Abstract
During the past decade, next-generation sequencing (NGS) technologies have become widely adopted in cancer research and clinical care. Common applications within the clinical setting include patient stratification into relevant molecular subtypes, identification of biomarkers of response and resistance to targeted and systemic therapies, assessment of heritable cancer risk based on known pathogenic variants, and longitudinal monitoring of treatment response. The need for efficient downstream processing and reliable interpretation of sequencing data has led to the development of novel algorithms and computational pipelines, as well as structured knowledge bases that link genomic alterations to currently available drugs and ongoing clinical trials. Cancer centers around the world use different types of targeted solid-tissue and blood based NGS assays to analyze the genomic and transcriptomic profile of patients as part of their routine clinical care. Recently, cross-institutional collaborations have led to the creation of large pooled datasets that can offer valuable insights into the genomics of rare cancers.
Keywords: next-generation sequencing, targeted sequencing, clinical sequencing, precision medicine, AACR GENIE
INTRODUCTION
Next-generation sequencing (NGS) genomic assays constitute a valuable tool for the genomic profiling of tumors within the clinical setting, offering advantages in cost-effectiveness and turn-around times 1. As such, they have become increasingly adopted as part of routine medical care across major cancer centers worldwide. Common applications include patient stratification based on clinically relevant molecular profiles and detection of biomarkers that can be used to guide therapeutic choices 2,3. The analysis of germline variants that contribute to cancer predisposition helps identify patients at risk for some heritable cancers 4. The large amount of tumors sequenced in recent years has also created unprecedented opportunities for translational research and scientific discovery 5.
Existing NGS-based platforms are used for profiling solid tumors and hematological malignancies. DNA assays enable efficient identification of somatic mutations and copy number alterations, as well as germline variants that can influence heritable cancer risks. RNA assays can be used to target gene fusions that help refine the diagnosis of certain cancer subtypes and may be therapeutically actionable in others. Ultra-sensitive cell-free DNA profiling platforms allow for minimally invasive monitoring of response to certain treatments, as well as for accurate detection of minimal residual disease following tumor resection or completion of treatment. The NGS platforms currently used by different cancer hospitals tend to include a core set of genes that are commonly considered to have functional and clinical relevance in cancer. Still, noticeable differences in their actual choice of gene panels, as well as in their sequencing and computational pipelines exist and constitute an important barrier for data integration across institutions.
In this short review, we provide an overview of current applications of NGS assays within the clinical and research setting. We enumerate and describe some common computational tools for NGS data analysis and interpretation and we provide examples of their use within published analyses of translational relevance. Finally, we end with a cross-institutional perspective based on our participation in AACR Project GENIE 6, which is an ongoing large multicenter collaborative effort where tens of thousands of samples are being sequenced and shared by cancer centers around the world.
TARGETED NEXT-GENERATION SEQUENCING PANELS IN CANCER RESEARCH AND CLINICAL CARE
The workflow that goes from sample acquisition to translational applications of NGS platforms involves a variety of components that are visually recapitulated in Figure 1. The process begins when patients are consented and their biological samples (typically solid tissue or blood) are collected in the clinic. DNA and/or RNA is extracted from these specimens using automated protocols and sequence libraries are prepared and captured using hybridization probes that cover genomic regions of interest. The resulting paired reads are then processed using customized bioinformatic pipelines that reliably identify presence of mutations, copy number alterations and genomic rearrangements in the case of DNA panels 7,8, as well as levels of gene expression or specific gene fusions in the case of RNA panels 9–11. DNA from matched blood or non-cancerous tissue samples can be used to reliably distinguish germline variants from somatic mutations; when these are not available, germline filtering can be attempted using databases of human genomic variation such as The Single Nucleotide Polymorphism Database (dbSNP) 12, as well as panels of “pooled normals” or dedicated computational tools 13,14. Matched blood controls can also be useful to distinguish between mutations that arise from clonal hematopoiesis and those that arise from solid-tumor cells, which is important in order to avoid erroneous treatment recommendations 15.
Figure 1. Targeted NGS-platforms for cancer research and clinical care.
Computational genomic analyses and multimodal integration with other molecular platforms and data extracted from the electronic health records are key elements of the workflow that goes from sample sequencing to clinically relevant translational applications.
The set of computational tools that we will reference in this section, together with relevant references and online resources, are recapitulated in Table 1. Genomic features of interest at the sample level for DNA sequencing panels include tumor mutational burden (TMB), which is usually expressed as the number of somatic mutations per sequenced megabase of DNA (mut/Mb). Accurate assessment of TMB has attracted a lot of interest in recent years due to the advent of immunotherapies, since high TMB has been demonstrated to be a biomarker predictive of activity for immune checkpoint blockade inhibitors across a variety of cancer types 16. Accordingly, the FDA approved pembroluzimab for the treatment of unresectable or metastatic solid tumors with TMB>10 mut/Mb as determined by an FDA-approved test in a tissue-agnostic manner 17. NGS-based estimates of TMB have shown overall good concordance with estimates from whole-exome sequencing (WES) approaches 8,18. However, technical variability and aspects such as the choice of genes included in each panel often make it necessary to use assay-specific cut-offs and quantitative harmonization approaches when analyzing associations between TMB and outcomes across diverse sequencing platforms 19,20. Furthermore, some studies have shown that additional genomic features that can be derived from current NGS panels such as human leukocyte antigen (HLA) status can be incorporated into multivariable models that outperform the use of TMB alone for predicting response to immunotherapy 21. NGS-platforms have also been proven to offer reliable performance for detecting microsatellite instability status across a wide variety of cancer types 22 and dedicated computational tools such as MSIsensor have been developed for this purpose 23. DNA sequencing data can also be used to identify germline variants that predispose to Lynch syndrome 24. Analysis of mutational signatures associated with other oncogenic processes, such as those defined by COSMIC 25, may also be attempted with targeted sequencing panels, but the reliability of the results depends strongly on the molecular profile of each individual signature due to the limited breadth of coverage. While signatures such as SBS4 or SBS7 (associated with tobacco smoking and ultraviolet light exposure, respectively) may have distinct profiles that allow for robust detection 26, other signatures such as those associated with homologous recombination deficiency may require the use of alternative platforms such as WES or whole-genome sequencing (WGS) 27. Users interested in these types of analyses can rely on dictionaries of signatures previously derived using large cohorts of sequenced tumors 25, or attempt to infer de novo signatures using their own cohorts of samples using computational packages like the SigProfiler set of tools 28–30. Throughout the past decade, a variety of algorithms like MutSig 31 and MuSic 32 have also been developed in order to identify recurrently mutated genes. These tools used sophisticated statistical models of background mutational rates in order to highlight genes that are mutated more often than one would expect by chance. Similar mathematical tools applied at the finer resolution of specific codon changes have identified large catalogs of mutational “hotspots”, both at the linear DNA chain and 3D protein level, that are publicly available and can be used to annotate alterations observed in analyzed cohorts 33–35.
Table 1.
Frequently used computational tools for downstream analyses of genomic data from NGS-platforms.
While not as widely adopted as TMB, chromosomal instability and cancer aneuploidy are also sample-level genomic features that have been proposed as markers of immune evasion 36 and associated with increased tumor aggressiveness and metastatic potential 37,38. Chromosomal instability can be quantified using a variety of metrics 39. A common approach consists in defining the fraction of the genome altered by copy-number alterations (FGA) as the proportion of sequenced bases where the inferred copy-number differs from the baseline diploid status. In the absence of further refinements, FGA estimates are quite vulnerable to differences in tumor purity. Recently, computational tools such as FACETS (Fraction and Allele-Specific Copy Number Estimates from Tumor Sequencing) 40 have made it possible to obtain computational estimates of tumor purity and ploidy, which can be used to derive corrected versions of the FGA. Additionally, these tools make it possible to reliably use targeted sequencing data from NGS-platforms to explore other genomic features that have been proven to have translational relevance, such as whole-genome duplication (WGD) events 41, or clonality and allelic imbalance (AI) of individual mutations 42. Traditionally, copy number alterations affecting specific genes have been identified using computational tools like GISTIC 43. More recently, methods like ASCETS have been developed for copy-number quantification of arm and chromosome level changes from panel-based, targeted NGS data 44.
Identification of gene fusions and structural variants from targeted NGS data can be performed using dedicated prediction methods such as Delly 45 and Manta 46. Some clinically relevant gene fusions, such as specific alterations involving NTRK, can be tested with high-sensitivity using targeted panel DNA based sequencing assays 10. However, RNA testing - possibly done through targeted assays using multiplex PCR in predefined sets of genes - may be desirable for confirmation of complex rearrangements as well as to identify structural alterations that may be missed based on targeted DNA sequencing alone 11.
After discrete somatic events such as mutations, copy-number changes, and gene fusions have been identified in a given sample, typical downstream analyses include identification of statistical enrichments within specific phenotypic groups, as well as analyses of co-occurrence and mutual exclusivity 47. Standard methods such as Fisher’s exact test can be used to assess statistical significance for these relationships in some simple scenarios, but more sophisticated models - such as the SELECT algorithm 48 - have been proposed in the literature to account for a variety of potentially confounding variables. Integrative downstream analyses can be performed at either the individual gene level, or by aggregating genes into functionally related signaling pathways in order to increase statistical power by decreasing signal sparsity and limiting the complexity of the candidate feature space in a biologically coherent manner 49. Curated sets of pathway templates 49 and online tools for pathway visualization 50 have been proposed to enhance reproducibility across studies. R packages like Maftools 51 also offer an efficient implementation of well-established computational methods for integrative analyses of genomic data with a descriptive or comparative focus.
In recent years, there has been a growing interest in the implementation of multimodal integration approaches leading to studies where DNA and RNA sequencing data is combined with other sources of “-omics” information, as well as additional data modalities including pathology, radiology, and clinical annotations extracted from the electronic health records (EHR) 52. Built upon the premise that the whole can be greater than the sum of its parts, these new computational frameworks often rely on sophisticated machine learning techniques for automatic feature extraction and deep learning architectures for biomarker discovery in large multimodal datasets 53.
Translational applications of NGS-platforms can be broadly categorized into diagnosis, prognosis, and treatment related. In many clinical workflows, after the sequencing results have been properly reviewed for quality and accuracy, they are added to the EHR and used to generate a clinical report that goes back to the treating physician and the patient 8,54. The information contained in this report can also act as a complement to conventional histologic review to improve diagnostic accuracy through genome-derived tumor type prediction tools 55. Furthermore, targeted NGS-panels can be used to identify germline variants in cancer predisposition genes and provide clinically actionable information for risk reduction in family members, reproductive planning, and selection of targeted therapies 56,57. In regards to this, the recently created SignalDB constitutes a good example of a useful resource that provides information about pathogenic germline variants and their corresponding tumor-specific zygosity changes stratified by gene, lineage, and cancer type based on a large clinical cohort of patients sequenced with NGS-panels 58. When it comes to prognosis, NGS-panels can be used within the clinical setting to identify biomarkers with FDA or professional guideline-recognized prognostic implications such as those curated by the OncoKB knowledge base in hematological malignancies 59. More generally, certain machine learning tools like Oncocast 60 have been designed to build personalized profiles that integrate clinical and genomic features derived from NGS-data for risk stratification and prediction of recurrence 61,62. In terms of guiding treatment, NGS-panels can be used to identify therapeutically targetable alterations and to inform the choice of the best drug available for each patient 63. To facilitate this task and provide support to treating physicians, knowledge bases such as OncoKB 59 and CIVIC 64, among others 3, have been developed in recent years and are kept regularly updated to reflect the latest advancements in the field. Multiple institutions are also currently using results from NGS-testing to match patients with ongoing clinical trials based on their genomic profiles 65,66 and computational pipelines like MatchMiner 67 have been specifically designed for this purpose. Liquid biopsy tests built upon panel-based NGS assays to detect and quantify tumor derived cell free DNA (cfDNA) can also be used for longitudinal monitoring of response to treatment, as well as early detection of tumor relapse and monitoring of minimal residual disease after certain treatments 68. The low concentration of cfDNA in blood that is encountered in many of these scenarios, particularly when dealing with early stage cancers, requires the use of ultra-high-depth sequencing and sophisticated statistical models aimed at reducing background error rates for very low variant allele frequencies 69.
A CROSS-INSTITUTION PERSPECTIVE ON TARGETED NGS PANELS: INSIGHTS FROM THE AACR GENIE PROJECT
The American Association for Cancer Research (AACR) Genomics, Evidence, Neoplasia, Information, Exchange (GENIE) project is a multi-institutional effort that aims to create an expansive database of targeted sequencing data for a wide array of tumors 6. The consortium was conceived in 2014 and released its first public data set in 2017, which included genomic and basic clinical information for more than 18,000 patients from eight founding institutions 70. Since then, the scope of the project has continued to grow and the cohort has been expanded to include additional cancer centers, patients, tumor types, and clinical data fields (Figure 2A). The most recent public release as of November 2021, GENIE v10.1, contains more than 120,000 sequenced samples from more than 110,000 patients across 19 institutions. The five largest contributing cancer centers account for more than 75% of the samples: MSK (41%), Dana Farber Cancer Institute (DFCI, 22%), Johns Hopkins University (JHU, 6%), Vanderbilt-Ingram Cancer Center (VICC, 4%) and Princess Margaret Cancer Center University Health Network (UHN, 4%). In this section, we provide an overview of the GENIE dataset and we analyze differences and similarities among the NGS-platforms and the data provided by these top five contributors.
Figure 2. Overview of clinical and genomic data in the GENIE cohort.
A. Number of samples included over time in each of the GENIE public releases, broken down by top contributing institutions. B. Distribution of patient race, sample type and cancer type for samples contributed by each institution. C. Number of genes within each of the most comprehensive panels from the five institutions that overlap with those in the list compiled by Vogelstein et al. and the COSMIC Cancer Gene Census (Tier 1). D. Distribution of oncogenes and tumor suppressor genes across the most comprehensive panels from each institution. E. Number of overlapping genes commonly sequenced across the smallest panel (left) and the most comprehensive panel (right) submitted from each institution. F. Number of genes targeting 10 canonical oncogenic signaling pathways of reference that are included in the smallest and largest panels from each institution. F. Distribution of mutations observed across the 23 genes covered in all panels in patients with colorectal cancer, melanoma and non-small cell lung cancer.
The patients in the GENIE cohort are predominantly white (69.8%) and non-Hispanic (72.5%). More than 70% of the patients are over 50 years old and there is a balanced gender ratio. Overall, the distributions for these demographic features are reasonably similar across the top contributing institutions (Figure 2B). More than half of the sequenced specimens in the cohort correspond to primary tumors (56.1%, Figure 2B). The most commonly sequenced cancer types are non-small cell lung and colorectal cancer, roughly reflecting overall incidence rates (Figure 2B). Breast tumors are also well represented across the top institutional cohorts, with the only exception of JHU. A relatively large fraction of cases from VICC and UHN correspond to hematologic malignancies (~42.3% and ~28.5% respectively). Ovarian tumors account for the largest subset of cases contributed by UHN (~17.8%), which explains the larger overall fraction of female patients sequenced at this institution. JHU and DFCI are the centers that contribute the largest and the smallest fraction of Black patients, respectively, possibly reflecting the underlying racial distributions in the Baltimore and Boston urban areas.
Each of the institutions contributing samples to the AACR GENIE cohort use their own NGS assays, which results in a high-degree of technical variability across samples coming from different centers. The most obvious difference among the submitted NGS-panels is the selection of genes sequenced by each assay, which largely coincides with published lists of cancer related genes such as the COSMIC Cancer Gene Census 71 or the landmark set of 125 driver genes curated by Vogelstein et al. in 2013 71 (Figure 2C, Supplemental Table S1). The distribution of oncogenes and tumor suppressor genes is balanced across all the panels and is roughly similar to the ratio observed in curated databases like OncoKB (Figure 2D). The overlap across the most restrictive gene panels from the top five contributing institutions includes 23 genes, whereas that overlap expands to 117 sequenced genes when the largest assay from each institution is considered (Figure 2E). While the most commonly mutated driver genes in cancer (such as TP53, KRAS, EGFR, etc.) were consistently included across all panels, there were also examples of genes with great translational relevance in certain cancer types (such as BRCA1/2 in breast cancer, or BAP1 in mesothelioma) that were missing from several of the panels (Figure 2E). As expected, oncogenic signaling pathways with a higher number of therapeutically actionable alterations, such as RTK/RAS, PI3K, or the cell cycle 49, tended to be the best represented across panels (Figure 2F). In order to fully exploit the large datasets assembled by GENIE, certain analyses will need to be restricted to the genes that are consistently interrogated by multiple panels. Analysts will need to weigh the benefits and drawbacks of using a smaller gene set with more samples or smaller set of panels/ samples with a larger gene list to determine which inclusion criteria best serves the analysis at hand.
Even when two panels cover the same gene, they may not capture the same mutations: while some of the assays may restrict sequencing to the vicinity of known mutational hotspots, other platforms will cover all coding exons, introns, and promoter regions (Figure 2C,G). All institutions report SNVs and small indels, but only a fraction report gene or intragenic CNAs and structural variants (Figure 2C). Germline variants are currently not reported in GENIE. Institutions that utilize matched normal samples are able to accurately characterize somatic versus germline variants and more efficiently remove germline variants (Figure 2C). Institutions that do not utilize tumor-normal paired samples need to rely on a tumor-only germline filtering approach that may fail to remove some of these variants 70. Besides the potential risk for patient reidentification, the lack of matched normals may result in increased TMB estimates. Similarly, TMB estimates can be inflated in smaller panels that tend to prioritize the most frequently altered genes in cancer, as well as in panels that only sequence hotspot regions, since these are more prone to harbor mutations. As an example of these technical biases, the number of mutations called in samples from the same cancer type can vary strongly across panels from different institutions (Figure 2G). Even in an analysis restricted to the core set of 23 genes shared across all panels, we observed differences in the distributions of mutations detected by different assays. The overwhelming majority of samples had at least one mutation in these 23 genes for the three cancer types that we evaluated. The panels that sequence hotspot regions only (JHU-500STP, JHU-50GP, UHN-48-V1) tended to have fewer samples with mutations in these 23 commonly sequenced genes. The VICC-01-SOLIDTUMOR panel had similar distributions to the other hotspot panels in CRC and NSCLC, but had a higher number of mutated samples in melanoma. The MSK-IMPACT panels (MSK-IMPACT341, 410, 468), which sequence introns, promoters, and exonic regions, tended to have more mutations than the hotspot panels, but less than those that utilized tumor samples only for sequencing in the absence of matched normals. The panels from DFCI skewed towards having more samples with at least one mutation in these genes (over 80% in each cancer type) and a higher number of samples with multiple mutations (over 50% in each cancer type).
Currently, one of the biggest limitations of the GENIE cohort is the lack of detailed clinicopathological annotation, as well as information about treatment and outcomes for the sequenced patients. As a first step, the most recent GENIE release (v.10.1) has included the survival endpoints of year of last follow up and year of death to facilitate analysis of overall survival. While this is an important improvement, there are still many clinical variables with prognostic implications for progression and survival that the GENIE database does not include. To address this gap in the data, GENIE has launched a collaboration with nine biopharmaceutical companies with the goal of obtaining detailed clinical data for an estimated subset of 50,000 patients within an initial period of five years. The effort aims to standardize and structure real-world clinical data according to the PRISSMM model proposed by DFCI65. The effort will first focus on bladder, breast, colorectal, lung, pancreatic and prostate cancer patients, with the goal of expanding to other cancer types and other institutions at a later phase. As of November 2021, the first analyses of integrated clinical and genomic data for the lung and colorectal cohort have already been published 72–74, highlighting the great potential for translational research of a resource of this type.
FUTURE DIRECTIONS
The AACR Project GENIE illustrates the importance of efficient data sharing across institutions. While TCGA has performed a comprehensive genomic characterization of the most common somatic alterations across major cancer types, larger sample sizes will be needed in order to characterize the distant tail of the distribution 31, and these will be more easily obtainable through institutional cooperation 75. Similarly, rare cancer types with limited rates of accrual at any single hospital - as well as rare genomic subtypes of common cancer types 76,77 - may greatly benefit from this type of multi-center data pooling. Still, in order to guarantee that data are effectively comparable, uniform guidelines and computational pipelines for data collection, annotation, processing, and sharing will need to be implemented. Community-driven efforts like OncoTree 78, which provides a dynamic classification platform for rare and common cancer types, or GenomeNexus, which integrates multiple commonly used resources for variant annotation and interpretation, will play an important role to ensure this much needed interoperability. Data sharing protocols will also become more robust through the use of machine learning and natural language processing approaches for automatic data extraction from the EHR, which may become a new standard that replaces manual curation in future large collaborative projects 79,80.
From a technical point of view, the continuous decrease in genomic sequencing costs suggests that NGS-assays will likely continue to evolve towards increasingly higher breadths and depths of coverage. Widespread use of WGS technologies within the clinic seems no longer a distant dream, but implementation of these approaches will pose formidable challenges for computational processing, long-term storage and meaningful biological interpretation. In the future, we can also expect to see novel NGS platforms that focus on other types of biochemical signals such as DNA methylation 81, microRNAs 82, metabolomics 83, and proteogenomics 84. We are also likely to evolve from a model where most tumors used to be sequenced just once for genomic characterization purposes to a new paradigm where longitudinal sampling at multiple time points can be used to characterize tumor evolution and monitor response to therapy, which in turn can inform the optimal choice of treatments. In this sense, the growing number of available platforms for ctDNA testing, ranging from targeted panels 69 to WES-informed and WGS-informed, patient-specific assays 85,86 are likely to play an important role within the clinic in the not too distant future. Multimodal integration of genomic features with features extracted from other data sources such as radiology and pathology is likely to lead to more sensitive methods for disease detection, improving diagnostic accuracy and early detection of local and distant recurrences 52. Seamless integration of -omics data with information from the EHR and additional variables of clinical relevance such as dietary habits, levels of physical activity or vital signs (body temperature, pulse rate, blood pressure) that can nowadays be monitored efficiently through portable and wearable devices has the potential to revolutionize the emerging digital health ecosystem. Of course, this evolving scenario will also bring forth novel privacy challenges and robust approaches to avoid genotypic and phenotypic information leakage 87, which may be particularly troubling when germline data is involved 88.
From a translational perspective, the continued success of NGS platforms will largely depend on their ability to demonstrate clinical utility and optimize treatment choices. Recent analyses suggest that, even though more than 90% of the tumors harbour at least one somatic alteration considered functional or “oncogenic”, the fraction of patients with at least one mutation classified as a predictive biomarker of response to an FDA-approved or investigational drug is only about one third 3. Similarly, compelling data from TCGA shows that a majority of these alterations are restricted to a reduced subset of oncogenic signaling pathways (namely RTK/RAS, PI3K, cell cycle, and TP53). The development of novel therapies that expand the catalogue of targetable alterations will incentivize a wider adoption of NGS-assays within the clinic, and at the same time the sequencing data generated by these panels will open additional doors for the discovery of new investigational biomarkers of response. This feedback loop will be instrumental in expanding the number of cancer patients that can benefit from genomic-driven therapies within the next few years.
Supplementary Material
Supplemental Table S1. Genes included in reference panels from the AACR GENIE project and overlaps with Sanger Cancer Gene Census and Vogelstein et al. sets.
ACKNOWLEDGEMENTS
The authors thank Michael F. Berger, Walid K. Chatila and Henry Walch for critical reading of the manuscript and related discussions. The authors would like to acknowledge the American Association for Cancer Research and its financial and material support in the development of the AACR Project GENIE registry, as well as members of the consortium for their commitment to data sharing. Interpretations are the responsibility of study authors.
DATA AVAILABILITY
The data used to generate the results shown in Figure 2 were obtained from the following resources available in the public domain: AACR Project GENIE public data releases (https://genie.cbioportal.org/). No new datasets were generated or analyzed to write the current review.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Supplemental Table S1. Genes included in reference panels from the AACR GENIE project and overlaps with Sanger Cancer Gene Census and Vogelstein et al. sets.
Data Availability Statement
The data used to generate the results shown in Figure 2 were obtained from the following resources available in the public domain: AACR Project GENIE public data releases (https://genie.cbioportal.org/). No new datasets were generated or analyzed to write the current review.