GENETEX - A GENomics Report TEXt Mining R Package and Shiny Application Designed to Capture Real-World Clinico-Genomic Data

2021
Merkel Cell Carcinoma
Clinical Informatics
R
NLP
Authors

David M. Miller

Sophia Z. Shalhout

Published

September 27, 2021

Abstract
We created GENETEX, an R package and Shiny application for text mining genomic reports from EHR and direct import into REDCap®.

Journal: JAMIA Open. Published September 28, 2021

Article type: Application Note

Authors: David M. Miller1,2^* MD PhD, Sophia Z. Shalhout1 PhD

1Department of Medicine, Division of Hematology/Oncology and the 2Department of Dermatology, Massachusetts General Hospital, Boston, MA

*Corresponding author: David M. Miller MD PhD
Massachusetts General Hospital
15 Parkman St. Room 132
Boston MA 02114

Funding sources: The Harvard Cancer Center Merkel Cell Carcinoma patient registry is supported by grants from Project Data Sphere, ECOG-ACRIN and the American Skin Association.

Conflicts of interest: None

Keywords: clinico-genomics, data abstraction, electronic health records, shiny app, REDCap, clinical informatics

Acknowledgments: We would like to acknowledge Ravikumar Komandur, PhD, Project Director at Project Data Sphere for review and critique of the manuscript; Guardant Health and Foundation Medicine for making sample reports available for the development of this package. GENETEX is for research purposes only. No clinical decisions should be made with the information obtained from its output. This article reflects the views and work (including development and use of GENETEX) of the authors and should not be construed to represent the work, policies, of any of the vendors whose reports were used to develop GENETEX and whose reports may be provided as part of the package.

Abbreviations: BWH: Brigham and Women’s hospital, CGD: clinico-genomic data, CNV: copy number variants, EDC: electronic data capture, EHR: electronic health record, MGH: Massachusetts General Hospital, MMR: mismatch repair, NSLS: numeric suffix linker system, REDCap: Research Electronic Data Capture, RWD: real-world data, TMB: tumor mutation burden

ABSTRACT

Objectives: Clinico-Genomic Data (CGD) acquired through routine clinical practice has the potential to improve our understanding of clinical oncology. However, these data often reside in heterogeneous and semi-structured data, resulting in prolonged time-to-analyses.
Materials and Methods: We created GENETEX: an R package and Shiny application for text mining genomic reports from EHR and direct import into REDCap®.
Results: GENETEX facilitates the abstraction of CGD from EHR and streamlines capture of structured data into REDCap®. Its functions include natural language processing of key genomic information, transformation of semi-structured data into structured data and importation into REDCap. When evaluated with manual abstraction, GENETEX had >99% agreement and captured CGD in approximately one-fifth the time.
Conclusions: GENETEX is freely available under the Massachusetts Institute of Technology license and can be obtained from GitHub(https://github.com/TheMillerLab/genetex). GENETEX is executed in R and deployed as a Shiny application for non-R users. It produces high-fidelity abstraction of CGD in a fraction of the time.

Lay Summary:

Advances in clinical oncology require a deep understanding of cancer biology. Data regarding the underlying genetic alterations in cancers obtained from routine clinical practice can greatly increase our comprehension of tumor biology. However, there are a number of barriers that impede capitalization of these critical real-world data. Paramount amongst these obstacles are prolonged time-to-analyses secondary to the difficulties of capturing data from heterogeneous sources, as well as the challenges of processing vast amounts of genomic information. These hurdles increase time-to-insight from these data and threaten our ability to fully maximize on advances in molecular and information technologies. In the real-world setting, genomic information resides in a variety of formats. The most common is a report from an institutional molecular pathology department or a commercial vendor. Manual abstraction of these data can be resource intensive. In order to facilitate the abstraction of these data, we created a user-friendly application called GENETEX. GENETEX utilizes natural language processing techniques, such as text mining, to efficiently capture and store genomic information acquired during routine clinical practice. Capturing clinical genomic data in an efficient and structured format can improve analysis and potentially lead to novel insights.

Objectives

Advances in clinical oncology require a deep understanding of cancer biology. Clinico-Genomic Data (CGD) obtained from routine clinical practice can greatly increase our comprehension of tumor biology. However, there are a number of barriers that impede capitalization of these critical real-world data (RWD). Paramount amongst these obstacles include prolonged time-to-analyses secondary to the difficulties of capturing data from heterogeneous sources, as well as the challenges of processing vast amounts of genomic information. These hurdles increase time-to-insight from RWD and threaten our ability to fully maximize on advances in molecular and information technologies.

In the real-world setting, genomic information resides in a variety of formats. The most common is a report from an institutional molecular pathology department or a commercial vendor. This information is often accessed by clinicians or clinical researchers as semi-structured data. Collecting CGD of a patient cohort in a structured electronic data capture (EDC) system can facilitate analysis and reduce time-to-analytics and time-to-action. CGD are often captured via classical (i.e. manual) abstraction by individual research teams. Easily adoptable methods of large-scale CGD collection are limited but include direct transfer from individual vendors. These types of data transfer often require collaborative agreements between vendors and end-users (e.g. investigators/institutions), which can limit their scalability.

We previously published an overview of a methodology and design of a Research Electronic Data Capture (REDCap®)1-based system to facilitate capture of RWD2. REDCap® is a web-based EDC utilized by researchers to collect structured data1. That platform incorporates a form entitled Genomics Instrument, which provides a structured format for the collection of CGD3. This instrument is freely available and can be incorporated into any existing REDCap project. It is currently being used by the Project Data Sphere led Merkel Cell Carcinoma Patient Registry4 to capture CGD.

Here we present GENETEX (pronounced “genetics”), an R package with a Shiny application front-end, which facilitates the abstraction of CGD from EHR and streamlines capture of structured data into the Genomics Instrument in REDCap®. Its functions include natural language processing of key genomic information, transformation of semi-structured data into structured data, and importation into REDCap® (Figure 1). GENETEX is executed in R but is deployed as a Shiny application to enhance the user interface for non-R users.

Figure 1. Schema of GENETEX Figure 1. Schema of GENETEX The GENETEX package takes clinico-genomic data, which is typically stored in semi-structured data, as an input via a Shiny application user interface. Once the input data has been captured, the package executes a series of server-side functions that text mine CGD reports for relevant genomic data. These structured data are then imported directly into the REDCap electronic data capture system (EDC), placing the data in the Genomics Instrument in REDCap.

Methods

Software Dependencies

GENETEX is written in R (version 4.0.0), organized using roxygen25, and utilizes the following packages dplyr6, tidyr7, readr8, stringr9, purrr10, REDCapR11, magrittr12, splitstackshape13 and Shiny14. For full details, instructions and examples refer to either our README (https://github.com/TheMillerLab/genetex/blob/main/README.md) or video demonstration (https://github.com/TheMillerLab/genetex/blob/main/Demo_Video.md), both of which can be viewed on the package GitHub page.

Clinical Informatics Dependencies

GENETEX facilitates abstraction of medical records for importation into the Genomics Instrument in REDCap®. The data dictionary for this form has been previously published3

Comparison of GENETEX to Manual Abstraction

Sample genomic reports were either generated or obtained from commercial vendors. These data were devoid of protected health information; thus, no IRB was required for this project. Two highly-trained abstractors manually abstracted the reports and recorded the time-to-capture for each report. GENETEX was used to abstract these same reports. To simulate the real-world experience, both techniques incorporated a manual visual quality-control step to verify if imported results were accurate. The time spent on this step was included in the total time-to-capture.. Agreement rates were compared using R. A paired wilcoxon test was used to compare the time of manual abstraction with the time to capture CGD with the GENETEX package.

Results

Inputs/User Interface

CGD in the real-world setting is predominantly contained in either portable document format (PDF) documents sent to providers by commercial vendors or in text files contained within EHR. Thus to facilitate abstraction of these data, we developed a browser-based user interface that incorporates text data captured on a clipboard as input in a Shiny application. Text is copied to a computer’s clipboard and pasted into the text area input in the Shiny application (Figure 2).

Figure 2: Browser-Based User Interface Depicted is the user interface (UI) of the Shiny app of GENETEX. This UI is produced by running the code in the R script “GENETEX Shiny app.R”, which can be found on GITHUB.



Users then control additional inputs including free text of the subject’s record id (required field), REDCap® instrument instance (required), lesion tag descriptor (optional field), and date the tissue was obtained (optional). Drop down inputs are also presented to the user including selection of the platform used to generate the genomics report (required) and the type of lesion the genomics report was generated from (e.g. primary lesion vs. metastases) (optional). Finally, to direct the data to REDCap®, users enter strings of the web address of the REDCap® platform (required) as well as the REDCap® API Token (required). These inputs are then called to the function genetex_to_redcap() by the action button “Run GENETEX to REDCap”.

Server Side Functions

The server side of the Shiny application contains the executable code of GENETEX. The package contains a set of functions that then parse, text mine and transform the input into structured data to serve as the substrate for import into REDCap® (Figure 1). Table 1 summarizes the functions and their respective function to extract key elements from the genomics report. We have methods to automatically mine HUGO Gene Nomenclature Committee-approved gene names and detected amino acid and/or nucleotide alterations, tumor mutational burden (tmb), mismatch repair status (mmr), copy number variants (cnv) and mutational signatures. In addition, our implementation transforms the data and links the appropriate CGD with the variables used in the Genomics Instrument so that they can be uploaded into REDCap®.

Function Functionality
genetex_to_redcap() integrates key verbs to provide NLP tools to abstract data from a variety of genomic reports and import them to REDCap
gene.variants() integrates various platform-specific NLP functions to text mine gene names and nucleotide variants from genomic reports and transforms them to structured data for import into REDCap
cnv() integrates various platform-specific NLP functions to text mine gene names and copy number variants data from a variety of genomic reports and transforms them to structured data for import into REDCap
mmr() text mines mismatch repair status from genomic reports and transforms it to structured data for import into REDCap
mutational.signatures() text mines mutational signatures data from a variety of genomic reports and transforms it to structured data for import into REDCap
tmb() text mines tumor mutation burden (TMB) data from a variety of genomic reports and transforms it to structured data for import into REDCap
platform() applies regular expressions to assign a numerical value for the various platforms used for genomic reports that aligns with the `genomics_platform` field in the REDCap Genomics Instrument
genes_regex() produces a regular expression of over 900 HGNC gene names
genes_boundary_regex() produces a regular expression of over 900 HGNC gene names as a unique string with word boundaries
genomics.tissue.type() applies regular expressions to assign a numerical value for the various platforms used for genomic reports that aligns with the `genomics_platform` field in the REDCap Genomics Instrument

Table 1 - GENETEX Functions. Key functions unique to GENETEX with brief description of action are shown. Description of other functions can be found in the package’s Help Page.


Text Mining CGD

Overview of Data Processing

Following the initial step of securing CGD into the Shiny application, GENETEX converts these character strings to a data frame for text mining. At this time, CGD from the following platforms are able to be processed by GENETEX: Guardant36015, FoundationOne®16, MGH SNaPshot17 and BWH Oncopanel18

Due to idiosyncratic differences between these reports, we developed platform-specific functions to text mine data. For example, gene.variants.isolate.oncopanel() and gene.variants.isolate.snapshot() isolate gene variant data from BWH Oncopanel and MGH SNaPshot reports, respectively. However, in general, following securing CGD, the data is tokenized using the cSplit() function from the splitstackshape package.

Isolating Genomic Data with Regular Expressions

To perform text mining of CGD, we created a number of regular expressions (regex) to identify gene names, nucleotide and amino acid sequences and cell-free DNA (cfDNA) data within genomic reports. For example, the function genes_boundary_regex() generates a regular expression of >900 gene names surrounded by word boundaries (Figure 3a). We further designed regular expressions to detect nucleotide and amino acid sequences and the magnitude of cfDNA (Figure 3b). The regular expressions were effectively combined to identify only those genomic information of interest (Figure 3C). Finally, we filter out unnecessary elements (e.g. the strings “RESULTS:” “Single” “nucleotide” “variants”) that are not intended to be captured in the Genomics instrument (Figure 3D)

genetex::genes_boundary_regex()
# A tibble: 1 × 1
  genes                                                                         
  <chr>                                                                         
1 "\\bABCB1\\b|\\bABCB11\\b|\\bABCC3\\b|\\bABL1\\b|\\bABL2\\b|\\bACTA2\\b|\\bAC…

Figure 3a: Regular expression of gene names. Depicted is a portion of the character vector output of the function genes_boundary_regex(). This function produces a regular expression that is used by GENETEX to identify gene names in CGD reports.


nuc_regex <- "[ACTG]>[ACTG]|del[ACTG]" 
aa_regex <- "(\\b([A-Z][0-9]{1,}(([A-Z])|(_[A-Z][1-9]{1,}del)|(fs\\*[1-9]{1,})|(\\*)|(fs)|(del)))|(p\\.[A-Z]))|([0-9]ins[A-Z])"

cfdna_regex <- "\\b[0-9]{1,2}\\.[0-9]{1,2}%"

Figure 3b: Regular expression of nucleotide and amino acid sequences, and cell-free DNA. Shown are the three regular expressions used to identify nucleotide sequences (“nuc_regex”), amino acid sequences (“aa_regex”) and cell-free DNA (“cfdna_regex”) contained within CGD reports.


X
RESULTS:
Single
nucleotide
variants:
ATM
2353C>T
R785C
0.1%
PTEN
L257V
769C>G
8.1%

Figure 3c: Tokenized genomics report. Depicted is a portion of a CGD report that has been tokenized. Here, each word of the report is partitioned into a single cell of the vector “X”.


genes_nuc_aa_cfdna_regex <- paste(genes_boundary_regex, aa_regex, nuc_regex, cfdna_regex, sep = "|")

dt.1 <- dt %>%
    dplyr::filter(stringr::str_detect(string = dt$X,
                                      pattern = stringr::regex(genes_nuc_aa_cfdna_regex)))
X
ATM
2353C>T
R785C
0.1%
PTEN
L257V
769C>G
8.1%

Figure 3d: Example of report filtered with genes_nuc_aa_cfdna_regex. Shown is vector “X” from Figure 3c, which has been filtered by a regular expression that selects only cells with elements relevant to gene names, nucleotide and amino acid sequences and cell-free DNA. The code used for this step is demonstrated above the output.


Group Correlated Text

In order to group correlated genomic information (e.g. gene name with the associated nucleotide/amino acid variant), GENETEX utilizes keyword-group pairing. A logical vector “keywords” is created using the dplyr function mutate() paired with str_detect() and the regular expression “genes_boundary_regex”. This logical vector becomes the object of the cumsum() function to create the numeric vector “group”; effectively grouping each unique gene name with its correlated data (Figure 3e).

dt.2 <- dt.1 %>%
    dplyr::mutate(keywords = stringr::str_detect(string = dt.1$X,
                                                 pattern = stringr::regex(genes_boundary_regex)),
                  group = base::cumsum(keywords))
X keywords group
ATM TRUE 1
2353C>T FALSE 1
R785C FALSE 1
0.1% FALSE 1
PTEN TRUE 2
L257V FALSE 2
769C>G FALSE 2
8.1% FALSE 2

Figure 3e: Example of report grouped by gene name. Related tokens are grouped using the stringr::str_detect() function by incorporating the regular expression “genes_boundary_regex”. With this method, HUGO gene names serve as the “keyword” and thus the boundary for each group. As a result, the appropriate nucleotide, amino acid and cell-free DNA data are linked with the corresponding gene name.


Mapping REDCap® Variables

In addition to isolating key genomic data from reports, the above regular expressions are also used to map variables used in the Genomics Instrument. The instrument uses the following variable prefixes “variant_gene”, “variant_nucleotide”, “variant_protein” and “variant_gene_perc_cfdna” to enable tidy data for gene names, nucleotide variants, amino acid variants and percent cfDNA, respectively. Using the following combination of ifelse() statements, str_detect() and the aforementioned regular expressions, these variables can be linked to the corresponding tidy data (Figure 3f).

dt.3 <- dt.2 %>%
  mutate(var = ifelse(test = str_detect(string = dt.2$X,
                                        pattern = regex(genes_boundary_regex)),
                      yes = "variant_gene",
                       no = ifelse(test = str_detect(string = dt.2$X,
                                                     pattern = regex(nuc_regex)),
                                   yes = "variant_nucleotide",
                                   no = ifelse(test = str_detect(string = dt.2$X,
                                                                 pattern = regex(aa_regex)),
                                               yes = "variant_protein",
                                               no = ifelse(test = str_detect(string = dt.2$X,
                                                                             pattern = regex(cfdna_regex)),
                                                           yes = "variant_gene_perc_cfdna",
                                                           no = "")))))
X keywords group var
ATM TRUE 1 variant_gene
2353C>T FALSE 1 variant_nucleotide
R785C FALSE 1 variant_protein
0.1% FALSE 1 variant_gene_perc_cfdna
PTEN TRUE 2 variant_gene
L257V FALSE 2 variant_protein
769C>G FALSE 2 variant_nucleotide
8.1% FALSE 2 variant_gene_perc_cfdna

Figure 3f: Mapping REDCap variables to data elements. . Each tokenized data element in vector “X” must be linked with an appropriate variable name from the Genomics Instrument. In this step, the four relevant variable stems, “variant_gene”, “variant_nucleotide”, “variant_protein” and “variant_gene_perc_cfdna” are matched with the relevant data in vector “X” by combining the ifelse() and str_detect() functions with the regular expressions “genes_boundary_regex”, “nuc_regex”, “aa_regex” and “cfdna_regex”.



As previously described3, in order to produce unique variables that can be linked with other related information, the Genomics Instrument utilizes a Numeric Suffix Linker System (NSLS). The NSLS links related elements of CGD with a character string in the variable name (e.g. “variant”) with an underscore and a numeric (e.g “_1”). Therefore, a given gene will be grouped with its correlated nucleotide/amino acid variants and/or cfDNA information with a unique character string and numeric. For example, the variable prefixes “variant_gene”, “variant_nucleotide”, “variant_protein” and “variant_gene_perc_cfdna” will all be linked with the same numeric suffix (e.g. “_1”). Consequently, these elements can be grouped during analysis with the unique pairing of “variant” and “_1”. Therefore, a final step in creating this unique variable linked system involves pasting the “var” vector with the “group” vector(Figure 3g).

dt.4 <- dt.3 %>%
    mutate(variables = paste(var, group, sep = "_"))
X keywords group var variables
ATM TRUE 1 variant_gene variant_gene_1
2353C>T FALSE 1 variant_nucleotide variant_nucleotide_1
R785C FALSE 1 variant_protein variant_protein_1
0.1% FALSE 1 variant_gene_perc_cfdna variant_gene_perc_cfdna_1
PTEN TRUE 2 variant_gene variant_gene_2
L257V FALSE 2 variant_protein variant_protein_2
769C>G FALSE 2 variant_nucleotide variant_nucleotide_2
8.1% FALSE 2 variant_gene_perc_cfdna variant_gene_perc_cfdna_2

Figure 3g: Complete mapping REDCap variables to data elements with numeric suffix linker system. Each data element in vector “X” must correspond to a unique variable name to be imported into REDCap. Therefore, in this final step the variable stems “variant_gene”, “variant_nucleotide”, “variant_protein” and “variant_gene_perc_cfdna” and linked to the number found in the column “group” which produces a unique variable. All of those variables with the suffix “_1” will be “linked” together using a numeric suffix linker system.



An analogous approach is used to identify and abstract data on cnvs, tmb, mmr and mutational signatures. Please see the description file and annotated R scripts contained within the package in GitHub for further details.

Package Outputs

The front-end Shiny application is executed with an action button that produces two easy-to-view outputs. The first, which can be viewed by clicking on “Report” in the sidebar, produces a verbatimTextOutput of the genomic report to ensure that the correct report was pasted into the textAreaInput. The second one, viewed by clicking on “Data” in the sidebar, is a table of the data executed by genetex_to_redcap() (Supplemental Table 1). This is intended to provide the user with an output of the data generated by the function so that a quality-control step can take place.

variables results
record_id Lewis Jones
redcap_repeat_instrument genomics
redcap_repeat_instance 1
genomics_yn 1
lesion_tag_genomics Liquid Biopsy
genomics_tissue_type 5
genomics_date 2020-12-22
genomics_platform 6
genomics_platform_other
mmr 2
tmb_yn 1
tmb 81.47
tmb_abs
mutation_signature_number 0
mutation_signature_1
mutation_signature_2
mutation_signature_3
mutation_signature_4
mutation_signature_5
mutation_signature_6
mutation_signature_7
mutation_signature_8
mutation_signature_9
mutation_signature_10
variant_number 20
variant_gene_1 NTRK2
variant_nucleotide_1
variant_protein_1 A31T
variant_gene_perc_cfdna_1 3.5%
variant_gene_2 CTNNB1
variant_nucleotide_2
variant_protein_2 R550R
variant_gene_perc_cfdna_2 3.2%
variant_gene_3 PTEN
variant_nucleotide_3
variant_protein_3 E157fs
variant_gene_perc_cfdna_3 3.2%
variant_gene_4 PIK3CA
variant_nucleotide_4
variant_protein_4 E545K
variant_gene_perc_cfdna_4 2.6%
variant_gene_5 FANCA
variant_nucleotide_5
variant_protein_5 R1400H
variant_gene_perc_cfdna_5 2.5%
variant_gene_6 PTEN
variant_nucleotide_6
variant_protein_6 K267fs
variant_gene_perc_cfdna_6 2.5%
variant_gene_7 BRCA2
variant_nucleotide_7
variant_protein_7 I605fs
variant_gene_perc_cfdna_7 2.3%
variant_gene_8 BRAF
variant_nucleotide_8
variant_protein_8 A762V
variant_gene_perc_cfdna_8 2.2%
variant_gene_9 PALB2
variant_nucleotide_9
variant_protein_9 R37C
variant_gene_perc_cfdna_9 1.9%
variant_gene_10 CHEK2
variant_nucleotide_10
variant_protein_10 R346H
variant_gene_perc_cfdna_10 1.8%
variant_gene_11 NTRK1
variant_nucleotide_11
variant_protein_11 T741T
variant_gene_perc_cfdna_11 1.7%
variant_gene_12 AR
variant_nucleotide_12
variant_protein_12 H875Y
variant_gene_perc_cfdna_12 0.5%
variant_gene_13 MTOR
variant_nucleotide_13
variant_protein_13 W1456R
variant_gene_perc_cfdna_13 0.4%
variant_gene_14 NOTCH1
variant_nucleotide_14
variant_protein_14 F357del
variant_gene_perc_cfdna_14 0.4%
variant_gene_15 DDR2
variant_nucleotide_15
variant_protein_15 P157L
variant_gene_perc_cfdna_15 0.3%
variant_gene_16 GNA11
variant_nucleotide_16
variant_protein_16 G208fs
variant_gene_perc_cfdna_16 0.3%
variant_gene_17 MPL
variant_nucleotide_17
variant_protein_17 P530P
variant_gene_perc_cfdna_17 0.3%
variant_gene_18 ALK
variant_nucleotide_18
variant_protein_18 Y1584Y
variant_gene_perc_cfdna_18 0.2%
variant_gene_19 NOTCH1
variant_nucleotide_19
variant_protein_19 S2486fs
variant_gene_perc_cfdna_19 0.2%
variant_gene_20 ARID1A
variant_nucleotide_20
variant_protein_20 P1710P
variant_gene_perc_cfdna_20 0.1%
variant_gene_21
variant_nucleotide_21
variant_protein_21
variant_gene_perc_cfdna_21
variant_gene_22
variant_nucleotide_22
variant_protein_22
variant_gene_perc_cfdna_22
variant_gene_23
variant_nucleotide_23
variant_protein_23
variant_gene_perc_cfdna_23
variant_gene_24
variant_nucleotide_24
variant_protein_24
variant_gene_perc_cfdna_24
variant_gene_25
variant_nucleotide_25
variant_protein_25
variant_gene_perc_cfdna_25
variant_gene_26
variant_nucleotide_26
variant_protein_26
variant_gene_perc_cfdna_26
variant_gene_27
variant_nucleotide_27
variant_protein_27
variant_gene_perc_cfdna_27
variant_gene_28
variant_nucleotide_28
variant_protein_28
variant_gene_perc_cfdna_28
variant_gene_29
variant_nucleotide_29
variant_protein_29
variant_gene_perc_cfdna_29
variant_gene_30
variant_nucleotide_30
variant_protein_30
variant_gene_perc_cfdna_30
variant_gene_31
variant_nucleotide_31
variant_protein_31
variant_gene_perc_cfdna_31
variant_gene_32
variant_nucleotide_32
variant_protein_32
variant_gene_perc_cfdna_32
variant_gene_33
variant_nucleotide_33
variant_protein_33
variant_gene_perc_cfdna_33
variant_gene_34
variant_nucleotide_34
variant_protein_34
variant_gene_perc_cfdna_34
variant_gene_35
variant_nucleotide_35
variant_protein_35
variant_gene_perc_cfdna_35
variant_gene_36
variant_nucleotide_36
variant_protein_36
variant_gene_perc_cfdna_36
variant_gene_37
variant_nucleotide_37
variant_protein_37
variant_gene_perc_cfdna_37
variant_gene_38
variant_nucleotide_38
variant_protein_38
variant_gene_perc_cfdna_38
variant_gene_39
variant_nucleotide_39
variant_protein_39
variant_gene_perc_cfdna_39
variant_gene_40
variant_nucleotide_40
variant_protein_40
variant_gene_perc_cfdna_40
variant_gene_41
variant_nucleotide_41
variant_protein_41
variant_gene_perc_cfdna_41
variant_gene_42
variant_nucleotide_42
variant_protein_42
variant_gene_perc_cfdna_42
variant_gene_43
variant_nucleotide_43
variant_protein_43
variant_gene_perc_cfdna_43
variant_gene_44
variant_nucleotide_44
variant_protein_44
variant_gene_perc_cfdna_44
variant_gene_45
variant_nucleotide_45
variant_protein_45
variant_gene_perc_cfdna_45
variant_gene_46
variant_nucleotide_46
variant_protein_46
variant_gene_perc_cfdna_46
variant_gene_47
variant_nucleotide_47
variant_protein_47
variant_gene_perc_cfdna_47
variant_gene_48
variant_nucleotide_48
variant_protein_48
variant_gene_perc_cfdna_48
variant_gene_49
variant_nucleotide_49
variant_protein_49
variant_gene_perc_cfdna_49
variant_gene_50
variant_nucleotide_50
variant_protein_50
variant_gene_perc_cfdna_50
variant_gene_51
variant_nucleotide_51
variant_protein_51
variant_gene_perc_cfdna_51
variant_gene_52
variant_nucleotide_52
variant_protein_52
variant_gene_perc_cfdna_52
variant_gene_53
variant_nucleotide_53
variant_protein_53
variant_gene_perc_cfdna_53
variant_gene_54
variant_nucleotide_54
variant_protein_54
variant_gene_perc_cfdna_54
variant_gene_55
variant_nucleotide_55
variant_protein_55
variant_gene_perc_cfdna_55
variant_gene_56
variant_nucleotide_56
variant_protein_56
variant_gene_perc_cfdna_56
variant_gene_57
variant_nucleotide_57
variant_protein_57
variant_gene_perc_cfdna_57
variant_gene_58
variant_nucleotide_58
variant_protein_58
variant_gene_perc_cfdna_58
variant_gene_59
variant_nucleotide_59
variant_protein_59
variant_gene_perc_cfdna_59
variant_gene_60
variant_nucleotide_60
variant_protein_60
variant_gene_perc_cfdna_60
variant_gene_61
variant_nucleotide_61
variant_protein_61
variant_gene_perc_cfdna_61
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cnv_gain_or_loss_436
cnv_gene_437
cnv_gain_or_loss_437
cnv_gene_438
cnv_gain_or_loss_438
cnv_gene_439
cnv_gain_or_loss_439
cnv_gene_440
cnv_gain_or_loss_440
cnv_gene_441
cnv_gain_or_loss_441
cnv_gene_442
cnv_gain_or_loss_442
cnv_gene_443
cnv_gain_or_loss_443
cnv_gene_444
cnv_gain_or_loss_444
cnv_gene_445
cnv_gain_or_loss_445
cnv_gene_446
cnv_gain_or_loss_446
cnv_gene_447
cnv_gain_or_loss_447
cnv_gene_448
cnv_gain_or_loss_448
cnv_gene_449
cnv_gain_or_loss_449
cnv_gene_450
cnv_gain_or_loss_450
cnv_gene_451
cnv_gain_or_loss_451
cnv_gene_452
cnv_gain_or_loss_452
cnv_gene_453
cnv_gain_or_loss_453
cnv_gene_454
cnv_gain_or_loss_454
cnv_gene_455
cnv_gain_or_loss_455
cnv_gene_456
cnv_gain_or_loss_456
cnv_gene_457
cnv_gain_or_loss_457
cnv_gene_458
cnv_gain_or_loss_458
cnv_gene_459
cnv_gain_or_loss_459
cnv_gene_460
cnv_gain_or_loss_460
cnv_gene_461
cnv_gain_or_loss_461
cnv_gene_462
cnv_gain_or_loss_462
cnv_gene_463
cnv_gain_or_loss_463
cnv_gene_464
cnv_gain_or_loss_464
cnv_gene_465
cnv_gain_or_loss_465
cnv_gene_466
cnv_gain_or_loss_466
cnv_gene_467
cnv_gain_or_loss_467
cnv_gene_468
cnv_gain_or_loss_468
cnv_gene_469
cnv_gain_or_loss_469
cnv_gene_470
cnv_gain_or_loss_470
cnv_gene_471
cnv_gain_or_loss_471
cnv_gene_472
cnv_gain_or_loss_472
cnv_gene_473
cnv_gain_or_loss_473
cnv_gene_474
cnv_gain_or_loss_474
cnv_gene_475
cnv_gain_or_loss_475
cnv_gene_476
cnv_gain_or_loss_476
cnv_gene_477
cnv_gain_or_loss_477
cnv_gene_478
cnv_gain_or_loss_478
cnv_gene_479
cnv_gain_or_loss_479
cnv_gene_480
cnv_gain_or_loss_480
cnv_gene_481
cnv_gain_or_loss_481
cnv_gene_482
cnv_gain_or_loss_482
cnv_gene_483
cnv_gain_or_loss_483
cnv_gene_484
cnv_gain_or_loss_484
cnv_gene_485
cnv_gain_or_loss_485
cnv_gene_486
cnv_gain_or_loss_486
cnv_gene_487
cnv_gain_or_loss_487
cnv_gene_488
cnv_gain_or_loss_488
cnv_gene_489
cnv_gain_or_loss_489
cnv_gene_490
cnv_gain_or_loss_490
cnv_gene_491
cnv_gain_or_loss_491
cnv_gene_492
cnv_gain_or_loss_492
cnv_gene_493
cnv_gain_or_loss_493
cnv_gene_494
cnv_gain_or_loss_494
cnv_gene_495
cnv_gain_or_loss_495
cnv_gene_496
cnv_gain_or_loss_496
cnv_gene_497
cnv_gain_or_loss_497
cnv_gene_498
cnv_gain_or_loss_498
cnv_gene_499
cnv_gain_or_loss_499
cnv_gene_500
cnv_gain_or_loss_500
amplifications_number 0
amplifications_gene_1
amplifications_gene_2
amplifications_gene_3
amplifications_gene_4
amplifications_gene_5
amplifications_gene_6
amplifications_gene_7
amplifications_gene_8
amplifications_gene_9
amplifications_gene_10
amplifications_gene_11
amplifications_gene_12
amplifications_gene_13
amplifications_gene_14
amplifications_gene_15
amplifications_gene_16
amplifications_gene_17
amplifications_gene_18
amplifications_gene_19
amplifications_gene_20
amplifications_gene_21
amplifications_gene_22
amplifications_gene_23
amplifications_gene_24
amplifications_gene_25
amplifications_gene_26
amplifications_gene_27
amplifications_gene_28
amplifications_gene_29
amplifications_gene_30
amplifications_gene_31
amplifications_gene_32
amplifications_gene_33
amplifications_gene_34
amplifications_gene_35
amplifications_gene_36
amplifications_gene_37
amplifications_gene_38
amplifications_gene_39
amplifications_gene_40
amplifications_gene_41
amplifications_gene_42
amplifications_gene_43
amplifications_gene_44
amplifications_gene_45
amplifications_gene_46
amplifications_gene_47
amplifications_gene_48
amplifications_gene_49
amplifications_gene_50
amplifications_gene_51
amplifications_gene_52
amplifications_gene_53
amplifications_gene_54
amplifications_gene_55
amplifications_gene_56
amplifications_gene_57
amplifications_gene_58
amplifications_gene_59
amplifications_gene_60
amplifications_gene_61
amplifications_gene_62
amplifications_gene_63
amplifications_gene_64
amplifications_gene_65
amplifications_gene_66
amplifications_gene_67
amplifications_gene_68
amplifications_gene_69
amplifications_gene_70
amplifications_gene_71
amplifications_gene_72
amplifications_gene_73
amplifications_gene_74
amplifications_gene_75
amplifications_gene_76
amplifications_gene_77
amplifications_gene_78
amplifications_gene_79
amplifications_gene_80
amplifications_gene_81
amplifications_gene_82
amplifications_gene_83
amplifications_gene_84
amplifications_gene_85
amplifications_gene_86
amplifications_gene_87
amplifications_gene_88
amplifications_gene_89
amplifications_gene_90
amplifications_gene_91
amplifications_gene_92
amplifications_gene_93
amplifications_gene_94
amplifications_gene_95
amplifications_gene_96
amplifications_gene_97
amplifications_gene_98
amplifications_gene_99
amplifications_gene_100
genomics_add_notes Data imported via genomics package
gen_daf_note
gen_dmf_note
gen_lpf_note
gen_flag_yn_1
gen_details_flag_1
gen_resolved_flag_1
gen_resp_flag_1
gen_flag_yn_2
gen_details_flag_2
gen_resolved_flag_2
gen_resp_flag_2
gen_flag_yn_3
gen_details_flag_3
gen_resolved_flag_3
gen_resp_flag_3
gen_unresolved_flag

Table 2 - Output of genetex_to_redcap()


Import to REDCap®

The data in Table 2 is imported to REDCap® from the Shiny application by calling the function redcap_write_oneshot() from the REDCapR package. An example of a portion of that form with data imported from GENETEX is seen in Supplemental Figure 1.

Supplemental Figure 1. Depcited is an example of the data form in REDCap® after the input clinico-genomic data has been passed to genetex_to_redcap().


Real-World Deployment

In order to evaluate the performance of augmented abstraction with GENETEX in the real-world setting compared to manual abstraction, we selected 7 genomic reports at random (3 Guardant, 2 Foundation Medicine, 1 MGH SNaPshot, 1 BWH Oncopanel) for abstraction. Each report was abstracted independently by two data abstractors via manual abstraction, as well as with GENETEX. In total, 744 data elements were captured from these 7 reports. Agreement rates between the two human abstractors was 99.19%. Importantly, >99% agreement was reached between both human abstractors and GENETEX (99.33% and 99.19%). Given that the agreement between classical and augmented abstraction was high, we next evaluated if the GENETEX pipeline would improve time-to-analysis. The mean time for manual abstraction for each report was 784.5 (range: 220.5-3096.5) seconds compared with 136 (range: 75- 216) seconds for augmented abstraction (Wilcoxon test p value = 0.015625).

Limitations and Solutions

GENETEX is to be used in conjunction with Genomics Instrument and thus, it is dependent on that form being installed into a REDCap® project. However, we have made the data dictionary freely available so that others may incorporate it into their individual project. Importantly, our regular expressions system of mapping variable names to key genomic data provides a high-degree of flexibility to map alternate variable names for REDCap® instruments with different data dictionaries. Additional limitations include the fact that at this time, GENETEX does not support all potential platforms available for CGD. However, due to its open-source position, external developers can perform pull-requests on GitHub for incorporation of additional platforms and future refinement. Lastly, genomic findings abstracted from individual reports describe results at the point in time at which they were provided are a historical record and may not represent the latest understanding of genomic science or precision oncology treatment paradigms.

Conclusions

Ideally, structured data objects that exactly match clinical genomic reports would be universally available directly from the providers of genomic profiling reports, and may be at some point in the future. Until then, there may be a desire to abstract or otherwise process these reports into structured formats. GENETEX is a browser-based application for natural language processing of CGD obtained in routine clinical practice. It facilitates extraction of data from EHR, transformation of semi-structured data into a structured format and loading into REDCap®. Its Shiny extension enables non-R users to execute the package without familiarity of R. Real-world deployment of the GENETEX demonstrated excellent agreement with classical abstraction in roughly 1/5 of the time. Thus, augmented abstraction with browser-based applications can decrease the barrier to data capture and importantly improve time-to-analysis of clinico-genomic data.

License

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

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