Metastatic solid tumors to the jaw and oral soft tissue: A retrospective clinical analysis of 44 patients from a single institution

PURPOSE

Metastatic solid tumors to the oral cavity are rare, frequently indicative of an end-stage disease process, and associated with poor survival rates. We performed a 20-year retrospective clinical analysis of our institution's cases of solid metastases to the oral cavity, and investigated these patients' clinical outcomes.

MATERIAL AND METHODS

A retrospective study of patients with metastatic solid tumors to the oral cavity over a 20-year period (October 1996 to September 2015) was conducted at Memorial Sloan Kettering Cancer Center. Patients were selected if they had a histopathologically confirmed diagnosis. Demographic, pathologic, and clinical information were reviewed to identify patient outcomes.

RESULTS

A total of 44 patients with metastatic non-melanocytic non-hematopoietic tumor to the oral cavity were identified: 24 males and 20 females (39 adults and 5 children) with a mean age of 54.3 years. In all, 24 cases involved the jaw and 20 cases involved the oral soft tissue. Eight patients (18.2%) had oral cavity metastases as the first indication of an occult malignancy. In adult patients, the common primary sites were the lungs (n = 9, 20%), kidney (n = 7, 16%), breast (n = 5, 11%), and colon (n = 4, 9%); and in pediatric patients the adrenal gland (3/5) was the most common site. Of the adult patients, 33 (84.6%) died of disease. From the time of metastasis diagnosis, patients with jaw metastases had a median and mean survival of 12 months and 27.7 months, respectively. In comparison, patients with oral soft tissue metastases had a median survival time of 5 months, and mean of 8 months. One pediatric patient (20%) died of disease 8 months after metastasis diagnosis.

CONCLUSION

Metastatic solid tumors to the oral cavity can be the first sign of a malignancy. Pediatric patients with oral cavity metastases have a better prognosis compared to adult patients. In this series, adults with oral soft tissue involvement had shorter survival times compared to patients with jaw involvement.

Powerful qPCR assays for the early detection of latent invaders: interdisciplinary approaches in clinical cancer research and plant pathology

Latent invaders represent the first step of disease before symptoms occur in the host. Based on recent findings, tumors are considered to be ecosystems in which cancer cells act as invasive species that interact with the native host cell species. Analogously, in plants latent fungal pathogens coevolve within symptomless host tissues. For these reasons, similar detection approaches can be used for an early diagnosis of the invasion process in both plants and humans to prevent or reduce the spread of the disease. Molecular tools based on the evaluation of nucleic acids have been developed for the specific, rapid, and early detection of human diseases. During the last decades, these techniques to assess and quantify the proliferation of latent invaders in host cells have been transferred from the medical field to different areas of scientific research, such as plant pathology. An improvement in molecular biology protocols (especially referring to qPCR assays) specifically designed and optimized for detection in host plants is therefore advisable. This work is a cross-disciplinary review discussing the use of a methodological approach that is employed within both medical and plant sciences. It provides an overview of the principal qPCR tools for the detection of latent invaders, focusing on comparisons between clinical cancer research and plant pathology, and recent advances in the early detection of latent invaders to improve prevention and control strategies.

Validation and Application of a Custom-Designed Targeted Next-Generation Sequencing Panel for the Diagnostic Mutational Profiling of Solid Tumors

The inevitable switch from standard molecular methods to next-generation sequencing for the molecular profiling of tumors is challenging for most diagnostic laboratories. However, fixed validation criteria for diagnostic accreditation are not in place because of the great variability in methods and aims. Here, we describe the validation of a custom panel of hotspots in 24 genes for the detection of somatic mutations in non-small cell lung carcinoma, colorectal carcinoma and malignant melanoma starting from FFPE sections, using 14, 36 and 5 cases, respectively. The targeted hotspots were selected for their present or future clinical relevance in solid tumor types. The target regions were enriched with the TruSeq approach starting from limited amounts of DNA. Cost effective sequencing of 12 pooled libraries was done using a micro flow cell on the MiSeq and subsequent data analysis with MiSeqReporter and VariantStudio. The entire workflow was diagnostically validated showing a robust performance with maximal sensitivity and specificity using as thresholds a variant allele frequency >5% and a minimal amplicon coverage of 300. We implemented this method through the analysis of 150 routine diagnostic samples and identified clinically relevant mutations in 16 genes including KRAS (32%), TP53 (32%), BRAF (12%), APC (11%), EGFR (8%) and NRAS (5%). Importantly, the highest success rate was obtained when using also the low quality DNA samples. In conclusion, we provide a workflow for the validation of targeted NGS by a custom-designed pan-solid tumor panel in a molecular diagnostic lab and demonstrate its robustness in a clinical setting.

"RADIOTRANSCRIPTOMICS": A synergy of imaging and transcriptomics in clinical assessment

Recent advances in quantitative imaging and "omics" technology have generated a wealth of mineable biological "big data". With the push towards a P4 "predictive, preventive, personalized, and participatory" approach to medicine, researchers began integrating complementary tools to further tune existing diagnostic and therapeutic models. The field of radiogenomics has long pioneered such multidisciplinary investigations in neuroscience and oncology, correlating genotypic and phenotypic signatures to study structural and functional changes in relation to altered molecular behavior. Given the innate dynamic nature of complex disorders and the role of environmental and epigenetic factors in pathogenesis, the transcriptome can further elucidate serial modifications undetected at the genome level. We therefore propose "radiotranscriptomics" as a new member of the P4 medicine initiative, combining transcriptome information, including gene expression and isoform variation, and quantitative image annotations.

Precision Medicine for Molecularly Targeted Agents and Immunotherapies in Early-Phase Clinical Trials

Precision medicine in oncology promises the matching of genomic, molecular, and clinical data with underlying mechanisms of a range of novel anticancer therapeutics to develop more rational and effective antitumor strategies in a timely manner. However, despite the remarkable progress made in the understanding of novel drivers of different oncogenic processes, success rates for the approval of oncology drugs remain low with substantial fiscal consequences. In this article, we focus on how recent rapid innovations in technology have brought greater clarity to the biological and clinical complexities of different cancers and advanced the development of molecularly targeted agents and immunotherapies in clinical trials. We discuss the key challenges of identifying and validating predictive biomarkers of response and resistance using both tumor and surrogate tissues, as well as the hurdles associated with intratumor heterogeneity. Finally, we outline evolving strategies employed in early-phase trial designs that incorporate omics-based technologies.

State of the art and future directions of pancreatic ductal adenocarcinoma therapy

Pancreatic ductal adenocarcinoma (PDAC) is expected to become the second cause of cancer-related death in 2030. PDAC is the poorest prognostic tumor of the digestive tract, with 80% of patients having advanced disease at diagnosis and 5-year survival rate not exceeding 7%. Until 2010, gemcitabine was the only validated therapy for advanced PDAC with a modest improvement in median overall survival as compared to best supportive care (5-6 vs 3 months). Multiple phase II-III studies have used various combinations of gemcitabine with other cytotoxics or targeted agents, most in vain, in attempt to improve this outcome. Over the past few years, the landscape of PDAC management has undergone major and rapid changes with the approval of the FOLFIRINOX and gemcitabine plus nab-paclitaxel regimens in patients with metastatic disease. These two active combination chemotherapy options yield an improved median overall survival (11.1 vs 8.5 months, respectively) thus making longer survival a reasonably achievable goal. This breakthrough raises some new clinical questions about the management of PDAC. Moreover, better knowledge of the environmental and genetic events that underpin multistep carcinogenesis and of the microenvironment surrounding cancer cells in PDAC has open new perspectives and therapeutic opportunities. In this new dynamic context of deep transformation in basic research and clinical management aspects of the disease, we gathered updated preclinical and clinical data in a multifaceted review encompassing the lessons learned from the past, the yet unanswered questions, and the most promising research priorities to be addressed for the next 5 years.