The Therapeutic Area Data Standards User Guide for Colorectal Cancer (TAUG-CrCa) was developed under the Coalition for Accelerating Standards and Therapies (CFAST) initiative.

The purpose of the TAUG-CrCa is to describe how to use CDISC standards to represent data pertaining to studies in colorectal cancer. TAUG-CrCa Version 1.0 focuses on clinical trials in metastatic colorectal cancer.

This document provides advice and examples for Clinical Data Acquisition Standards Harmonization (CDASH), the Study Data Tabulation Model (SDTM), the SDTM Implementation Guide for Medical Devices (SDTMIG-MD), and the Analysis Data Model (ADaM), including

• guidance on the use of domains and variables;

• annotated sample case report forms (CRFs) compliant with CDASH;

• examples of SDTM datasets, with text describing the situational context and pointing out records of note; and
• discussion of the concepts and common analysis endpoints relevant to the analysis of colorectal cancer studies.

The biomedical concepts covered in this guide were selected from concepts identified by one or more stakeholders as important, and which were not addressed or were not completely addressed by existing CDISC implementation guides. This TAUG does not provide guidance on what data are needed for regulatory submission or approval; it only provides advice on how to represent data in a standard form.

This user guide emphasizes that examples are only examples and should not be over-interpreted. For guidance on the selection of biomedical concepts and endpoints, please refer to the appropriate clinical and regulatory authorities. Clinical guidelines, articles, and other works consulted by the team during the creation of this document are referenced where appropriate, using the American Medical Association (AMA) style for citation. For a full list of references, see Appendix D: References.

1. First, read foundational standards upon which this document is based: CDASH v1.1, CDASHUG v1.0, SDTM v1.4, SDTMIG v3.2, SDTMIG-PGx v1.0, SDTMIG-MD v1.0, ADaM v2.1, and ADaMIG v1.1 to gain some familiarity with data models and the basic rules for how they are implemented. These standards are available from: http://www.cdisc.org/.
   • For guidance on how to review the CDASH annotated case report forms (aCRFs) that were created based on the CDASH metadata included with a CRF, see Section 1.3, CDASH Metadata and Annotated CRFs.
2. Next, read Introduction to Therapeutic Area Standards (https://wiki.cdisc.org/x/SSy8AQ) to be sure to know what to expect from such a document.
3. Read this guide all the way through (without skipping any sections) at least once.
4. Consult any sections of particular interest as the need arises.

Some things to bear in mind while reading this TAUG:

• This TAUG does not replace or supersede the foundational CDISC standards or their implementation guides, and should not be used as a substitute for any other CDISC standard.
• This document does not repeat content already published in another CDISC standard.
• This document is not and does not try to be an exhaustive documentation of every possible kind of data that could be collected in relation to colorectal cancer. Instead, the team has tried to focus on those areas that CFAST's resources have identified as most likely to be relevant and useful more often than not.  
• The advice and examples presented in this document are influenced by ongoing internal standards development at CDISC. If a modeling approach seems inconsistent with a published standard, it may be a genuine error, but it could also be a reflection of potential or upcoming changes to the standard.
• The examples in this document use CDISC Controlled Terminology where possible, but some values that seem to be controlled terminology may still be under development at the time of publication, or even especially plausible "best-guess" placeholder values. Do not rely on any source other than the CDISC value set in the NCI Thesaurus (available at http://www.cancer.gov/research/resources/terminology/cdisc) for controlled terminology.
• With time, some parts of this document may become outdated. Those parts will be updated in the next version.

This document is divided into the following sections:

• Section 1, Introduction, provides an overall introduction to the purpose and goals of the colorectal cancer project.
• Section 2, Overview of Colorectal Cancer, provides a brief overview of the focus of this document in relation to the stage of colorectal cancer.
• Section 3, Subject and Disease Characteristics, covers data that are usually collected once at the beginning of a study.
• Section 4, Disease Assessments, covers data that are used to evaluate disease severity, control, or progression. These are usually collected repeatedly during a study and may be used as, or for the derivation of, efficacy and/or safety endpoints.
• Section 5, Questionnaires, Ratings, and Scales
• Section 6, Routine Data, provides information on routine data collected particular to colorectal cancer.
• Section 7, Analysis Data, includes key data analysis concepts for a colorectal cancer study.
• Appendices provide additional background material and describe other supplemental material relevant to colorectal cancer.

CDASH examples include both metadata tables and sample case report forms (CRFs). Each table of CDASH metadata corresponds to a sample annotated CRF (aCRF), built directly from the metadata. The annotations show the variables associated with each field in the context of data collection (CDASH) and submission (SDTM). Which context is applicable is denoted by color. Data that are collected using the same variable name as defined in the SDTMIG are in RED. If the CDASHIG variable differs from the one defined in the SDTMIG, the CDASHIG variable is in GREY. Data collected, but not submitted in SDTM-based datasets, are denoted as NOT SUBMITTED.

The following diagram illustrates how to interpret the annotations.

CDASH variables may also be mapped to or used to populate other SDTMIG variables that are not shown. The information in the attached CRF metadata has been "customized" following the conformance rules included in the CDASHIG to illustrate data collection instruments relevant to this TAUG. Users should always refer to the CDASHIG and CDASH Model when creating or adapting these CRFs to other studies.

When viewing sample aCRFs, bear in mind that:

• More information may also be found in the CDASH Model and CDASHIG.
• Example CRFs are provided to illustrate data collection instruments. They are only examples and are not meant to imply that any particular layout is preferable over another.
• Example CRFs are best understood in conjunction with their respective metadata tables and/or the CDASH Domain Metadata Tables.
• Most example CRFs do not include the Highly Recommended header variables. The population of these values is usually determined by the sponsor's data management system.
• Sponsors are responsible for understanding and implementing CDISC Controlled Terminology where applicable.
• CDASH variable names for denormalized variables are examples. Sponsor may use other conventions for creating denormalized CDASH variable names.
• CDASH variable names that are annotated as "NOT SUBMITTED" may be used to contribute towards the population of other appropriate variables when the SDTM-based datasets are created.
• CDASH variables may also be mapped to or used to populate other SDTMIG variables that are not shown.

• Non-Standard Variables: Non-standard variables (NSVs) are shown as though they were appended to a dataset rather than being represented as supplemental qualifiers because this makes examples easier to understand. It is also consistent with a proposed future structure for representing NSVs. That structure is a modification of the NSV proposal that went for public review, and is still under development. For a list of all NSVs used in this document, and the variable-level metadata that might become normative for the NSVs should they be promoted to standard variables, see Appendix B: Non-Standard Variables (NSVs).

  

• NSV Naming Convention: In this document, NSV names include the 2-letter domain code before the variable name. The naming convention for these variables is under discussion.  

• Treatment Regimens: The strategy used to treat cancer is often a "regimen" that may consist of multiple drugs or of drug treatment combined with radiation and/or surgery. The SDTMIG does not give clear guidance on how to indicate that multiple treatment modalities are combined to create a regimen or a product. The modeling of this data is under consideration in the Combination Therapy Focus Area User Guide and will not be part of this TAUG.
• If Tumor Is Inevaluable, Reason Not Done (Target and Non-Target Lesions): Current modeling of tumor state of inevaluable shows that the result will be missing, status will be NOT DONE, and the reason will be mapped to the --REASND variable. This modeling does not capture that the tumor was inevaluable. At the time of publication of this document, there were ongoing discussions on how best to model this data; users are cautioned that the current modeling may be subject to change. The pre-specified terms used for --REASND are simply examples of collected terms to encourage standardized terminology (as opposed to free-text). Sponsors are encouraged to use generic reasons, and if necessary to provide any other detailed reasons in the Comments (CO) domain. The CDASH metadata tables provide suggestions on the use of these generic reasons.
• CDASH Variable Name, Question Text, and Prompt: The aCRFs were developed considering the CDASHIG v2.0 and CDASH Model v1.0 that are anticipated to be published. Thus, users are cautioned that the CRF prompts, question texts, and CDASH variable names used in this TAUG are subject to change.
• Collection of Date of Birth: In several countries, the collection of date of birth is restricted in order to protect patient confidentiality. There are some concepts (e.g., "Age at Diagnosis") that rely on the date of birth in order for that concept to be derived. Collection of age for these critical age-related data points has been raised to the CDISC Submission Data Standards Committee for discussion on the best way to represent this in the SDTM. Guidance on how to collect this information will be provided in future versions of this TAUG.
• Baseline Disease Characteristics and Identification of Primary Tumor: These concepts are being developed in the Lung Cancer TAUG (currently in development at the time of publication of this TAUG). Once modeling of these concepts has been approved they may be added to this TAUG in a future version.
• SDTMIG-PGx Recommendations: Genetic variations data were not parsed using PFORRESF, PFORRES and PFGENLOC. Although this parsing approach is recommended by the SDTMIG-PGx, the non-parsing approach was used to facilitate historical data collected on CRFs. This approach is under discussion within the PGx Team.

Colorectal cancer is a growth of malignant cells in the colon and/or rectum. Globally, colorectal cancer is the third most common cancer in men and the second in women.^[1] Although important efforts in the prevention and early detection of colorectal cancer are ongoing, some patients present with metastases at initial diagnosis and many patients with non-metastatic colorectal cancer will develop metastases.

The concept map below provides a high-level overview of the stages of colorectal cancer, including the American Joint Committee on Cancer (AJCC) TNM staging system (the most commonly used colorectal cancer staging system). This system looks at T (tumor expansion), N (extent of cancer spread to lymph nodes) and M (metastases or spread of cancer to other organs). Also included is the transition from non-metastatic to metastatic colorectal cancer, where the cancer has spread through the colon wall and may have spread to nearby organs and/or lymph nodes. The content of this user guide has been developed in relation to Stage IV colorectal cancer. In Stage IVA, cancer has spread to 1 organ (e.g., liver, lung, ovary) or to a non-regional lymph node. In Stage IVB, cancer has spread to more than 1 organ/site or to the peritoneum (note that the peritoneum is not included in the Japanese classification of colorectal carcinoma) . Once cancer has spread to another part of the body it is unlikely to be curable. However, for some people with colorectal cancer that has only spread to the liver or lungs, a cure may still be possible.^[2] 

For readers unfamiliar with colorectal cancer, basic information in layman's language can be found at the following websites:

• U.S. National Library of Medicine (https://www.nlm.nih.gov/medlineplus/colorectalcancer.html)
• National Cancer Institute (https://www.cancer.gov)

• Japanese Society for Cancer of the Colon and Rectum (JSCCR) Guidelines 2014 (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4653248/)

This section provides details of subject and disease characteristics relevant to colorectal cancer.

Colon cancer is cancer of the large intestine and rectal cancer is cancer of the last several inches of the colon. Together, they are often referred to as colorectal cancers. Most colorectal cancer begins as a growth of a polyp (in particular, an adenomatous polyp), which may form on the inner wall of the colon or rectum. Screening and work-up for colorectal cancer may include:

• Fecal occult blood test, DNA stool tests (note that DNA stool tests are still exploratory in Japan and not commonly performed)
• Flexible sigmoidoscopy, barium enema, or colonoscopy
• Clinical examinations (e.g., digital rectal examination)
• A biopsy or polypectomy, performed during a test to determine whether cancer is present
• Imaging such as magnetic resonance imaging (MRI), computed tomography (CT), positron emission tomography/computed tomography (PET/CT), ultrasound (US), bone scan, virtual colonoscopy
• Laboratory testing and molecular work-up (including serum carcinoembryonic antigen [CEA] and DNA testing)

For clinical trials, data collected on initial diagnosis of colorectal cancer include the age at initial diagnosis, the date of diagnosis, family history, and diagnostic work-up (including location of primary tumor, histology, and molecular work-up). Studies in metastatic colorectal cancer may collect data both on the initial diagnosis and from additional diagnostic work-ups (including molecular work-up) conducted when the subject enters the study. Several genes have been identified as being associated with colorectal cancer.

For the purposes of this TAUG, the definition of diagnosis can refer to the following categories:

• Confirmation that colorectal cancer is present
• Determination of the severity/extent of the colorectal cancer

Disease staging describes the extent to which the malignancy has spread in the body. It contributes to the determination of treatment options and to the estimation of a patient's prognosis.

The AJCC's Cancer Staging Manual (available at https://cancerstaging.org/Pages/default.aspx) is a named and copyrighted instrument which describes TNM Classification. Guidance on mapping its components to SDTM will be provided as part of a supplement developed by the Questionnaires, Ratings, and Scales (QRS) Team, once permission to do so is granted by the copyright holder. It is anticipated that TNM staging will be represented in the Disease Response and Clin Classification (RS) domain.

T, N, and M stand for primary Tumor, regional lymph Node, and distant Metastasis, respectively.

T, N, and M determinations can be based on indirect measurements from physical examination and imaging tests (clinical staging), and/or on observations made directly on surgically sampled tissue (pathologic staging).

Fo llowing a biopsy, the resected tissue is examined by a pathologist. Pathologic assessments are both macroscopic and microscopic, and provide further detail as to the specific type, staging, and aggressiveness of the cancer. Specimens are prepared and processed as illustrated in the following concept map.

The assessment of genes and proteins helps in the determination of the specific type and aggressiveness of the cancer. The following concept map provides a high-level overview of a molecular work-up that may be performed.

The concept map below may be used to resolve questions about where particular tests should be represented in SDTM domains.

The following tables provide details on the more common tests and findings associated with colorectal cancer. The abbreviations and test/finding names in these tables are commonly used in clinical practice. Abbreviations are not necessarily the --TESTCD and --TEST values in CDISC Controlled Terminology. Similarly, the descriptions of these tests and findings are given in the context of colorectal cancer, and may not be Controlled Terminology definitions. When constructing standard datasets, consult the current version of CDISC Controlled Terminology (available at http://www.cancer.gov/cancertopics/cancerlibrary/terminologyresources/cdisc) for values of --TEST and --TESTCD.

The Prespecified Findings table below lists findings that may be collected as results of a general microscopic examination looking for abnormalities, or collected via individual questions with present/absent responses. In some cases (e.g., bowel obstruction/perforation) the data may be represented as medical history. Please note that this table is not an exhaustive list but details the more common findings associated with colorectal cancer.

The findings in the following table may be collected as results of laboratory tests or microscopic examinations, or via prespecified questions with present/absent responses. Please note that the table does not provide an exhaustive list but rather describes the most common types of colorectal cancer assessments.

The following table lists genetic and molecular analysis terms related to colorectal cancer. Tests can be run on different sample sources (e.g., tumor tissue, plasma). Please note that this list is not comprehensive but rather includes the most common analyses associated with colorectal cancer.

Examples of how a sponsor represented results of molecular and genetic assessments that may be performed in colorectal clinical trials are provided in the following sections.

The College of American Pathologists "Template for Reporting Results of Biomarker Testing of Specimens From Patients With Carcinoma of the Colon and Rectum" (available at https://www.ncbi.nlm.nih.gov/pubmed/23808403) was referenced when creating these examples.

The Study Data Tabulation Model Implementation Guide for Pharmacogenomics/Genetics future version was considered when the following Pharmacogenomics/Genetics Findings (PF) domain example were created, especially proposed controlled terminology. However, all variables used in these examples were defined in SDTMIG-PGx v1.0. Please note that the variables PFGENRI, PFGENTYP, PFGENLI, PFGENTRG, PFGENLOC, PFGENSR, and PFMUTYP were not standard variables in the current SDTM model at the time this guide was published.

Microsatellite Instability (MSI) and Mismatch Repair (MMR) data collected in a colorectal cancer clinical trial. The Revised Bethesda Guidelines for Hereditary Nonpolyposis Colorectal Cancer (Lynch Syndrome) and Microsatellite Instability(available at https://www.ncbi.nlm.nih.gov/pubmed/14970275) provides guidance on testing that may be performed in colorectal cancer patients. The Evaluation of Genomic Applications in Practice and Prevention (EGAPP) recommends testing of colorectal tumors of individuals with newly-diagnosed colorectal cancer.^[21] 

In the examples below, the investigator was requested to report any information available on MSI or MMR testing performed at the time of the initial diagnosis. MMR IHC testing is used to detect the presence or absence of the protein products of the mismatch repair genes (e.g., MLH1, MSH2, PMS2 and MSH6), whereas MSI PCR testing will determine the DNA mismatch repair loss in tumors.

The SDTM domain used to represent the results depends on the method employed. DNA-based MSI testing was represented in the PF domain, and results for protein-based MMR using IHC methodology were represented in the MI domain.

DNA MSI testing results may be reported as:

• the number of markers exhibiting instability;
• the percentage of markers tested exhibiting instability;
• the specific marker present; or
• an interpretation of the MSI testing, where results are commonly reported using the terminology of MSI-H, MSI-L, and MSS.

Different panels of markers may be tested depending upon the laboratory and kit used.

The MSI test results are illustrated below for MSI-summary results and MSI-individual marker results. It is recommended that users discuss with the relevant regulatory authority whether to represent summary results and individual markers. Both summary results and individual marker results can be presented in one dataset. However, in the examples that follow the datasets were split to enhance the readability for users. When datasets are combined, sponsors may use PFGRPID to link the appropriate records together.  

The following modeling of MMR Proteins in SDTM is under discussion and users are cautioned that this modeling may be subject to change.

In this example colorectal cancer trial, the sponsor planned exploratory analyses involving tumor-associated somatic variations (mutations), so they collected any available data on genetic variations included in the subject's historical records (e.g., KRAS, NRAS, BRAF, PIK3CA). The amount of available information varied across subjects. Some subjects only had information on whether any variations were detected for each gene (or protein) of interest, whereas other subjects had information on the actual variation detected in each gene (or protein) of interest. The method may or may not have been available. Because the name of the vendor was collected, the sponsor may have the ability to obtain more technical information about the specific tests. These technical details were not modeled in any SDTM dataset.  

The sponsor carefully considered the limitations of historical data collection reported by investigators on CRFs and represented this data based on a pragmatic approach. When collecting pre-study data on CRFs, certain data (e.g., reference sequence) may not be readily available. If data are collected using a central vendor, the sponsor should work with the central vendor to ensure that the required details are provided. This TAUG does not include an example of data collected using a central vendor. However, this sponsor collected the name of the vendor used for testing, and this allowed the sponsor to obtain more technical information about the specific tests (if needed for analysis).

This example deviates from the current recommendation in the SDTMIG-PGx v1.0 as the information on the genetic variations were not parsed out using the variables PFORRES, PFORREF, and PFGENLOC. This parsing out is recommended, but in this example the sponsor did not want to manually parse out the information to avoid potential errors. This pragmatic representation of the genetic data is under discussion within the PGx Team. Sponsors are urged to discuss such pragmatic representation with the appropriate regulatory agency. For the reader's information, the appropriate parsing of "c.1637A>T" (indicates nucleotide 1637 an A is changed to a T) is: PFORREF would be A, the PFORRES would be T, and PFGENLOC would be 1637.

However, if a central lab was used, the PGx Team recommends that the genetic data be parsed out into the appropriate items.

In addition, the published controlled terminology for PFTEST "Genetic Variation" was used instead of the PFTEST values of "Amino Acid" or "Nucleotide" that are suggested in the SDTMIG-PGx v1.0.

This example also used the CAP reporting template (available at https://www.ncbi.nlm.nih.gov/pubmed/23808403) as a possible CRF collection format.

Sponsors may collect information on the kit used for testing and the characteristics of the kit. If collected, each kit may be treated as a device. The SDTM device domains may be used to represent the relevant information. See Section 3.3.1.3.1, Microsatellite Instability (MSI) and Mismatch Repair (MMR), for examples on how to show information on a kit used for testing.

When looking at the example below please bear in mind the following:

• The representation of the data in the PF domain focused on ensuring consistency of the information within the dataset. The sponsor decided to include the results of the question whether or not a generic variation was detected in the SDTM-based dataset. Some sponsors may decide that when a variation is detected, the question indicating that the variation was detected may not be represented in the SDTM-based dataset as the specific variation are represented.
• The SDTM example reports the results based on the actual data collected. When CRFs are used to collect pre-study results, sponsors may allow various formats for reporting the results to accommodate the various formats that may have been used when the original data was reported by the lab.
• The protein variation may use the single-letter amino acid symbols or the 3-letter amino acid symbols. For example, a variation in a protein in which the 12th amino acid is Glycine in the reference sequence and Alanine in the subject's sequence can be represented as either Gly12Ala or G12A. Both 3-letter and single-letter symbols for amino acids were published by the International Union of Pure and Applied Chemistry (IUPAC) and the International Union of Biochemistry (IUB) in their 1983 recommendations for the nomenclature and symbolism for amino acids and peptides (available at http://iupac.org/publications/pac/56/5/0595/). Many labs report the results using either the 3-letter or single-letter amino acid names. The CAP reporting template (available at https://www.ncbi.nlm.nih.gov/pubmed/23808403) uses the 3-letter abbreviation for reporting some the amino acid (protein) names. Because the 3-letter amino acid symbols were used, they are reported in the SDTM variable PFORRES/PFSTRESC. This avoided the manual translation of the data into another format. A sponsor is encouraged to design the data collection instrument using the most appropriate format that avoids any manual translation of the data. Sponsors should provide instructions to the site regarding the nomenclature to use within a specific clinical trial. The Human Genome Variation Society (HGVS) nomenclature may be used to report information regarding DNA, RNA, and protein sequences; these standards were updated in 2017.^[23]
• A list of pre-specified gene variations may be defined on a CRF either because a set of targeted tests was performed for the study in order to identify specific variations, or as a data entry convenience when collecting information about tests that may have been performed in the past. If a set of targeted tests was performed for a study then, as described in the SDTMIG-PGx v1.0, the PFGENLI and PFGENTRG variables should be used to define the pre-specified variations, PFORRES should be used to indicate whether the variation was detected, and any detected variations should be recorded in PFSTRESC using standard nomenclature. However, when using a pre-specified list of gene variations as a data entry convenience to collect information about tests that may have been performed in the past, sponsors may choose either to transcribe previous results to the format described above or to collect the results in their originally reported format. In this example study, the sponsor chose to represent any variations selected from the pre-specified list or written in by the investigator using the original collection format. PFSTRESC, the standardized result, also used the format associated with the collected variation to avoid any manual translation to another format (e.g., p.Gly12Asp to c.35G>A). Note that the PFSTREC still followed the convention of reporting an amino acid using p. and a nucleotide using c.
• Typically for variations that are not pre-specified, PFGENLOC and PFORREF would be populated. PFORREF would be compared with PFORRES to determine if a mutation is present. However, in this case, only subjects with a mutation had the variation reported. Because the data was collected on CRFs, and these fields would have to be manually populated, the sponsor elected not to include these "permissible" variables in the SDTM-based dataset.
• The PFDTC is the date of the original specimen collection. PFDY is based on the date of the original sample, and not the date the testing was performed.
• Some variables (e.g., PFREFSEQ) are not populated. In this study, the sponsor did not consider this information needed to interpret the data.
• PFRUNDTC is a new proposed variable being considered to be added to the PF domain. This variable is used to represent the date that the sample was analyzed. Because this variable has not be approved, it is represented as an NSV variable.
• In this study, the sponsor standardized the results in an ADaM dataset to facilitate data analysis. This ADaM dataset is not shown.

The table below lists gross assessments and histologic findings associated with colorectal cancer, including diagnostic terms derived from conventional light microscopic examination of the tissues. Please note that the table is not an exhaustive list.

Please note that in some geographical regions the anatomical definition of "rectum" may differ which may affect whether a tumor is classified as a rectal tumor.

This section provides details of disease assessments relevant to colorectal cancer.

Treatments for colorectal cancer may include elimination of the cancer or controlling the effects of the cancer on the body. Some treatments target only the anatomic region in which the cancer exists; others act on a systemic level. In general, types of treatments are sequential rather than concurrent. The concept map below provides an overview of the treatment options.

Medications

In cancer trials, it is important to collect information on prior anti-cancer therapies, and any anti-cancer therapies given after the study drug of interest has been discontinued. Typically, in colorectal cancer, anti-cancer treatments are given as regimens.

A regimen is a treatment plan that specifies the dosage, the schedule, and the duration of treatment (NCI definition: https://www.cancer.gov/publications/dictionaries/cancer-terms/def/regimen). In cancer trials, subjects are often prescribed treatment plans that contain multiple treatment components. These multiple treatment plans are commonly known by an acronym (e.g., FOLFOX, FOLFIRI). Sponsors often collect information about the regimen itself (e.g., start date, end date, best response, reason for discontinuation) as well as information on the individual treatment components within the regimen. In other disease areas, subjects are also treated with a combination of treatments but these treatments are often administered as a single product which is a combination of several active ingredients. It is difficult to represent the information about treatments that include multiple components in the SDTM Concomitant Medications (CM) domain.

CDISC is putting together a team that will focus on the SDTM modeling of combination products/regimens. This topic is applicable to many therapeutic areas. CDISC anticipates that this modeling would be published in a separate document providing guidelines for the representation of combination products or combination regimens.

Surgeries

In some colorectal cancer trials, it may be important to collect information on prior anti-cancer surgeries as well as any such surgeries during the study. The type of surgery performed at the time of initial diagnosis depends on the extent of the cancer. In some situations the anus may be weak or damaged, or must be removed, and a colostomy or an ileostomy may be required. Permanent stomas are required when stool cannot pass through its normal route after surgery. Sphincter-sparing surgery is fairly common for some rectal cancers. Depending upon the study, the sponsor may collect the type of surgery performed at the time of the initial diagnosis. Although not shown, this prior surgery information would be represented in the SDTM Procedures (PR) domain.

In some studies, liver resections may be performed for metastases. It is important to provide the dates of surgery and whether disease was found during surgery. Other surgeries with curative intent may also be allowed. If these surgeries are allowed during the study, a sponsor must specify how this will be handled in the analysis of relevant endpoints (e.g., BOR, PFS).

Response Evaluation Criteria In Solid Tumors (RECIST: https://www.ncbi.nlm.nih.gov/pubmed/19097774) is usually used to assess disease response in metastatic colorectal cancer clinical trials. In this document, RECIST is used as a generic term to refer to the various versions of the RECIST criteria. However, sponsors should always specify which RECIST version was used in a study.

The response criteria chosen to assess disease response may depend on the type of therapy being evaluated. Due to the different response patterns in subjects receiving anti-cancer immunotherapies, other criteria such (e.g., irRC, irRECIST, iRECIST) may also be used.

irRC (immune related response criteria)

The irRC criteria were proposed by Wolchok and colleagues^[28] to determine the extent of anti-tumor response to an immunotherapeutic agent. The irRC criteria were based on the WHO tumor response criteria (http://apps.who.int/iris/handle/10665/37200)^[29] and bidimensional tumor measurements are used.

irRECIST (irRC using unidimensional measurements)

Mizuki Nishino and colleagues^[30] have proposed new criteria using 1-dimensional lesion measurements following RECIST. A poster presented by Bohnsack^[31] provides a table comparing irRC and irRECIST immunotherapy criteria.

iRECIST (RECIST 1.1 for immune based therapeutics)

A March 2017 publication by the RECIST Working Group Team^[32] describes the consensus iRECIST guideline intended to standardize design and collection guidelines for the evaluation of disease in cancer immunotherapy trials.

Other CDISC oncology TAUGs have provided examples of RECIST assessments represented in the SDTM. This TAUG only shows examples of the irRC criteria. The Lung Cancer TAUG will provide examples of the newly published standardized iRECIST criteria. This TAUG does not provide any recommendation regarding which criteria to use in a clinical trial. Sponsors should always consult regulatory authorities to obtain advice on this topic.

The following concept map provides an overview of the measurement of tumor burden and response assessments.

Tumor Results referenced in the previous concept map are based on the response criteria and version used by the sponsor. In RECIST, results are based on the sum of the diameters (longest for non-nodal lesions, short axis for nodal lesions) for all target lesions (Target), with qualitative assessments of non-measurable tumors (Non-Target). The development of new lesions is also assessed. irRC use the WHO criteria for tumor response^[29]. In irRC, results are based on the sum of the diameters of bidimensional measurable tumors (Index Lesions + New lesions). Non-measurable lesions are not considered in progression, but are considered in complete response.

It is recommended that the user be familiar with the tumor response guidelines before reviewing the following examples. The sponsor should determined the relevant version of the guideline to be used in a study. General guidance on managing data pertaining to the identification, monitoring, and assessment of tumors and lesions is covered by three SDTMIG Findings domains: Tumor Identification (TU), Tumor Results (TR), and Disease Response (RS). Consult the SDTMIG v3.2 for specifications, assumptions, and examples for these domain models.

The following are examples of tumor identification, tumor assessment, and tumor response for subjects in a colorectal cancer clinical trial. These examples employ the irRC immune-related response criteria. SDTM dataset examples and aCRFs using RECIST have been provided in the TAUGs for Breast Cancer (Section 4.2) and Prostate Cancer (Section 4.2). The sample aCRFs in this TAUG use the irRC criteria.

Annotated CRF: Tumor Identification/Results - irRC - Index Lesions

This CRF is used to identify Index lesions/tumors at baseline and to report the results of these Index lesions/tumors at all subsequent tumor assessment visits.

It illustrates the collection of the tumor identification information and the tumor results on a combined CRF. When SDTM submission datasets are created, the appropriate information must be mapped to TU and TR.

Tumor Identification/Result CRFs assume that measurable lesions that are followed and measured according to the criteria are classified as "Index lesions." All tumors that are followed but not measured according to the criteria are considered as "Non-Index lesions." These CRFs do not collect any tumor measurements for Non-Index lesions.

CDASH variable names for denormalized variables are examples, and are used to ensure unique CDASH variable names. Sponsors may use other conventions for creating denormalized CDASH variable names.

See Section 1.3, CDASH Metadata and Annotated CRFs, for explanation of annotations.

Annotated CRF: Tumor Identification/Results - irRC - Non-Index Lesions

This CRF is used to identify non-Index lesions/tumors at baseline and to report the results of these non-index lesions/tumors at all subsequent tumor assessment visits.

It illustrates the collection of the new tumor identification information and the new tumor results on a combined CRF. When SDTM submission datasets are created, the appropriate information must be mapped to TU and TR.

Tumor Identification/Result CRFs assume that new measurable lesions that are followed and measured according to the criteria are classified as "Index lesions." All new tumors that are followed but not measured according to the criteria are considered as "Non-Index lesions." These CRFs do not collect any tumor measurements for Non-Index lesions.

CDASH variable names for denormalized variables are examples, and are used to ensure unique CDASH variable names. Sponsors may use other conventions for creating denormalized CDASH variable names.

See Section 1.3, CDASH Metadata and Annotated CRFs, for explanation of annotations.

Annotated CRF: Tumor Identification/Results - irRC - New Lesions

This CRF is used to identify new lesions/tumors identified during the study and to report the results of these new lesions/tumors.

It illustrates the collection of the new tumor identification information and the new tumor results on a combined CRF. When SDTM submission datasets are created, the appropriate information must be mapped to TU and TR.

Tumor Identification/Result CRFs assume that new measurable lesions that are followed and measured according to the criteria are classified as "Index lesions." All new tumors that are followed but not measured according to the criteria are considered "Non-Index lesions." These CRFs do not collect any tumor measurements for new Non-Index lesions.

CDASH variable names for denormalized variables are examples, and are used to ensure unique CDASH variable names. Sponsors may use other conventions for creating denormalized CDASH variable names.

See Section 1.3, CDASH Metadata and Annotated CRFs, for explanation of annotations.

Annotated CRF: Tumor Response - irRC

This CRF is used to collect tumor responses during the study.

CDASH variable names for denormalized variables are examples, and are used to ensure unique CDASH variable names. Sponsors may use other conventions for creating denormalized CDASH variable names.

See Section 1.3, CDASH Metadata and Annotated CRFs, for explanation of annotations.

This example shows a representation of tumor assessments using irRC, which is based on bidimensional measurements following the WHO criteria^[29]. The SDTM variable RSCAT is used to indicate which response criteria and version were used in a study.

An example of the dataset relationships (RELREC) is not shown as it has been described in the SDTMIG.

Colorectal cancer studies may use measures which assess symptoms, a particular symptom such as functional status (i.e., ability to perform functions of daily living), or health-related quality of life.

Questionnaires, Ratings, and Scales (QRS) are maintained as standalone guides on the CDISC website: https://www.cdisc.org/foundational/qrs. The table below lists assessments that are being pursued as potential supplements as part of the development work for this TAUG. Supplements may or may not be finalized at the time of publication, and depend on copyright approval where applicable. CDISC cannot produce supplements for copyrighted measures without the express permission of the copyright holder.

Sponsors should refer to QRS guides on the CDISC website for measures of interest not included below, as they may have been developed for another therapeutic area; new measures are implemented on an ongoing basis by the CDISC QRS Terminology and Standards Development sub-teams. See CDISC COP 001 (https://www.cdisc.org/about/bylaws) for details on implementing or requesting development of standards for SDTM-based submissions.

Identified QRS Measures of Interest to Colorectal Cancer

Some routinely collected data are not specific to a therapeutic area. These include data collected to assess the subject's history, the safety of the study treatment, and/or factors in assessing the subject's condition or reaction to a treatment.

For many cancer treatments, one of the aims of early development is the characterization of side effects, including the identification of dose-limiting toxicities. This aspect of colorectal cancer study data is beyond the scope of this version of the TAUG.

In later development, when considering adverse events that have been found to be associated with a drug, data collection will depend on the particular side effects. As this varies considerably among cancer treatments, it is not covered here. Advice for analysis of adverse events within ADaM can be found in the ADaM Structure for Occurrence Data (OCCDS) available at https://www.cdisc.org/standards/foundational/adam.

Pre-specified adverse events that may be of interest but often depend on the treatment given. Advice for collecting data on pre-specified adverse events may be found in the CDASH and SDTM foundational standards.

Medications may be given for pain management. Time to first use of opiates may be used as an endpoint in a study. CDISC forming a team that will focus on the SDTM modeling of combination products/regimens. This topic is applicable to many therapeutic areas. CDISC anticipates that this modeling will be published in a stand-alone publication and would provide guidelines for the representation of combination products or combination regimens.

This section presents some of the concepts and common analysis endpoints relevant to the analysis of colorectal cancer studies. Colorectal cancer is considered a solid tumor. Clinical trial data would be analyzed like other clinical studies in solid tumors (e.g., breast cancer, prostate cancer). Therefore, most of the methodologies described in those TAUGs would be the same for colorectal cancer.

Colorectal cancer efficacy analysis mainly falls into two groups: response analysis and time-to-event analysis. Typical time-to-event endpoints are:

• Progression-Free Survival (PFS)
• Overall Survival (OS)
• Disease-Free Survival (DFS)

Other time-to-event endpoints may be considered (e.g., cancer-specific survival) depending on the protocol planned analyses.  

The methodologies for creating datasets to support these analyses have been covered in previous TAUGs (Breast Cancer and Prostate Cancer).

Best Overall Response (BOR) analysis for colorectal cancer studies is based on RECIST 1.1, irRC, irRECIST, or iRECIST. BOR is defined as the best response recorded from the start of the trial—normally the date of randomization—until death, until disease progression occurs either by RECIST or irRC, or until the patient discontinues treatment. BOR analysis based on RECIST criteria is described in the BrCa and PrCa TAUGs.

The immune related response criteria (irRC)^[28] has been developed to assess response and progression where patients are treated with an immune-oncology drug. RECIST criteria, which are typically used to assess solid tumor cancers, are not adequate because they do not account for the time gap in many patients between initial treatment and the apparent action of the immune system to reduce the tumor burden.

In metastatic colon cancer clinical trials irRC is emerging as a method to evaluate efficacy endpoints.

Some of the differences between RECIST and irRC are:

• RECIST looks at "Target" and "Non-target" lesions whereas irRC looks at "Index" and "Non-index" lesions.
• In irRC, new lesions are considered a change in tumor burden and not necessarily progressive disease, whereas in RECIST a new lesion is considered to be a sign of disease progression.
• Lesions in RECIST are measured unidimensionally based on the longest diameter whereas lesions in irRC are measured bidimensionally.
• Algorithms for determining responses (including progression) differ between RECIST and irRC.

Documenting new lesion measurements is especially important in irRC. Whereas in RECIST new lesions always constitute progression, in irRC new index lesion measurements are included with the other index lesions measurements to determine tumor burden, and do not always constitute progression. This is due to the specific reaction (pseudo progression) to the immunologic drugs that may occur initially and subside in the subsequent assessments.

The response values for RECIST versus irRC are similar and are typically analyzed in the same way. When performing a best overall response analysis, the ranking for best response is considered to be complete response (CR/irCR), partial response (PR/irPR), stable disease (SD/irSD; Non-CR/Non-PD for subjects without target or index lesions), and progressive disease (PD/irPD), in that order. BOR can be calculated either for the entire time a subject was in the trial or for a specific period of time, such as for the combination therapy period and the maintenance therapy period.

Unlike the survival analysis, BOR analysis is based on tumor assessments performed until progressive disease is determined. Considerations for missing assessments are not generally taken into account. The BOR is analyzed based on the number of subjects achieving each of the RECIST or/and irRC categories during the defined clinical trial period.

Almost all oncology studies will include the stage of cancer for each subject at the time the subject was included in the study. The most commonly used colorectal cancer staging system is known as the TNM system, established by the American Joint Committee on Cancer (AJCC). This system looks at T (tumor expansion), N (extent of cancer spread to lymph nodes), and M (metastases or spread of cancer to other organs). In addition to TNM information, colorectal cancer trials should consider collecting anatomical location details of the primary tumor and the distance from anal verge to the primary tumor. Anal sphincter preservation status at the time of the original surgery may also be of interest.

The performance of 2 types of surgical intervention, primary tumor resection and resection of liver metastases, may be important for efficacy analysis of colorectal cancer; information related to these procedures is often collected in colorectal cancer studies. If the primary tumor is rectal, information on whether total mesorectal excision (TME) was performed can also be important.

The following subject-level values should be considered to be captured for analysis:

• A flag to indicate the finding of No Existing Disease (NED) during the surgery
• The date of the surgical NED (there could be more than one surgery performed)

Performance status at baseline should be captured; the Eastern Cooperative Oncology Group-Performance Status (ECOG), Karnofsky Performance Status, and WHO Performance Status measures are all different methodologies for recording performance status.^[33,34]

Some colorectal cancer characteristics that are considered risk factors include obstruction or perforation of the bowel, lymphovascular invasion, poorly differentiated histology, and number of resected lymph nodes. Other risk factors may also be assessed. The following are specific tests that assess some of these risk factors; all of these tests are used in the adjuvant setting only:

• Oncotype DX, which gives a score that measures risk of recurrence of colorectal cancer
• ColoPrint, which predicts Stage II recurrence of colorectal cancer
• CDX2 expression, which is a marker for gastrointestinal differentiation in colorectal cancer

Prior therapy information can also serve as a stratification indicator. Some of the interventions that might be captured are surgical interventions, non-surgical liver directed intervention, systemic therapy, and radiation. The PrCa TAUG has examples of variables that indicate types of intervention and important dates associated with these therapies.

Disease characteristics used for prognostic stratification or covariates may be included in the subject-level analysis dataset (ADSL). Examples of disease characteristics that might be included in ADSL include mutations, microsatellite instability, and gene expression as determined by immunohistochemistry (IHC).

Mutation

Collecting the mutation type and subtype associated with a tumor specimen can help predict responses to EGFR-targeted immunotherapies. Method of mutation detection would also be important.

Microsatellite Instability (MSI)

Cancers with MSI account for approximately 15% of all colorectal cancers.^[14] However, in some geographical regions (e.g., Japan) this may be lower. A tumor is classified as MSI-High if 2 or more of the 5 microsatellite sequences have been mutated and MSI-Low if only 1 has mutated. Detection of MSI is important to diagnose Lynch syndrome as well as to determine an effective chemotherapy regimen. MSI tumors do not respond well to 5-fluorouracil (5-FU), which is the most common treatment for colorectal cancer. Testing for MSI mutations might also be performed for prognostic stratification. This might be captured in ADSL.

Immunohistochemistry (IHC)

Immunohistochemistry (IHC) is a diagnostic technique used to identify discrete tissue components in a tissue specimen. Using specific tumor markers, IHC can be used to diagnose a cancer as benign or malignant, determine the stage and grade of a tumor, and identify the cell type and origin of a metastasis to find the site of the primary tumor. It also allows detection of expression of certain protein markers in a tumor which can be a prognostic factor in treatment and expected outcome to the disease. Over-expression of a protein may or may not indicate a gene mutation. The protein parameters may be target protein detection, intensity score, and percent cell staining. Intensity score and percent staining can be used to derive the outcome of the test as negative, positive, or other.

Carcinoembryonic Antigen (CEA) is an important indicator in colorectal cancer trials. CEA levels at the baseline and throughout a particular study can indicate how widespread the cancer is as well indicating the success or lack thereof of the treatment. It can also be used to identify recurrences after surgical resection.

The following table lists the non-standard variables used in this document, and gives their parent domain and variable-level metadata. Note that names of the NSVs are listed using the -- notation to represent the 2 letter domain name abbreviations. These NSVs may be used in other domains, if appropriate.

Works Cited

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2. Bowel cancer: Surger for advanced cancer. Cancer Research UK. ( https://www.cancerresearchuk.org/about-cancer/bowel-cancer/advanced/treatment/surgery ). Accessed October 27, 2016.
3. Nivatvongs S, Rojanasakul A, Reiman HM, et al. The risk of lymph node metastasis in colorectal polyps with invasive adenocarcinoma. Dis Colon Rectum. 1991;34(4):323-328. doi:10.1007/BF02050592.
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8. Koukourakis MI, Giatromanolaki A, Sivridis E, et al. Prognostic and predictive role of lactate dehydrogenase 5 expression in colorectal cancer patients treated with PTK787/ZK 222584 (vatalanib) antiangiogenic therapy. Clin Cancer Res. 2011;17(14):4892-4900. doi:10.1158/1078-0432.CCR-10-2918.
9. Marmorino F, Salvatore L, Barbara C, et al. Serum LDH predicts benefit from bevacizumab beyond progression in metastatic colorectal cancer. Br J Cancer. 2017;116(3):318-323.
10. Dalerba P, Sahoo D, Paik S, et al. CDX2 as a prognostic biomarker in stage II and stage III colon cancer. N Engl J Med. 2016;374(3):211-222. doi:10.1056/NEJMoa1506597.
11. Genetics of colorectal cancer (PDQ®) - health professional version. Executive summary. National Cancer Institute Web site. (https://www.cancer.gov/types/colorectal/hp/colorectal-genetics-pdq). Updated June 14, 2018. Accessed September 14, 2018.
12. Schmoll HJ. Targeting HER2: precision oncology for colorectal cancer. Lancet Oncol. 2016;17(6):685-686. doi:10.1016/S1470-2045(16)30039-0.
13. Sartore-Bianchi A, Trusolino L, Martino C, et al. Dual-targeted therapy with trastuzumab and lapatinib in treatment-refractory, KRAS codon 12/13 wild-type, HER2-positive metastatic colorectal cancer (HERACLES): a proof-of-concept, multicentre, open-label, phase 2 trial. Lancet Oncol. 2016;17(6):738-746. doi:10.1016/S1470-2045(16)00150-9.
14. Horvat M, Stabuc B. Microsatellite instability in colorectal cancer. Radiol Oncol. 2011;45(2):75-81. doi:10.2478/v10019-011-0005-8.
15. Nazemalhosseini Mojarad E, Kashfi SM, Mirtalebi H, et al. Low level of microsatellite instability correlates with poor clinical prognosis in stage II colorectal cancer patients. J Oncol. 2016;2016:2196703. doi:10.1155/2016/2196703.
16. Markman B, Javier Ramos F, Capdevila J, Tabernero J. EGFR and KRAS in colorectal cancer. Adv Clin Chem. 2010;51:71-119.
17. De Roock W, Claes B, Bernasconi D, et al. Effects of KRAS, BRAF, NRAS, and PIK3CA mutations on the efficacy of cetuximab plus chemotherapy in chemotherapy-refractory metastatic colorectal cancer: a retrospective consortium analysis. Lancet Oncol. 2010;11(8):753-762. doi:10.1016/S1470-2045(10)70130-3.
18. Di Nicolantonio F, Martini M, Molinari F, et al. Wild-type BRAF is required for response to panitumumab or cetuximab in metastatic colorectal cancer. J Clin Oncol. 2008;26(35):5705-5712. doi:10.1200/JCO.2008.18.0786.
19. Semrad TJ, Kim EJ. Molecular testing to optimize therapeutic decision making in advanced colorectal cancer. J Gastrointest Oncol. 2016;7(Suppl 1):S11-20. doi:10.3978/j.issn.2078-6891.2015.094.
20. Fodde R. The APC gene in colorectal cancer. Eur J Cancer. 2002;38(7):867-871.
21. Recommendations from the EGAPP Working Group: genetic testing strategies in newly diagnosed individuals with colorectal cancer aimed at reducing morbidity and mortality from Lynch syndrome in relatives. Genet Med. 2009;11(1):35-41. doi:10.1097/GIM.0b013e31818fa2ff.
22. Libutti SK, Willett CG, Saltz LB. Cancer of the rectum. In: DeVita VT, Lawrence TS, Rosenberg SA, eds. Cancer: Principles and Practice of Oncology. 9th ed. Philadelphia, PA: Lippincott Williams & Wilkins; 2011:1127-1141.
23. Den dunnen JT, Dalgleish R, Maglott DR, et al. HGVS recommendations for the description of sequence variants: 2016 update. Hum Mutat. 2016;37(6):564-569. doi:10.1002/humu.22981.
24. Adenocarcinoma (code C2852). National Cancer Institute Thesaurus. (https://ncit.nci.nih.gov/). Accessed September 14, 2018.
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26. Xiao Y, Wu B2, Qiu H, et al. Effects of tumor distance from anal verge and types of operations on survival outcomes for low rectal cancer after neoadjuvant chemoradiotherapy [in Chinese]. Zhonghua Yi Xue Za Zhi. 2014;94(22):1705-1709.
27. Restivo A, Zorcolo L, Cocco IM, et al. Elevated CEA levels and low distance of the tumor from the anal verge are predictors of incomplete response to chemoradiation in patients with rectal cancer. Ann Surg Oncol. 2013;20(3):864-871. doi:10.1245/s10434-012-2669-8.
28. Wolchok JD, Hoos A, O'Day S, et al. Guidelines for the evaluation of immune therapy activity in solid tumors: immune-related response criteria. Clin Cancer Res. 2009;15(23):7412-7420. doi:10.1158/1078-0432.CCR-09-1624.
29. Miller AB, Hoogstraten B, Staquet M, Winkler A. Reporting results of cancer treatment. Cancer. 1981;47:207–214.
30. Nishino M, Giobbie-hurder A, Gargano M, Suda M, Ramaiya NH, Hodi FS. Developing a common language for tumor response to immunotherapy: immune-related response criteria using unidimensional measurements. Clin Cancer Res. 2013;19(14):3936-3943. doi:10.1158/1078-0432.CCR-13-0895.
31. Bohnsack O, Hoos A, Ludajic K. Adaptation of the immune related response criteria: irRECIST. Ann Oncol. 2014;25(suppl 4):iv361-iv372. doi:10.1093/annonc/mdu342.23.
32. Seymour L, Bogaerts J, Perrone A, et al. iRECIST: guidelines for response criteria for use in trials testing immunotherapeutics. Lancet Oncol. 2017;18(3):e143-e152. doi:10.1016/S1470-2045(17)30074-8.
33. Oken M, Creech R, Tormey D, et al. Toxicity and response criteria of the Eastern Cooperative Oncology Group. Am J Clin Oncol. 1982;5:649-655.
34. Karnofsky DA, Burchenal JH. The Clinical Evaluation of Chemotherapeutic Agents in Cancer. In: MacLeod CM, ed. Evaluation of Chemotherapeutic Agents. New York: Columbia Univ Press; 1949.

Further Reading

• Bartley AN, Hamilton SR, Alsabeh R, et al. Template for reporting results of biomarker testing of specimens from patients with carcinoma of the colon and rectum. Arch Pathol Lab Med. 2014;138(2):166-170. doi:10.5858/arpa.2013-0231-CP.

• Eisenhauer EA, Therasse P, Bogaerts J, et al. New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). Eur J Cancer. 2009;45(2):228-247. doi:10.1016/j.ejca.2008.10.026.

• Gray RG, Quirke P, Handley K, et al. Validation study of a quantitative multigene reverse transcriptase-polymerase chain reaction assay for assessment of recurrence risk in patients with stage II colon cancer. J Clin Oncol. 2011;29(35):4611-4619. doi:10.1200/JCO.2010.32.8732
• Dalerba P, Sahoo D, Paik S, et al. CDX2 as a prognostic biomarker in stage II and stage III colon cancer. N Engl J Med. 2016;374(3):211-222. doi:10.1056/NEJMoa1506597

• IUPAC-IUB Joint Commission on Biochemical Nomenclature. Nomenclature and symbolism for amino acids and peptides. Recommendations 1983. Pure Appl. Chem. 1984;56(5):595-624. doi:10.1351/pac198456050595.

• Marmorino F, Salvatore L, Barbara C, et al. Serum LDH predicts benefit from bevacizumab beyond progression in metastatic colorectal cancer. Br J Cancer. 2017;116(3):318-323.

• Salazar R, Roepman P, Capella G, et al. Gene expression signature to improve prognosis prediction of stage II and III colorectal cancer. J Clin Oncol. 2011;29(1):17-24. doi:10.1200/JCO.2010.30.1077

• Umar A, Boland CR, Terdiman JP, et al. Revised Bethesda Guidelines for hereditary nonpolyposis colorectal cancer (Lynch syndrome) and microsatellite instability. J Natl Cancer Inst. 2004;96(4):261-268.

• US Department of Health and Human Services, Food and Drug Administration, CDER. Guidance for Industry: Clinical Trial Endpoints for the Approval of Cancer Drugs and Biologics. Silver Spring, MD: U.S. Food and Drug Administration; 2007. ( http://www.fda.gov/downloads/drugs/guidancecomplianceregulatoryinformation/guidances/ucm071590.pdf). Accessed October 27, 2016.

• Wain HM, Bruford EA, Lovering RC, Lush MJ, Wright MW, Povey S. Guidelines for human gene nomenclature. Genomics. 2002;79(4):464-470. doi:10.1006/geno.2002.6748.

• World Health Organization. WHO handbook for reporting results of cancer treatment. Geneva, Switzerland: World Health Organization; 1979. WHO offset publication no. 48. ( http://www.who.int/iris/handle/10665/37200 ). Accessed September 5, 2018.

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