Compliance with the provisions in this LCD may be monitored and addressed
through post payment data analysis and subsequent medical review audits.  
  
 **History/Background and/or General Information  
  
** With advancement in science and technology comes the ability to incorporate
genetic testing for oncology biomarkers into clinical care, with the goal of
improved patient outcomes. The scope of this LCD is DNA and RNA genetic
testing in the practice of oncology in the Medicare population.  
  
As defined by the Food and Drug Administration (FDA) and National Institutes
of Health (NIH) Biomarker Working Group, a biomarker is "A defined
characteristic that is measured as an indicator of normal biological
processes, pathogenic processes, or biological responses to an exposure or
intervention, including therapeutic interventions," and may include molecular,
histologic, radiographic, or physiologic characteristics.2 (p.45)  
  
Cancer is a disease caused by changes or alterations to a person's genome.
Some genetic changes or alterations can be inherited (also known as germline
mutations). About 5-10% of all cancer diagnoses result from germline
mutations, and over 50 hereditary cancer syndromes are known. Other cancer-
causing (oncogenic) genetic changes or alterations result from acquired
genetic damage (also known as somatic mutations). Somatic mutations can arise
in numerous scenarios, including exposure to chemicals that alter DNA
(carcinogens) or ultraviolet (UV) radiation from the sun.3  
  
Biomarker testing is a part of precision medicine (also known as personalized
laboratory medicine). Precision medicine is a tailored approach to medical
care and treatment. Because each patient has a unique combination of genetic
heritage and somatic changes, and therefore, a unique pattern of biomarkers,
precision medicine for oncology involves the use of biomarker testing to
pinpoint the disease management needs of individual patients and avoid the use
of treatments which are unlikely to be successful.4 Much of this testing
involves direct evaluation of the genetics of the malignancy through various
testing methodologies. These methodologies can include high level genetic
evaluations such as karyotyping (analysis of chromosomes) to more detailed
evaluations such as identifying specific pathogenic point variations (analysis
of specific nucleotide changes).  
  
Additionally, testing may be used to check for a single biomarker or multiple
biomarkers at the same time via a multigene test or panel.3 As a result, the
growing compendium of products described as biomarkers requires careful
evaluation to determine what testing configurations are medically reasonable
and necessary under Medicare.  
  
Biomarkers for oncology can be generally classified into four functional
types5:  
  

  1. **Diagnostic** biomarkers detect or confirm the presence of a disease or condition.
  2. **Prognostic** biomarkers provide information about the likely course of a disease process and potential patient outcomes if left untreated.
  3. **Predictive** biomarkers forecast a patient's response and/or benefit to a specific treatment.
  4. **Therapeutic** biomarkers identify potential targets for a medical intervention. (e.g., targeted drug therapy)  
  

In certain circumstances, genetic testing for oncology biomarkers in patients
with the corresponding appropriate medical condition could have the potential
to assist patient management in the Medicare population. However, given the
complexity and rapidly expanding knowledge in this topic area, we must
vigilantly avoid testing that generates confusion and that does not improve
patient outcomes. In order for services to be considered medically reasonable
and necessary, they must impact the management of the patient and lead to
improved patient outcomes. Specialized clinical expertise in oncology in
addition to advanced knowledge in both genetic variation and effect on gene
function is required to facilitate optimal outcomes for patients.  
  
 **Definitions  
  
Analytical validation** is a process intended to determine if a test, tool, or
instrument has acceptable technical performance (sensitivity, specificity,
accuracy, precision, etc.). Analytical validation is an assessment of the
test's technical performance (the test measures what it was designed to
measure), not its usefulness or clinical significance.2,6 Analytical validity
includes the ability of the test to accurately and reliably detect the
mutation and/or variant. 6  
  
 **Biomarkers** A biological molecule found in blood, other body fluids, or
tissues that is a sign of a normal or abnormal process, or of a condition or
disease. A biomarker may be used to see how well the body responds to a
treatment for a disease or condition. Also called molecular marker and
signature molecule.7  
  
 **Cancer Screening** An attempt to detect cancer early by routine examination
of apparently healthy people.8  
  
 **Cancer Surveillance** is closely watching a patient's condition but not
treating it unless there are changes in test results. Surveillance is also
used to find early signs that a disease has come back. During surveillance,
certain exams and tests are done on a regular schedule.9  
  
 **Cell-free DNA (cfDNA)** is a laboratory method that involves analyzing free
(i.e., no longer within the cell) DNA contained within a biological sample,
most often to look for genomic variants associated with a hereditary or
genetic disorder. For example, prenatal cfDNA testing is a non-invasive method
used during pregnancy that examines the fetal DNA that is naturally present in
the maternal bloodstream. Cell-free DNA testing is also used for the detection
and characterization of some cancers and to monitor cancer therapy.10  
  
 **Clinical Indication for Germline testing** is a sign, symptom, laboratory
test result, or medical condition, or a combination of these indications, that
leads to the recommendation of a treatment, laboratory test, or procedure for
a hereditary disease or condition. 11  
  
 **Circulating tumor DNA (ctDNA)** are small pieces of DNA that are released
into a person's blood by tumor cells as they die. A sample of blood can be
used to look for and measure the amount of ctDNA and identify specific
mutations (changes) in the DNA. Circulating tumor DNA is being used as a
biomarker to help diagnose some types of cancer, to help plan treatment, or to
find out how well treatment is working or if cancer has come back.12  
  
 **Clinical validity** is defined as the ability of a test to classify a
patient's specific circumstance into a diagnostic, prognostic, or predictive
functional category. It should be noted that clinical validity is not a fixed
value. Clinical validity includes the ability of the test to accurately and
reliably detect the disease of interest in the defined population. 6,13  
  
 **Clinical utility** can be defined as the ability of a test to provide
information related to the patient's care and management, and thus, its
ability to inform treatment decisions. CMS is most focused on assessing
clinical utility in the context of whether or not a test is used to guide
patient management and whether or not use of the test results leads to
treatment that improves health outcomes. 6,13  
  
 **Comprehensive Genomic Profiling (CGP)** is, at this time, a term with many
potential interpretations depending on which entity uses the term and in what
context the term is used. _Because of this, the term Comprehensive Genomic
Profiling will not be utilized within this LCD_. It is recognized that
knowledge bases like NCCN sometimes use the term CGP, but it must be
recognized that the term's precise definition depends on the unique context in
which it is utilized. Because of this, the way CGP is defined in one guideline
will not necessarily translate to other guidelines.  
  
 **FDA-cleared or approved test system** means a test system was cleared or
approved by the FDA through the premarket notification (510(k)) or premarket
approval (PMA) process for in-vitro diagnostic use. (See CFR, Title 42, Volume
2, Chapter IV, Part 493.2 Laboratory Requirements: Definitions)  
  
 **Genetic Testing** , for the purposes of this LCD, describes any and all
assays evaluating DNA and/or RNA without regard for a test's purpose,
methodology, or output. Examples of "genetic testing" include DNA sequencing
of genes and surrounding non-coding regions, quantification of RNA expression,
identification of epigenetic changes to the DNA, and evaluation of overall
changes in chromosome structure. Moreover, this term encompasses all testing
that includes genetic evaluation without regard to other included test
components such as simultaneous protein testing and algorithmic analyses
(e.g., multianalyte assays with algorithmic analyses [MAAAs]). Widely accepted
terminology used in oncology such as "biomarker testing," "genetic testing for
an inherited mutation," and "genetic testing for inherited cancer risk" are
included under the umbrella term "genetic testing" for the purposes of this
LCD, recognizing that molecular oncology is a highly complex and rapidly
evolving field and thus, more inclusive terminology is required.  
  
 **Genomic Testing** , for the purposes of this LCD, _will not be used as a
defining term_. Depending on the context, genomic testing can describe testing
that includes both genes and non-coding sequence; however, given how this term
is not always used precisely, we chose to avoid using this term to define
policy in this LCD.  
  
 **Genomic Sequencing Procedures (GSPs)** are DNA and/or RNA sequence analysis
methods that simultaneously assay multiple genes or genetic regions relevant
to a clinical situation. They may target specific combinations of genes or
genetic material, or assay the exome or genome. 14  
  
 **Germline** The sequence of cells in the line of direct descent from zygote
to gametes, as opposed to somatic cells (all other body cells). Mutations in
germline cells are transmitted to offspring; mutations in somatic cells are
not transmitted to offspring. 15  
  
 **Kit** means all components of a test that are packaged together. (See CFR,
Title 42, Volume 2, Chapter IV, Part 493.2 Laboratory Requirements:
Definitions)  
  
 **Laboratory Developed Tests** **(LDT)** defined by the FDA as an in vitro
diagnostic test that is manufactured by and used within a single laboratory.16  
  
 **Liquid biopsy** is a test performed on blood to either look for cancer
cells circulating in the blood or for DNA from tumor cells that are in the
blood.17  
  
 **Minimal residual disease (MRD)** is a term used to describe a very small
number of cancer cells that remain in the body during or after treatment.
Minimal residual disease can be found only by highly sensitive laboratory
methods. Also called measurable residual disease.18  
  
 **Multianalyte Assays with Algorithmic Analyses (MAAAs)** are procedures that
utilize multiple results derived from panels of analyses of various types,
including molecular pathology assays, fluorescent in situ hybridization
assays, and non-nucleic acid based assays. Algorithmic analysis using the
results of the assays as well as other patient information is performed and
typically reported as a numeric score(s) or as a probability.19  
  
 **Neoplasm** An abnormal mass of tissue that forms when cells grow and divide
more than they should or do not die when they should. Neoplasms may be benign
(not cancer) or malignant (cancer).20  
  
 **Next Generation Sequencing (NGS)** is a high-throughput method used to
sequence a part or the whole of an individual's genome. This technique
utilizes DNA sequencing technologies that are capable of processing multiple
DNA sequences in parallel. Also called massively parallel sequencing. Note
that NGS is also utilized to analyze RNA, however, the RNA is typically
converted to complementary DNA (cDNA) before analysis.21  
  
 **Reflex testing** means confirmatory or additional laboratory testing that
is automatically requested by a laboratory under its standard operating
procedures for patient specimens when the laboratory's findings indicate test
results that are abnormal, are outside a predetermined range, or meet other
pre-established criteria for additional testing.  (See CFR, Title 42, Volume
2, Chapter IV, Part 493.2 Laboratory Requirements: Definitions)

**Risk Factor for Germline testing** is a variable associated with an
increased risk of a disease, such as age, gender, or family history of
disease.11  
  
 **Somatic,** a term synonymous with acquired, refers to genetic code
alterations that develop after birth (e.g., occurring in neoplastic cells)22  
  
A **Substantiated Suspicion of Cancer** requires direct, physical sampling of
a lesion, such as a needle aspiration or excision of tissue, followed by
microscopic (histologic or cytologic) or flow cytometric evaluation of the
sample. _For the purposes of this LCD, radiologic suspicion of cancer is not
considered "substantiated."_ (Except where otherwise specified in the Covered
Indications as reasonable and necessary.)  
  
 **Treating physician** is the physician who is treating the beneficiary, that
is, the physician who furnishes a consultation or treats a beneficiary for a
specific medical problem and who then _uses the results in the management of
the beneficiary's specific medical problem_. Tests not ordered by the
physician who is treating the beneficiary are not medically reasonable and
necessary. Nonphysician practitioners that are enrolled in the program, who
are operating within the scope of their authority under State law and within
the scope of their Medicare statutory benefit, are also considered for this
purpose, as treating and managing a beneficiary's specific medical problem.
(see CFR, Title 42, Volume 2, Chapter IV, Part 410.32(a))  
  
 **Tumor mutation burden (TMB)** is the total number of mutations (changes)
found in the DNA of cancer cells.23  
  
 **Covered Indications  
  
** Three evidence-based databases and/or knowledge bases have been identified
as valid and reliable sources. Note that a specific genetic test may be listed
in one database or knowledge base, but not others; therefore, providers may
choose to utilize guidelines from any of the three databases/knowledge bases.
However, **for services to be considered medically reasonable and necessary,
#1 below is required regardless of which guidelines are utilized.** Genetic
testing for oncology will be considered medically reasonable and necessary if:  
  

  1. The provider has either established a diagnosis of cancer or found significant evidence to create suspicion for cancer in their patient. (See SSA 1862(a)(1)(A)) Both a clinical evaluation **AND** abnormal results from histologic, cytologic and/or flow cytometric examination are required to establish a diagnosis of cancer or suspicion of cancer. If then, as a next step in the clinical management of the patient, genetic testing would directly impact the management of the patient's specific condition, the testing would be indicated. (see CFR, Title 42, Volume 2, Chapter IV, Part 410.32(a))  
  

**Note:** In rare circumstances where patients have significant evidence to
create suspicion for cancer **AND** are not candidates for a tissue biopsy due
to high risk for complications **AND** genetic testing would directly impact
the management of the patient's specific condition, cell-free genetic testing
could be indicated.  
  
 **Note:** See below regarding NCD 90.2 Next Generation Sequencing (NGS). The
NCD does not allow for suspicion of cancer as a covered indication for DNA-
only NGS testing of non-hematologic malignancies.  
  
 **AND ONE OF THE FOLLOWING (2-4):  
  
**

  2. The evidence for the gene-disease association is evaluated by the evidence-based, transparent, peer-reviewed process of the National Institutes of Health (NIH) sponsored Clinical Genome Resource (ClinGen)24 and is determined to demonstrate actionability in clinical decision making, **meeting the criteria for all 5 categories below.** At least **one** of the items listed under each of the categories (severity, likelihood of disease, nature of the intervention, effectiveness, and validity) must be satisfied:

    * **Disease severity equal to:**

      * Sudden death (Level 3), or
      * Possible death or major morbidity (Level 2), or
      * Modest morbidity (Level 1)

  *     * **Likelihood of disease equal to:**

      * Substantial evidence of a >40% chance (Level 3A), or
      * Moderate evidence of a >40% chance (Level 3B)

  *     * **Nature of the intervention is equal to:**

      * Low risk/medically acceptable/low intensity (Level 3), or
      * Moderately acceptable/risk/intensive (Level 2)

  *     * **Effectiveness equal to:**

      * Substantial evidence of a highly effective intervention (Level 3A), or
      * Moderate evidence of a highly effective intervention (Level 3B), or
      * Substantial evidence of a moderately effective intervention (Level 2A), or
      * Moderate evidence of a moderately effective intervention (Level 2B)

  *     * **Validity of the gene-disease relationship equal to:**

      * Definitive, or
      * Strong, or
      * Moderate

**Table 1:** NIH ClinGen coverage criteria, scores meeting LCD medical
reasonableness and necessity criteria. One from each of all 5 categories must
be met.

**Disease Severity equal to:**

|

**Likelihood of disease equal to:**

|

**Nature of intervention equal to:**

|

**Effectiveness equal to:**

|

**Validity of the gene-disease relationship equal to:**  
  
---|---|---|---|---  
  
**Level 1**

Modest morbidity



**OR**



**Level 2**

Possible death or major morbidity



**OR**

****

**Level 3**

Sudden death

|

**Level 3A**

Substantial evidence of a >40% chance



**OR**



**Level 3B** Moderate evidence of a >40% chance

|

**Level 2**

Moderately acceptable/risk/intensive



**OR**



**Level 3**

Low risk/medically acceptable/low intensity

|

**Level 2A**

Substantial evidence of a moderately effective intervention



**OR**

****

**Level 2B**

Moderate evidence of a moderately effective intervention



**OR**

****

**Level 3A**

Substantial evidence of a highly effective intervention

**OR**

**Level 3B**

Moderate evidence of a highly effective intervention

|

**Definitive**

**OR**

****

**Strong**

****

**OR**

****

**Moderate**  
  
****

****

**OR  
  
**

  3. The evidence for the intervention is evaluated by the NCCN25 and is determined to demonstrate actionability in clinical decision making, meeting the following metric:

    * Based upon high-level evidence, there is uniform NCCN consensus that the intervention is appropriate (Category 1), or
    * Based upon lower-level evidence, there is uniform NCCN consensus that the intervention is appropriate (Category 2A)  
  

**Table 2:** NCCN Compendium and/or Guideline coverage criteria, scores
meeting LCD medical reasonableness and necessity criteria. Either 1 or 2A must
be met.



**Category**  
  
---  
  
1 - Based upon high-level evidence, there is uniform NCCN consensus that the
intervention is appropriate



**OR**



2A - Based upon lower-level evidence, there is uniform NCCN consensus that the
intervention is appropriate  
  


**OR  
  
**

  4. The evidence for the intervention is evaluated by the Memorial Sloan Kettering Cancer Center-sponsored Oncology Knowledge Base (OncoKB)26 and is determined to demonstrate actionability in clinical decision making, meeting one of the following metrics:  
  

    * **For therapeutic use cases** :
      * The intervention is an FDA-recognized biomarker predictive of response to an FDA-approved drug in this indication (Level 1)  
  

**OR**



      * The intervention is a standard care biomarker recommended by the NCCN or other professional guidelines predictive of response to an FDA-approved drug in this indication (Level 2)  
  

**OR  
  
**

      * The intervention is a standard care biomarker predictive of resistance to an FDA-approved drug in this indication (Level R1)  
  

  * **For diagnostic use cases** :  
  

      * The intervention is an FDA and/or professional guideline-recognized biomarker required for diagnosis in this indication (Level Dx1)  
  

**OR  
  
**

      * The intervention is an FDA and/or professional guideline-recognized biomarker that supports diagnosis in this indication (Level Dx2)  
  

  * **For prognostic use cases** :  
  

      * The intervention is an FDA and/or professional guideline-recognized biomarker prognostic in this indication based on a well-powered study (or studies) (Level Px1)  
  

**OR  
  
**

      * The intervention is an FDA and/or professional guideline-recognized biomarker prognostic in this indication based on a single study or multiple small studies (Level Px2)



**Table 3:** OncoKB coverage criteria, scores meeting LCD medical
reasonableness and necessity criteria. Any one from any of the three
categories must be met.



**For therapeutic use cases:**

|

**For diagnostic use cases:**

|

**For prognostic use cases:**  
  
---|---|---  
  
**Level 1**

The intervention is an FDA-recognized biomarker predictive of response to an
FDA-approved drug in this indication  
**  
OR**



**Level 2**

The intervention is a standard care biomarker recommended by the NCCN or other
professional guidelines predictive of response to an FDA-approved drug in this
indication  
  
 **OR**  
  
 **Level R1**

The intervention is a standard care biomarker predictive of resistance to an
FDA-approved drug in this indication  
  

|

**Level Dx1**

The intervention is an FDA and/or professional guideline-recognized biomarker
required for diagnosis in this indication  
  
**OR**  
  
 **Level Dx2**

The intervention is an FDA and/or professional guideline-recognized biomarker
that supports diagnosis in this indication

|

**Level Px1**

The intervention is an FDA and/or professional guideline-recognized biomarker
prognostic in this indication based on a well-powered study (or studies)



**OR**  
  

**Level Px2**  
The intervention is an FDA and/or professional guideline-recognized biomarker
prognostic in this indication based on a single study or multiple small
studies  
  
**_  
NOTE:_** The Contractor will accept LCD reconsideration requests as to whether
a specific genetic test or specific genetic content meets CMS IOM Publication
100-08, Chapter 13, Section 13.5.4 reasonable and necessary criteria.  
  
Additionally, other knowledge bases with well-established evidence-based
recommendations may be submitted for consideration for inclusion into this LCD
via the reconsideration process. Note that these knowledge bases will be
considered under the same criteria utilized for the above three knowledge
bases.  
  
 **Additional Requirements for Next Generation Sequencing (NGS) tests:  
**

Per NCD 90.2, specific coverage indications must be met before coverage for
DNA sequencing is permitted under Medicare. First and foremost, per the NCD,
the FDA status of an NGS test determines whether a test is evaluated under the
NCD or under Medicare Administrative Contractor (MAC) discretion. Secondly,
coverage is permitted only when the patient and their cancer meets very
specific criteria as detailed in the NCD. This patient criteria is clearly
stated for tests under MAC discretion.  
  
Of note, these required indications include limitations for cancer status or
stage, which providers can order testing, and how test results are used.
According to Medicare regulations, NCD requirements take precedence over Local
Coverage Determinations. As a result, for NGS testing, all requirements in NCD
90.2 must be met before coverage can be considered through this LCD. For all
DNA-only NGS testing of non-hematologic malignancies where MACs determine
coverage, the NCD and LCD criteria must both be met for coverage.  
  
In the January 27, 2020 decision memo for NCD 90.2 released by the CMS
Coverage Analysis Group (CAG), all coverage determinations for NGS testing of
hematologic malignancies were deferred to the MACs.11 The CAG cited
difficulties differentiating between somatic versus germline origins and
difficulties clearly staging these malignancies. This memo indicates that the
requirements listed under somatic (acquired) and germline (inherited)
categories only apply to non-hematologic malignancies.  
  
Finally, NCD 90.2 only addresses NGS tests that evaluate DNA. RNA sequencing
and/or in conjunction with protein analysis are not considered under the NCD.  
  
 **Limitations  
  
** The following are considered not medically reasonable and necessary:

    1. A genetic test with unestablished analytical validity, clinical validity, and/or clinical utility.
    2. Interventions with levels of evidence not identified by ClinGen24, NCCN25, or OncoKB26 as demonstrating actionability in clinical decision making OR interventions that are non-covered per MAC review.
    3. Genetic testing in patients who do not have either an established diagnosis of cancer or substantiated suspicion of cancer as determined by a clinical evaluation and abnormal results (cancer or suspicious for cancer) from histologic, cytologic, and/or flow cytometric examination. (Except where otherwise specified in the Covered Indications as reasonable and necessary) (See SSA Section 1862(a)(1)(A))
    4. Genetic testing of asymptomatic patients for the purposes of screening the patient or their relatives. (See SSA Section 1862(a)(1)(A))
    5. Repetitions of the same genetic test on the same genetic material. (see Medicare NCCI Policy Manual, Chapter 10, Section A Introduction)
    6. DecisionDx-Melanoma*
    7. DecisionDx-SCC*
    8. UroVysion fluorescence in situ hybridization (FISH)*
    9. Cxbladder Detect*
    10. Enhanced Cxbladder Detect*
    11. Cxbladder Monitor*
    12. Cxbladder Triage*
    13. Enhanced Cxbladder Triage*
    14. Cxbladder Resolve*
    15. Colvera*
    16. PancreaSeq Genomic Classifier*
    17. PancraGEN*
    18. ThyroSeq CRC*

*Please see below Summary and Analysis of Evidence sections for citations.  
  
Note: Genetic tests for hereditary cancer syndromes, which are considered
germline testing, may only be performed _once_ per beneficiary's lifecycle.

**Provider Qualifications  
**

The following provider qualification requirements must be met for the service
to be considered medically reasonable and necessary. **The ordering provider
of a genetic test for a patient with an established diagnosis of cancer or
substantiated suspicion of cancer must:  
  
**

  * Be the treating clinician who is responsible for the management of the patient's cancer; and,
  * Understand how the test result will impact the patient's condition; and,
  * Have presented this information to the patient eliciting patient understanding.

  
(See Code of Federal Regulations, Title 42, Volume 2, Chapter IV, Part
410.32(a)).  
  
 **Notice:** Services performed for any given diagnosis must meet all of the
indications and limitations stated in this LCD, the general requirements for
medical necessity as stated in CMS payment policy manuals, any and all
existing CMS national coverage determinations, and all Medicare payment rules.

**Introduction**

Please refer to the "History/Background and/or General Information" section
for general information on testing of RNA and DNA as it applies to oncology.  
  
This evidence review focuses on genetic testing used to guide oncologic
treatment and whether the evidence behind this testing is adequate to draw
conclusions about improved health outcomes for the Medicare population. In
general, health outcomes of interest include patient mortality, morbidity,
quality of life, and function.  
  
For use in the Medicare population, tests themselves must demonstrate analytic
validity, clinical validity, and clinical utility. Tests should enhance
clinical decision-making, directly informing clinical management and improving
patient outcomes.  
  
In the context of oncology, genetic testing endeavors to improve patient
outcomes through both prognostic and predictive means. For instance, oncologic
genetic testing can optimize treatment choice (predictive), avoiding
ineffective treatments and reducing adverse events. Ultimately, patient-
centered outcomes must be the underlying justification for oncologic testing.  
  
 **Internal Technology Assessment for Databases and Knowledge Bases  
  
** Google, PubMed and Google Scholar were searched for literature or links
that provided information regarding available peer-reviewed, publicly
accessible, regularly updated, knowledge bases with biological and clinical
information about genomic alterations in cancer. Key words available to search
in various combination included: genetics, cancer, database(s), molecular
testing, clinical, diagnostic, prognostic, genomics, biomarker(s), genomic
alteration(s), peer-reviewed, FDA, genetic test, knowledge base(s), valid
evidence, level of evidence, updated, guideline(s), public, oncology, and
genetic mutations. From this search, a number of knowledge bases were found.
As identified in the Institute of Medicine's seminal work _Clinical Practice
Guidelines: Directions for a New Program_ , there are eight recommended
attributes for clinical practice guidelines.27 These include validity,
reliability/reproducibility, clinical applicability, clinical flexibility,
clarity, development via a multidisciplinary process, scheduled review, and
documentation. These attributes were referenced in the selection of
databases/knowledge bases for genetic testing.  
  
In order to be included, the database/knowledge base was required to be
evidence-based, widely available, and created and/or facilitated by an
organization with a focus on either oncology or genetics. Each
database/knowledge base was also required to include a scoring metric which
could be utilized to determine clinical actionability for specific genetic
tests. Additionally, the database/knowledge bases and their scoring metrics
were required to demonstrate the attributes listed in the _Clinical Practice
Guidelines_. All countries of origin were included as long as the
database/knowledge base met the criteria, with only sources in English
considered. Based on the above criteria, three databases/knowledge bases were
identified that ideally met the needs of this LCD.  
  
 **Databases and Knowledge Bases  
  
** **National Comprehensive Cancer Network (NCCN)**

The NCCN is a nonprofit alliance of U.S. National Cancer Institute
(NCI)-designated comprehensive cancer centers. NCCN strives to improve the
effectiveness and quality of care for patients with cancer and has published
clinical practice guidelines applying to more than 97% of cancers affecting
individuals in the U.S.28 According to the organization, their guidelines are
"intended to assist all individuals who impact decision-making in cancer care
including physicians, nurses, pharmacists, payers, patients and their
families, and many others."28 (p.1)  
  
In addition to a Guidelines Steering Committee (which provides oversight and
planning), and a Guidelines Panel Chair and Vice Chair (who provide oversight
of content development activities), each NCCN guideline has an individual
Guidelines Panel that includes multidisciplinary representation from all of
the core medical specialties relevant to the guideline, a primary care
physician, and a patient advocate. NCCN notes that "any Panel Member with a
meaningful conflict of interest is excluded from participating in Panel
presentations, reviews, discussions, and voting relevant to the area of the
conflict of interest."28 (p.2)  
  
The development and update of NCCN guidelines is an ongoing process which
includes "critical evaluation of evidence, integrated with the clinical
expertise and consensus of a multidisciplinary panel of cancer specialists,
clinical experts and researchers in those situations where high-level evidence
does not exist."28 (p.5) Recommendations for treatment are based on the level
of clinical evidence available as well as consensus among the Guidelines Panel
regarding the efficacy and safety of the intervention. Active NCCN guidelines
are reviewed and updated at least annually.  
  
NCCN evidence and consensus categories are as follows: **Category 1** (high
level of evidence with uniform Panel consensus that the intervention is
appropriate); **Category 2A** (lower level of evidence with uniform Panel
consensus that the intervention is appropriate); **Category 2B** (lower level
of evidence with at least 50% [but less than 85%] panel consensus); and
**Category 3** (any level of evidence, but major Panel disagreement regarding
whether the intervention is appropriate).28 As discussed by Birkeland and
McClure, the majority of recommendations in the NCCN guidelines fall into
Category 2A "because high-level evidence is not available for most decisions
across the continuum of care." 29 (p.608)  
  
Due to rapid development of biomarker and companion diagnostic testing in the
field of oncology, the NCCN Biomarkers Compendium was established "to
facilitate identification of biomarker tests recommended for use by NCCN
guideline panels." 29 (p.609) As discussed by Birkeland and McClure, the
Biomarkers Compendium "focuses on the clinical usefulness of biomarker testing
rather than specific tests or test kits," and therefore includes all tests
measuring genes or gene products, regardless of their functional category
(predictive, prognostic, diagnostic, screening, monitoring, surveillance). 30
(p.611) NCCN assigns a category of evidence and consensus to individual
alterations (as opposed to the entire gene). Furthermore, NCCN states
guideline recommendations (including those relative to the Biomarkers
Compendium) are "intended to apply to the vast majority of patients in a
particular clinical situation"28 (p.6) and are therefore not exhaustive or
expected to apply to all patients or all situations.  
  
NCCN is a widely available resource, which is frequently utilized by
oncologists and other clinicians. Poonacha and Go discuss that clinical
practice guidelines published by NCCN are "the most comprehensive and widely
used standard in clinical practice in the world."30 (p.187) In their study,
the authors investigated the level of scientific evidence behind NCCN
guidelines for the ten most common types of cancer in the U.S. (breast,
prostate, lung [both small-cell and non-small cell subtypes], colorectal,
melanoma, non-Hodgkin's lymphoma, kidney, pancreas, urinary bladder, and
uterus). Of the ten clinical practice guidelines reviewed, Poonacha and Go30
identified that on average, guidelines contained over 100 intervention
recommendations; the NCCN lung cancer guideline included the most
recommendations (238) while the kidney cancer guideline had the least (45).  
  
Of the ten guidelines reviewed, most intervention recommendations (83%) were
from Category 2A, and only 6% were from Category 1.30 Categories of evidence
were found to be highly variable based on diagnosis; the authors identified
that the guidelines for kidney and breast cancers included the highest
proportion of recommendations with Category 1 evidence (20% and 19%,
respectively), eight of the cancer types had between 1% and 6% recommendations
with Category 1 evidence, and neither urinary, bladder nor uterine cancer had
any recommendations with Category 1 evidence in their respective NCCN
guidelines. Poonacha and Go30 also noted that of the ten guidelines reviewed,
Category 3 evidence (major panel disagreement regarding whether the
intervention is appropriate) was rare.  
  
Also of note, in another paper written by Poonacha, Go, and colleagues in
2021, NCCN is called "the most comprehensive standard for clinical care in
malignant hematology, and they are widely used by clinicians and payers."31  
  
 **National Institute of Health funded Clinical Genome Resource (ClinGen)  
  
** The NIH-funded ClinGen was designed as an open-access resource to support
clinical decision making by aggregating, curating, and defining the clinical
relevance and actionability of gene-disease relationships.32 As an open-access
resource, ClinGen is publicly available to all clinicians and patients. Their
database "provides a structure to enable research and clinical communities to
make clear, streamlined, and consistent determinations of clinical
actionability based on transparent criteria to guide analysis and reporting of
genomic variation."33 ClinGen is also included in the FDA's Recognition of
Public Human Genetic Variant Databases.34  

ClinGen's consortium of experts includes a Steering Committee (responsible for
establishing standards and overseeing all ClinGen processes), a Clinical
Domain Working Groups Oversight Committee (responsible for overseeing the
development and approval of variant curation), and a Sequence Variant
Interpretation (SVI) workgroup (comprised of industry experts responsible for
providing guidance relevant to variant assessment activities, including
education tasks).32 ClinGen's Variant Curation Expert Panels (VCEPs) are
comprised of "individuals with scientific expertise regarding gene function,
clinical expertise regarding disease manifestations, and biocurators who are
trained in evaluating evidence sources that support a variant assertion."32
(p.2) Variant Curation Expert Panels follow a standard operating procedure
(SOP) during the process of gene curation and assessment; this SOP is publicly
available via their website. Among other things, the SOP details the
organization's transparency and public accessibility (all variant assertions
and summary evidence are publicly available), as well as conflict of interest
disclosures (all conflicts are publicly declared).  
  
ClinGen has taken great measures to ensure staff involved in variant curation
and evaluation are adequately trained.32 ClinGen expects their VCEPs to
demonstrate the diversity of expertise in the field of genetics (including the
major areas of clinical, diagnostic laboratory, and research). While VCEPs
include disease/gene experts, they also include biocurators, who are not
required to be experts (and are primarily responsible for assembling evidence
for expert review). Regardless of their level of expertise, each VCEP member
is required to demonstrate competence through completion of extensive training
and an evaluation of their proficiency. All individuals are also required to
obtain HIPAA and human subjects training (based on their level of access to
human subjects' data). Finally, the SVI workgroup provides organization-wide
guidance regarding the evaluation and curation of human variant data.  
  
ClinGen requires that variant curation and preliminary evaluation must be
conducted by at least two reviewers.32 The requirements for variant evaluation
are described in the ClinGen Variant Curation Expert Panel Protocol, publicly
available via ClinGen's website. Part of this process includes evaluating
supporting data against rules and criteria developed by the VCEP, and ranking
them as either standalone, very strong, strong, moderate, or supporting. These
ranks are then used to determine a classification assertion (pathogenic [P],
likely pathogenic [LP], benign [B], likely benign [LB], or uncertain
significance [VUS]). Final evaluation and decisions about variant assertions
are made by consensus of the relevant VCEP. Consensus can be indicated by
either unanimous agreement by all members of the VCEP or a majority vote. In
order to be published as an approved assertion, variant classifications must
have at least a majority vote. If a majority vote cannot be obtained, the
variant may be considered an unclassified variant (which are reevaluated every
two years to determine if additional evidence has been made available to
support a classification) or may be classified as a lower-ranking class (for
instance, a variant may be considered VUS if a majority vote cannot be
obtained for a LP or LB classification). In order to receive final approval
and publication, all variant interpretations are reviewed by the full VCEP
membership (which includes non-biocurator, clinical, and disease experts).
Furthermore, all evidence curated by the ClinGen team is readily accessible
via their website.  
  
The framework established by ClinGen attempts to define and evaluate the
clinical validity of gene-disease relationships by evaluating the evidence
supporting or contradicting them.35 This standardized framework was developed
because there is substantial variability in the level of evidence supporting
claims of gene-disease relationships. As noted by Strande et al, "This
framework aims to provide a systematic, transparent method to evaluate a gene-
disease relationship in an efficient and consistent manner suitable for a
diverse set of users." 35 (p.905)  
  
ClinGen's database validates gene-disease relationships by evaluating both
quantity and quality of evidence.35 Gene-disease relationships are then
identified under one of the following levels, with each level building upon
the previous: **Definitive** (requires that the relationship has been
repeatedly demonstrated in research and clinical diagnostic settings, as well
as upheld over time), **Strong** (requires that the relationship has been
featured in two or more independent studies with multiple unrelated probands
with pathogenic variants, as well as several types of supporting experimental
data), **Moderate** (requires that the relationship has been featured in at
least one independent study with several unrelated probands with pathogenic
variants, as well as having some supporting experimental data), **Limited**
(requires that the relationship has been featured in at least one independent
study with more than three unrelated probands with pathogenic variants, or
multiple unrelated probands without pathogenicity), and **No Known Disease
Relationship** (where no pathogenic variants have been identified to date,
therefore no evidence supports a causal role).  
  
There are additionally two levels of evidence reserved for when conflicting
evidence has been reported - **Disputed** (which suggests that disputing
evidence has been discovered but does not necessarily outweigh existing
evidence in support of the gene-disease association) and **Refuted** (which
suggests that disputing evidence has been discovered, and significantly
outweighs existing evidence in support of the gene-disease association). The
refuted status is applied at the discretion of clinical experts, after
analysis of all available evidence. Experimental evidence is scored based on a
separate framework.  
  
The evidence supporting clinical actionability for genetic disorders varies
significantly. Therefore, ClinGen developed and implemented a standardized,
evidence-based method to determine actionability of genomic testing. Hunter et
al explains that the assessment of clinical actionability is part of the
effort to create a central resource of information for the clinical relevance
of genomic variation.30 As discussed by Strande et al, the ultimate goal of
the ClinGen database is to "enhance the incorporation of genomic information
into clinical care."35 (p.905) That said, ClinGen has also created a semi-
quantitative scoring metric to be utilized to assess actionability for
clinical decision making. As discussed by Berg et al, it should be noted that
clinical actionability "is a continuum, not a binary state."36 (p.467-468)
That said, the ClinGen semi-quantitative scoring metric is used to score
interventions, not genes; ClinGen assigns a level of evidence to individual
alterations (rather than the entire gene). The scoring metric assesses four
categories: disease severity, likelihood of disease, effectiveness of the
intervention, and nature of the intervention. The scoring matrix also assesses
level of available evidence for two categories: likelihood of disease and
intervention effectiveness.  
  
Using the ClinGen framework, Strande et al evaluated a number of gene-disease
pairs and examined reproducibility of the scoring metric by having two
independent clinical domain experts evaluate each gene-disease relationship.35
Clinical domain experts agreed with the preliminary classifications for 87.1%
of ClinGen's gene-disease relationship curations with published evidence.
Discrepancies between expert and curator classification were discussed and
explained; additionally, it was noted that when the expert and curator
classifications differed, they did so by only a single category (moderate
versus limited). The authors concluded that ClinGen's evidence-based method
for evaluating gene-disease associations "will provide a strong foundation for
genomic medicine."35 (p.902)  
  
As concluded by Hunter et al, "The ClinGen framework for actionability
assessment will assist research and clinical communities in making clear,
efficient, and consistent determinations of actionability based on transparent
criteria to guide analysis and reporting of findings from clinical genome-
scale sequencing."33 (p.10)  
  
 **Memorial Sloan Kettering Cancer Center Oncology Knowledge Base (OncoKB)  
  
** OncoKB was established as a comprehensive precision oncology tool to
deliver evidence-based information about tumor mutations and alterations and
distill NCCN guidelines, expert recommendations, and scientific literature, in
order to support treatment decisions.26 OncoKB provides a resource which is
available to all clinicians and patients. The database is publicly available
through their website, organized by gene, alteration, tumor type, and clinical
implication, and is searchable by any of the above. OncoKB has received FDA
recognition for a portion of the database and is also included in the FDA's
Recognition of Public Human Genetic Variant Databases.34  
  
OncoKB's staff is made up of highly qualified scientists, physicians, and
engineers, each meeting specific qualifications criteria including educational
background, professional training, and skills.37 Individuals with Lead
Scientist, Clinical Genomics Annotation Committee (CGAC), or Scientific
Content Management Team (SCMT) roles are required to be physicians or
Ph.D-level scientists who are considered experts in their field and disease
specialty. These individuals' responsibilities include "coordinating and
monitoring training and proficiency of curators in procuring the appropriate
data, assessing the data in the context of variant interpretation, and
entering the data with sufficient detail into the OncoKB curation platform."37
(p.5) Curators, who are responsible for assessing and curating gene
alterations, their biological effects, and associated treatment implications,
can be either pre-doctoral graduate students, postdoctoral fellows, or
clinical fellows. Curators receive extensive in-person training in variant
classification, including mapping variants to FDA levels. All OncoKB staff are
also evaluated for potential conflicts of interest, with financial conflicts
being publicly disclosed on the OncoKB website. Any CGAC member with a
conflict of interest relevant to a specific Level of Evidence assignment is
not permitted to work on the assignment.  
  
CGAC reviews and approves all OncoKB/FDA level associations prior to internal
review.37 (p.3-5) Additionally, data curated by OncoKB staff does not become
publicly available until it has undergone an internal, independent review by a
different OncoKB staff member. Specific protocols exist to manage conflicting
data or conflicting assertations regarding alterations, including an
independent review of curated data, as well as evaluation and discussion of
decisions until a consensus has been reached.37 (p.20-21) In instances where a
consensus is reached, the alteration is accepted into the knowledge base with
a notation that there was majority but not uniform consensus; in instances
where consensus cannot be reached, the alteration is not assigned a level of
evidence within the knowledge base.  
  
As discussed by Chakravarty et al, OncoKB contains a classification system for
clinical utility and potentially actionable alterations. 26 "Potentially
actionable alterations in a specific cancer type are assigned to one of four
levels that are based on the strength of evidence that the mutation is a
predictive biomarker of drug sensitivity to FDA-approved or investigational
agents for a specific indication."26 (p.2) OncoKB delineates separate levels
of evidence for therapeutic, diagnostic, and prognostic use cases. OncoKB
assigns a level of evidence to individual alterations (as opposed to the
entire gene).  
  
The OncoKB therapeutic levels of evidence are as follows: **Level 1** gene
alterations have been recognized by the FDA as "predictive of response to an
FDA-approved drug in a particular disease context."26 (p.2) **Level 2** gene
alterations are considered "standard care" predictive biomarkers. They are not
FDA-recognized but are recommended by professional guidelines (including NCCN)
and predict response to FDA-approved therapy in a particular disease context.
**Level 3A and 3B** are considered investigational; **3A** requires compelling
clinical evidence to support the biomarker as predictive of response to a drug
in a particular disease context and only applies to investigational biomarkers
for which there has been clinical activity (such as a clinical or preclinical
trial). **3B** could be either a standard care or investigational biomarker
predictive of response to an FDA-approved or investigational drug in another
indication. **Level 4** is considered hypothetical and requires compelling
biological evidence to support the biomarker as predictive of response to a
drug.  
  
Additionally, there are two therapeutic levels of evidence for treatment
resistance; **Level R1** __ is for standard care biomarkers predictive of
resistance to an FDA-approved drug in a particular disease context, while
**Level R2** requires compelling clinical evidence to support the biomarker as
being predictive of resistance to a drug.  
  
OncoKB also offers scoring of evidence for both diagnostic and prognostic use
cases. For diagnostic indications, level **Dx1** biomarkers have been
recognized by the FDA or professional guidelines as a requirement for
diagnosis in a particular disease context. Level **Dx2** biomarkers have been
recognized by the FDA or professional guidelines as supportive of diagnosis in
a particular disease context. Biomarkers in level **Dx3** may assist disease
diagnosis based upon clinical evidence. Similarly, for prognostic indications,
Level **Px1** biomarkers have been recognized by the FDA or professional
guidelines as prognostic for a particular disease context based on at least
one well-powered study. Level **Px2** biomarkers have been recognized by the
FDA or professional guidelines as prognostic for a particular disease context
based on at least one small study. Biomarkers in level **Px3** are considered
prognostic for a particular disease context based on clinical evidence from
well-powered studies.  
  
As a portion of the OncoKB database has been approved by the FDA, the
therapeutic levels of evidence indicated above can be mapped to one of three
FDA Levels of Evidence within the database.38 **FDA Level 1** requires
companion diagnostics (CDx) tests, which "are supported by analytical validity
of the test for each specific biomarker and a clinical study establishing
either the link between the result of that test and patient outcomes or
clinical concordance to a previously approved CDx."38 Level 1 is the highest
level of recognition by the FDA; however, OncoKB does not include any
companion diagnostic claims, and therefore no genes or variants are currently
considered Level 1. **FDA Level 2** is designated for mutations with evidence
of clinical significance, which allows providers to utilize information about
their patients' health alongside clinical evidence presented in professional
guidelines. "Such claims are supported by a demonstration of analytical
validity (either on the mutation itself or via a representative approach, when
appropriate) and clinical validity (typically based on publicly available
clinical evidence, such as professional guidelines and/or peer-reviewed
publications)."38 **FDA Level 3** is reserved for mutations with potential
clinical significance, but not identified as a higher level. "Such claims are
supported by analytical validation, principally through a representative
approach, when appropriate, and clinical or mechanistic rationale for
inclusion in the panel" (to include peer-reviewed publications or in vitro
pre-clinical models).38 OncoKB has a validation protocol in place to assess
the consistency of variant classification to FDA levels of evidence; mapping
OncoKB levels of evidence to FDA levels ranges from 85.7% to 100%.38 (p.20)

****

**Summary of Evidence for Specific Lab Tests**

**Cxbladder (Detect, Triage, Monitor, Resolve)  
  
** PubMed and Google Scholar were searched for peer-reviewed, evidence-based
literature which provided information regarding analytic and clinical validity
and clinical utility for the Cxbladder test. Key words used to search in
combination included: Cxbladder, Cxbladder detect, Cxbladder triage, Cxbladder
monitor, molecular testing, urine test, bladder cancer, urine biomarker(s),
mRNA, uRNA, gene expression profile test, GEP test, 5 gene expression assay,
prognostic, and TERT and FGFR3 mutations. Outside of publications from Pacific
Edge Diagnostics or studies with funding from that company, only a few peer-
reviewed papers have been published addressing the performance of Cxbladder
tests.  
  
The Cxbladder line of tests are currently represented by six variations on a
five gene expression assay: Cxbladder Detect, Cxbladder Triage, Cxbladder
Monitor, Cxbladder Resolve, enhanced Cxbladder Triage, and enhanced Cxbladder
Detect. The sequential development of each test variant may be traced through
a series of publications, beginning in 2008 with a paper describing the
development of Cxbladder's precursor, the uRNA test, a four gene expression
profile (GEP) test.39 Each of the Cxbladder tests rely on a different set of
statistical parameters to optimize the function of the five gene expression
assay, sometimes synthesizing gene expression data with other inputted data
such as patient demographics, cancer history, other clinical history, and
single nucleotide variants associated with the FGFR3 and TERT genes. Cxbladder
Detect is optimized for sensitivity and specificity as initially described in
the seminal 2012 paper.40 Both Cxbladder Triage and Cxbladder Monitor are
optimized for sensitivity, Negative Predictive Value (NPV), and test negative
rate as initially described in 2015 and 2017 papers, respectively.41,42
Cxbladder Resolve, with a published validation in 2021, is optimized for
sensitivity and specificity.43 Most recently, in 2022, the enhanced versions
of Cxbladder Triage and Detect were described, with the basic purpose of each
parent test unchanged but the parameters of each new test modified by adding
data from sequencing six "single nucleotide polymorphisms" associated with two
genes: FGFR3 and TERT.44  
  
In 2008, Holyoake and colleagues described a precursor to the Cxbladder tests,
uRNA.39 The four gene (CDC2, MDK, IGFBP5, and HOXA13) expression profile test
was designed to detect and characterize transitional cell carcinoma (TCC) from
patients' urine. Development of the test involved selection of RNA expression
markers that best detected and characterized both early and late stage TCC
tumors. The best candidate markers were identified through comparison of
tissue from 58 tumors of different stages (Ta-T4) and normal urothelial
tissue. Validation of the test utilized urine samples from a cohort of 142
patients that was comprised of 75 patients who were diagnosed with Ta-T4
tumors and 77 "control" patients. The overall specificity of this test was 85%
with a range of sensitivities depending on tumor stage (from 48% for Ta tumors
to 100% for tumors with a stage greater than T1).  
  
In 2012, O'Sullivan and colleagues developed and validated the first Cxbladder
test (Cxbladder Detect), using a foundation of the uRNA four GEP test and
adding an additional gene (CXCR2) to this profile.40 The 2012 publication also
compared the new Cxbladder test to its precursor test (uRNA-D), urine
cytology, and other urine tests on the market (NMP22 ELISA and BladderChek).
The patient cohort was comprised of 485 patients presenting with gross
hematuria. Cxbladder demonstrated an 81.8% sensitivity at a fixed specificity
of 85%; all other tests in the comparison fell below a sensitivity of 65%,
although the specificity of all other tests was higher than Cxbladder, with
the highest specificity at 96.4% for the BladderChek test.  
  
In 2015, Breen and colleagues further evaluated the Cxbladder Detect test in a
comparative study with other tests used to detect urothelial carcinoma in
urine.45 The other tests evaluated included cytology, UroVysion FISH, and
NMP22. The study utilized five cohorts of patients, only one of which
evaluated all four tests for the entire cohort. Data from the five cohorts
were evaluated and integrated, with several different imputation analyses
utilized to fill in for missing test values and create a "new, imputed,
comprehensive dataset." From this data, the authors found that before
imputation, Cxbladder Detect had superior sensitivity (79.5%) as compared to
the other three tests (the second highest sensitivity being 45.5%) but
inferior specificity (82.2%), with the second lowest specificity being 87.3%.
Utilizing several different imputation methodologies, similar findings for
comparative sensitivities and specificities were seen, leading to the
conclusion that the imputed data sets were valid, with the best imputation
methodology being the 3NN model. Finally, with the new imputed data set, the
authors re-assessed the comparisons between tests and found that Cxbladder
Detect "outperformed" the other tests in this study.  
  
In 2015, Kavalieris and colleagues developed another version of the Cxbladder
test (later to be called Cxbladder Triage), this time evaluating the impact of
adding clinical data (age, gender, frequency of macrohematuria, and smoking
history) to the testing algorithm.41 Genetic input into the algorithm was
termed the G INDEX while clinical data was termed the P INDEX. The study
utilized 517 patients with macrohematuria from the 2012 Cxbladder study
population, an additional 178 patients with macrohematuria from two separate
cohorts, and 45 patients from a small cohort of patients with
microhematuria.38 Combining the G and P indices provided a better bias-
corrected receiver operating characteristic curve (AUC) (0.86) than either of
the indices alone (0.83 and 0.61 respectively). When set at a test-negative
rate of 0.4, the G + P INDEX performed with a sensitivity of 95% and NPV of
98%, improving on the G INDEX sensitivity and NPV of 86% and 96% respectively.
The authors envisioned the G + P INDEX being used to triage outpatients with a
low probability of having urothelial carcinoma, reducing the need for
diagnostic procedures.

In 2017, Kavalieris and colleagues developed another version of the Cxbladder
test (Cxbladder Monitor) utilizing a cohort of 763 patients under surveillance
for recurrence of bladder urothelial carcinoma.42 In addition to the data from
the five gene expression profile, Cxbladder Monitor also used clinical data in
its algorithm which included previous tumor status (primary tumor or recurrent
tumor) and the number of years elapsed since the previous tumor. The paper
analyzed several subgroups including different stages of tumor and patients
who had received adjuvant bacillus Calmette-Guerin (BCG) treatment. With a
test negativity rate of 0.34, Cxbladder Monitor demonstrated a sensitivity of
93% and NPV of 97%.  
  
Also from 2017, Lotan and colleagues utilized the same patient cohort found in
the Kavalieris (2017) study to perform a comparative analysis between
Cxbladder Monitor and other noninvasive urine tests that were used to rule out
recurrent urothelial carcinoma.42,46 The authors found that Cxbladder
"outperformed" all comparative tests (which included cytology, NMP22 ELISA,
NMP22 BladderChek, and UroVysion FISH), with higher sensitivity (91% versus
sensitivities ranging from 11% to 33%) and higher NPV (96% versus NPVs ranging
from 86% to 92%).  
  
In 2017, Darling and colleagues performed a clinical utility study for
Cxbladder Triage and Detect.47 The study used previously obtained clinical
data and Cxbladder test results to create clinical scenarios for twelve
urologists. These scenarios centered on patients presenting with hematuria and
evaluated how the urologists would hypothetically work-up these patients in
the context of Cxbladder results. The study found that when the urologists had
access to Cxbladder results, they would hypothetically change their clinical
decisions in caring for these patients, ultimately leading to fewer invasive
diagnostic procedures.  
  
In 2018, Lough and colleagues performed a clinical utility study for Cxbladder
Monitor.48 The study used previously obtained clinical data and Cxbladder test
results to create clinical scenarios for eighteen physicians. These scenarios
centered on patients with a history of urothelial carcinoma and evaluated how
the physicians would hypothetically manage these patients in the context of
Cxbladder results. The study found that when the physicians had access to
Cxbladder results, they would hypothetically change their clinical decisions
in caring for these patients, leading ultimately to fewer tests and procedures
for patients classified as low risk by Cxbladder and an increased number of
tests and procedures for patients classified as higher risk.  
  
In 2019, Konety and colleagues performed a retrospective analysis of pooled
data (from four patient cohorts) in the context of Cxbladder Triage, Detect,
and Monitor.49 A total of 436 samples were evaluated from patients with
hematuria and 416 samples from patients with potential recurrence of
urothelial carcinoma. These Cxbladder results were then compared with cytology
results for the same samples. The authors found that overall, Cxbladder
demonstrated a better NPV than cytology (97.4% versus 92.6%) and missed less
tumors (false negatives) than cytology (eight missed versus 59 missed).  
  
In 2020, Koya and colleagues performed a retrospective audit of a new
surveillance protocol that incorporated Cxbladder Monitor for patients with a
history of urothelial carcinoma.50 The patients involved were divided into two
cohorts: low risk (n = 161) and high risk (n = 47), noting that these numbers
represent only patients who completed the study with both Cxbladder testing
and follow up cystoscopy. There were 309 patients who were initially enrolled
in the study but only 208 that completed the study. In the low-risk cohort,
patients who received a negative result for the Cxbladder test were permitted
to wait longer (12 months as opposed to within two to three months of a
positive result) before receiving a follow-up cystoscopy. In the high-risk
cohort, patients were managed the same regardless of the Cxbladder result,
although the data was used to speculate on potential changes to the protocols
for this type of patient. Over the course of the study and in the 35 months of
the follow-up period, no cases positive for urothelial carcinoma were missed
by the first Cxbladder test (although there was at least one false negative
result in a second, follow-up round of Cxbladder testing) and no patients
developed newly invasive or metastatic urothelial carcinoma. For the low-risk
cohort, confirmed recurrence occurred in three of the patients who initially
tested negative in the Cxbladder test; only two of those three patients had a
follow-up, second Cxbladder test, with one true positive and one false
negative (as demonstrated in the supplementary figures). There was confirmed
recurrence in three low risk patients who tested positive with Cxbladder. For
high risk patients, four patients demonstrated a recurrence of urothelial
carcinoma and all four of these patients tested positive with Cxbladder.  
  
In 2023, Li and colleagues evaluated Cxbladder Monitor through a prospective
study of 92 patients diagnosed with non-muscle invasive bladder cancer (NMIBC)
from two different clinical sites that were ready for follow up regarding
their diagnosis (primary or recurrent), a previous procedural visit, and/or
other therapy (e.g., BCG instillation).51 The study sought to triage
scheduling these patients' for follow-up cystoscopy through use of at home
Cxbladder Monitor testing, delaying the follow-up appointment if patients
received a lower risk score (<3.5) on the Monitor test. Patients with either
gross hematuria or active UTI were excluded from the study. Moreover, of the
92 patients, a total of 16 were lost to follow-up, although data was still
included in summary tables for these 16 patients. Of the 24 patients followed-
up earlier due to a higher risk score ( _>_ 3.5), nine were found to have
tumors on cystoscopy. Of the 52 patients with delayed cystoscopy due to a
lower risk score, none were found to have tumor. Note that of the 66 patients
with a lower risk score, 14 were not evaluated via the delayed cystoscopy for
the following reasons: did not show up for cystoscopy, chose another round of
Cxbladder Monitor testing instead of cystoscopy, stopped surveillance for
reasons not given, or died of "unrelated" but undescribed causes. The paper
also noted that the patients who opted out of the follow-up cystoscopy in
favor of a second Cxbladder Monitor test were only found at one of the two
sites. The authors concluded that using at-home Cxbladder Monitor testing to
triage patients and allow delayed cystoscopy for patients with a lower risk
score was an effective new protocol.  
  
In 2021, Raman and colleagues developed a fourth version of the Cxbladder
test, Cxbladder Resolve, utilizing three different patient cohorts (total of
863 patients) in the internal validation and one separate cohort (548
patients) in the external validation.43 In the external validation, testing
was also performed with other versions of the Cxbladder test: Cxbladder Triage
and Detect.

Cxbladder Resolve was designed to identify patients with a high probability of
high-impact tumors (HIT), namely high grade urothelial carcinomas, by
stratifying patients into one of three categories: high priority for HIT
evaluation, work-up for HIT based on physician-directed protocol (PDP), or
manage by observation. In the internal validation, Cxbladder Resolve was found
to have a bootstrap-adjusted estimated sensitivity of 92.4% and specificity of
93.8% for HIT; note that the overall sensitivity and specificity for all
tumors during the internal validation was 91.2% and 61.0% respectively. In the
external validation, Cxbladder Resolve correctly identified all HIT diagnoses
and missed three low grade tumors, with a cumulative sensitivity of 90.0% and
specificity of 96.3%. The authors also found that using a reflexive test
algorithm with Cxbladder Triage, Detect, and Resolve together would correctly
identify 87.6% of patients who did not need further work-up (NPV of 99.4%).  
  
In 2022, Lotan and colleagues published a study describing two newer versions
of the Cxbladder tests (enhanced Cxbladder Triage [CxbT+] and Detect
[CxbD+]).46 The enhancement (digital droplet PCR testing of urine specimens)
was used to identify the presence or absence of six single nucleotide
polymorphisms (SNPs) associated with the genes FGFR3 and TERT. These SNPs,
mostly somatic (acquired) genetic variants, can be found in urothelial
carcinomas, as described in the literary references provided in Lotan and
colleagues' paper. Lotan and colleagues performed an internal validation of
these new biomarkers, testing urine from two cohorts: 344 patients from the
United States and 460 patients from Singapore. The six SNPs were evaluated
both as stand-alone tests and as enhancement to original Cxbladder Triage and
Detect tests. The authors concluded that the addition of the six SNPs to the
Cxbladder tests improved test performance, particularly specificity.  
  
Two publications from Davidson and colleagues in 2019 and 2020 evaluated the
performance of the Cxbladder Triage test when integrated into hematuria work-
up protocols.52,53 Notably, these studies were not performed or funded by
Pacific Edge Diagnostics. The 2019 paper prospectively evaluated the new
protocol without enacting it within the clinical setting while the 2020 paper
described the outcome of a fully enacted protocol. In the 2019 study, which
included 478 patients with hematuria referred to the urology practice and 73
patients with hematuria who had not been referred, the Cxbladder test
correctly triaged 42 of 44 patients with urothelial malignancy; the two false
negatives were either confirmed or suspected (no histology obtained) low grade
lesions. From their cohort, the authors found that Cxbladder Triage had a
sensitivity of 95% and NPV of 98%. The authors concluded "the risk of missing
a significant cancer from the adoption of the theoretical pathway appears very
low and clinically acceptable," while later stating that larger studies were
still needed to "prove the true clinical value of inclusion of these
biomarkers in investigative pathways." In the 2020 study, Davidson and
colleagues retrospectively evaluated the clinical courses of 884 patients with
hematuria who were worked-up with the new protocol and subsequently followed
for a median of 21 months. The protocol identified 46 histologically confirmed
urothelial carcinomas. Cxbladder Triage results included five false negatives,
four of which were detected upon imaging and one of which was discovered in a
three month follow up. Cxbladder Triage results also demonstrated low
specificity with 39% of results being false positives. Overall, the authors
found that the protocol that included Cxbladder Triage had a sensitivity of
98.1% and NPV of 99.9%. The authors concluded that their findings "add to the
increasing evidence that biomarkers have a place in the assessment of
hematuria, but that the results of these assays need to be supported by
imaging of the bladder."  
  
In terms of systemic reviews and meta-analyses, two publications in 2015 were
identified, both from Chou and colleagues, in contract with the Agency for
Healthcare Research and Quality, AHRQ, U.S. Department of Health and Human
Services.54,55 The publications discussed several urinary biomarker tests
including Cxbladder Detect. However, both publications only discussed a single
Cxbladder study performed by O'Sullivan and colleagues in 2012.40 In the
shorter publication by Chou and colleagues, a systematic review and meta-
analysis in the Annals of Internal Medicine, the sensitivity and specificity
of Cxbladder was given a low grade for strength of evidence (as determined by
study quality, precision, consistency and directness). The process of evidence
assessment was covered in greater detail in the 923 page document from Chou
and colleagues entitled "Emerging Approaches to Diagnosis and Treatment of
Non-Muscle-Invasive Bladder Cancer;" however, the key points concerning
Cxbladder were covered in the shorter publication and mostly just reiterated
in the longer document.

Another systemic review and meta-analysis was published more recently in 2022
by Laukhtina and colleagues.56 The study reviewed five different urinary
biomarker tests (UBT) used to detect recurrent urothelial carcinoma, including
Cxbladder Monitor. The authors assessed statistical values associated with
each test, such as sensitivity, specificity, positive predictive value (PPV),
NPV, and accuracy. Additionally, the authors assessed the risk of bias using
the Quality Assessment of Diagnostic Accuracy Studies tool (QUADAS-2), using
cystoscopy and histology results for reference. The study also performed
network meta-analysis on the tests as compared with cytology. Two Cxbladder
Monitor studies were analyzed: Koya (2020) and Lotan (2017).46,50 At the end
of the paper, the authors concluded the performances of the five tests
"support[ed] their potential value in preventing unnecessary cystoscopies."
The authors also assessed other diagnostic tests from four of the five test
companies, including Cxbladder Triage and Detect (O'Sullivan [2012] and
Davidson [2019]) and concluded "there are not enough data to support their use
in the initial diagnosis setting."40,52  
  
 **ThyroSeq CRC, CBLPath, Inc, University of Pittsburgh Medical Center  
**

PubMed and Google Scholar were searched for peer-reviewed, evidence-based
literature bases which provided information regarding analytic and clinical
validity and clinical utility for the ThyroSeq Cancer Risk Classifier test.
Key words used to search in combination included: ThyroSeq, ThyroSeq Cancer
Risk Classifier, ThyroSeq CRC, molecular testing, thyroid nodule(s), thyroid
cancer, Bethesda VI, Bethesda VI nodules, FNA(s), fine needle aspiration
biopsy, formalin fixed paraffin-embedded (FFPE), prognostic, and TERT, TP53,
AKT1, and PIK3CA mutations.  
  
Only three peer-reviewed publications were identified evaluating the clinical
validity of the ThyroSeq CRC test. The first paper written by Yip and
colleagues in 2021 represented the first assessment of the ThyroSeq CRC test
and evaluated 287 patients with Bethesda VI (malignant) cytology.57 The second
paper written by Skaugen and colleagues in 2022 assessed ThyroSeq CRC in 100
patients with Bethesda V (suspicious for malignancy) cytology.58 The third
paper from Liu and colleages retrospectively assessed the three tier Molecular
Risk Group classification system in 578 patients who had received a
thyroidectomy for primary thyroid cancer and who had been subsequently tested
using the ThyroSeq v3 molecular test.59 The primary outcome measure was
recurrence of the thyroid cancer following completion of the initial oncologic
treatment. A recurrence event was defined as either structural recurrence or
(if no structural recurrence was detected) biochemical recurrence. No papers
significantly assessing clinical utility were identified.  
  
 **PancraGEN- Interpace Diagnostics  
  
** PubMed and Google Scholar were searched for peer-reviewed, evidence-based
literature that provided information regarding the analytic and clinical
validity and clinical utility for the PancraGEN test. Key words used to search
in combination included: PancraGEN, PathfinderTG, molecular testing,
topographic genotyping, pancreatic cyst(s), pancreatic cyst fluid, solid
pancreatic lesions, and KRAS and/or GNAS mutations.  
  
Thirty-five total publications addressing the analytical validity, clinical
validity, or clinical utility of the PancraGEN prognostic test (from Interpace
Diagnostics) were identified. The papers identified focused on both
individuals with pancreatic cysts and with solid pancreaticobiliary lesions.

Of the 35 publications identified, 25 were excluded for the following reasons.
Seventeen of these studies described an earlier version of the test that no
longer resembles the currently offered PancraGEN test, and thus, these studies
were excluded.60-76 Moreover, two of these 17 studies did not include a
reference standard such as survival or time to tumor recurrence, further
justifying exclusion.75,76 One study did not pair the PancraGEN test results
with corresponding patient data and was thus excluded.77 This study and
another study did not adequately describe patient selection criteria, and
thus, both were excluded.77,78 Three papers which did not address patient
characteristics were excluded. 65,75,78 Six studies that compared the
guidelines for molecular testing in pancreatic cysts but made no specific
mention of the PancraGEN test were excluded.79-84  
  
The remaining 10 papers were included in this LCD's analysis of the evidence.
Three of these papers were retrospective studies and addressed PancraGEN's
clinical validity and clinical utility for pancreatic cysts.85-87
Additionally, two retrospective studies that only addressed the clinical
utility of PancraGEN for pancreatic cysts were identified.88,89 Three studies
that addressed PancraGEN's clinical validity and clinical utility for solid
pancreaticobiliary lesions were also found.90-92 Finally, two technical
reviews that examined clinical utility and clinical validity of PancraGEN were
identified.93,94  
  
 **DecisionDx - Castle Biosciences  
  
** **DecisionDx-Melanoma  
  
** PubMed and Google Scholar were searched for peer-reviewed, evidence-based
literature which provided information regarding analytic and clinical validity
and clinical utility for the DecisionDx-Melanoma test. Key words used to
search in combination included: DecisionDx, DecisionDx-Melanoma, Castle
Biosciences, molecular testing, melanoma, skin cancer, sentinel lymph node
biopsy, SLNB, GEP test, gene expression profile, stage I melanoma, stage II
melanoma, stage III melanoma, 31-gene profile test, 28-gene profile test,
cutaneous melanoma, and formalin-fixed paraffin embedded (FFPE) tissue.  
  
The DecisionDx-Melanoma prognostic test (from Castle Biosciences) is described
in numerous peer-reviewed publications. Forty total publications addressing
analytic validity, clinical validity, or clinical utility were identified.
Five papers were immediately excluded from review due to their publication in
a non-peer reviewed journal (not found on PubMed).95-99 For the remaining 35
papers, three were editorial in nature or in response to editorial comments
and four were evidence review papers without meta-analysis.100-106 There were
two primarily meta-analysis papers identified, and additionally, one of these
papers was frequently used as a source of raw data for other
publications.107,108 The remaining 26 papers were identified as either
validation papers or cohort studies (either prospective or retrospective, or
both). Of the four validation papers, one paper described the development of
the original GEP test and the other three papers described clinicopathologic
syntheses with the GEP data that resulted in new information beyond the
results of the original GEP test.109-112 It should be noted that these latter
three papers were relatively new and do not currently have any follow-up
publications that further evaluate the clinical validity and/or utility of the
clinicopathologic and GEP combined results.110-112 One paper primarily
assesses the analytic validity of DecisionDx-Melanoma, while seven papers
address aspects of clinical utility.113 The remaining 14 papers mostly assess
clinical validity through retrospective and/or prospective cohorts of
patients, typically evaluating five-year outcomes in recurrence free survival
(RFS), disease free survival (DFS), distant metastasis free survival (DMFS),
melanoma specific survival (MSS), and/or overall survival (OS).114-127  
  
 **DecisionDx-SCC  
**

PubMed and Google Scholar were searched for peer-reviewed, evidence-based
literature which provided information regarding analytic and clinical validity
and clinical utility for the DecisionDx-SCC test. Key words used to search in
combination included: DecisionDx, DecisionDx-SCC, Castle Biosciences,
molecular testing, melanoma, skin cancer, prognostic test, SCC, cutaneous
squamous cell carcinoma, cSCC, GEP test, gene expression profile, metastasis,
40-gene profile test, and formalin-fixed paraffin embedded (FFPE) tissue.

Twelve total publications addressing the analytical validity, clinical
validity, and/or clinical utility of the DecisionDx-SCC prognostic test (from
Castle Biosciences) were identified. All 12 identified studies were funded by,
or written by employees of, Castle Biosciences. The papers identified included
one panel review and three surveys of medical professionals.128-131
Additionally, two papers were evidence reviews without meta-analyses.132,133
The remaining six papers included two that addressed analytical validity,
three cohort studies (both prospective and retrospective) that addressed
clinical validity, and one case series that addressed clinical utility.134-139

  
**UroVysion fluorescence in situ hybridization (FISH)** **- Abbott  
**

PubMed and Google Scholar were searched for peer-reviewed, evidence-based
literature which provided information regarding analytic and clinical validity
and clinical utility for the UroVysion FISH test. Key words used to search in
combination included: UroVysion, UroVysion FISH, UroVision, UroVision FISH,
UroVysion outcomes, UroVysion utility, and fluorescence in situ hybridization.

The UroVysion FISH test for bladder cancer (Abbott) is described in numerous
peer-reviewed publications, and 23 papers addressing analytical validity,
clinical validity, or clinical utility were identified. Meta-analyses,
systematic reviews, and literature reviews accounted for 10 of these papers,
seven of which assessed multiple biomarkers for urothelial cancer, not just
the UroVysion FISH test.140-149 The remaining 13 papers were identified as
case-control, cross-sectional, or cohort studies (either prospective or
retrospective).45,150-161 Only two papers were identified that addressed
clinical utility, while the rest addressed analytical and clinical validity -
typically evaluating outcomes such as recurrence free survival, disease free
survival, and overall survival.  
  
 **Colvera - Clinical Genomics  
**

PubMed and Google Scholar were searched for peer-reviewed, evidence-based
literature which provided information regarding analytic and clinical validity
and clinical utility for the COLVERA test. Key words used to search in
combination included: COLVERA, Colvera, blood test, molecular testing,
colorectal cancer (CRC), CEA test, ctDNA, colorectal adenocarcinoma, real-time
PCR test, DNA methylation, CRC surveillance and _BCAT1_ and _IKZF1_.

Ten total publications addressing the analytic validity, clinical validity, or
clinical utility of the Colvera test for colorectal cancer (from Clinical
Genomics) were identified. All 10 identified studies were funded by, and
written by employees of, Clinical Genomics. The papers identified included
three validation papers and seven cohort studies (both prospective and
retrospective).162-171 One paper addressed analytic and clinical validity,
while the rest addressed clinical validity. No papers were found that
addressed the clinical utility of the Colvera test.  
  
Note that several papers that are not listed in this LCD were identified
addressing testing of _BCAT1_ and _IKZF1_ in colorectal cancer patients;
however, since these papers did not address the Colvera test itself, they were
excluded from this Summary and subsequent Analysis of Evidence for Colvera.  
  
 **PancreaSeq  Genomic Classifier, Molecular and Genomic Pathology
Laboratory, University of Pittsburgh Medical Center  
  
**PubMed and Google Scholar were searched for peer-reviewed, evidence-based
literature bases which provided information regarding analytic and clinical
validity and clinical utility for the PancreaSeq genetic test. Key words used
to search in combination included: PancreaSeq, 22-gene panel, molecular
testing, PancreaSeq Genomic Classifier, pancreatic cyst(s), pancreatic
neuroendocrine tumors (PanNETs), pancreatic adenocarcinoma, DNA targeted next-
generation sequencing, DNA-targeted NGS, and KRAS and/or GNAS mutations. The
PancreaSeq DNA and mRNA-based targeted NGS test (from University of Pittsburgh
Medical Center) as currently offered and described in the methods section of
clinical reports does not currently have any corresponding peer-reviewed
literature.

**Databases and Knowledge Bases**

Potential knowledge bases were evaluated against the Good Practice Guidelines
found in the Clinical Practice Guidelines: Directions for a New Program
written by the Institute of Medicine in 1990. Per the publication, the
Institute recommended eight Attributes of Good Practice Guidelines: Validity,
Reliability/Reproducibility, Clinical Applicability, Clinical Flexibility,
Clarity, Multidisciplinary Process, Scheduled Review, and Documentation. While
the definitions and descriptions for each attribute will be only briefly
detailed in this LCD, the rationale supporting how a potential knowledge base
was considered in the context of these attributes will be detailed below.27  
  
Per the Good Practice Guidelines, the **_Validity_** attribute addresses both
the guidelines as a whole in addition to the content within the guidelines.
For the former, validity is demonstrated when the guidelines lead to
improvement in health and cost outcomes, a metric that would best be assessed
through an external study of the guidelines in clinical practice. For the
latter, validity is demonstrated when the guideline content assesses how a
clinical action/recommendation affects health and cost outcomes. Validity of
the guideline content is ideally evaluated using 11 elements: Projected health
outcomes, Projected costs, Relationship between the evidence and the
guidelines, Preference for empirical evidence over expert judgment, Thorough
literature review, Methods used to evaluate the scientific literature,
Strength of the evidence, Use of expert judgment, Strength of expert
consensus, Independent review, and Pretesting.  
  
The NCCN generally meets the definition of validity. In 2022, the NCCN was the
subject of a retrospective study performed by CVS Health that was presented at
that year's American Society of Clinical Oncology conference. The study found
that adherence to NCCN guidelines lowered the total cost of care in the
treatment of colorectal and breast cancers in Medicare patients.172,173
Similarly, adherence to NCCN guidelines has been shown to improve health
outcomes in cancer patients.174-177 As for the guideline content, the NCCN
thoroughly details its recommendations using language that acknowledges the
potential complexities and variations found in patient cases (terminology such
as "consider" or "preferred" or "should be guided by") and supports these
recommendations with ample peer reviewed literature. Often the discussion of
the evidence highlights strengths and weaknesses in studies and tailors the
strength of a recommendation based on such evaluation. Additionally, the
guidelines are created, reviewed, and updated by subject matter experts
through consensus panels.  
  
Unlike NCCN, OncoKB acts as more of a catalogue of genetic variants with
proven clinical validity and utility. OncoKB does not make formal
recommendations regarding specific clinical actions, but rather provides
expert and evidentiary analysis of known variants and explains how confident
one can be in the actionability of a variant. For instance, a genetic variant
with a low score on OncoKB may not have enough research and clinical evidence
to prove a certain drug is more or less effective when that variant is
present. This does not mean that testing for the variant will not (in OncoKB's
view) become useful in the future, but OncoKB suggests, based on expert
consensus and evidence, that the variant should not yet be used in clinical
decision making. Because of these characteristics, OncoKB does not meet the
above definition of the attribute of validity, although it does provide
valuable information from expert and evidentiary analysis.178  
  
ClinGen demonstrates both features of NCCN and OncoKB as a knowledge base in
the context of the attribute of validity. In terms of similarities to OncoKB,
ClinGen assesses the relationship between a gene and disease (including
hereditary cancer) and makes determinations whether or not there is sufficient
evidence supporting this causative relationship. In this instance, again, the
knowledge base acts as guidance for whether or not a gene should be evaluated
as part of a particular cancer workup. However, more like NCCN, ClinGen also
provides very specific recommendations on "Clinical Actionability." The
recommendations provided by this section of ClinGen, however, are relatively
simplistic, including clinical actions such as "Surveillance" and preventative
surgery. Given that ClinGen is designed to evaluate hereditary diseases, it is
unsurprising that most clinical actions evaluated by ClinGen revolve around
screening and preventative interventions as opposed to therapeutic actions.
Moreover, the prospective nature of clinical actions evaluated on ClinGen are
difficult to assess health and cost outcomes as an external study.  
  
The **_Reliability/Reproducibility_** attribute in the Good Practice
Guidelines refers to consistency in guideline development and procedures. In
practice, by following a knowledge base's standard operating procedures and
criteria in evaluating a topic, different groups of reviewers should come up
with the same guideline for that topic. Reliability/reproducibility also
refers to consistency in _how_ a guideline is interpreted and utilized by its
audience. This means that patients under different providers who use the same
guideline should see equivalent care and management of their needs. Providers
who use the same guideline should also come to the same conclusions and
management decisions when evaluating similar patient complaints.

Evaluating knowledge bases for their reliability and reproducibility is
difficult, as Good Practice Guidelines has noted. Guidelines, by definition,
are meant to guide (not necessarily mandate) clinical decisions. As described
in the Validity attribute, the practice of medicine requires flexibility in
clinical management to address different patient presentations. This means
that guidelines should not only be clear in their recommendations but also
provide alternatives, when available.

As stated earlier, the NCCN guidelines thoroughly details its recommendations
using language that acknowledges the potential complexities and variations
found in patient cases (terminology such as "consider" or "preferred" or
"should be guided by") and supports these recommendations with ample peer
reviewed literature. Given the complexity and variation inherent to managing
even similar appearing clinical presentations, external comparison of the
reliability/reproducibility of NCCN guidelines between these presentations
would not be productive. However, one could interpret improvements in health
outcomes for those using NCCN guidelines (as opposed to not using them) as an
indication that the guidelines reliably improve patient outcomes.174-177  
  
OncoKB provides a very detailed standard operating procedure detailing how it
reviews, scores, and updates the knowledge base. The standard operating
procedure, while providing step by step instructions in granular detail, still
requires OncoKB's software and personnel infrastructure to review evidence and
score genetic content. In this sense, while consistency within the
organization is likely robust, it cannot be translated to external reviewers
outside the OncoKB team, preventing evaluation of reproducibility as described
in the Good Practice Guidelines. Additionally, several steps rely on the
judgement of experienced individuals as can be seen in the procedure. The
thought processes of these individuals are not provided publicly. As for how
OncoKB is used by clinicians, currently no studies were identified addressing
this topic. This lack of studies, however, is to be expected given that OncoKB
provides scored data without direct clinical management recommendations.
Overall, OncoKB meets the Reliability/Reproducibility attribute, albeit, in
the context of the OncoKB organization without ability to translate to
external reviewers.  
  
ClinGen, like OncoKB, supplies clinicians with scored data but additionally
includes minimal clinical management recommendations (e.g., when the clinical
action of cancer surveillance is given a high score and is thus implicitly
recommended for patients with relevant genetic findings). ClinGen, however, is
less centralized than OncoKB in standard operating procedures. Generalizable
standard operating procedures applying to all workgroups are provided,
however, workgroups sometimes have panel-specific operating procedures as
well. Given the broad scope of ClinGen as a whole (pediatric and adult
hereditary disorders, both non-cancer and cancer related), having different
protocols per workgroup with an overarching more general policy makes sense,
even if it leads to variability in how genetic material is evaluated. The
disadvantage to this decentralization of standardized operating procedures is
consistency can be lost from workgroup to workgroup, making reproducibility
more difficult and less consistent. Given the simplicity of clinical
recommendations, with more focus on providing a user with information to help
make their decisions, external studies comparing the reproducibility of
ClinGen recommendations in clinical practice would be difficult if not
impractical. ClinGen, while having some internal variation in standard
operating procedures, still captures the general features of the
Reliability/Reproducibility attribute.  
  
To fulfill the attribute **_Clinical Applicability_** , a knowledge base
should clearly define in what circumstances and/or patient populations a
guideline applies. For all three knowledge bases in this LCD, Clinical
Applicability was demonstrated de facto by gene-disease correlations; namely,
every guideline relevant to this LCD identified genes in context to a
cancer(s) or risk of cancer. More specifically, NCCN further contextualizes
its recommendations based on several factors including strength of evidence,
cost of intervention, and special patient population considerations. ClinGen
similarly expounds upon its scored recommendations in attached Summary Reports
that address details such as relevant ages, gender, and clinical
presentations. OncoKB categorizes its scoring based on the clinical indication
(e.g., prognostic scores Px1, Px2, Px3 and therapeutic scores 1, 2, 3A, 3B, 4,
R1, R2) with ties to the FDA scoring where indicated.  
  
 ** _Clinical Flexibility_** , per the Good Practice Guidelines, describes the
practice of providing alternatives and exceptions to primary recommendations
in a guideline. At best, a guideline would be systematic, considering not only
all major alternatives and exceptions, but also describing the rationales
behind them and describing which circumstances support not utilizing a primary
recommendation, including a consideration of patient preferences, clinical
judgement, and logistics behind an alternative recommendation.  
  
Again, the NCCN's guidelines demonstrate thoroughness backed by evidence, as
is seen in the details surrounding each recommendation statement. Often, NCCN
guidelines will provide parameters to their recommendations, including what
elements should be considered before performing a clinical action and what
exceptions and alternatives should be considered, when appropriate. The NCCN
guidelines also tend to defer clinical actions to clinical judgement,
including recommending team-based decision making in collaboration with the
patient.  
  
ClinGen acts less as guidelines and more as a knowledge repository in respect
to providing recommendations. As stated earlier, the clinical actionability
statements in ClinGen generally refer to hereditary cancer syndromes, which
focus primarily on surveillance and prophylactic interventions. However,
ClinGen is designed to provide scoring and evidence for clinical actions,
allowing the clinicians to decide if the scores are high enough and the
evidence strong enough to move forward with a clinical action. In this way,
ClinGen does not directly provide clinical instructions, but rather indirectly
guides clinical behavior through supplying evidence without frank conclusions.
Thus, in this sense, the Clinical Flexibility attribute does not readily apply
to ClinGen.  
  
Likewise, OncoKB acts as a knowledge base, however, with even less description
of clinical actions. OncoKB also supplies clinicians with information and
evidence, but without recommending a certain clinical action and providing
alternatives (such as selecting one chemotherapy over another). As a result,
the Clinical Flexibility attribute is not applicable to OncoKB.  
  
According to the Good Practice Guidelines, a knowledge base should also
demonstrate **_Clarity_** , namely be straightforward, logical, organized, and
well defined. Terminology and clinical descriptions (e.g., anemia versus
hemoglobin level) should be as precise as possible.

The attribute of Clarity is exemplified by NCCN guidelines in many respects.
The guidelines use several modalities to present their recommendations,
including a searchable database and frequent use of flow chart, diagrammatic
algorithms. Additionally, NCCN provides numerous resources describing its
process of developing recommendations and what the scoring means. It should be
noted that clarity varies per NCCN guideline, with some guidelines less clear
in terminology and defining recommendations. In fact, embedding
recommendations within long paragraphs of discussion can make a recommendation
harder to find. Another weakness of the NCCN guidelines is the practice of
saying any unscored recommendation within a guideline should be treated as
Level 2A, namely: "Based upon lower-level evidence, there is uniform NCCN
consensus that the intervention is appropriate."179 The great majority of NCCN
recommendations are Level 2A but given the fact that the recommendation
received "uniform NCCN consensus," the Level 2A recommendations were
considered strong enough to warrant coverage as clinically reasonable and
necessary in Medicare. Overall, the NCCN guidelines aim to be transparent and
clear with their recommendations and use a variety of modalities to ensure the
recommendations are clearly communicated.  
  
ClinGen addresses the Clarity attribute through thorough explanation of its
scoring systems, separation of different topics such as gene-disease
relationships versus clinical actionability of genetic testing, use of
searchable databases, and presentation of scores in consistently structured
reports. ClinGen allows its users to determine how to use the presented
information, only providing recommendations indirectly through listing
potential clinical actions, ranking the strength of evidence, and consensus
scoring of the clinical actions. ClinGen, however, provides information with
the apparent audience being people with more experience in genetics. Due to
the complexity of the ClinGen website, the technical nature of evidence
review, and the various topics assessed per gene (gene-disease relationship,
dosage sensitivity, clinical actionability, variant pathogenicity, and
pharmacogenetics), the ClinGen knowledge base can be challenging to use
depending on the user's background and foundation of knowledge. ClinGen
requires its users to learn how to work with the data before they can properly
use the website. Moreover, the absence of stated recommendations requires
users to understand the details provided before arriving at a conclusion. In
fact, per the website: "The information on this website is not intended for
direct diagnostic use or medical decision-making without review by a genetics
professional."24 Overall, these weaknesses reduce the clarity of the knowledge
base; however, the website does provide a lot of definitional and procedural
details that add to its transparency.  
  
OncoKB, much like ClinGen, addresses the Clarity attribute through its clearly
described scoring system. Additionally, OncoKB does not provide direct
recommendations, but rather provides the strength of evidence and expert
assessment of genetic content in the context of cancer(s). Also, like ClinGen,
the absence of expert recommendations requires users to come to their own
decisions on the provided scoring and data. However, OncoKB's website is
comparatively straightforward/intuitive and has a searchable database.  
  
Noted as "one of the committee's [Institute of Medicine] strongest
recommendations," good practice guidelines should focus on the attribute of
**_Multidisciplinary Process_**.27 The committee for Good Practice Guidelines
felt that guideline development should include all stakeholders potentially
affected by the guideline, suggesting even patients and payors be considered.
Part of the process of organizing a guideline committee would be determining
the participants' conflicts of interest, not necessarily to exclude
individuals but rather recognize and account for potential biases. It is
noteworthy that even among the committee writing the Good Practice Guidelines
there was debate on who should be included in guideline development and who
should lead the guideline development.  
  
Per the NCCN's Disclosure Policies: "The NCCN Guidelines are updated at least
annually in an evidence-based process integrated with the expert judgment of
multidisciplinary panels of experts from NCCN Member Institutions. NCCN
depends on the NCCN Guidelines Panel Members to reach decisions objectively,
without being influenced or appearing to be influenced by conflicting
interests."179 Panel members are overseen by various levels of staff and
leadership with conflicts of interest updated at least semiannually. It is
recognized that panel members will have outside activities with other entities
such as industry and patient advocacy groups, and thresholds for untenable
conflicts of interest have been set, as described on the NCCN website. It
should also be noted that while panels are comprised of subject matter
experts, the guidelines also receive input from patient advocates. Overall,
the process of selecting and maintaining panel members as well as other NCCN
leadership, participants, and staff are well-detailed on the NCCN website. As
a whole, the NCCN system robustly meets the Multidisciplinary Process
attribute.  
  
As described on their website and documented in their standard operating
procedures, OncoKB maintains its knowledge base through use of curators, a
CGAC, and External Advisory Board. The CGAC consists of "Core" members with
broad skillsets that include clinical management, research, and translational
cancer biology expertise. The CGAC also includes "Extended" members that
include service chiefs, physicians, and scientists that represent
multidisciplinary clinical leadership within the Memorial Sloan Kettering
Cancer Center (MSKCC). Each member must submit their conflicts of interest and
the system of developing and approving genetic assertions including scoring
takes conflicts of interest into consideration before a member is allowed to
approve or deny these assertions. The CGAC is additionally overseen by an
External Advisory Board consisting of leaders from the oncology and genomics
community that are not employed by MSKCC. One of the inherent weaknesses of
the OncoKB knowledge base is the limited representation from experts outside
of the MSKCC. Moreover, involvement from other stakeholders such as patient
advocacy groups do not appear to be included in OncoKB development and
maintenance. The current CGAC committee appears to consist of doctoral level
members (MDs and PhDs) only. While these weaknesses are noted, the overall
system utilized by OncoKB meets the core elements of the Multidisciplinary
Process attribute.  
  
ClinGen utilizes Expert Panels to score, review, and report recommendations.
Expert Panels are open to public volunteers who first receive training, are
required to disclose conflicts of interest, and finally are assigned to a
topic specific panel(s) by ClinGen leadership. Members of Expert Panels are
listed on the ClinGen website, and their conflicts of interest disclosures
usually can be found within their workgroup's webpage. These members represent
a variety of professions in research, medicine, academia, and/or industry and
diverse educational backgrounds including both doctoral and non-doctoral
degrees. Additionally, membership is not limited to the United States. While
ClinGen provides one of the most inclusive memberships of the three knowledge
bases discussed, the transparency around these members (including leadership)
is lacking. Moreover, as would be expected given the scope of ClinGen's
content, the predominance of membership leans towards genetics research as
opposed to clinical management. ClinGen does demonstrate weaknesses that would
benefit from improvement, but the essence of the knowledge base, inclusivity
guided by centralized review and reporting standardized operating procedures
and multidisciplinary input, meets the core elements of the Multidisciplinary
Process attribute.  
  
The Institute of Medicine committee also endorsed **_Scheduled Reviews_** for
knowledge bases. While the committee does not specify exact timelines for
scheduled reviews, the Good Practice Guidelines does state that reviews should
be scheduled based on the known or expected frequency of updates in the topic
of interest. Additionally, scheduled reviews do not preclude ad hoc reviews as
new evidence is identified.  
  
NCCN guidelines are reviewed and updated as necessary on a yearly basis.
However, NCCN guidelines are often updated on a more frequent basis as
stakeholder inquiries are submitted or new information is learned. A good
example of this process can be seen in NCCN's transparency documents which
record guideline specific meetings and the topics discussed at those meetings.
NCCN clearly meets the Scheduled Reviews attribute.  
  
OncoKB, of the three discussed knowledge bases, appears to have the most
rigorous and frequent scheduled reviews of its content. OncoKB's team reviews
various data sources at frequencies from weekly for databases like cBioPortal
and COSMIC to monthly for peer-reviewed literature from a variety of well-
established journals. Additionally, OncoKB keeps abreast of new information
from major annual conferences (e.g., ASH Annual Meeting) and evaluates other
sources ad hoc, such as user feedback or data from clinical trials. All of
these procedures can be found in the Standard Operating Procedure for the
website.178 This procedure strongly meets the Scheduled Review attribute
described in the Good Practice Guidelines.  
  
Based on the structure of ClinGen, the Scheduled Review attribute is not well
met. In 2018, McGlaughon and colleagues published an analysis asking how
frequently a gene curation should be re-assessed and updated.180 The
retrospective study recommended different timelines for re-evaluation based on
the initial strength of a gene-disease association (limited, moderate,
strong), ranging from greater than five years to three years. In 2019, ClinGen
did develop a policy along these lines, with the shortest time to recuration
being two years for gene-disease associations with a Moderate classification,
mandating the policy for "all current and future GCEPs" (Gene Curation Expert
Panels).181 However, a similar policy for recuration of Clinical Actionability
scores and recommendations was not found. Based on what is available, it
appears that ClinGen may re-evaluate Clinical Actionability on an ad hoc
basis, such as the recent re-evaluation of hereditary cardiac disease scores
in 2021.182 It should also be noted that for all released Clinical
Actionability reports, the oldest update was only in April 2020, meaning that
all released reports appear to be current up to at least 2020 (with a vast
majority of reports showing updates in 2022). While the absence of a clear re-
curation policy for Clinical Actionability is problematic, ClinGen's reports
are based on current evaluations and recent updates.

The eighth and final attribute described in Good Practice Guidelines is
**_Documentation_**. This attribute refers to the written procedures,
policies, scoring metrics, etc. describing how a knowledge base makes its
determinations and guidelines. Good documentation should also record
activities of the knowledge base, including which individuals developed a
guideline, the evidence they utilized, their rationales and assumptions, and
any analytical methodology they used.  
  
NCCN demonstrates robust standard operating procedures and vetting of its
guideline developers. Additionally, NCCN both records submissions from
external sources and documents the subsequent panel meetings where submissions
are considered. Much of the text in NCCN guidelines outlines both the evidence
and rationale for NCCN scoring and recommendations. However, NCCN is less
transparent on the dialogue and content of its guideline panel meetings, for
instance not providing comprehensive meeting minutes or transcription of the
meeting's discussions. The votes of the panel on an issue are likewise
anonymous even if the members attending a discussion are disclosed. These
practices can obscure full understanding of the rationales and assumptions
behind a panel's final decisions and recommendations. As a whole, the NCCN is
fairly transparent in both the procedures used to develop guidelines and the
people creating and updating these guidelines. There are areas in which more
transparency would be needed to fully meet the Documentation attribute, but as
a whole, NCCN demonstrates good documentation.  
  
OncoKB provides a very detailed standard operating procedure on its website
and lists all team members with their respective Conflicts of Interest
disclosures. However, a major weakness of OncoKB is the lack of transparency
as to what data and/or citations support their score for genetic content. For
instance, although alterations are listed with their score, disease, and drug
associations, only the number (but not the actual references) of citations are
listed. Moreover, meeting minutes do not appear to be publicly available,
limiting insight into the rationales and assumptions behind scores. OncoKB,
because of these weaknesses, does not completely meet the Documentation
attribute as described above, although the knowledge base does do a good job
explaining its general procedures and documenting who is curating the
knowledge base.  
  
ClinGen provides overall scoring criteria and parameters for its knowledge
base, but some expert panels create their own working group protocols. Given
the broad scope of ClinGen as a whole (pediatric and adult hereditary
disorders, both non-cancer and cancer related), having different protocols per
workgroup with an overarching more general policy makes sense, even if it
leads to variability in how genetic material is evaluated. Members of Expert
Panels are listed on the ClinGen website, and their conflicts of interest
disclosures usually can be found within their workgroup's webpage. ClinGen,
because of these weaknesses, does not completely meet the Documentation
attribute as described above, although the knowledge base does do a good job
explaining its general procedures and documenting who is curating the
knowledge base.  
  
 **Specific Lab Tests  
  
** **Cxbladder (Detect, Triage, Monitor, Resolve)  
  
** The fundamental methodology of Cxbladder tests is founded on a 2008 paper
from Holyoake and colleagues describing the creation of an RNA expression
assay that could predict the likelihood of urothelial carcinoma from urine.39
Several gene expression profiles were examined between urothelial carcinoma
tissue and normal urothelial tissue, the latter of which was collected as non-
malignant tissue from patients with renal cell carcinoma who had undergone a
radical nephrectomy. The gene expression profiles that demonstrated the most
promise in differentiating between cancer and non-cancer were gathered into a
four gene expression panel and then optimized to discriminate between urine
from patients with any grade/stage of urothelial cancer and patients without
urothelial cancer. Gene expression tests that are used to predict the presence
or absence of cancer, however, must take into consideration many potential
complicating and confounding factors. The absence of a rigorous approach to
addressing complicating/confounding factors undermined the clinical validity
of Cxbladder tests, as will be detailed below.  
  
As evidenced in the publications reviewed in the Summary of Evidence, the key
weakness of the Cxbladder tests is found within their test design. Cxbladder
tests are founded on the concept that differences in gene expression between
urothelial cancer and non-urothelial cancer (including non-neoplastic tissue)
can be measured in urine to determine if urothelial cancer is present or not
present. This means that a well-designed test will be able to not only
discriminate between cancer and normal tissue, but also between different
types of malignancy.  
  
For the precursor test uRNA, Holyoake and colleagues started with a custom-
printed array from MWG Biotech that allowed gene expression profiling of
26,600 genes.39 This array was used to analyze normal tissue (18 specimens)
and urothelial carcinoma tissue (28 specimens from Ta tumors and 30 specimens
from T1-T4 tumors). The preliminary data was then analyzed to select the most
promising genes for creation of a GEP test. This subset of promising genes was
further pruned by testing urine from patients with transitional cell carcinoma
(TCC) (urothelial carcinoma) (n=75), patients with other "urological cancer"
(n=33), and patients without cancer, including patients with infection (n=20)
and "other benign urinary tract disease" (n=24). Additionally, the paper
mentions testing blood to get gene expression levels for blood and
inflammatory cells, but the results of this subset of tests were not provided
in this paper. It must be noted here that the characteristics of the non-TCC
cancers were also not disclosed in the paper.  
  
After this additional testing, Holyoake and colleagues settled on four gene
expression test (uRNA-D) that utilized the genes _MDK_ , _CDC2_ (now
officially known as _CDK1_ ), _IGFBP5_ , and _HOXA13_.39 Unfortunately, the
false positives and false negatives received little attention from the paper,
including false positives for patients with other non-TCC cancers (n=3). The
authors concluded that their results "will need to be further validated in a
prospective setting to more accurately determine test characteristics,
particularly in patients presenting with hematuria and other urological
conditions."  
  
Standing alone, the 2008 paper from Holyoake and colleagues lacks the
scientific rigor to establish the uRNA-D test as able to accurately
distinguish between urothelial carcinoma and other cancers or other non-cancer
urological conditions.39 One very notable gap included a lack of details or
definition for non-urothelial cancers, of which many would feed into the
urinary system, including prostate cancers, renal cancers, and metastatic or
locally invasive cancers from other organs. It would be expected that a well-
designed test would assess not only the full spectrum of potential cancers,
but that the test design would include a much higher count of specimens
(beyond the 33 undefined cancers found in this study). In the same sense, the
absence of details regarding non-malignant specimens, which included only 20
"urinary tract infections," was a major and notable gap in this test's
development. There were many other issues identified with this paper including
a strong population bias towards male patients, but altogether, this
validation of uRNA-D was insufficient to support that the test performed
accurately as a tool for distinguishing between urine from patients with and
without urothelial carcinoma.  
  
It is critical to understand the limitations of the 2008 publication from
Holyoake and colleagues because the test uRNA-D was used to create the
Cxbladder line of tests, with the main difference between uRNA-D and Cxbladder
being the addition of a single gene, _CXCR2_ , to the gene expression profile
of the Cxbladder assay.39,40 It is noted that other versions of Cxbladder use
non-genetic data in an overarching algorithm to produce results, but the focus
of this discussion will be upon the gene expression profile technology of
Cxbladder tests.

In 2012, the first paper describing a Cxbladder test was published by
O'Sullivan and colleagues.40 This paper acted as both a test validation and a
comparison of the new Cxbladder test with other urine tests on the market.
While the statistical results of Cxbladder seem promising, we must return to
the foundation of the test, namely its ability to distinguish between
urothelial carcinoma and other cancerous or non-cancerous conditions (or
patients without disease). In this paper, other malignancies (n=7) were
assessed only when they were found in patients with urothelial carcinoma.
Moreover, the types of other malignancy were not disclosed in this paper.
There are also 255 "nonmalignant disease" specimens, which included
representations of "benign prostatic hyperplasia/prostatitis",
"cystitis/infection or inflammation of urinary tract", calculi, and "hematuria
secondary to warfarin," and 164 specimens from patients with "no specific
diagnosis." This first paper from 2012 also does not sufficiently address
Cxbladder's ability to distinguish between urothelial carcinoma and other
malignancies, which is of particular relevance when a majority of the patient
population were male (78%) with a median patient age of 64 years and thus,
with higher risk of prostate carcinoma. The paucity of clinical data also
created gaps in the data integrity, failing to answer questions such as how
many of the urothelial carcinoma specimens had coincident inflammation and
what other medical conditions (and medications) were present in this patient
population? Additionally, the paper does not spend significant time discussing
the potential reasons for false positives and false negatives. These issues
are compounded by a short follow-up period (only 12-months) with participating
patients.  
  
In the most recent paper published for Pacific Edge Diagnostics by Lotan and
colleagues in 2022, Cxbladder Triage and Detect were "enhanced" by addition of
a different test methodology, digital droplet PCR, adding a different approach
to detecting urothelial carcinoma: identification genetic variants associated
with urothelial carcinoma.44 This approach was based on the premise that the
six variants (called single nucleotide polymorphisms or SNPs in the paper) are
either acquired as mutations in the carcinogenesis of urothelial carcinoma or
already present as an inherited genetic variant in the patient's germline DNA,
representing a higher risk of urothelial carcinoma. However, it is known that
these SNPs can also show up in the context of other malignancies (such as
papillary renal cell carcinoma), which is not addressed by Lotan and
colleagues.183,184 Moreover, as mentioned in the paper's discussion, the
presence of these SNPs in urine may not coincide with clinically detectable
(e.g., cystoscopically visible) carcinoma. This could lead to further
confusion with false positives, especially when the PPV of Cxbladder tests
tends to be very low. If numerous false positive results in Cxbladder are
accepted as an inherent trait of the test, providers may not be as vigilant in
closely following patients with a positive Cxbladder result after a negative
cystoscopy. In addition, providers may not search for other malignancies
(e.g., papillary renal cell carcinoma) as a potential cause for the "false
positive" Cxbladder result. Another weakness of the 2022 study was seen in the
differences between cohorts. Notably, the six SNPs alone were less sensitive
for urothelial carcinoma in the Singapore cohort (66%) than in the United
States cohort (83%). This could indicate differences in the genetic etiology
of urothelial carcinoma in different populations, meaning that the six SNPs
may not be as representative in populations not evaluated in this study.
Furthermore, while this study claimed to evaluate multiple ethnicities, the
paper does not disclose which ethnicities were evaluated and the numbers of
patients from each ethnicity.

Each new Cxbladder test builds on their predecessors, often utilizing the same
specimens from prior studies in their test validations and performance
characterizations. Moreover, the insufficient assessment of potential
confounding factors is perpetuated through these studies. For instance, if we
look at assessment of non-urothelial cancer through all major published uDNA
and Cxbladder studies, we see the following:

  * Holyoake 2008: 33 undefined cancers were noted39
  * O'Sullivan 2012: Seven other malignancies (undefined) were noted, all in patients with urothelial carcinoma40
  * Kavalieris 2015: Non-urothelial neoplasms were not discussed (study population included 517 patients from the O'Sullivan 2012 study)40,41
  * Breen 2015: Non-urothelial neoplasms were not discussed (study population included patients from the O'Sullivan 2012 study)40,45
  * Kavalieris 2017: Non-urothelial neoplasms were not discussed (same patient population as Lotan 2017)42,46
  * Lotan 2017: Non-urothelial neoplasms were not discussed (same patient population as Kavalieris 2017) 42,46
  * Konety 2019: In some subpopulations, patients with history of prostate or renal cell carcinoma were excluded from the study; otherwise, non-urothelial neoplasms were not discussed (study population included patients from O'Sullivan 2012 and Kavalieris/Lotan 2017) 40,42,46,49
  * Davidson 2019: Other non-bladder malignancies and neoplasms were identified (but not subclassified) in a study evaluating hematuria; notably, Cxbladder-Triage was positive in most of these other malignancies (seven of nine total) and neoplasms (two of three total)52
  * Koya 2020: Non-urothelial neoplasms were not discussed50
  * Davidson 2020: Other non-bladder malignancies and neoplasms were identified in the study but data was not presented to allow association of these other malignancies and neoplasms with positive or negative results from Cxbladder.53
  * Raman 2021: In some subpopulations, patients with history of prostate or renal cell carcinoma were excluded from the study; otherwise, non-urothelial neoplasms were not discussed (study population included patients from O'Sullivan 2012 and Konety 2021)40,43,49
  * Lotan 2022: In some subpopulations, patients with history of prostate or renal cell carcinoma were excluded from the study; otherwise, non-urothelial neoplasms were not discussed44
  * Li 2023: Some patients (24 of 92 patients) noted to have "other cancers;" except for one mention (a patient with breast cancer who missed their nine month follow-up due to conflict with breast cancer treatment), other types of cancers are not described or significantly discussed51

There are numerous potential malignancies that can contribute to urine genetic
composition (e.g., renal cell cancer, bladder cancer, prostate cancer).
However, by using only 40 unspecified neoplasm specimens, 33 of which were
tested only for uDNA ( ** _not_** Cxbladder), the validation from Pacific Edge
Diagnostics underrepresents potentially confounding variables. This
underrepresentation is further substantiated through data found in studies not
performed or funded by Pacific Edge Diagnostics. In an independent study from
Davidson and colleagues in 2019 performed on patients with hematuria, seven of
nine malignant _prostate or kidney_ lesions were discovered in patients with a
positive Cxbladder-Triage result.52 False positive Cxbladder-Triage results
were also seen in a majority of patients without bladder cancer but instead
diagnosed with radiation cystitis, vascular prostate, bladder stones,
anticoagulation related bleeding, post-TURP bleeding, and urethral stricture.
No cause for hematuria was found in 225 patients, with 137 of them having a
positive Cxbladder-Triage result. This 2019 study as well as others indicate
that Cxbladder is less sensitive for detecting smaller, low grade malignancies
making it unlikely the false positives could represent urothelial malignancies
below the limit of detection by cystoscopy and other conventional
evaluations.52  
  
The exclusion criteria further weakened the development and validation of
Cxbladder tests. Consistent with the aforementioned confounding variables of
other urinary tract cancers and metastases, the Cxbladder studies generally
excluded patients with a history of prostate or renal cancer; however, these
exclusions were not always seen in studies funded and/or published by Pacific
Edge Diagnostics. The validation studies for Cxbladder also typically excluded
inflammatory disorders such as pyelonephritis and active urinary tract
infections and also excluded known causes for hematuria like bladder or renal
calculi or recent manipulation of the genitourinary tract (e.g.,
cystoscopy).40-43, 46 In the first published validation study for Cxbladder,
the authors stated that the additional 5th RNA marker, CXCR2, "was predicted
to reduce the risk of false-positive results in patients with acutely or
chronically inflamed urothelium."40 However, in this same study, the authors
went on to exclude patients with "documented urinary tract infection."
Fortunately, publications from other sources such as Davidson and colleagues
in 2019 provide insight into how Cxbladder tests (namely Cxbladder Triage)
perform under these benign conditions.52 Davidson and colleagues found that
false positives were seen in a majority of patients where the underlying
etiology of hematuria was radiation cystitis, vascular prostate, bladder
stones, anticoagulation related bleeding, post-TURP bleeding, or urethral
stricture. False positives were also seen in 10 of 23 (43%) patients with
urinary tract infection and 8 of 10 (80%) patients with "other" inflammatory
etiologies. Altogether, in the inflammatory category of the study, over half
(59%) of patients with an inflammatory etiology of their hematuria received a
false positive result from the Cxbladder Triage (CxbT) test. In a subsequent
study by Davidson and colleagues in 2020, "approximately 10% of patients (85
of 884) required a repeat CxbT assay because quality control failures, mainly
caused by interference of inflammatory products or a large number of white
blood cells."53 The couple of systemic reviews and meta-analyses that included
Cxbladder tests were mixed in their assessment of this line of tests. Chou and
colleagues in 2015 only reviewed one of the Cxbladder papers (O'Sullivan 2012)
and came to the conclusion that the strength of evidence for the study was
graded low.40,54,55 In 2022, Laukhtina and colleagues performed a more
involved assessment of Cxbladder tests, particularly Cxbladder Monitor, coming
to the conclusion that it had "potential value in preventing unnecessary
cystoscopies."56 The authors also determined that there was "not enough data
to support" using Cxbladder Triage and Detect in the "initial diagnosis
setting." Laukhtina and colleagues did acknowledge that their study had
several potential limitations which included the "absence of data on blinding
to pathologist and urologists" and the inability to "perform subgroup analyses
for HG [High Grade urothelial carcinoma] recurrence detection only." However,
it should be noted that for both the 2015 and 2022 systemic reviews and meta-
analyses, the evaluation of the actual clinical features of each Cxbladder
study was relatively superficial, focusing more on the statistical values and
less on the quality of the studies and the designs underlying those
values.54-56  
  
In conclusion, the Cxbladder line of tests all suffer from the foundational
problem of insufficient validation of their test in potentially confounding
clinical circumstances including non-urothelial carcinoma malignancies and
inflammatory conditions of the urinary tract. Cxbladder also demonstrates
several population biases, including early papers with a strong bias towards
male patients of European ancestry. The majority of Cxbladder papers avoid
disclosing the PPV and number of false positives of their tests. Cxbladder
tests generally have low PPVs (down to 15-16% as seen in Konety, et al 2019)
and high numbers of false positives (also in Konety, et al 2019, there were
464 false positive results as compared to 86 true positive results).49 These
values are significant in that false test results, particularly false
positives, can lead to patient anxiety and distress among other procedural
issues related to follow up for an inaccurate result. Most of the primary
literature regarding Cxbladder test development and performance is funded, if
not directly written by, the test's parent company, Pacific Edge Diagnostics.
This conflict of interest must be taken into account when reviewing these
papers. Finally, and most importantly, due to the insufficient representation
of confounding factors in the validation populations, the Cxbladder tests have
not been adequately vetted in the context of the Medicare population. Given
all of these findings, the Cxbladder line of tests are considered not
medically reasonable and necessary for Medicare patients.  
  
 **ThyroSeq** **CRC, CBLPath, Inc, University of Pittsburgh Medical Center  
  
** ThryoSeq CRC is a prognostic test for malignant cytology that predicts the
five year likelihood of cancer recurrence (low, intermediate, or high risk)
based on algorithmic synthesis of raw data from the next generation sequencing
(NGS) of DNA and RNA from 112 genes. Given that fine needle aspirated (FNA)
nodules proven to be malignant on cytology are typically surgically resected,
sometimes with coincident lymph node dissections as warranted, and the total
features of the cancers are then assessed on permanent pathology of the
resection specimen, having a prognostic test predicting risk of cancer
recurrence on cytology before assessment of the entire resection seems
preemptive. However, ThyroSeq CRC is proposed to direct extent of surgery for
Bethesda VI nodules, increasing aggressiveness of surgery for more aggressive
cancers. Therefore, ThyroSeq CRC must not only supply information that is not
obtained through standard clinical and pathologic procedures prior to a
resection, but also provide results that are subsequently confirmed on patient
follow-up after the resection. Ultimately, a prognostic test should provide
information that predicts the course of a patient's disease before therapy is
implemented and thus informs future clinical management to preemptively reduce
adverse outcomes. For a prognostic test to be clinically useful, it must
ultimately improve patient outcome.  
  
In the first publication describing the evaluation of the ThyroSeq CRC test, a
small population of patients (n=287) with differentiated thyroid cancer (DTC)
were evaluated with the CRC prognostic algorithm and their molecular risk
group (Low, Intermediate, High) was compared to their outcome in terms of
distant metastases (DM) as identified through pathology or whole body scans
with iodine-131.57 Patients were divided into two groups: control (n=225) and
DM within five years (n=62). In the control group, precise numbers of how many
patients fell into each CRC risk category were not supplied by the paper.
Instead the control group was further segregated into a subcategory of
propensity matched patients where each DM positive patient was compared with a
control patient with similar demographic and pathologic characteristics,
although the authors clearly state histologic subtype was not used to perform
this propensity match. Using this propensity matching technique, comparisons
were provided between the 53 DM positive patients and 55 control patients. In
this subgroup comparison, the DM positive patients demonstrated more high risk
scores (Low=1 patient; Intermediate=17 patients; High=35 patients) than the
control patients (Low=28 patients; Intermediate=19 patients; High=8 patients).
This comparison was felt to be adequate by the authors to conclude that their
"molecular profile can robustly and quite accurately stratify the risk of
aggressive DTC defined as DM."

This study had numerous limitations and drew dramatic conclusions from a very
small sample size that was poorly presented by the paper.57 The immediate
issue with this study was the lack of transparency. Thyroid cancer is a
complex category of malignancy that includes many different subtypes of
cancer, each with a variety of behaviors depending on numerous demographic,
clinical, and pathologic factors. Considerations for management of cancer
patients is thus a multifactorial and interdisciplinary process that requires
careful evaluation. The study from Yip and colleagues not only oversimplifies
the descriptions of patient populations, but the background data for each
patient is not provided to allow for objective review by their readers. We are
not given crucial details such as key findings in pathology reports (mitoses,
lymphovascular invasion, capsular invasion, histologic subtype of the cancer)
nor the number of patients with positive lymph nodes found during resection of
the cancer. Instead, the patient demographics and molecular characteristics
provided (Table 1) include simplifications such as generalized cancer types
without subclassification (Papillary, Follicular, or Oncocytic) and non-
specific metastatic locations (Bone, Lung, ">1" and Other). Additionally, the
propensity matched description table (Table 2) only lists mean age at
diagnosis, mean tumor size, and gender ratio.  
  
Yip and colleagues also did not provide significant insight into why some
controls (n=8) were ranked as high risk while one patient with DM was
categorized as low risk.57 The purpose of the intermediate risk category is
unclear and concerningly unhelpful when the number of patients with this risk
category were basically the same between propensity matched DM and control
patients (n=17 versus n=19 respectively).  
  
Ultimately, it was unclear how this test would be used in patient care.57
Given that the test is performed on cytology before resection, the authors
conjectured their test could be used to guide extent of surgery (lobectomy
versus total thyroidectomy) or help direct patients to therapeutic trials.
However, these potential clinical utilities were not assessed in this paper.

In the second publication evaluating ThyroSeq CRC, Skaugen and colleagues
performed a single-institution retrospective cohort study assessing 128
Bethesda V (suspicious for malignancy) cytology specimens.58 The study
assessed both the ThyroSeq v3 diagnostic test as well as the ThyroSeq CRC
test. For the CRC portion of the study, 100 specimens were assessed, with
exclusion of five due to a benign diagnosis upon resection and three excluded
due to concurrent metastatic disease discovered at resection. For the
remaining 92 specimens, there was a mean follow-up of 51.2 months (about four
years). The shortest follow-up time was less than one month, and the longest
follow-up time was of 470 months (nearly 40 years). It must immediately be
noted here that the ThyroSeq CRC test claims to predict a five year risk of
DM, which means over half of the CRC tested specimens (more than 46 specimens)
demonstrated potentially inadequate follow-up to assess the core five year
prognostic claim. The importance of these follow-ups become even more evident
when the authors drew conclusions about the prognostic power of the CRC's
three risk categories: High, Intermediate, and Low. Distant metastases was
identified in 12 of the 92 specimens: 6 of 11 specimens with the high risk
result and 6 of 63 specimens with the intermediate risk result. The authors
did not provide a deeper analysis of the five high risk specimens without DM,
including no speculation as to why the test potentially misclassified these
specimens. Additionally, the authors did not provide significant discussion
into the meaning of the intermediate-risk result that was given to 66 of the
100 specimens tested. In the paper's conclusions, the ThyroSeq CRC was again
proposed as potentially helpful in deciding the extent of surgery required.  
  
Much like the first paper, the second paper (Skaugen and colleagues) lacked
data transparency, making further assessments by readers difficult.58 While
Table 3 provided patient characteristics, surgical findings, and pathologic
findings, all to a much greater extent than the first paper, readers were
still unable to synthesize how data categories corresponded to each other
(e.g., of the patients who received lymph node dissection, what subtypes of
thyroid cancer were represented).

Ultimately, Skaugen and colleagues lacked sufficient follow-up to draw
significant conclusions about the accuracy of the ThyroSeq CRC results.58 The
paper, while data rich, was not transparent nor thorough enough for readers to
draw their own conclusions about the validity of the test. Moreover, the
conclusions given by the authors regarding the prognostic test were overly
simplified, such as highlighting the presence of DMs in some patients with
intermediate and high risk results, and considering this correlation to be
significant when they also noted absence of DMs in patients with low risk
results. Finally, the actual use of ThyroSeq CRC in the clinical setting is
still unclear based on the discussion of the paper.

In the third publication, Liu and colleagues assessed their three tier
classification system (low-risk, intermediate-risk, and high-risk for
recurrence) in the context of primary thyroid cancer recurrence after a
primary thyroidectomy and subsequent initial oncologic therapy.56 Notably, the
test name ThyroSeq CRC was never used in this paper, even though the three
tier system of risk stratification appeared to be the same. This raises a
concern that the classification system used in this paper may not be the same
methodology as used for the marketed ThyroSeq CRC. With that caveat and for
the purposes of this Analysis of Evidence, this third paper will be considered
contribuatory to the body of literature evaluating ThyroSeq CRC.

Just from the methodology section of the publication alone, we can see
immediate differences between this paper and the previous two papers
described.57-59 Firstly, surgical specimens were permitted in the study, not
just cytology specimens. This allowance of a non-cytology specimen type
("final surgical specimens," without specification of post resection handling
of tissue, formalin fixation versus fresh-frozen preservation) in a test that
was presumably designed for cytologic specimen would require a separate
validation of the test for a new type of specimen. Validation for this change
in pre-analytic procedure was not evidenced in this paper nor either of the
two prior publications. Secondly, the study was not blinded due to its
retrospective nature. Thirdly, in cases where multifocal cancer was
identified, only samples from the "most aggressive biology" were selected for
molecular testing; however, the paper does not define what constitutes "most
aggressive biology." Fourthly, the specimens included in the study included
patients with preoperative Bethesda I, II, III, and IV cytology as well as
Bethesda V and VI cytology. This starkly contrasts the inclusion criteria seen
in the prior two studies. Overall, these methodologic differences between
papers reduces the comparability of results between the three studies.

Data collection in this study from Liu and colleagues was also different from
the previous two papers.57-59 For instance, Liu and colleagues recorded
several details on the surgical and post-surgical treatments of the patients.
This data included types of lymph node dissection (central versus lateral and
prophylactic versus therapeutic), postoperative complications (e.g., hematoma,
hypercalcemia, surgical site infection), and long-term complications (such as
hypocalcemia and recurrent laryngeal nerve paresis). Several of these data
categories were similar to those seen in Skaugen and colleagues' study, but
differences found in Liu's publication included post-operative details,
grouping of several types of papillary thyroid cancer (such as tall cell
variant) into a more general category ("Papillary, high risk"), evaluating
only all-cause mortality (not substratifying into disease specific mortality),
and detailing AJCC prognostic stages. Note that as mentioned above, there was
a paucity of clinical and pathologic data provided for samples in the study
from Yip and colleagues, and the amount of data from Liu and colleagues was
far more diverse than that prior study.  
  
The study followed up patients for a median of 19 months ([Interquartile
range] IQR of 10-31 months).59 None of the patients were followed for a total
of five years, which means the data in this study is insufficient to
substantiate the five year prognostication claims of the ThyroSeq CRC test.  
  
The above analysis captures only some of the issues identified with the study
from Liu and colleagues.59 In fact, careful reading of the paper's discussion
brings up numerous other "limitations" to the study, not already described
above, as identified by the authors. While the authors' discussion remains
upbeat, statements such as "how to manage the intermediate group?" draw
attention to the novelty of this classification schemea and the uncertainty of
what and how the results can impact patient care and outcome. While the
authors suggest numerous ways their classifications can affect patient
management, and even suggest that they use this test within their institution
to guide their decision-making, the lack of evidence demonstrating this
prognostic test's clinical utility through carefully designed studies suggests
that currently the test may not be adequately studied for use in patient care.
Ultimately, in spite of the numerous data supplied, this paper still failed to
adequately evaluate the clinical validity and utility of the prognostic three
tier system.  
  
In summary, the validity of the ThyroSeq CRC test is not sufficiently
supported by the three peer-reviewed papers identified. The three papers were
exceptionally difficult to compare to each other due to differences in
information provided, types of samples tested, and methodologies described.
The clinical utility of the test is not significantly evaluated by any of the
papers. Due to the inadequate quality of the papers and the insufficiency of
data, this test does not have sufficient evidence to prove clinical
reasonableness and necessity and will be considered non-covered in Medicare
patients.  
  
 **PancraGEN- Interpace Diagnostics  
  
** A 2006 patent described a topographic genotyping molecular analysis test
(which would later become PathfinderTG and then renamed PancraGEN) for risk
classification of pancreatic cysts and solid pancreaticobiliary lesions when
first line results were inconclusive.185 PancraGEN integrates the molecular
results (loss of heterozygosity markers and oncogene variants) with a
pathologist interpretation to provide four categories of risk (benign,
statistically indolent, statistically higher-risk, or aggressive).  
  
Topographic genotyping (also called integrated molecular pathology [IMP]) was
created to integrate molecular and microscopic analyses when a definitive
pathologic diagnosis or prognosis was inconclusive. Typically, investigation
of a pancreatic cyst or solid pancreaticobiliary lesion is an
interdisciplinary process that involves a battery of clinical evaluations
including imaging, cytology, and, when applicable, cyst fluid analysis. Given
the complexity of this work-up process, it is surprising that only a small
number of PancraGEN studies compared its test results with the histology,
cytology, and/or pathology of surgical biopsy specimens. For the PancraGEN
studies addressing pancreatic cysts, all three studies were retrospective in
nature and contained significant limitations. The largest study by Al-Haddad
in 2015, analyzed 492 patients registered with the National Pancreatic Cyst
Registry (NPCR).85 The majority of the patients reviewed for inclusion
(n=1,732) did not meet the study inclusion criteria, due to insufficient or
inaccessible documentation, which resulted in many cases not meeting the
follow-up threshold of 23 months. Researchers evaluated how well PancraGEN
(PathfinderTG), and the 2012 Sendai International Consensus Guideline
classification categorized patients with pancreatic cysts in terms of their
chance of developing cancer. However, the publication from Al-Haddad and
colleagues did not adequately address the validity of the PancraGEN due to
several shortcomings and limitations, including, but not limited to:

  * Data that would be used for the categorization of patients according to Sendai 2012 criteria were also not specified for most patients, as the collection of information was started prior to publication of the 2012 guidelines.
  * Only a small fraction of all enrolled patients (26%) met inclusion guidelines.
  * The study only used a retrospective design without randomization or prognostic data.
  * All patients in the study had been scheduled for surgery, while typically not all patients with pancreatic cysts get surgery referrals.
  * The mean follow-up period for benign disease in this study was too short for firm conclusions to be made beyond three years (insufficient follow-up times).

Of note, during the study, the criteria for the test evolved and older cases
on the registry had to be recategorized based on new criteria.  
  
Two other retrospective studies by Malhotra et al (2014) and Winner et al
(2015) analyzed data from patients with pancreatic cysts in 2006 and 2012 who
had surgical resection and analysis with PancraGEN (Pathfinder TG).86,87 The
study by Winner and colleagues had an extremely small cohort of 36 patients,
85% of whom were "white" with no other race/ethnicity reported. All patients
in the study were scheduled for surgery even though not all patients with
pancreatic cysts undergo surgery. Moreover, the authors were unable to include
the majority of patients recruited because of the lack of final pathology
results. The study by Malhotra and colleagues utilized 26 patients with no
demographic characteristics reported and only three months of follow-up. In
Malhotra (2014), no clinical validity outcomes such as sensitivity,
specificity, and predictive values were calculated or reported. Both Malhotra
et al (2014) and Winner et al (2015) were performed at single institutions
with no blinding.  
  
Al-Haddad and colleagues assessed clinical utility by describing how PancraGEN
might provide incremental improvement to international consensus guidelines
(Sendai [2006] and Fukuoka [2012]).85 Of the 289 patients who met the
consensus criteria for surgery, 229 had a benign outcome. The PancraGEN test
correctly classified 84% as benign and correctly categorized four out of six
as high risk. A 2016 study by Loren and colleagues evaluated clinical utility
by comparing the association between PancraGEN diagnoses and international
consensus guidelines for the classification of intraductal papillary mucinous
neoplams and mucinous cystic pancreatic neoplasms.88 In the study, 491
patients were categorized as (1) "low-risk" or "high-risk" using the PancraGEN
diagnostic algorithm; (2) meeting "surveillance" criteria or "surgery"
criteria using consensus guidelines; and (3) having "benign" or "malignant"
outcomes during clinical follow-up. Additionally, the real-world management
decision was categorized as "intervention" if there was a surgical report,
surgical pathology, chemotherapy, or positive cytology within 12 months of the
index EUS-FNA, and otherwise categorized as "surveillance". A 2016 study by
Kowalski and colleagues analyzed false negatives from the NPCR to examine
clinical utility.89 The study hypothesized that PancraGEN might appropriately
classify some pancreatic cysts that had been misclassified by consensus
guidelines, but the numbers where the PancraGEN and consensus guidelines
disagreed were small, limiting the value of these results.  
  
The clinical validity of PancraGEN has been addressed in several retrospective
studies. Most evaluated performance characteristics of PancraGEN for
classifying pancreatic cysts according to the risk of malignancy without
comparison to current diagnostic algorithms. The best evidence regarding
incremental clinical validity comes from the report from the NPCR, which found
that PancraGEN has slightly lower sensitivity (83% vs. 91%), similar NPV (97%
vs. 97%), but better specificity (91% vs. 46%) and PPV (58% vs. 21%) than
consensus guidelines.85  

Throughout their publications, Interpace Diagnostics has indicated that the
PancraGEN test is meant to support first-line testing, but no process for
combining PancraGEN with consensus guidelines for decision making has been
proposed, and the data reporting outcomes in patients where the PancraGEN and
consensus guideline diagnoses disagreed was limited. There are no prospective
studies with a simultaneous control population that proves PancraGEN can
affect patient-relevant outcomes (e.g., survival, reduction in unnecessary
surgeries). The evidence reviewed moreover does not demonstrate that PancraGEN
has incremental clinical value in the prognosis of pancreatic cysts and
associated cancer.  
  
The evidence for the clinical validity of using PancraGEN to evaluate solid
pancreaticobiliary lesions consists of three retrospective studies by Khosravi
and colleagues (2018), Kushnir and colleagues (2018), and Gonda and colleagues
(2017).90-92 One study assessed the ability of PancraGEN for classifying solid
pancreatic lesions while the other two evaluated the classification of biliary
strictures. Biliary strictures can be caused by solid pancreaticobiliary
lesions but also other causes such as pancreatitis or trauma. Additionally,
the studies did not specify what percentage of patients with biliary stricture
had solid pancreaticobiliary lesions. While the three retrospective studies
noted that the use of cytology plus FISH plus PancraGEN increased sensitivity
significantly, the incremental value of using cytology plus FISH plus
PancraGEN over cytology plus FISH is unclear. Interpace Diagnostics has
indicated that PancraGEN is meant as an adjunct to first-line testing for
pancreatic cysts but has not effectively tested or assessed how PancraGEN
performs for solid pancreaticobiliary lesions. Therefore, the evidence
reviewed does not demonstrate that PancraGEN has incremental clinical value
for the diagnosis of solid pancreaticobiliary lesions.  
  
Notably, there are no studies assessing the analytical validity of the
PancraGEN test. Without such data, the technical performance of the test
cannot be truly determined. A Technology Assessment and systematic review of
Pathfinder TG prepared by CMS found no studies that evaluated the analytic
validity of LOH analyses in the Pathfinder framework as compared to a
reference standard such as pathology reports or radiologic findings.91 The
systematic review addressed questions about analytical validity, clinical
validity, and clinical utility, but found no studies which "directly measured
whether using LOH-based topographic genotyping with PancraGEN/[PathfinderTG]
improved patient-relevant clinical outcomes." The study also found that sample
sizes were small, had methodological limitations, and were all retrospective
without any prospective studies. To date, no study has been performed to
identify how PancraGEN impacts patient outcomes such as reducing patient
mortality from pancreatic cancer or leading to improved survival.  
  
In summary, the body of peer-reviewed literature concerning PancraGEN is
insufficient to establish the analytic validity, clinical validity, and
clinical utility of this test in the Medicare populace. There is insufficient
literature evidence to demonstrate that the topographic genotyping used in
PancraGEN is an effective method to aid in the management of individuals with
pancreatic cysts or solid pancreaticobiliary lesions when other testing
methods are inconclusive or unsuccessful. There is also a lack of peer-
reviewed evidence demonstrating that the use of topographic genotyping in the
management of individuals with pancreatic cysts results in improved clinical
outcomes. As such, this test will not be covered currently as medically
reasonable and necessary in Medicare patients.  
  
 **DecisionDx - Castle Biosciences  
  
** **DecisionDx-Melanoma  
  
** In order to systematically evaluate such a large body of publications, we
will discuss both test design and study design of DecisionDx-Melanoma in the
context of what information is offered to providers and patients. Looking at
an example report from the Castle Biosciences website, we can see that their
test provides prognostic information based on GEP data alone (class
assignment) and GEP data in combination with clinicopathologic data (i31-ROR
and i31-SLNB).186 We will spend most of the discussion below focused on the
class assignment portion of the results since most of the peer-reviewed
literature focuses on this result alone and the i31-ROR and i31-SLNB were
developed much more recently.  
  
Fundamentally, DecisionDx-Melanoma is a GEP that analyzes 28 genes of interest
(and considered by the company to be significantly informative of the
prognosis of melanoma) and anchors these 28 genes to three reference genes. As
controls, the three reference genes should provide a consistent baseline
across all types of melanoma and non-melanoma tissue. Unfortunately, the
literature described in the summary of evidence did not provide insight into
the consistency of expression of the three reference genes across tissue types
and other pre-analytic variables (for instance fixation time and age of
formalin-fixed paraffin-embedded [FFPE] tissue). Of note, one of the reference
genes ( _FXR1_ ) serves as a gene of interest (not a control gene) in Castle
Bioscience's DecisionDx-UM.187  

Gene expression profiles are founded on the principle of differential gene
expression in a cell of interest, like a cancer cell, when compared to
background cells, namely all other cells in the surrounding tissue. Tissues
are comprised of many different types of cells. Skin, for instance, is
comprised of a variety of cell types including melanocytes, keratinocytes,
immune cells such as lymphocytes, structural cells such as fibroblasts and
fibrocytes, hair generating cells, and specialized glandular cells such as
apocrine cells. Many different factors influence gene expression in a cell
including changes in the cell's surroundings (as is seen in a response to sun
damage) and the cell's stage of development, such as whether a cell is part of
a germinal layer (and mitotically active) or terminally differentiated. This
means that even among cells of the same type (same lineage) the GEP can be
different. Considering all of these complexities, a GEP from cells of interest
can be difficult to untangle from the GEP of background cells.  
  
In terms of DecisionDx-Melanoma, the test development publication from Gerami
and colleagues did not adequately address these complexities inherent to
GEPs.109 Many key questions were left unaddressed, such as: how did the
relative quantity of tumor versus non-tumor cells affect test sensitivity, did
lighter or darker skin tones affect the test outcome, did the test perform
differently between histologically distinct melanomas such as acral
lentiginous melanoma versus desmoplastic melanoma, and did the presence of sun
damage affect test results? As a more specific example, we would expect the
presence of tumor-infiltrating lymphocytes (TILs) to affect the outcome of a
GEP test. Tumor-infiltrating lymphocytes change the composition of the
background tissue, increasing the density of background cells. Additionally,
TILs are thought to interact with tumor cells, which would suggest tumor cells
would respond with differential GEP expression. These factors alone could
influence the expression profile of the 28 genes in DecisionDx-Melanoma test,
but until this scenario is tested, its impact is unknown. In summary,
understanding the potential pitfalls of GEP testing is critical for
understanding the reliability, performance, and accuracy of a GEP test.  
  
During the development of any test, consideration and documentation of pre-
analytic variables is critical for establishing test accuracy and precision.
For instance, RNA extracted from formalin-fixed, paraffin-embedded tissue is a
challenging analyte. Of all the macromolecules (like DNA, carbohydrates,
lipids, and proteins), RNA is one of the most fragile and unstable
macromolecules, meaning that adverse conditions such as delayed tissue
fixation can result in the rapid degradation of RNA.188 When creating a valid
clinical test (such as a GEP that uses RNA extracted from FFPE specimens), the
material used to develop a test must be comparable to the material tested in
the clinics. One cannot legitimately develop a test for blood using only
urine, a test for females using only male specimens, or a test for breast
cancer using only prostate cancer specimens. Similarly, a test using archived,
older specimens cannot represent newer, <1 year-old specimens without, at
minimum, comparisons demonstrating that the specimens behave similarly in the
test. In the case of DecisionDx-Melanoma, test development and validation
utilized archival specimens with ages up to 14 years.109 The effect of aged
material on RNA integrity was not thoroughly addressed (only a brief statement
about quantity and quality assessed using NanoDrop 1000 and Agilent
Bioanalyzer 2100 was provided), and older material was not differentiated from
newer material.  
  
In a later study assessing the analytic validity of DecisionDx-Melanoma, Cook
and colleagues presented data supporting the performance and reproducibility
of their test.113 Per the paper introduction, Cook and colleagues stated that
they performed their study in accordance with published guidelines,
specifically the 2010 publication "Quality, Regulation and Clinical Utility of
Laboratory-developed Molecular Tests" from the AHRQ and the 2011 publication
"NCCN molecular testing white paper: effectiveness, efficiency, and
reimbursement."189,190 In the study, several points of concern were
identified, a few of which are described below.  
  
First, RNA stability studies only extracted RNA once from each specimen,
relying on downstream analysis of the same pool of RNA (kept at -80F) to
confirm analytic validity.113 This means that the study did not assess the
processes of macrodissection and RNA extraction for reproducibility and
reliability. Note that per the AHRQ, "if the assay incorporates an extraction
step, reproducibility of the extraction step should be incorporated into the
validation studies, and likewise for any other steps of the procedure."189  
  
Second, while Cook and colleagues did try to evaluate the affect of FFPE block
age on GEP testing, the study did not compare GEP results from the _same tumor
specimen_ at different time points.113 Instead, the study evaluated whether or
not a GEP result could be obtained from an older FFPE block. Interestingly,
Figure 3 in the paper diagrams test failure rates in yearly increments (for
specimens aged up to four years) and then lumps together all data from
specimens older than four years. Although 6,772 FFPE specimens were
represented by Figure 3, the break down of how many specimens were found in
each age category was not given. Also, it is not clear as to the origin or
handling of these 6,772 specimens. Nowhere else in the paper are such large
numbers (thousands versus hundreds) of specimens evaluated despite the
apparent availability of 6,772 FFPE specimens. Not only does Figure 3
demonstrate an expected decline in the testability of older specimens, but it
also highlights the quandary of using older, less reliable specimens to
develop a test intended for clinical specimens that will invariably be under
one year of age. Moreover, data regarding the measurement of RNA integrity (as
was done in the Gerami publication from 2015) was not provided, even though
this would be valuable for comparing specimens of different ages.109
Altogether, the evaluation represented by Figure 3 does not answer the
question of whether an older specimen would have the same test result as a
younger version of itself.

Cook and colleagues (along with other studies from Castle Biosciences) also
failed to sufficiently address many other pre-analytic variables including,
but not limited to:113

  * Protocols for central pathologic review of cohort specimens:
  * How many pathologists participated? 
    * What specific features were evaluated in each slide?
    * How were discrepancies between outside report and internal review handled?
  * Protocols for diagnosis of sentinel lymph nodes? 
    * How many sections/levels were evaluated per lymph node?
    * Was immunohistochemistry used for every specimen to identify occult or subtle tumor deposits?
  * How much time passed between biopsy or wide excision and placing the specimen in fixative?
  * Was the fixation time (time in fixation before processing to FFPE) consistent for each specimen?
  * Was the same fixative (e.g., formalin) used for each specimen?
  * What was the time between tissue sectioning for slide creation and RNA extraction? Was this time consistent between different specimens?
  * When cDNA was "preamplified" prior to testing, was the process confirmed to consistently amplify all relevant genes to the same, consistent degree or were some amplifications more efficient than others?

These questions address known pitfalls in both the comparability of specimens
and the integrity of extracted RNA. Moreover, even if some of the above
questions were addressed during test development, the lack of transparency in
the published literature prevents clear assessment of the integrity of the
test development.  
  
In terms of study design, a prognostic test should ideally evaluate itself in
the context of the current standard of care. We would anticipate that a
prognostic test for malignancy would both compare its accuracy with the best
prognostic standards available and would also compare itself against real
world outcomes. Once accuracy is sufficiently established, proving clinical
utility becomes crucial. One of the key factors in determining clinical
utility is a test's impact on patient outcome. A test without an improvement
in patient outcome is not clinically utile for the purposes of Medicare
coverage.

The initial assessment of newly diagnosed melanoma is complicated. For the
primary melanoma alone, clinical and pathologic evaluations are critical for
developing a proper plan of management for the patient. This plan must
consider many factors both in the primary melanoma and the surrounding
clinical context, including exposure and family histories. The American Joint
Committee on Cancer (AJCC) acts as an authority on the grading and staging of
primary melanomas based on many clinical, radiologic, and pathologic
factors.191,192 Additionally, the AJCC provides extensive prognostic data tied
to the factors used in the grading and staging of melanomas. Many of these
factors are assessed during pathologic evaluation and include histologic
features such as tumor mitotic rate, surface ulceration, and Breslow depth. At
the same time, it must be recognized that AJCC staging is only one
consideration in a multitude of data points that are considered by the
clinical team when developing plans for patient management. For instance, a
melanoma subtype, which is not explicitly factored into the AJCC scoring, can
play a significant role in determining patient management. In general, the
term "melanoma" represents a category of malignancies that are actually
comprised of a spectrum of subtypes, each with their own etiologies,
behaviors, and properties. For instance, acral lentiginous melanoma is known
to be more aggressive than other subtypes of melanoma and have a poorer
prognosis.193 Without consideration of this subtype, a patient could be
misclassified as having a less dangerous form of melanoma based on AJCC
clinical and pathologic staging alone. For this reason, while AJCC staging is
invaluable to patient management in melanoma, it does not represent the only
clinical consideration in patient care.  
  
The development and assessment of DecisionDx-Melanoma relied heavily on
comparisons to AJCC clinical and pathologic staging and the factors used in
these AJCC scores. Often, the authors of DecisionDx-Melanoma studies would
focus primarily on a single factor, such as sentinel lymph node positivity,
and compare the prognostic value of that factor to the prognostic value of
DecisionDx-Melanoma. This strategy often set up false dichotomies since in
clinical practice a single prognostic factor such as sentinel lymph node
biopsy is not considered in isolation without considering other clinical data.
Even in more complicated, multifactorial comparisons, studies involving
DecisionDx-Melanoma failed to account for the whole clinical and pathologic
picture, sometimes only evaluating a limited number of factors used in AJCC
scoring when attempting to establish the prognostic validity of the test. This
can be seen in the variability of demographics and clinical information
provided from study to study. In general, most studies at least provided
information on patient age, Breslow thickness, presence/absence of tumor
ulceration, and AJCC clinical and pathologic stages. Conversely, most studies
_did not provide_ information regarding the subtype of melanoma, location of
primary tumor, presence/absence of transected tumor base, and presence/absence
of lymphovascular invasion. Moreover, none of the studies identified in the
Summary of Evidence provided sufficient information to determine the
interrelationships between demographic and clinicopathologic data points. For
instance, despite knowing the count of patients with a specific subtype of
melanoma, one could not further explore other characteristics within a
melanoma subtype group such as the average Breslow thickness per subtype group
or the AJCC clinical stages represented in a subtype group.116  
  
Of all the clinicopathologic factors used in describing melanoma, the Breslow
thickness is a central factor, critical in both AJCC clinical and pathologic
staging. Measuring Breslow thickness requires histologic identification of
both the surface of the melanoma and the deepest point of tumor growth.
Obviously, transection of the tumor base during biopsy or wide excision would
compromise the accurate measurement of Breslow thickness. Moreover, since AJCC
pathologic staging is primarily based on Breslow thickness (with subgrouping
currently based on presence or absence of ulceration), undermeasurement of
Breslow thickness can dramatically affect both clinical and pathologic stage
assignment. For instance, according to AJCC's 8th edition, the cutoff between
pathologic stage T2 and T3 tumors is a Breslow thickness of 2 mm. Just looking
at the pathologic stage without consideration of nodal or metastasis status, a
T2 could be AJCC clinical stage I or II depending on the presence or absence
of ulceration.192 However, a T3 melanoma will always be at least a clinical
stage II tumor. Undermeasurement of a T3 melanoma without ulceration would
drop the melanoma at least one clinical stage, from II to I. While this seems
to be a minor technicality, several DecisionDx-Melanoma studies draw
conclusions through comparison of clinical stage I and stage II melanomas
(such as Zager, 2018).117 Interestingly, most of the DecisionDx-Melanoma
studies do not present data on how many specimens were transected at the base
of tumor, although this metric does appear in three more recent
studies.110,126,194 In fact, the rate of transection in the more recent
studies is striking, seen in 39.5%, 34.9%, and 53.29% of all specimens
respectively.110,126,194 It is further notable that even with the presence of
transection, the specimens were still used in the papers' analyses and
conclusions.  
  
Limited patient follow-up proved to be another critical weakness in many of
the DecisonDx-Melanoma studies. DecisionDx-Melanoma advertises its results as
five year prognosticators for risk of recurrence, metastases, and/or death.186
At a baseline, data supporting this assertion must account for a minimum of
five years of patient follow-up, even if the patient experiences a recurrence
event. If the patient experiences a local recurrence, they may still develop
distant metastases and/or pass away from the melanoma within the five year
time frame, both events of which would be relevant to the DecisionDx-Melanoma
prognostics. Of all the studies reviewed in the summary of evidence, only one
study monitored all of its patients for a minimum of five years. 115 Even the
publication from Gerami and colleagues in 2015 that described the development
and validation of DecisionDx-Melanoma reported use of specimens with well
under five years of follow-up.109 Their training cohort included patients with
0.06 years of follow up (claiming a median of 6.8 years for all training
specimens), and their validation cohort included patients with 0.5 years of
follow-up (claiming a median of 7.3 years all validation specimens).109
Overall, studies demonstrated _median_ follow-ups of patients without disease
recurrence that ranged from 1.5 to 7.5 years.115,117  
  
Publications involving DecisionDx-Melanoma also lacked consistent definitions
from study to study. Definitional inconsistency was well captured by Marchetti
and colleagues in the Melanoma Prevention Working Group, which convened in
2020 to discuss prognostic GEP tests for melanoma.108 For instance, the
definition of "melanoma recurrence," as used in the outcome metrics of
Disease-Free Recurrence (DFS) or Recurrence-Free Survival (RFS), differed from
study to study. In Hsueh (2017), RFS was defined by regional and distant
metastases while in Zager (2018) RFS included local metastases in addition to
regional and distant metastases and excluded sentinel lymph node
positivity.115,117 Podlipnik (2019) used the term DFS, defining it by
"relapse" without further detail, and Keller (2019) used the term RFS without
providing a clear definition altogether.121,122 A majority of studies
indicated that the outcome risk estimates represented the first five years
following a primary diagnosis of melanoma with only a few studies reducing the
risk estimate to cover only the first three years. Note again that only one
study from Zager and colleagues in 2018 included patients all followed-up for
a minimum of five years.117  
  
As evidenced in the previous paragraphs, there are several weaknesses in both
the quality and thoroughness of data collection in DecisionDx-Melanoma studies
as well as methodologic and definitional inconsistencies. In terms of
conclusions and results, we see the potential corollaries of these weaknesses.
For instance, the PPV and NPV of these studies are particularly striking. Not
all papers used these metrics when evaluating their results, but when PPV and
NPV were provided, their values changed dramatically from study to study. This
finding is particularly relevant when examining the latest version of the
DecisionDx-Melanoma report.186 The DecisionDx-Melanoma report supplies a three
to four tier prognostic classification of melanomas (Classes 1A, 1B/2A, 2B).
In one of the interpretation tables in the DecisionDx-Melanoma report, the
classes (1A, 1B/2A, and 2B) are paired with the AJCC clinical stages (I, II,
or III) to provide five year risk estimates for three potential outcomes:
Melanoma-Specific Survival(MSS), Distant Metastasis-Free Survival (DMFS), and
RFS. According to the report, this interpretation table may be referenced to
Greenhaw and colleague's publication in 2020.107 If we look at Greenhaw's
meta-analysis study, we find the PPV and NPV is only provided for RFS (PPV
46%; NPV 92%) and DMFS (PPV 35%; NPV 93%). Remember that PPV and NPV scores
represent the number of true results divided by the number of all positive or
negative results respectively, both true and false results. This means that
for patients with a positive result, 35 of 100 patients will experience a
distant metastasis (positive for this event) within five years of their
original melanoma diagnosis and 65 _will not experience_ a distance metastasis
within five years of their original diagnosis. Negative predictive value
provides the opposite reassurance, namely that a negative result means 93 of
100 patients will NOT experience a distant metastasis within five years while
7 of 100 patients will still experience a distant metastasis within five
years. The reason the concept of PPV and NPV is described here in such basic
detail is to highlight the risks of relying on the Class designation provided
by DecisionDx-Melanoma to prognosticate patient outcome. The concern for test
accuracy is further compounded when one considers that the PPV and NPV are
different from study to study. The PPVs for DMFS for studies as described in
the Summary of Evidence ranged from 14.6% to 62%.117,124 For reference, the
only study with a minimum of five years of follow-up for all patients recorded
a PPV of 40% for DMFS.117  
  
Several studies were published addressing the clinical utility of DecisionDx-
Melanoma. All of these studies focused on how DecisionDx-Melanoma would impact
patient management, typically by measuring to what degree and how the test
result changed patient management. Several of the studies utilized
hypothetical scenarios and polled providers (ranging from trainee residents to
practicing attendings) on how they would respond to these scenarios with and
without DecisionDx-Melanoma results.195-198 These studies did not assess real
world interactions of the test with patient management. A couple of studies
prospectively measured changes in physician behavior and patient management
when provided with DecisionDx-Melanoma results for their patients.184,199
However, as defined for the purposes of this LCD, a clinically utile test must
positively affect patient outcome. While these six studies altogether
demonstrated changes in physician behavior and/or patient management when
DecisionDx-Melanoma was used, _none of the studies demonstrated how this
positively impacted patient outcome_ , ie, increasing patient survival. A
demonstration of clinical utility could be accomplished in a clinical trial
where patients' overall survival is compared between patients where the test
is used or patients managed without test results. To date, such a trial has
not been performed for DecisionDx-Melanoma.  
  
Finally, as discussed in an editorial review by WH Chan, MS and H Tsao MD, PhD
in 2020, management of cutaneous melanoma has dramatically changed within the
past few years.102 Prognosis determination plays less of a role in determining
patient management when other factors are used to determine predictive
(therapy-related) outcomes. For instance, sentinel lymph node biopsy status is
used to determine if a patient should receive adjuvant chemotherapy. Targeted
sequence analyses for specific gene mutations (such as _BRAF_ V600E) now can
inform clinicians on which targeted therapy would most benefit their patients.
This changing landscape appears to be recognized by Castle Biosciences, who
most recently added clinicopathologic algorithmic prognostic results to their
test.186 Unfortunately, there is currently insufficient peer-reviewed
literature to establish the clinical validity and utility of these new
features: two papers as of the writing of this LCD, both of which are
validation papers, one for i31-SLNB and the other for i31-ROR.110,111 Without
more published literature, including clinical trials, the i31-SLNB and i31-ROR
cannot be considered clinically valid or utile for Medicare patients.  
  
It is beyond the scope of this LCD to provide comprehensive analysis of all
individual papers reviewed. While the major concerns regarding peer-reviewed
literature for DecisionDx-Melanoma are well characterized above, many other
concerns were not detailed and still should be addressed, even if not detailed
in this Analysis of Evidence. Examples of concerns not expounded in the
analysis of evidence include:

  * Inadequate information regarding patients with hereditary melanoma disorders
  * Inadequate study of the effects of therapies on measured outcomes
  * Inadequate information comparing melanomas with different mutational profiles (e.g., tumors with _BRAF_ V600E)

In summary, the body of peer-reviewed literature concerning DecisionDx-
Melanoma is insufficient to establish the analytic validity, clinical
validity, and clinical utility of this test in the Medicare population. As
such, this test does not currently meet medically reasonable and necessary
criteria for Medicare patients and will not be currently covered.

**DecisionDx-SCC  
  
** In 2020, Wysong and colleagues described a 40 GEP test (which would later
become DecisionDx-SCC) for risk classification in cases of cutaneous squamous
cell carcinoma (cSCC).134 Their biomarker study aimed to validate a GEP test
that could assess the risk of metastasis in cSCC. Fundamentally, DecisionDx-
SCC is a GEP that analyzes 34 genes of interest (considered by Castle
Biosciences to be significantly informative of the prognosis of cSCC) and six
control genes.  
  
Gene expression profiles are founded on the principle of differential gene
expression in a cell of interest, like a cancer cell, when compared to
background cells, namely all other cells in the surrounding tissue. Tissues
are comprised of many different types of cells. Skin, for instance, is
comprised of a variety of cell types including melanocytes, keratinocytes,
immune cells such as lymphocytes, structural cells such as fibroblasts and
fibrocytes, hair generating cells, and specialized glandular cells such as
apocrine cells. Many different factors influence gene expression in a cell
including changes in the cell's surroundings (as is seen in a response to sun
damage) and the cell's stage of development, such as whether a cell is part of
a germinal layer (and mitotically active) or terminally differentiated. This
means that even among cells of the same type (same lineage) the gene
expression profile can be different. Considering all of these complexities, a
GEP from cells of interest can be difficult to untangle from the GEP of
background cells.  
  
In terms of DecisionDx-SCC, the test development publication from Wysong and
colleagues did not adequately address these complexities inherent to GEPs.134
Many key questions were left unaddressed, such as: how did the relative
quantity of tumor versus non-tumor cells affect test sensitivity, did lighter
or darker skin tones affect the test outcome, did the test perform differently
between histologically distinct cSCCs, and did the presence of sun damage
affect test results? As a more specific example, we would expect the presence
of TILs to affect the outcome of a GEP test. Tumor infiltrating lymphocytes
change the composition of the background tissue, increasing the density of
background cells. Additionally, TILs are thought to interact with tumor cells,
which would suggest tumor cells would respond with differential GEP
expression. These factors alone could influence the expression profile of the
40 genes in the DecisionDx-SCC test, but until this scenario is tested, its
impact is unknown. In summary, understanding the potential pitfalls of GEP
testing is critical for understanding the reliability, performance, and
accuracy of a GEP test.  
  
An additional validation study from Borman and colleagues was published in
2022.135 This paper primarily focused on whether or not the DecisionDx-SCC
test would provide "actionable class call outcomes." They did not provide any
information regarding patient follow-up or accuracy of the class call
outcomes. While they did test for replication and precision, the sample sizes
for these assessments were considerably smaller than the overall cohort used
in the study. Additionally, they (as seen in other studies from Castle
Biosciences) failed to sufficiently address many other pre-analytic variables
including, but not limited to:

  * Protocols for central pathologic review of cohort specimens: 
    * How many pathologists participated?
    * What specific features were evaluated in each slide?
    * How were discrepancies between outside report and internal review handled?
  * How much time passed between biopsy or wide excision and placing the specimen in fixative?
  * Was the fixation time (time in fixation before processing to FFPE) consistent for each specimen?
  * Was the same fixative (e.g., formalin) used for each specimen?
  * What was the time between tissue sectioning for slide creation and RNA extraction? Was this time consistent between different specimens?
  * When cDNA was "preamplified" prior to testing, was the process confirmed to consistently amplify all relevant genes to the same, consistent degree or were some amplifications more efficient than others?

These questions address known pitfalls in both the comparability of specimens
and the integrity of extracted RNA. Moreover, even if some of the above
questions were addressed during test development, the lack of transparency in
the published literature prevents clear assessment of the integrity of the
test development.  
  
The additional observational studies produced by Castle Biosciences included
three cohort studies and a case series, all published between 2020 and
2022.136-138  

Farberg and colleagues published a paper, using the same dataset as the
validation study, aiming to assess whether or not the DecisionDx-SCC test
could be integrated into the existing NCCN guidelines for the management of
patients with cSCC.136 Another paper, from Aaron and colleagues, also used
samples from the same dataset as the original validation study.137 This paper
assessed whether DecisionDx-SCC could predict recurrence and "provide
independent prognostic value to complement current risk assessment methods."
The third cohort study was from Ibrahim and colleagues whose paper attempted
to clinically validate the DecisionDx-SCC test.138 In general, these studies
had the same issues as those outlined above, and in the two studies that
assessed rates of recurrence, patient follow-up data was not reported. The
studies stated that "cases had a documented regional or distant metastasis, or
documented follow-up of at least three years post-diagnosis of the primary
tumor without a metastatic event" but did not give any further
information.136-138 DecisionDx-SCC advertises its results as three year
prognosticators for risk of recurrence, metastases, and/or death. At a
baseline, data supporting this assertion must account for a minimum of three
years of patient follow-up, even if the patient experiences a recurrence
event. If the patient experiences a local recurrence, they may still develop
distant metastases and/or death may result from the cSCC within the three year
time frame, both events of which would be relevant to the DecisionDx-SCC
prognostics.  
  
Au and colleagues described two cases of cSCC, one with fatal recurrence and
one without recurrence, and the retrospective results of DecisionDx-SCC
testing on tissue samples from each case.139 While the results did show that
the recurrent case was classified as high risk of recurrence and the non-
recurrent case was classified as low risk, two cases are insufficient to
provide meaningful insight into generalizability of the test in the normal
population. Additionally, there is no evidence that the test results would
have resulted in a change of management decisions for the cases or in eventual
patient outcomes.  
  
Aside from the paper from Au and colleagues, papers addressing clinical
utility included surveys, a panel review, and literature reviews.128-133 These
papers had a number of shortcomings and limitations, including, but not
limited to:

  * A high likelihood of selection and response bias in the surveys
  * No description of survey participant recruitment methods
  * An expert panel composed of Castle Bioscience employees, consultants, and researchers
  * Reviews that cited the authors', or their colleagues', previous work without acknowledgement
  * Lack of methods descriptions or, in review papers, inclusion criteria

Notably, there are no significant studies assessing patient outcomes or
clinician treatment decisions in a real-world setting following a DecisionDx-
SCC test. Without such data, clinical utility cannot be determined. For
example, a demonstration of clinical utility could be accomplished in a
clinical trial where patients' overall survival is compared between patients
tested with DecisionDx-SCC and patients managed without this test. To date,
such a trial has not been performed for DecisionDx-SCC.  
  
In summary, the body of peer-reviewed literature concerning DecisionDx-SCC is
insufficient to establish the analytic validity, clinical validity, and
clinical utility of this test in the Medicare population. As such, this test
does not currently meet medically reasonable and necessary criteria for
Medicare patients and will not be currently covered.  
  
 **UroVysion fluorescence in situ hybridization** **(FISH) - Abbott  
  
**In order to systematically evaluate the available research surrounding the
uFISH test for bladder cancer it is important to first discuss the test within
the context of the test information provided for clinicians and patients. The
product page on Abbott Molecular's website states that the uFISH test "is
designed to detect aneuploidy for chromosomes 3, 7, 17, and loss of the 9p21
locus via fluorescence in situ hybridization (FISH) in urine specimens from
persons with hematuria suspected of having bladder cancer."200 UroVysion
fluorescence _in situ_ hybridization (uFISH) is often compared to urine
cytology, and the manufacturer specifically states that the uFISH test has
"greater sensitivity in detecting bladder cancer than cytology across all
stages and grades." A positive result of the test is defined by the
manufacturer as four or more cells out of 25 showing gains for two or more
chromosomes (3, 7, or 17) in the same cell, or 12 or more out of 25 cells
having no 9p21 signals detected. However, not all bladder cancers have these
alterations, and these chromosomal changes can also be seen occasionally in
healthy tissues and other types of cancer, as noted by Bonberg and colleagues
(2014) and Ke and colleagues (2022).140,161 Additionally, genomic profiles and
chromosomal abnormalities can vary between low grade and high grade bladder
cancer which can make the detection of low grade cancer less likely.  
  
As noted by Lavery and colleagues in 2017 and Mettman and colleagues in 2021,
much of the literature assessing uFISH uses a variety of definitions for
positivity.150,157 Lavery aimed to overcome these shortcomings by using a
strict definition for a positive uFISH test - which used the manufacturer's
definition along with the addition of "tetraploidy in at least 10
morphologically abnormal cells."157 Tetraploidy can be seen in normal cell
division and in other non-cancerous processes, so this addition was made to
account for false-positive results from the uFISH test. The blinded study
described in the paper found no significant difference between uFISH and urine
cytology, with sensitivities of 67% and 69% and specificities of 72% and 76%,
respectively. Additionally, the authors found that inclusion of the
tetraploidy requirement in their definition effectively reduced false-positive
rates, but also determined that some bladder cancer tumors do not have the
chromosomal alterations for which uFISH assesses (30% of the tumors tested by
the authors). Mettman similarly attempted to increase the accuracy of the
uFISH test by including tetraploidy in their positivity definition.150 The
authors reported considerably different results than the paper from Lavery,
with sensitivity of uFISH ranging from 58-95% depending on the definition
used, and a specificity using each definition of 99%. The study was
specifically evaluating the test in patients suspected of having
_pancreatobiliary stricture malignancies_ , however, which could account for
the differences seen between the two papers.  
  
Sassa and colleagues (2019) compared the uFISH test to urine cytology in 113
patients prior to nephrouterectomy and 23 volunteers with no history of
urothelial carcinoma.153 In cases of high-grade urothelial carcinoma (HGUC),
the sensitivity, specificity, positive PPV, and NPV for detection by urinary
cytology were 28.0%, 100.0%, 100.0%, and 31.6%, respectively. For uFISH, these
values were 60.0%, 84.0%, 93.8%, and 41.2%, respectively. In cases of low-
grade urothelial carcinoma (LGUC), however, the results were significantly
worse, with sensitivities for both UroVysion and urine cytology of only 30%.  
  
UroVysion fluorescence in situ hybridization (uFISH) has also been assessed as
a prognostic test for the recurrence of bladder cancer in patients and as a
means of identifying recurrence in patients sooner. A paper from Guan and
colleagues in 2018 evaluated the value of uFISH as a prognostic risk factor of
bladder cancer recurrence and survival in patients with upper tract urothelial
cancer (UTUC).155 One hundred and fifty-nine patients in China received a
uFISH test prior to surgery and were then monitored for recurrence. While the
authors did indicate that there was a relationship between uFISH results and
recurrence, the results were non-significant (p=.07). Liem and colleagues
(2017) conducted a prospective cohort study to evaluate whether uFISH can be
used to early identify recurrence during treatment with Bacillus Calmette-
Guerin (BCG).156 During the study, three bladder washouts at different time
points during treatment (t0 = week 0, pre-BCG, t1 = 6 weeks following
transurethral resection of bladder tumor [TURBT], t2 = 3 months following
TURBT) were collected for uFISH from patients with bladder cancer that were
treated with BCG. The authors found no significant association between a
positive uFISH result at t0 or t1 but found that a positive uFISH result at t2
was associated with a higher risk of recurrence. Additionally, in 2020, Ikeda
and colleagues published a paper that aimed to evaluate the relationship
between uFISH test results following TURBT and subsequent intravesical
recurrence.158 They indicated that uFISH test positivity was a prognostic
indicator for recurrence following TURBT. However, recurrence in patients with
two positive uFISH tests was only 33.3%, and in patients with one positive
uFISH test (out of two tests total) the recurrence rate was only 16.5%.  
  
Limited patient follow-up was a repeated weakness in papers evaluating uFISH
to detect or predict recurrence. For example, the paper from Guan had a median
follow-up of 27 months (range: 3-55 months), the paper from Liem had a median
of 23 months of follow-up (range: 2-32 months), and the paper from Ikeda had a
median follow-up of 27 months (range: 1-36.4).155,156,160 The ranges of
follow-up indicate that at least one patient was only followed for one month,
and at least half of all patients had less than the median follow-up time.
This limited follow-up means that cases of recurrence were likely overlooked
in the studies. Even in cases where shorter follow-up may have been due to the
early detection of recurrence, lack of continued follow-up could result in
overlooking a patient with reduced survival following a recurrence; this
additional information would be relevant to the uFISH prognostics.  
  
Other observational studies identified included two cohort studies from Nagai
and colleagues (2019) and Gomella and colleagues (2017), a case-control study
from Freund and colleagues (2018), and a cross-sectional study from Todenhofer
and colleagues (2014).152,154,158,159 Each of these studies reported similar
results and limitations to the papers described above. Additionally, Breen and
colleagues (2015)45 evaluated uFISH in a comparative study with other tests
used to detect urothelial carcinoma in urine. The other tests included
Cxbladder Detect, cytology, and NMP22. The study utilized five cohorts of
patients, only one of which evaluated all four tests for the entire cohort.
Data from the five cohorts were evaluated and integrated, with several
different imputation analyses utilized to fill in for missing test values and
create a "new, imputed, comprehensive dataset." The authors report that before
imputation uFISH had a sensitivity of 40% (the lowest of the four tests) and a
specificity of 87.3% (the second lowest of the four tests). Utilizing several
different imputation methodologies, similar findings for comparative
sensitivities and specificities were seen, leading to the conclusion that the
imputed data sets were valid, with the best imputation methodology being the
3NN model. In this 3NN model, uFISH had considerably lower sensitivity than
the other three tests and lower specificity than two of the three tests.  
  
In recent years, other authors have conducted reviews and meta-analyses in
order to better address the validity and utility of uFISH, and other urinary
biomarkers in general. In 2022, Zheng and colleagues published a meta-analysis
and review that assessed the prognostic value of uFISH to detect recurrence in
the surveillance of non-muscle invasive bladder cancer (NMIBC).141 They
identified 15 studies from 2005-2019 that met their inclusion criteria and in
their meta-analysis determined that the pooled sensitivity of uFISH in
detecting recurrence was 68% (95% CI:58-76%) and the pooled specificity was
64% (95% CI: 53-74%).  
  
Sciarra and colleagues (2019) conducted a systematic review to evaluate the
diagnostic performance of urinary biomarkers for the initial diagnosis of
bladder cancer.146 The review identified 12 studies addressing uFISH, with a
combined sample size of 5,033 uFISH test results. The mean sensitivity was
64.3% and the median was 64.4%, with a range of 37-100%. Additionally, the
mean specificity was 88.4% and the median was 91.3%, with a range of 48-100%.  
  
Another recent paper identified was from Soputro and colleagues (2022), who
conducted a literature review and meta-analysis to evaluate the diagnostic
performance of urinary biomarkers to detect bladder cancer in primary
hematuria.145 The authors identified only two studies assessing uFISH that met
their inclusion criteria. The pooled sensitivity and specificity of uFISH in
the identified studies was 0.712 and 0.818, respectively. The authors noted
that the "current diagnostic abilities of the FDA-approved biomarkers remain
insufficient for their general application as a rule out test for bladder
cancer diagnosis and as a triage test for cystoscopy in patients with primary
hematuria."145  
  
Sathianathen and colleagues also conducted a literature review and meta-
analysis to evaluate the performance of urinary biomarkers in the evaluation
of primary hematuria.148 The authors were only able to identify one paper
addressing uFISH which met their inclusion criteria, which determined that
uFISH was comparable to the other biomarker tests being evaluated. However,
given the fact that only one paper was identified which met the authors'
criteria for inclusion, the findings regarding uFISH could not be properly
assessed.  
  
The most recent meta-analysis identified was written by Papavasiliou and
colleagues (2023) who assessed the diagnostic performance of urinary
biomarkers potentially suitable for use in primary and community care
settings.143 The authors identified 10 studies addressing the diagnostic
performance of uFISH between 2000 and 2022. These studies had a wide range of
sensitivities (0.38-0.96) but a narrower range of specificities (0.76-0.99).  
  
Three additional literature reviews were identified from Bulai and colleagues
(2022), Miyake and colleagues (2018), and Nagai and colleagues (2021). Each of
these papers noted significant issues with the literature support for these
biomarkers in general, and uFISH in particular, but also lacked non-ambiguous
inclusion criteria, search methods, and other necessary information to
validate their assessments.142,144,147  
  
Only two identified papers significantly addressed the clinical utility of the
uFISH test - Guan (2018) and Meleth (2014).149,155 Guan noted that they did
not find any association between a positive uFISH test and survival in
patients; however, as noted above, limited follow-up was a significant
shortcoming of their study.155 Meleth and colleagues conducted a review of the
available literature and were unable to find any papers that met their
inclusion criteria which directly assessed patient survival, physician
decision-making, or downstream health outcomes in relation to uFISH test
results.149 This lack of information regarding clinical utility is notable and
without studies assessing for improvement in patient outcomes in a real-world
setting, the evidence supporting the uFISH test for use in the Medicare
population is severely lacking.  
  
It is also important to note that no studies were identified that established
that uFISH was able to accurately distinguish between urothelial carcinoma and
other cancers or other non-cancer urological conditions. As noted above, the
specific chromosomal changes that uFISH uses to identify urothelial carcinoma
have been identified in non-cancerous tissues and other types of carcinomas.
This very notable gap in the identified research included a lack of details or
definitions for non-urothelial cancers, of which many would feed into the
urinary system, including prostate cancers, renal cancers, and metastatic or
locally invasive cancers from other organs. With the knowledge that the
chromosomal changes that uFISH uses to identify urothelial carcinoma can also
be found in the context of other malignancies and non-malignancies, and that
their identification in urine may not coincide with clinically detectable
(e.g., cystoscopically visible) carcinoma, confusion could arise with false
positives, especially when the PPV of uFISH tests tends to be very low. If
numerous false positive results in uFISH are accepted as an inherent trait of
the test, providers may not be as vigilant in closely following patients with
a positive uFISH result after a negative cystoscopy. In addition, providers
may not search for other malignancies as a potential cause for the "false
positive" uFISH result.  
  
It is beyond the scope of this LCD to provide comprehensive analysis of all
individual papers reviewed. However, the major concerns regarding peer-
reviewed literature for uFISH are well represented above.  
  
In summary, the body of peer-reviewed literature concerning UroVysion FISH is
insufficient to establish the analytic validity, clinical validity, and
clinical utility of this test in the Medicare population. As such, this test
does not currently meet medically reasonable and necessary criteria for
Medicare patients and will not currently be covered.  
  
 **Colvera - Clinical Genomics  
  
**In April 2015, Pedersen and colleagues published a validation paper in which
they described a blood test which would later come to be named Colvera.162 The
test was designed to identify two methylated genes, namely branched-chain
amino acid transaminase 1 ( _BCAT1_ ) and ikaros family zinc finger protein 1
( _IKZF1_ ). Clinical Genomics had previously identified both genes as being
important in the screening of colorectal cancer (CRC). Their study used
methylation-specific PCR assays to measure the level of methylated _BCAT1_ and
_IKZF1_ in DNA extracted from plasma obtained from colonoscopy-confirmed 144
healthy controls and 74 CRC cases. The authors found that their test was
positive in 77% of cancer cases and 7.6% of controls. This study, however,
failed to sufficiently address many pre-analytic variables, such as the
protocols for pathologic review and diagnosis.  
  
Later that same year, another validation paper (also led by Pedersen) was
published.161 This cohort study included both prospective and retrospective
methods to collect plasma samples from 2,105 volunteers and reported a test
sensitivity of 66%, (95% CI: 57-74). For CRC stages I-IV, respective
positivity rates were 38% (95% CI: 21%-58%), 69% (95% CI: 53%-82%), 73% (95%
CI: 56%-85%) and 94 % (95 % CI: 70%-100%). Specificity was 94% (95% CI:
92%-95%) in all 838 non-neoplastic pathology cases and 95% (95% CI: 92%-97%)
in those with no colonic pathology detected (n = 450). It is important to note
that case diagnosis was performed by one independent physician and that there
was no control of colonoscopy or pathology procedures. The authors stated that
this was due to their aim "to assess marker performance relative to outcomes
determined in usual clinical practice."

An additional validation paper was published by Murray and colleagues in 2017
which assessed both the analytic and clinical validity of the Colvera blood
test.164 The authors reported using archived samples from the previous study
from Pedersen and colleagues (n=2,105 samples), but only used a subset of
these archived samples (n=222 specimens, 26 with cancer).162,163 The authors
did not describe selection criteria for these samples specifically, namely,
whether or not sample selection was a randomized process or why a majority of
the archived specimens were not selected. Murray and colleagues found that the
Colvera test had good reproducibility and repeatability with a reported
sensitivity of 73.1% and specificity of 89.3%. In addition to questions
regarding sample selection, other questions were left unanswered in the paper
including, but not limited to:

  * Does the precision of the test vary in different stages of cancer?
  * Does treatment (such as chemotherapy/radiation) impact the precision of the test?
  * For apparent false positives, would a longer follow-up reveal them to be true positives?
  * In general, would serial sampling or longitudinal data impact the precision estimates of the test?

An additional paper published in 2018 from Murray and colleagues, sought to
establish the clinical validity of the Colvera test.170 In the paper, the
authors tested patients post-surgery (median of 2.3 months after surgery) and
followed them to establish whether or not recurrence was detected. Median
follow-up for recurrence was 23.3 months, with an IQR 14.3-29.5 months.
Twenty-three participants were diagnosed with recurrence, but the Colvera test
was positive in 28 participants. It should be noted that the cancer treatment
varied considerably between cases, even between patients with a positive
Colvera test result and those with negative result. Only 61% of patients with
a positive Colvera result completed their initial course of treatment, while
87% of patients with a negative result completed the initial course of
treatment. The authors state that this was due "to either patients declining
ongoing therapy, or due to comorbidities or complications precluding a full
course of treatment."170 This could have significantly confounded the results
given the higher likelihood of recurrence in a patient who did not receive a
full course of treatment as opposed to patients who did receive a full course.
Additionally, while the median follow-up was 23.3 months, half of the patients
had a shorter follow-up than the median, and without long-term follow-up,
additional cases of recurrence were likely missed.  
  
Five other papers, all cohort studies, were identified that assessed the
clinical validity of the Colvera test, in particular, Colvera's performance
compared to carcinoembryonic antigen (CEA) and/or fecal immunochemical tests
(FIT).165-167,169,171 These papers from Clinical Genomics found the
sensitivity of Colvera to be 62-68% and the specificity to be 87-97.9%, better
than the results for both CEA and FIT in the same studies.  
  
Young and colleagues (2016) assessed 122 patients that were being monitored
for recurrent CRC (28 of whom had confirmed recurrence) to determine if
Colvera or CEA was more accurate.165 The study only obtained a blood sample 12
months prior to or three months following verification of a patient's
recurrence status. This method of determining test accuracy was problematic,
in particular because the follow-up lengths varied considerably between
patients. In patients with confirmed recurrence, the median follow-up was 28.3
months, with an IQR of 21.9-41.0. In patients without confirmed recurrence,
the median follow-up was only 17.3 months, with an IQR of 12.0-29.2. This
indicates that the majority of "confirmed" cases of no recurrence were
followed for less time than the median follow-up in recurrent cases. Without
an adequate length of follow-up, it is certainly possible that cases of
recurrence would be missed. Additionally, while the authors did report on some
longitudinal data (the concordance of test results in the same patient taken
at different times), that data was limited to only 30 cases out of the total
122. Of the cases that did have longitudinal data, multiple cases were
reported to have false-positive test results. When combined with the
insufficient follow-up already discussed, the likelihood of incorrectly
identified false-positive tests increases considerably.  
  
Musher and colleagues (2020) and Symonds and colleagues (2020) also compared
Colvera to CEA for detecting recurrent CRC.166,167 Musher, similar to the
paper from Young (2016), also had short follow-up periods and insufficient
longitudinal data (median follow-up was 15 months, range: 1-60 months).166,167
Symonds (2020), however, did obtain relatively more longitudinal data and
longer follow-up periods, and showed that months prior to imaging
confirmation, Colvera could show a positive result.167 However, without any
assessment of the impact of test results on clinical outcomes, the utility of
the test cannot be ascertained. Also, in the papers from Young, Musher, and
Symonds, CEA sensitivity was considerably lower than normally reported in
other literature (32%, 48%, and 32%, respectively).165-167 While not a direct
reflection on the validity or utility of Colvera, it is important to note this
discrepancy since the authors were comparing Colvera to the CEA test.  
  
The two additional cohort studies evaluating Colvera, Symonds and colleagues
(2016) and Symonds and colleagues (2018), had similar findings and
shortcomings as the three studies described above, with test sensitivities of
62% in both papers and similar study designs.169,171  
  
Finally, the paper from Cock and colleagues (2019) assessed the precision of
both Colvera and FIT testing in the detection of sessile serrated
adenomas/polyps (SSPs).168 For this study, the authors used the same samples
that were used in Symonds and colleagues (2016).169 While the paper did
address pre-analytic variables and other shortcomings more sufficiently than
the previous studies discussed, the results do not support the use of Colvera
for the detection of SSPs. Forty-nine SSPs were identified during the
colonoscopies of 1,403 participants who were also tested with the Colvera
test. In those patients with SSPs, the Colvera test only had a sensitivity of
8.8%, and when combined with FIT, the sensitivity only increased to 26.5%.  
  
Notably, there are no studies assessing patient outcomes or clinician
treatment decisions in a real-world setting following a Colvera test. Without
such data, clinical utility cannot be determined. One of the key factors in
determining clinical utility is a test's impact on patient outcomes. For
example, a demonstration of clinical utility could be accomplished in a
clinical trial where patients' overall survival is compared between patients
tested with Colvera and patients managed without this test. To date, such a
trial has not been performed for Colvera.  
  
In general, papers assessing the validity of the Colvera test for CRC have a
number of shortcomings, including short follow-up time, insufficient
longitudinal data, insufficient description of study methodology, and a
failure to sufficiently address important pre-analytic variables.
Additionally, no paper has been published addressing the clinical utility of
Colvera; a test without an improvement in patient outcomes is not clinically
useful for the purposes of Medicare coverage.  
  
In summary, the body of peer-reviewed literature concerning Colvera is
insufficient to establish the analytic validity, clinical validity, and
clinical utility of this test in the Medicare population. As such, this test
does not currently meet medically reasonable and necessary criteria for
Medicare patients and will not be currently covered.  
  
 **PancreaSeq  Genomic Classifier, Molecular and Genomic Pathology
Laboratory, University of Pittsburgh Medical Center  
  
**Due to the lack of peer-reviewed literature identified, and thus a lack of
peer-reviewed evidence detailing analytic and clinical validity and clinical
utility, this test is currently non-covered for the Medicare population.