Knowledge bases and software support for variant interpretation in precision oncology

Precision oncology is a rapidly evolving interdisciplinary medical specialty. Comprehensive cancer panels are becoming increasingly available at pathology departments worldwide, creating the urgent need for scalable cancer variant annotation and molecularly informed treatment recommendations. A wealth of mainly academia-driven knowledge bases calls for software tools supporting the multi-step diagnostic process. We derive a comprehensive list of knowledge bases relevant for variant interpretation by a review of existing literature followed by a survey among medical experts from university hospitals in Germany. In addition, we review cancer variant interpretation tools, which integrate multiple knowledge bases. We categorize the knowledge bases along the diagnostic process in precision oncology and analyze programmatic access options as well as the integration of knowledge bases into software tools. The most commonly used knowledge bases provide good programmatic access options and have been integrated into a range of software tools. For the wider set of knowledge bases, access options vary across different parts of the diagnostic process. Programmatic access is limited for information regarding clinical classifications of variants and for therapy recommendations. The main issue for databases used for biological classification of pathogenic variants and pathway context information is the lack of standardized interfaces. There is no single cancer variant interpretation tool that integrates all identified knowledge bases. Specialized tools are available and need to be further developed for different steps in the diagnostic process.

Implementing the FAIR Data Principles in precision oncology: review of supporting initiatives

Compelling research has recently shown that cancer is so heterogeneous that single research centres cannot produce enough data to fit prognostic and predictive models of sufficient accuracy. Data sharing in precision oncology is therefore of utmost importance. The Findable, Accessible, Interoperable and Reusable (FAIR) Data Principles have been developed to define good practices in data sharing. Motivated by the ambition of applying the FAIR Data Principles to our own clinical precision oncology implementations and research, we have performed a systematic literature review of potentially relevant initiatives. For clinical data, we suggest using the Genomic Data Commons model as a reference as it provides a field-tested and well-documented solution. Regarding classification of diagnosis, morphology and topography and drugs, we chose to follow the World Health Organization standards, i.e. ICD10, ICD-O-3 and Anatomical Therapeutic Chemical classifications, respectively. For the bioinformatics pipeline, the Genome Analysis ToolKit Best Practices using Docker containers offer a coherent solution and have therefore been selected. Regarding the naming of variants, we follow the Human Genome Variation Society's standard. For the IT infrastructure, we have built a centralized solution to participate in data sharing through federated solutions such as the Beacon Networks.

Targeting DNA damage repair pathways in pancreas cancer

Pancreas ductal adenocarcinoma (PDAC) is the third most common cause of cancer death in the USA. While other cancers with historically poor prognoses have benefited from new immunotherapies and targeted agents, the 5-year survival rate for PDAC patients has remained static. The accessibility to genomic testing has improved in recent years, and it is now clear that PDAC is a heterogenous disease, with a subset of patients harboring actionable mutations. There are several targeted therapies approved by the Food and Drug administration (FDA) in PDAC: EGFR inhibitor erlotinib (combined with gemcitabine) in unselected patients, TRK inhibitors larotrectinib and entrectinib for patients with NTRK fusion mutation, the PD-1 inhibitor pembrolizumab for mismatch repair-deficient patients, and the poly-ADP-ribose polymerase (PARP) inhibitor olaparib in patients with germline BRCA mutation as a maintenance therapy. DNA damage repair (DDR) is paramount to genomic integrity and cell survival. The defective repair of DNA damage is one of the hallmarks of cancer, and abnormalities in DDR pathways are closely linked with the development of malignancies and upregulation of these pathways linked with resistance to treatment. The prevalence of somatic and germline mutations in DDR pathways in metastatic PDAC is reported to be approximately 15-25%. Patients with DDR gene alterations benefit from a personalized approach to treatment. Recently, the POLO trial demonstrated a progression-free survival (PFS) benefit in metastatic PDAC patients with a germline BRCA1/2 mutation treated with maintenance olaparib following platinum-based induction chemotherapy. This was the first phase 3 randomized trial to establish a biomarker-driven approach in the treatment of PDAC and establishes a precedent for maintenance therapy in PDAC. The review herein aims to outline the current treatment landscape for PDAC patients with DDR gene-mutated tumors, highlight novel therapeutic approaches focused on surmounting tumor resistance, and explore new strategies which may lead to an expansion in the number of patients who benefit from these targeted treatments.

Leveraging Pathology Informatics Concepts to Achieve Discrete Lab Data for Clinical Use and Translational Research

Clinical practice is most efficient when physicians have the right information, including pathology and laboratory results, at the point of contact with the patient. In downstream workflows, subsequent groups using lab data want to have it available in a format that is easy to manipulate. With the complexity of electronic medical records, hospital information systems, and the need to accommodate data from outside systems, this is not easy to accomplish. By utilizing a group of concepts from clinical and pathology informatics, system implementations may be improved to achieve relevant laboratory data in a format that is usable by healthcare entities to improve patient care and forward endeavors in precision medicine.

Comparison of Open-access Databases for Clinical Variant Interpretation in Cancer: A Case Study of MDS/AML

BACKGROUND

Recently, guidelines for variant interpretation in cancer have been established. However, these guidelines do not mention which databases are most suited to performing this task.

MATERIALS AND METHODS

We give an overview of existing databases and evaluate their benefit in practical application. We compared three meta-databases and 12 databases for a dataset of patients with myelodysplastic syndrome or acute myeloid leukemia.

RESULTS

Clinical implications were found for 13% of all variants. One-third of variants with therapeutic implications were uniquely contained in one database. The VICC meta-database was the most extensive source of information, featuring 92% of variants with a drug association. However, a comparison of meta-databases and original sources indicated that some variants are missing in those meta-databases.

CONCLUSION

Public databases provide decision support for interpreting variants but there is still need for manual curation. Meta-databases facilitate the use of multiple resources but should be interpreted with caution.

Molecular Profiling-Based Precision Medicine in Cancer: A Review of Current Evidence and Challenges

Matched therapy based on next-generation sequencing is now a part of routine care to guide the treatment of patients with advanced solid tumors. However, whether and to what extent patients can benefit from this strategy on a large scale remains uncertain. In the past decade, several clinical studies were performed in this field, among which only one was a randomized trial. We reviewed the literature on this topic and summarize the existing data about the efficacy of this treatment strategy. Currently, the evidence is promising but not solid. Multiple ongoing trials are also summarized. We also discuss the limitations of this treatment strategy and certain unsolved important problems, including how to select the sample and target level, how to interpret the results, and the problem of drug accessibility. All these issues should receive more attention in future clinical trial design and the application of target therapy in cancer treatment.

Impact of tumor heterogeneity and tissue sampling for genetic mutation testing: a systematic review and post hoc analysis

OBJECTIVE

The objective of the study was to identify guidelines to assist systematic reviewers or clinical researchers in identifying sampling bias due to tumor heterogeneity (TH) in solid cancers assayed for somatic mutations. We also assessed current reporting standards to determine the impact of TH on sample bias.

STUDY DESIGN AND SETTING

We conducted a systematic review searching 13 databases (to January 2019) to identify guidelines. A post hoc analysis was performed using 12 prostate tumor somatic mutation data sets from a previous systematic review to assess reporting on TH.

RESULTS

Searches identified 2,085 records. No formal guidelines were identified. Forty publications contained incidental recommendations across five major themes: using multiple tumor samples (n = 29), sample purity thresholds (n = 14), using specific sequencing methods (n = 8), using liquid biopsies (n = 4), and microdissection (n = 4). In post hoc analyses, 50% (6 of 12) clearly reported pathology methods. Forty-two percent (5 of 12) did not report pathology results. Forty-two percent (5 of 12) confirmed the pathology of the sample by direct diagnosis rather than inference. Forty-two percent (5 of 12) used multiple samples per patient. Fifty-eight percent (7 of 12) reported on tumor purity (reported ranges 10% to 100%).

CONCLUSIONS

As precision medicine progresses to the clinic, guidelines are required to help evidence-based decision makers understand how TH may impact sample bias. Authors need to clearly report pathology methods and results and tumor purity methods and results.

Molecular profiling for precision cancer therapies

The number of druggable tumor-specific molecular aberrations has grown substantially in the past decade, with a significant survival benefit obtained from biomarker matching therapies in several cancer types. Molecular pathology has therefore become fundamental not only to inform on tumor diagnosis and prognosis but also to drive therapeutic decisions in daily practice. The introduction of next-generation sequencing technologies and the rising number of large-scale tumor molecular profiling programs across institutions worldwide have revolutionized the field of precision oncology. As comprehensive genomic analyses become increasingly available in both clinical and research settings, healthcare professionals are faced with the complex tasks of result interpretation and translation. This review summarizes the current and upcoming approaches to implement precision cancer medicine, highlighting the challenges and potential solutions to facilitate the interpretation and to maximize the clinical utility of molecular profiling results. We describe novel molecular characterization strategies beyond tumor DNA sequencing, such as transcriptomics, immunophenotyping, epigenetic profiling, and single-cell analyses. We also review current and potential applications of liquid biopsies to evaluate blood-based biomarkers, such as circulating tumor cells and circulating nucleic acids. Last, lessons learned from the existing limitations of genotype-derived therapies provide insights into ways to expand precision medicine beyond genomics.

Genetic database software as medical devices

This article provides a primer on medical device regulations in the United States, Europe, and Canada. Software tools are being developed and shared globally to enhance the accessibility and usefulness of genomic databases. Interactive software tools, such as email or mobile alert systems providing variant classification updates, are opportunities to democratize access to genomic data beyond laboratories and clinicians. Uncertainty over the reliability of outputs, however, raises concerns about potential harms to patients, especially where software is accessible to lay users. Developers may also need to contend with unfamiliar medical device regulations. The application of regulatory controls to genomic software could improve patient and user safety, but could also stifle innovation. Legal uncertainty for developers is compounded where software applications are made available globally (implicating multiple regulatory frameworks), and directly to lay users. Moreover, there is considerable uncertainty over the application of (evolving) medical device regulations in the context of both software and genetics. In this article, criteria and examples are provided to inform determinations of software as medical devices, as well as risk classification. We conclude with strategies for using genomic communication and interpretation software to maximize the availability and usefulness of genetic information, while mitigating the risk of harm to users.

This article provides a primer on medical device regulation of software that interprets and exchanges genomic data. We compare tests for determining if software qualifies as a medical device across the United States, Europe, and Canada, as well as risk classifications and regulatory controls. Medical device regulation of both software and genetic tools remains uncertain, raising competing concerns: insufficient regulation allows low quality outputs to undermine patient care, while excessive regulation stifles development of innovative tools delivering precision medicine.

First Responder to Genomic Information: A Guide for Primary Care Providers

With rapid advances in genetics and genomics, the commercialization and access to new applications has become more widespread and omnipresent throughout biomedical research. Thus, increasingly, more patients will have personal genomic information they may share with primary care providers (PCPs) to better understand the clinical significance of the data. To be able to respond to patient inquiries about genomic data, variant interpretation, disease risk, and other issues, PCPs will need to be able to increase or refresh their awareness about genetics and genomics, and identify reliable resources to use or refer patients. While provider educational efforts have increased, with the rapid advances in the field, ongoing efforts will be needed to prepare PCPs to manage patient needs, integrate results into care, and refer as indicated.

Challenges of Providing Concordant Interpretation of Somatic Variants in Non-Small Cell Lung Cancer: A Multicenter Study

Background: Success of multiple-gene mutation tests by next-generation sequencing (NGS), associated with molecular targeting therapies for cancers, depending on the accuracy and consistency of interpreting variants. Here, we summarized reports from clinical laboratories for cases with non-small cell lung cancer (NSCLC) and discussed conflicting interpretations of somatic variants. Methods: Three mimetic DNA samples, containing six somatic mutations, were prepared based on three clinical case reports of NSCLC. Clinical reports and genetic testing questionnaires were collected from 67 laboratories enrolled in this investigation. Results: Thirty-four laboratories with correct variant results identified two variants, based on FDA approval of targeted drugs for the same tumor, consistently, with strong clinical significance, whereas the other variants were classified with conflicting interpretations. Discordant interpretations were reported for ERBB2 with three different classifications, including strong clinical significance (53.0%, 18/34), potential clinical significance (38.2%, 13/34), and unknown significance (8.8%, 3/34). In the variant therapeutic drug recommendation section, 32.4% of the laboratories (11/34) did not recommend all the available therapeutic drugs designated by the National Comprehensive Cancer Network (NCCN). In the remaining group of 33 laboratories with incorrect variant results, less correct classifications were acquired for the variants with strong clinical significance. Conclusions: Owing to numerous reasons, the interpretation of variants differed greatly, which might in turn lead to the inappropriate clinical care of patients with NSCLC. By analyzing the limitations of different databases used by laboratories, we integrated various types of databases with different levels of evidence to form a comprehensive and detailed variant interpretation pipeline, aiming to standardize the variant classification and provide accurate and sufficient therapeutic drug recommendation to clinicians for minimal-inappropriate therapeutic options.

Genomics-Enabled Precision Medicine for Cancer

Genomic information is increasingly being incorporated into clinical cancer care. Large-scale sequencing efforts have deepened our understanding of the genomic landscape of cancer and contributed to the expanding catalog of alterations being leveraged to aid in cancer diagnosis, prognosis, and treatment. Genomic profiling can provide clinically relevant information regarding somatic point mutations, copy number alterations, translocations, and gene fusions. Genomic features, such as mutational burden, can also be measured by more comprehensive sequencing strategies and have shown value in informing potential treatment options. Ongoing clinical trials are evaluating the use of molecularly targeted agents in genomically defined subsets of cancers within and across tumor histologies. Continued advancements in clinical genomics promise to further expand the application of genomics-enabled medicine to a broader spectrum of oncology patients.

Precision Therapy of Head and Neck Squamous Cell Carcinoma

Precision medicine is an approach to disease prevention and treatment that takes into account genetic variability and environmental and lifestyle influences that are unique to each patient. It facilitates stratification of patient populations that vary in their susceptibility to disease and response to therapy. Shared databases and the implementation of new technology systems designed to advance the integration of this information will enable health care providers to more accurately predict and customize prevention and treatment strategies for patients. Although precision medicine has had a limited impact in most areas of medicine, it has been shown to be an increasingly successful approach to cancer therapy. Despite early promising results targeting aberrant signaling pathways or inhibitors designed to block tumor-driven processes such as angiogenesis, limited success emphasizes the need to discover new biomarkers and treatment targets that are more reliable in predicting response to therapy and result in better health outcomes. Recent successes in the use of immunity-inducing antibodies have stimulated increased interest in the use of precision immunotherapy of head and neck squamous cell carcinoma. Using next-generation sequencing, the precise profiling of tumor-infiltrating lymphocytes has great promise to identify hypoimmunogenic cancer that would benefit from a rationally designed combinatorial approach. Continued interrogation of tumors will reveal new actionable targets with increasing therapeutic efficacy and fulfill the promise of precision therapy of head and neck cancer.

Comprehensive Computed Tomography Radiomics Analysis of Lung Adenocarcinoma for Prognostication

BACKGROUND

In this era of personalized medicine, there is an expanded demand for advanced imaging biomarkers that reflect the biology of the whole tumor. Therefore, we investigated a large number of computed tomography-derived radiomics features along with demographics and pathology-related variables in patients with lung adenocarcinoma, correlating them with overall survival.

MATERIALS AND METHODS

Three hundred thirty-nine patients who underwent operation for lung adenocarcinoma were included. Analysis was performed using 161 radiomics features, demographic, and pathologic variables and correlated each with patient survival. Prognostic performance for survival was compared among three models: (a) using only clinicopathological data; (b) using only selected radiomics features; and (c) using both clinicopathological data and selected radiomics features.

RESULTS

At multivariate analysis, age, pN, tumor size, type of operation, histologic grade, maximum value of the outer 1/3 of the tumor, and size zone variance were statistically significant variables. In particular, maximum value of outer 1/3 of the tumor reflected tumor microenvironment, and size zone variance represented intratumor heterogeneity. Integration of 31 selected radiomics features with clinicopathological variables led to better discrimination performance.

CONCLUSION

Radiomics approach in lung adenocarcinoma enables utilization of the full potential of medical imaging and has potential to improve prognosis assessment in clinical oncology.

IMPLICATIONS FOR PRACTICE

Two radiomics features were prognostic for lung cancer survival at multivariate analysis: (a) maximum value of the outer one third of the tumor reflects the tumor microenvironment and (b) size zone variance represents the intratumor heterogeneity. Therefore, a radiomics approach in lung adenocarcinoma enables utilization of the full potential of medical imaging and could play a larger role in clinical oncology.

Resources for Interpreting Variants in Precision Genomic Oncology Applications

Precision genomic oncology-applying high throughput sequencing (HTS) at the point-of-care to inform clinical decisions-is a developing precision medicine paradigm that is seeing increasing adoption. Simultaneously, new developments in targeted agents and immunotherapy, when informed by rich genomic characterization, offer potential benefit to a growing subset of patients. Multiple previous studies have commented on methods for identifying both germline and somatic variants. However, interpreting individual variants remains a significant challenge, relying in large part on the integration of observed variants with biological knowledge. A number of data and software resources have been developed to assist in interpreting observed variants, determining their potential clinical actionability, and augmenting them with ancillary information that can inform clinical decisions and even generate new hypotheses for exploration in the laboratory. Here, we review available variant catalogs, variant and functional annotation software and tools, and databases of clinically actionable variants that can be used in an ad hoc approach with research samples or incorporated into a data platform for interpreting and formally reporting clinical results.