"Personalized Cancer Therapy": A Publicly Available Precision Oncology Resource

Two-stage stratified designs with survival outcomes and adjustment for misclassification in predictive biomarkers

Biomarker stratified clinical trial designs are versatile tools to assess biomarker clinical utility and address its relationship with clinical endpoints. Due to imperfect assays and/or classification rules, biomarker status is prone to errors. To account for biomarker misclassification, we consider a two-stage stratified design for survival outcomes with an adjustment for misclassification in predictive biomarkers. Compared to continuous and/or binary outcomes, the test statistics for survival outcomes with an adjustment for biomarker misclassification is much more complicated and needs to take special care. We propose to use the information from the observed biomarker status strata to construct adjusted log-rank statistics for true biomarker status strata. These adjusted log-rank statistics are then used to develop sequential tests for the global (composite) hypothesis and component-wise hypothesis. We discuss the power analysis with the control of the type-I error rate by using the correlations between the adjusted log-rank statistics within and between the design stages. Our method is illustrated with examples of the recent successful development of immunotherapy in nonsmall-cell lung cancer.

Molecular tumour boards - current and future considerations for precision oncology

Over the past 15 years, rapid progress has been made in developmental therapeutics, especially regarding the use of matched targeted therapies against specific oncogenic molecular alterations across cancer types. Molecular tumour boards (MTBs) are panels of expert physicians, scientists, health-care providers and patient advocates who review and interpret molecular-profiling results for individual patients with cancer and match each patient to available therapies, which can include investigational drugs. Interpretation of the molecular alterations found in each patient is a complicated task that requires an understanding of their contextual functional effects and their correlations with sensitivity or resistance to specific treatments. The criteria for determining the actionability of molecular alterations and selecting matched treatments are constantly evolving. Therefore, MTBs have an increasingly necessary role in optimizing the allocation of biomarker-directed therapies and the implementation of precision oncology. Ultimately, increased MTB availability, accessibility and performance are likely to improve patient care. The challenges faced by MTBs are increasing, owing to the plethora of identifiable molecular alterations and immune markers in tumours of individual patients and their evolving clinical significance as more and more data on patient outcomes and results from clinical trials become available. Beyond next-generation sequencing, broader biomarker analyses can provide useful information. However, greater funding, resources and expertise are needed to ensure the sustainability of MTBs and expand their outreach to underserved populations. Harmonization between practice and policy will be required to optimally implement precision oncology. Herein, we discuss the evolving role of MTBs and current and future considerations for their use in precision oncology.

PredictONCO: a web tool supporting decision-making in precision oncology by extending the bioinformatics predictions with advanced computing and machine learning

PredictONCO 1.0 is a unique web server that analyzes effects of mutations on proteins frequently altered in various cancer types. The server can assess the impact of mutations on the protein sequential and structural properties and apply a virtual screening to identify potential inhibitors that could be used as a highly individualized therapeutic approach, possibly based on the drug repurposing. PredictONCO integrates predictive algorithms and state-of-the-art computational tools combined with information from established databases. The user interface was carefully designed for the target specialists in precision oncology, molecular pathology, clinical genetics and clinical sciences. The tool summarizes the effect of the mutation on protein stability and function and currently covers 44 common oncological targets. The binding affinities of Food and Drug Administration/ European Medicines Agency -approved drugs with the wild-type and mutant proteins are calculated to facilitate treatment decisions. The reliability of predictions was confirmed against 108 clinically validated mutations. The server provides a fast and compact output, ideal for the often time-sensitive decision-making process in oncology. Three use cases of missense mutations, (i) K22A in cyclin-dependent kinase 4 identified in melanoma, (ii) E1197K mutation in anaplastic lymphoma kinase 4 identified in lung carcinoma and (iii) V765A mutation in epidermal growth factor receptor in a patient with congenital mismatch repair deficiency highlight how the tool can increase levels of confidence regarding the pathogenicity of the variants and identify the most effective inhibitors. The server is available at https://loschmidt.chemi.muni.cz/predictonco.

S. B, Chatterjee M, Prabhash K, Sreekanthreddy P, Rishi KD, Goswami HM, Veldore VH. An absolute approach to using whole exome DNA and RNA workflow for cancer biomarker testing

Introduction

The concept of personalized medicine in cancer has emerged rapidly with the advancement of genome sequencing and the identification of clinically relevant variants that contribute to disease prognosis and facilitates targeted therapy options. In this study, we propose to validate a whole exome-based tumor molecular profiling for DNA and RNA from formalin-fixed paraffin-embedded (FFPE) tumor tissue.

Methods

The study included 166 patients across 17 different cancer types. The scope of this study includes the identification of single-nucleotide variants (SNVs), insertions/deletions (INDELS), copy number alterations (CNAs), gene fusions, tumor mutational burden (TMB), and microsatellite instability (MSI). The assay yielded a mean read depth of 200×, with >80% of on-target reads and a mean uniformity of >90%. Clinical maturation of whole exome sequencing (WES) (DNA and RNA)- based assay was achieved by analytical and clinical validations for all the types of genomic alterations in multiple cancers. We here demonstrate a limit of detection (LOD) of 5% for SNVs and 10% for INDELS with 97.5% specificity, 100% sensitivity, and 100% reproducibility.

Results

The results were >98% concordant with other orthogonal techniques and appeared to be more robust and comprehensive in detecting all the clinically relevant alterations. Our study demonstrates the clinical utility of the exome-based approach of comprehensive genomic profiling (CGP) for cancer patients at diagnosis and disease progression.

Discussion

The assay provides a consolidated picture of tumor heterogeneity and prognostic and predictive biomarkers, thus helping in precision oncology practice. The primary intended use of WES (DNA+RNA) assay would be for patients with rare cancers as well as for patients with unknown primary tumors, and this category constitutes nearly 20–30% of all cancers. The WES approach may also help us understand the clonal evolution during disease progression to precisely plan the treatment in advanced stage disease.

Somatic Genomic Testing in Patients With Metastatic or Advanced Cancer: ASCO Provisional Clinical Opinion

PURPOSE

An ASCO provisional clinical opinion offers timely clinical direction to ASCO's membership following publication or presentation of potentially practice-changing data from major studies. This provisional clinical opinion addresses the appropriate use of tumor genomic testing in patients with metastatic or advanced solid tumors.

CLINICAL CONTEXT

An increasing number of therapies are approved to treat cancers harboring specific genomic biomarkers. However, there is a lack of clarity as to when tumor genomic sequencing should be ordered, what type of assays should be performed, and how to interpret the results for treatment selection.

PROVISIONAL CLINICAL OPINION

Patients with metastatic or advanced cancer should undergo genomic sequencing in a certified laboratory if the presence of one or more specific genomic alterations has regulatory approval as biomarkers to guide the use of or exclusion from certain treatments for their disease. Multigene panel-based assays should be used if more than one biomarker-linked therapy is approved for the patient's disease. Site-agnostic approvals for any cancer with a high tumor mutation burden, mismatch repair deficiency, or neurotrophic tyrosine receptor kinase (NTRK) fusions provide a rationale for genomic testing for all solid tumors. Multigene testing may also assist in treatment selection by identifying additional targets when there are few or no genotype-based therapy approvals for the patient's disease. For treatment planning, the clinician should consider the functional impact of the targeted alteration and expected efficacy of genomic biomarker-linked options relative to other approved or investigational treatments.Additional information is available at www.asco.org/assays-and-predictive-markers-guidelines.

Artificial intelligence in clinical research of cancers

Several factors, including advances in computational algorithms, the availability of high-performance computing hardware, and the assembly of large community-based databases, have led to the extensive application of Artificial Intelligence (AI) in the biomedical domain for nearly 20 years. AI algorithms have attained expert-level performance in cancer research. However, only a few AI-based applications have been approved for use in the real world. Whether AI will eventually be capable of replacing medical experts has been a hot topic. In this article, we first summarize the cancer research status using AI in the past two decades, including the consensus on the procedure of AI based on an ideal paradigm and current efforts of the expertise and domain knowledge. Next, the available data of AI process in the biomedical domain are surveyed. Then, we review the methods and applications of AI in cancer clinical research categorized by the data types including radiographic imaging, cancer genome, medical records, drug information and biomedical literatures. At last, we discuss challenges in moving AI from theoretical research to real-world cancer research applications and the perspectives toward the future realization of AI participating cancer treatment.

Application of third-generation sequencing in cancer research

In the past several years, nanopore sequencing technology from Oxford Nanopore Technologies (ONT) and single-molecule real-time (SMRT) sequencing technology from Pacific BioSciences (PacBio) have become available to researchers and are currently being tested for cancer research. These methods offer many advantages over most widely used high-throughput short-read sequencing approaches and allow the comprehensive analysis of transcriptomes by identifying full-length splice isoforms and several other posttranscriptional events. In addition, these platforms enable structural variation characterization at a previously unparalleled resolution and direct detection of epigenetic marks in native DNA and RNA. Here, we present a comprehensive summary of important applications of these technologies in cancer research, including the identification of complex structure variants, alternatively spliced isoforms, fusion transcript events, and exogenous RNA. Furthermore, we discuss the impact of the newly developed nanopore direct RNA sequencing (RNA-Seq) approach in advancing epitranscriptome research in cancer. Although the unique challenges still present for these new single-molecule long-read methods, they will unravel many aspects of cancer genome complexity in unprecedented ways and present an encouraging outlook for continued application in an increasing number of different cancer research settings.

The Community Oncology and Academic Medical Center Alliance in the Age of Precision Medicine: Cancer Genetics and Genomics Considerations

Recent public policy, governmental regulatory and economic trends have motivated the establishment and deepening of community health and academic medical center alliances. Accordingly, community oncology practices now deliver a significant portion of their oncology care in association with academic cancer centers. In the age of precision medicine, this alliance has acquired critical importance; novel advances in nucleic acid sequencing, the generation and analysis of immense data sets, the changing clinical landscape of hereditary cancer predisposition and ongoing discovery of novel, targeted therapies challenge community-based oncologists to deliver molecularly-informed health care. The active engagement of community oncology practices with academic partners helps with meeting these challenges; community/academic alliances result in improved cancer patient care and provider efficacy. Here, we review the community oncology and academic medical center alliance. We examine how practitioners may leverage academic center precision medicine-based cancer genetics and genomics programs to advance their patients' needs. We highlight a number of project initiatives at the City of Hope Comprehensive Cancer Center that seek to optimize community oncology and academic cancer center precision medicine interactions.

Collaborative, Multidisciplinary Evaluation of Cancer Variants Through Virtual Molecular Tumor Boards Informs Local Clinical Practices

PURPOSE

The cancer research community is constantly evolving to better understand tumor biology, disease etiology, risk stratification, and pathways to novel treatments. Yet the clinical cancer genomics field has been hindered by redundant efforts to meaningfully collect and interpret disparate data types from multiple high-throughput modalities and integrate into clinical care processes. Bespoke data models, knowledgebases, and one-off customized resources for data analysis often lack adequate governance and quality control needed for these resources to be clinical grade. Many informatics efforts focused on genomic interpretation resources for neoplasms are underway to support data collection, deposition, curation, harmonization, integration, and analytics to support case review and treatment planning.

METHODS

In this review, we evaluate and summarize the landscape of available tools, resources, and evidence used in the evaluation of somatic and germline tumor variants within the context of molecular tumor boards.

RESULTS

Molecular tumor boards (MTBs) are collaborative efforts of multidisciplinary cancer experts equipped with genomic interpretation resources to aid in the delivery of accurate and timely clinical interpretations of complex genomic results for each patient, within an institution or hospital network. Virtual MTBs (VMTBs) provide an online forum for collaborative governance, provenance, and information sharing between experts outside a given hospital network with the potential to enhance MTB discussions. Knowledge sharing in VMTBs and communication with guideline-developing organizations can lead to progress evidenced by data harmonization across resources, crowd-sourced and expert-curated genomic assertions, and a more informed and explainable usage of artificial intelligence.

CONCLUSION

Advances in cancer genomics interpretation aid in better patient and disease classification, more streamlined identification of relevant literature, and a more thorough review of available treatments and predicted patient outcomes.

A Review of Precision Oncology Knowledgebases for Determining the Clinical Actionability of Genetic Variants

The increased availability of tumor genetic testing and targeted cancer therapies contributes to the advancement of precision medicine in the field of oncology. Precision oncology knowledgebases provide a way of organizing clinically relevant genetic information in a way that is easily accessible for both oncologists and patients, facilitating the genetic-based clinical decision making. Many organizations and companies have built precision oncology knowledgebases, intended for multiple users. In general, these knowledgebases offer information on cancer-related genetic variants as well as their associated diagnostic, prognostic, and therapeutic implications, but they often differ in their information curations, designs, and user experiences. It is advisable that oncologists use multiple knowledgebases during their practice to have them complement each other. In the future, convergence toward common standards and formats is needed to ensure that the comprehensive knowledge across all sources can be unified to bring the oncology community closer to the achievement of the goal of precision oncology.

Next-generation sequencing for the general cancer patient

Next-generation sequencing is a novel method of DNA sequencing that has become a cornerstone of precision oncology. This sequencing method detects differences in specific DNA sequences between a sample and a reference genome or matched normal DNA. In addition to single-nucleotide variants, other insertions, deletions, copy number changes, and fusions may be drivers of cancer growth, and thus represent therapeutic opportunities. As a result, genomic characterization has been increasingly used to guide treatment decisions, especially in patients with advanced disease. This review discusses the basic technologies involved in next-generation sequencing, the applications of this method, and limitations in the clinical realm.

Comprehensive elaboration of database resources utilized in next-generation sequencing-based tumor somatic mutation detection

The rapid evolution of next-generation sequencing (NGS)-based tumor genomic profile detection and the emergence of molecularly targeted therapies have enabled precision oncology. In NGS-based analysis, various types of databases have been developed to perform different functions. However, many problems still exist when using these public databases. Therefore, it is important to better understand the characteristics and limitations of each database and have them complement each other to provide useful clinical evidence for NGS testing. In this review, we elaborate on the important role of databases and their concrete applications in NGS-based somatic mutation detection. We introduce the typically used databases for sequence alignment, variant filtration, and variant interpretation, and compare the differences between the databases with similar functions. Subsequently, we determine the limitations of each database and provide the corresponding solutions. Furthermore, we present an overview diagram to clearly illustrate the database used in the entire NGS-based somatic mutation detection pipeline.

Disease classification: from phenotypic similarity to integrative genomics and beyond

A fundamental challenge of modern biomedical research is understanding how diseases that are similar on the phenotypic level are similar on the molecular level. Integration of various genomic data sets with the traditionally used phenotypic disease similarity revealed novel genetic and molecular mechanisms and blurred the distinction between monogenic (Mendelian) and complex diseases. Network-based medicine has emerged as a complementary approach for identifying disease-causing genes, genetic mediators, disruptions in the underlying cellular functions and for drug repositioning. The recent development of machine and deep learning methods allow for leveraging real-life information about diseases to refine genetic and phenotypic disease relationships. This review describes the historical development and recent methodological advancements for studying disease classification (nosology).

[Detection and interpretation of somatic variants in molecular pathology]

BACKGROUND

Due to the increasing amount of data and sources of information, data evaluation is a crucial step in parallel sequencing.

OBJECTIVES

Illustration of pitfalls in evaluating the variant list of parallel sequencing and recommendations regarding software tools and databases.

METHODS

Description of filtering steps used, demonstration of criteria and recommendations for annotation by examples from everyday work, comparative analysis of databases with somatic variants, description of the installation of an individualized database.

RESULTS

Variant filtering is a multistep process using information from different databases. The plausibility of variant calling should be verified using the Integrative Genomics Viewer and variants should be described according to the Human Genome Variation Society (HGVS) recommendations. Different databases, which all show advantages and disadvantages, are available for variant interpretation. An individualized database can be built up with the open-source tool cBioPortal.

CONCLUSIONS

Different tools and databases might be used for the analysis of parallel sequencing data. The application depends on, amongst other things, the local situation and has to be extensively validated.

The Status and Impact of Clinical Tumor Genome Sequencing

Since the discovery that DNA alterations initiate tumorigenesis, scientists and clinicians have been exploring ways to counter these changes with targeted therapeutics. The sequencing of tumor DNA was initially limited to highly actionable hot spots-areas of the genome that are frequently altered and have an approved matched therapy in a specific tumor type. Large-scale genome sequencing programs quickly developed technological improvements that enabled the deployment of whole-exome and whole-genome sequencing technologies at scale for pristine sample materials in research environments. However, the turning point for precision medicine in oncology was the innovations in clinical laboratories that improved turnaround time, depth of coverage, and the ability to reliably sequence archived, clinically available samples. Today, tumor genome sequencing no longer suffers from significant technical or financial hurdles, and the next opportunity for improvement lies in the optimal utilization of the technologies and data for many different tumor types.

Prediction of response to anti-cancer drugs becomes robust via network integration of molecular data

Despite the widening range of high-throughput platforms and exponential growth of generated data volume, the validation of biomarkers discovered from large-scale data remains a challenging field. In order to tackle cancer heterogeneity and comply with the data dimensionality, a number of network and pathway approaches were invented but rarely systematically applied to this task. We propose a new method, called NEAmarker, for finding sensitive and robust biomarkers at the pathway level. scores from network enrichment analysis transform the original space of altered genes into a lower-dimensional space of pathways. These dimensions are then correlated with phenotype variables. The method was first tested using in vitro data from three anti-cancer drug screens and then on clinical data of The Cancer Genome Atlas. It proved superior to the single-gene and alternative enrichment analyses in terms of (1) universal applicability to different data types with a possibility of cross-platform integration, (2) consistency of the discovered correlates between independent drug screens, and (3) ability to explain differential survival of treated patients. Our new screen of anti-cancer compounds validated the performance of multivariate models of drug sensitivity. The previously proposed methods of enrichment analysis could achieve comparable levels of performance in certain tests. However, only our method could discover predictors of both in vitro response and patient survival given administration of the same drug.

Modern Information Technology for Cancer Research: What's in IT for Me? An Overview of Technologies and Approaches

Information technology (IT) can enhance or change many scenarios in cancer research for the better. In this paper, we introduce several examples, starting with clinical data reuse and collaboration including data sharing in research networks. Key challenges are semantic interoperability and data access (including data privacy). We deal with gathering and analyzing genomic information, where cloud computing, uncertainties and reproducibility challenge researchers. Also, new sources for additional phenotypical data are shown in patient-reported outcome and machine learning in imaging. Last, we focus on therapy assistance, introducing tools used in molecular tumor boards and techniques for computer-assisted surgery. We discuss the need for metadata to aggregate and analyze data sets reliably. We conclude with an outlook towards a learning health care system in oncology, which connects bench and bedside by employing modern IT solutions.

Use of Next-Generation Sequencing Tests to Guide Cancer Treatment: Results From a Nationally Representative Survey of Oncologists in the United States

Purpose

There are no nationally representative data on oncologists' use of next-generation sequencing (NGS) testing in practice. The purpose of this study was to investigate how oncologists in the United States use NGS tests to evaluate patients with cancer and to inform treatment recommendations.

Methods

The study used data from the National Survey of Precision Medicine in Cancer Treatment, which was mailed to a nationally representative sample of oncologists in 2017 (N = 1,281; cooperation rate = 38%). Weighted percentages were calculated to describe NGS test use. Multivariable modeling was conducted to assess the association of test use with oncologist practice characteristics.

Results

Overall, 75.6% of oncologists reported using NGS tests to guide treatment decisions. Of these oncologists, 34.0% used them often to guide treatment decisions for patients with advanced refractory disease, 29.1% to determine eligibility for clinical trials, and 17.5% to decide on off-label use of Food and Drug Administration-approved drugs. NGS test results informed treatment recommendations often for 26.8%, sometimes for 52.4%, and never or rarely for 20.8% of oncologists. Oncologists younger than 50 years of age, holding a faculty appointment, having genomics training, seeing more than 50 unique patients per month, and having access to a molecular tumor board were more likely to use NGS tests.

Conclusion

In 2017, most oncologists in the United States were using NGS tests to guide treatment decisions for their patients. More research is needed to establish the clinical usefulness of these tests, to develop evidence-based clinical guidelines for their use in practice, and to ensure that patients who can benefit from these new technologies receive appropriate testing and treatment.

Cancer Informatics in 2017: A New Beginning and a Bright Future

Objective:  To summarize significant research contributions on cancer informatics published in 2017.

Methods:  An extensive search using PubMed/Medline, Google Scholar, and manual review was conducted to identify the scientific contributions published in 2017 that address topics in cancer informatics. The selection process comprised three steps: (i) 15 candidate best papers were first selected by the two section editors, (ii) external reviewers from internationally renowned research teams reviewed each candidate best paper, and (iii) the final selection of three best papers was conducted by the editorial board of the Yearbook.

Results:  Results: The three selected best papers present studies addressing many facets of cancer informatics, with immediate applicability in the research and clinical domains.

Conclusion: Cancer informatics is a broad and vigorous subfield of biomedical informatics. Strides in knowledge management, crowdsourcing, and visualization are especially notable in 2017.

Multidisciplinary molecular tumour board: a tool to improve clinical practice and selection accrual for clinical trials in patients with cancer

BACKGROUND

The complexity of delivering precision medicine to oncology patients has led to the creation of molecular tumourboards (MTBs) for patient selection and assessment of treatment options. New technologies like the liquid biopsy are augmenting available therapeutic opportunities. This report aims to analyse the experience of our MTB in the implementation of personalised medicine in a cancer network.

MATERIALS AND METHODS

Patients diagnosed with solid tumours progressing to standard treatments were referred to our Phase I unit. They underwent comprehensive next generation sequencing (NGS) of either tumour tissue or cell-free circulating tumour DNA (ctDNA) or both. The MTB expressed either a positive or negative opinion for the treatment of the patients with discovered druggable alterations inside a clinical trial, in an expanded access programme, with a compassionate use. Afterwards, discovered alterations were matched with OncoKB levels of evidence for the choice of alteration-specific treatments in order to compare MTB outcomes with a standardised set of recommendations.

RESULTS

NGS was performed either on ctDNA or tumour tissue or in both of them in 204 patients. The MTB evaluated 173 of these cases. Overall, the MTB proposed alteration-specific targeted therapy to 72 patients (41.6%). 49 patients (28.3% of the total evaluated) were indicated to enter a clinical trial. In 29 patients with matched liquid biopsy NGS (lbNGS), tumour tissue NGS (ttNGS) and MTB evaluation, the MTB changed the treatment strategy coming from standardised recommendations based on lbNGS and ttNGS alone in 10 patients (34.5%), thanks to the evaluation of other clinical parameters. In our cohort, lbNGS was more likely, compared with ttNGS, to detect point mutations (OR 11, 95% CI 2.9 to 24.1, p<0.001) and all-type alterations (OR 13.6, 95% CI 5.5 to 43.2, p<0.001) from the same genes of matched patients.

CONCLUSIONS

Our MTB allows patients with refractory cancer to be included in clinical trials and improves the precision of clinical decisions compared with a standardised set of mutation-driven recommendations.

IMPACT web portal: oncology database integrating molecular profiles with actionable therapeutics

Background

With the advancement of next generation sequencing technology, researchers are now able to identify important variants and structural changes in DNA and RNA in cancer patient samples. With this information, we can now correlate specific variants and/or structural changes with actionable therapeutics known to inhibit these variants. We introduce the creation of the IMPACT Web Portal, a new online resource that connects molecular profiles of tumors to approved drugs, investigational therapeutics and pharmacogenetics associated drugs.

Results

IMPACT Web Portal contains a total of 776 drugs connected to 1326 target genes and 435 target variants, fusion, and copy number alterations. The online IMPACT Web Portal allows users to search for various genetic alterations and connects them to three levels of actionable therapeutics. The results are categorized into 3 levels: Level 1 contains approved drugs separated into two groups; Level 1A contains approved drugs with variant specific information while Level 1B contains approved drugs with gene level information. Level 2 contains drugs currently in oncology clinical trials. Level 3 provides pharmacogenetic associations between approved drugs and genes.

Conclusion

IMPACT Web Portal allows for sequencing data to be linked to actionable therapeutics for translational and drug repurposing research. The IMPACT Web Portal online resource allows users to query genes and variants to approved and investigational drugs. We envision that this resource will be a valuable database for personalized medicine and drug repurposing. IMPACT Web Portal is freely available for non-commercial use at http://tanlab.ucdenver.edu/IMPACT.

Personalized cancer therapy-leveraging a knowledge base for clinical decision-making

Next-generation sequencing (NGS), also known as massively parallel sequencing, is rapidly being incorporated into oncology practice. Interpretation of genomic reports and selecting treatments based on the tumor's genomic analysis becomes more and more complicated for the treating oncologist because of the use of larger panels covering dozens to hundreds of genes and the amount of rapidly emerging clinical/translational data. To help guide personalized treatments in oncology, The Sheikh Khalifa Bin Zayed Al Nahyan Institute for Personalized Cancer Therapy (IPCT) at MD Anderson Cancer Center has developed a knowledge base, available at https://personalizedcancertherapy.org or https://pct.mdanderson.org (PCT). This knowledge base provides information on the function of common genomic alterations and their therapeutic implications. Here, we describe how such genomic information can be used by health-care providers to identify genomically matched therapies.

Systematic Functional Annotation of Somatic Mutations in Cancer

The functional impact of the vast majority of cancer somatic mutations remains unknown, representing a critical knowledge gap for implementing precision oncology. Here, we report the development of a moderate-throughput functional genomic platform consisting of efficient mutant generation, sensitive viability assays using two growth factor-dependent cell models, and functional proteomic profiling of signaling effects for select aberrations. We apply the platform to annotate >1,000 genomic aberrations, including gene amplifications, point mutations, indels, and gene fusions, potentially doubling the number of driver mutations characterized in clinically actionable genes. Further, the platform is sufficiently sensitive to identify weak drivers. Our data are accessible through a user-friendly, public data portal. Our study will facilitate biomarker discovery, prediction algorithm improvement, and drug development.

Genomic Landscape of Cell-Free DNA in Patients with Colorectal Cancer

"Liquid biopsy" approaches analyzing cell-free DNA (cfDNA) from the blood of patients with cancer are increasingly utilized in clinical practice. However, it is not yet known whether cfDNA sequencing from large cohorts of patients with cancer can detect genomic alterations at frequencies similar to those observed by direct tumor sequencing, and whether this approach can generate novel insights. Here, we report next-generation sequencing data from cfDNA of 1,397 patients with colorectal cancer. Overall, frequencies of genomic alterations detected in cfDNA were comparable to those observed in three independent tissue-based colorectal cancer sequencing compendia. Our analysis also identified a novel cluster of extracellular domain (ECD) mutations in EGFR, mediating resistance by blocking binding of anti-EGFR antibodies. Patients with EGFR ECD mutations displayed striking tumor heterogeneity, with 91% harboring multiple distinct resistance alterations (range, 1-13; median, 4). These results suggest that cfDNA profiling can effectively define the genomic landscape of cancer and yield important biological insights.Significance: This study provides one of the first examples of how large-scale genomic profiling of cfDNA from patients with colorectal cancer can detect genomic alterations at frequencies comparable to those observed by direct tumor sequencing. Sequencing of cfDNA also generated insights into tumor heterogeneity and therapeutic resistance and identified novel EGFR ectodomain mutations. Cancer Discov; 8(2); 164-73. ©2017 AACR.This article is highlighted in the In This Issue feature, p. 127.