Coverage Policy Manual
Policy #: 2004021
Category: Laboratory
Initiated: May 2004
Last Review: December 2023
  Proteomics, Screening and Detection of Cancer (e.g., OvaCheck)

The genetic basis of cancer has been an intense research focus; however, genetic mutations do not reflect the complicated interactions between individual cells, tissue, and organs. Proteins are the functional units of cells and represent the end product of the interactions among the underlying genes. Research interest has been increasing in the field of proteomics (referring to the protein product of the genome), in an effort to improve upon screening and detection efforts for malignancies.
Serum protein biomarkers
Current diagnostic and follow-up serum biomarkers in clinical oncology (e.g., prostate specific antigen [PSA, prostate cancer], CA-125 [ovarian cancer]), involve identifying and quantifying specific proteins, but limitations may include non-specificity and elevation in benign conditions.
Ovarian cancer is the leading cause of death from gynecologic malignancy in the U.S., as most patients present with advanced disease with a 5-year survival rate between 15–45%. However, if the disease is caught in Stage I, survival rates are 95%. Therefore, there is great interest in a biomarker to detect ovarian cancer in its earliest stages, as current screening methods are inadequate.
Serum measurements of PSA are used as a screening method for detecting prostate cancer. Very low or very high serum PSA results are most reliable in determining cancer risk. However, values often fall within a range that is nonspecific, and thus many patients end up undergoing biopsy for benign disease. Proteomics has been proposed as a technique to further evaluate cancer risk in this diagnostic “gray” zone.
Proteomics involves the use of mass spectrometry to study differences in patterns of protein expression. While patterns of protein expression have been proposed to yield more biologically relevant and clinically useful information than assays of single proteins, many limitations in the use of proteomics exist:
In contrast to genomics, where amplification techniques like polymerase chain reaction (PCR) allow for the investigation of single cells, no technology is available at the protein level. Other issues between studies have been lack of uniform patient inclusion and exclusion criteria, small patient numbers, absence of standardized sample preparations and limited analytical reproducibility.
Proteomic tests
Correlogic Systems, Inc. has developed a serum-based test using proteomics for the early detection of epithelial ovarian cancer called OvaCheck®. The test is based on proteomic patterns detected in the serum, which are further analyzed with the use of a mass spectrometer to profile a population of proteins based on their size and electrical charge. This type of analysis contains thousands of data points, which undergo further sophisticated computer analysis using artificial intelligence-based algorithms to identify a pattern that is consistent with ovarian cancer.
Originally, the manufacturer had assumed that the test would not be subject to approval by the U.S. Food and Drug Administration (FDA), since the test would be performed exclusively at one reference laboratory and testing materials do not cross state lines (i.e., a “home brew” test). However, in 2004, the FDA determined that the software used to perform the analysis was considered a medical device and under the FDA premarket review jurisdiction. At this time, Correlogic Systems, Inc. is conducting clinical trials on OvaCheck® at sites in the U.S. and abroad, and it is not commercially available.
Correlogic is also in the process of developing proteomic blood tests for the detection of colorectal, breast (MammoCheck®) and prostate cancer (ProstaCheck®).  
Biodesix  developed a proteomic test used to identify patients that are likely to be good or poor candidates for therapy with EGFR-TKI therapy.  The test uses matrix assisted laser desorption/ionization time of flight (MALDI-TOF) mass spectrometry to identify patients as VeriStrat Good or VeriStrat Poor.  This test has not been reviewed or approved by the US Food and Drug Administration.  
There are no CPT codes that are specific to this type of testing.  A combination of molecular diagnostic codes or one of the following codes might be used to report the test:
    • 83788 Mass spectroscopy and tandem mass spectrometry (MS, MS/MS), analyte not elsewhere specified, qualitative, each specimen
    • 83789 Mass spectroscopy and tandem mass spectrometry (MS, MS/MS), analyte not elsewhere specified, quantitative, each specimen
    • 84999 Unlisted chemistry procedure

Does Not Meet Primary Coverage Criteria Or Is Investigational For Contracts Without Primary Coverage Criteria
Analysis of proteomic patterns in serum for screening and detection of any type of cancer does not meet member benefit certificate primary coverage criteria that there be scientific evidence of effectiveness.
For members with contracts without primary coverage criteria, analysis of proteomic patterns in serum for screening and detection of any type of cancer is considered investigational. Investigational services are specific contract exclusions in most member benefit certificates of coverage.

There has been considerable discussion regarding the potential role for proteomics for cancer screening and detection (Reymond,2007; Lin,2009; Dziadziuszko,2008; Lomnystska, 2007; Unwin,2007); however, there are inadequate data in the peer-reviewed literature to permit scientific conclusions regarding ovarian, prostate, or other malignancies.
Ovarian Cancer
Petricoin and colleagues (2002) reported on the technical feasibility of proteomic screening in a test series of serum from 50 patients with and 50 patients without ovarian cancer.  The spectra of proteins were analyzed by an iterative searching algorithm that identified a cluster pattern that segregated the patients with cancer from those without. This discovered pattern was then used to classify an independent set of 116 masked serum samples; 50 from women with ovarian cancer and 66 from unaffected women or those with nonmalignant conditions. Patients without cancer were considered at high risk, due either to familial breast or cancer syndrome or positivity of BRCA 1 or BRCA 2 mutations. All 50 with ovarian cancer were correctly identified, including the 18 with Stage I cancer. Of the 66 benign cases, 63 were identified as not cancer, yielding a sensitivity of 100% and a positive predictive value of 94%. The authors note that while a positive predictive value of 94% may be acceptable for those high-risk patients, in the larger population of average-risk patients, the positive predictive value must be close to 100% to avoid a high number of false-positive results, which, in turn, would generate additional workup. One of the key outcomes of an ovarian cancer screening test is the ability to identify Stage I ovarian cancer that is potentially curable with surgery. The above study only included 18 patients with Stage I ovarian cancer. The authors state that an important future goal is the confirmation of the diagnostic performance of proteomic screening for the prospective detection of Stage I ovarian cancer in trials of both high- and low-risk women.
It should also be noted that the technology used in the Petricoin study is not the same as the technology proposed for the OvaCheck® test. According to the National Cancer Institute, 2005, “The two techniques use different mass spectrometry instrumentation and detection methods, as well as different sample handling and processing methods. Therefore the class of molecules analyzed by these two approaches, and thus the molecules that constitute the diagnostic patterns would be expected to be entirely different.”  Other comments and correspondence in the literature (Lancet, 2002) also question the statistical analysis used by Petricoin and other technical issues. (Diamandis,2004) The results of the Petricoin study have not been reproduced elsewhere. (Unwin,2007)
Prostate Cancer
Ornstein and colleagues (2004) reported the results of serum proteomic profiling in 154 men with serum PSA ranging from 2.5 to 15.0 ng/mL.  A total of 63 samples (30 malignant, 33 benign) were used as the training set to identify a proteomic pattern that could distinguish benign from malignant disease. The results of the training set were then applied to the remaining 91 samples (i.e., the “testing” set) in a blinded fashion. In this testing set of 63 with negative biopsies and 28 with positive biopsies, there was 100% sensitivity and 67% specificity. These data imply that if the results of proteomic profiling were used to deselect patients for biopsy, 42 of 63 (67%) patients without prostate cancer could have avoided biopsy. The authors note that using a training set of only 63 samples may be inadequate, and that “before this new technology can be applied in clinical practice, much larger and diverse training and testing sets will be needed.”
McLerran and colleagues (2008) selected serum samples from biorepositories from patients with 1) prostate cancer with Gleason score 7 or higher; 2) prostate cancer with Gleason score less than 7; or 3) negative prostate biopsies with PSA 10 mcg/L or less and no history of cancer of any kind, a normal digital rectal exam, and no inflammatory disease. They also selected two control groups: one with a history of inflammatory disease but no cancer and one with no history of prostate cancer but a history of another type of cancer.  Four hundred specimens were analyzed by mass spectrometry after random selection from the 5 groups of patients, with 125 from the group with high Gleason grade, 125 with low Gleason grade, 125 from the biopsy-negative group and 50 from each of the control groups. The investigators sought to derive a decision algorithm for classification of prostate cancer from the mass spectrometry data, but found that they were unable to separate the patients with prostate cancer from biopsy-negative controls, nor were they able to separate patients with high and low Gleason scores. The conclusion was made that in the validation process, this protein-expression profiling approach did not perform well enough to advance to the prospective study stage.
Miscellaneous Cancers
A number of preliminary proteomic studies are available for many cancers including lung, colorectal, gastric, pancreatic, liver, cervical, endometrial, bladder, lymphoma/leukemia, melanoma and astrocytomas. (Reymond,2007; Garrisi,2008; Li, 2006; Leman,2007; Belluco, 2007; Bast,2007).
As of May 2009, there are 8 ongoing phase III trials for a variety of malignancies, which are secondarily analyzing proteomic profiles in serum as predictors of survival, risk of disease progression and response to treatment: non-small cell lung cancer (NCT00300729 and NCT00738881), melanoma (NCT00389571), ovarian cancer (NCT00426257), breast cancer (NCT00516425), prostate cancer (NCT00567580), hepatoblastoma (NCT00652132), and colon cancer (NCT00647530).
2010 Update
The VeriStrat test, developed by Biodesix, was used to analyze serum or plasma samples from 230 patients treated with cetuximab, EGFR-TKI or chemotherapy for recurrent/metatstatic head and neck squamous cell carcinoma or colorectal cancer (Chung, 2010).  Pretreatment samples were analyzed and classified as either VeriStrat good or VeriStrat poor.   Survival analyses of each cohort were done based on the classifications.  In the EGFR inhibitor-treated cohorts, the classification predicted survival but no survival difference was noted in the chemotherapy treatment cohort. For colorectal cancer patients, tumor EGFR ligand RNA levels were significantly associated with the proteomic classification, and combined KRAS and proteomic classification proteomic classification provided improved survival classification.  The author reports, “prospective studies are necessary to confirm these finding”.
The VeriStrat test is currently being studied in clinical trials in patients with non-small-cell lung cancer.   At this time there is insufficient evidence that testing with the VeriStrat test improves health outcomes and the policy statement is unchanged.
2011 Update
A literature search was conducted through May 2011.  There was no new literature identified that would prompt a change in the coverage statement.
2013 Update
A search of the MEDLINE database through 2013 did not reveal any new literature that would prompt a change in the coverage statement.
2015 Update
A literature search conducted through September 2015 did not reveal any new information that would prompt a change in the coverage statement. The key identified literature is summarized below.
Birse and colleagues developed a broad-based proteomics discovery program, integrating liquid chromatography/mass spectrometry (LC/MS) analyses of freshly resected lung tumor specimens (n = 13), lung cancer cell lines (n = 17), and conditioned media collected from tumor cell lines (n = 7) (Birse, 2015). To enrich for biomarkers likely to be found at elevated levels in the peripheral circulation of lung cancer patients, proteins were prioritized based on predicted subcellular localization (secreted, cell-membrane associated) and differential expression in disease samples. 179 candidate biomarkers were identified. Several markers selected for further validation showed elevated levels in serum collected from subjects with stage I NSCLC (n = 94), relative to healthy smoker controls (n = 189). An 8-marker model was developed (TFPI, MDK, OPN, MMP2, TIMP1, CEA, CYFRA 21-1, SCC) which accurately distinguished subjects with lung cancer (n = 50) from high risk smokers (n = 50) in an independent validation study (AUC = 0.775). Integrating biomarker discovery from multiple sample types (fresh tissue, cell lines and conditioned medium) has resulted in a diverse repertoire of candidate biomarkers. This unique collection of biomarkers may have clinical utility in lung cancer detection and diagnoses.
Sun and colleagues conducted a study, with the purpose of the study, to identify alterations in the serum proteome profile during the development of ovarian cancer and to provide an experimental basis for discovering new and valuable serum biomarkers for the early detection of ovarian carcinoma (Sun, 2014). Surface-enhanced laser desorption/ionization-time of flight (SELDI-TOF-MS) was used to profile changes in the serum proteome of Fischer 344 rats with ovarian cancer during the progress of tumor development. Sera were collected from the rats on day A (1 week before injection of tumor cells), day B (4 weeks after injection), and day C (6 weeks after injection). Each sample was subjected to SELDI-TOF-MS testing. Peak detection and alignment and selection of peaks with the highest discriminatory power were performed using proteinchip biomarker software. Decision tree analyses were performed using biomarker pattern software. Finally, 3 peaks were found to be the most valuable ones (3759, 4659 and 9318 Da). The expression frequency of m/z 3759-Da peaks was downregulated and another two frequencies (4659 and 9318 Da) were upregulated, and the levels of expression of these three proteins showed the same tendency as the expression frequency during the development of ovarian cancer. The total accuracy rate of diagnosis at 4 and 6 weeks post-injection was 94.7 and 97.3%, respectively. Profiling the serum proteome changes during the process of the cancer development using SELDI-TOF-MS may provide useful information regarding carcinogenesis and facilitate discovery of novel serum biomarkers for early detection.
2016 Update
A literature search conducted through September 2016 did not reveal any new information that would prompt a change in the coverage statement.   
2017 Update
A literature search conducted using the MEDLINE database through September 2017 did not reveal any new information that would prompt a change in the coverage statement.
2018 Update
A literature search was conducted through September 2018.  There was no new information identified that would prompt a change in the coverage statement.  
2019 Update
A literature search was conducted through September 2019.  There was no new information identified that would prompt a change in the coverage statement.  
2020 Update
Annual policy review completed with a literature search using the MEDLINE database through September 2020. No new literature was identified that would prompt a change in the coverage statement.
2021 Update
Annual policy review completed with a literature search using the MEDLINE database through September 2021. No new literature was identified that would prompt a change in the coverage statement.
2022 Update
Annual policy review completed with a literature search using the MEDLINE database through November 2022. No new literature was identified that would prompt a change in the coverage statement.
2023 Update
Annual policy review completed with a literature search using the MEDLINE database through November 2023. No new literature was identified that would prompt a change in the coverage statement.

0256UTrimethylamine/trimethylamine N-oxide (TMA/TMAO) profile, tandem mass spectrometry (MS/MS), urine, with algorithmic analysis and interpretive report
83789Mass spectrometry and tandem mass spectrometry (eg, MS, MS/MS, MALDI, MS TOF, QTOF), non drug analyte(s) not elsewhere specified, qualitative or quantitative, each specimen
84999Unlisted chemistry procedure

References: Agency for Health Research & Quality, Evidence Report/Technology Assessment, Number 145, October 2006. Genomic tests for ovarian cancer detection and management. Duke University Evidence Based Practice Center.

Apweiler R, Aslanidis C. et al.(2009) Approaching clinical proteomics: current state and future fields of application in fluid proteomics. Clin Chem Lab Med, 2009; 47(6):724-44.2004

Bast RC Jr, Brewer M, et al.(2007) Prevention and early detection of ovarian cancer: mission impossible? Recent Results Cancer Res, 2007; 174:91-100.

Belluco C, Petricoin EF, et al.(2007) Serum proteomic analysis identifies a highly sensitive and specific discriminatory pattern in stage 1 breast cancer. Ann Surg Oncol, 2007; 14(9):2470-6.

Birse CE, Lagier RJ, FitzHugh W, et al.(2015) Blood-based lung cancer biomarkers identified through proteomic discovery in cancer tissues, cell lines and conditioned medium. Clin Proteomics. 2015 Jul 16;12(1):18.

Chung CH, Seeley EH, Roder H, et al.(2010) Detection of tumor epidermal growth factor receptor pathway dependence by serum mass spectrometry in cancer patients. Cancer Epidemiol Biomarkers Prev. 2010 Feb;19(2):358-65.

Conrads TP, Zhou M, et al.(2003) Cancer diagnosis using proteomic patterns. Expert Rev Mol Diagn 2003; 3:411-20.

Diamandis EP.(2004) Analysis of serum proteomic patterns for early cancer diagnosis: drawing attention to potential problems. J Natl Cancer Inst, 2004; 96(5):353-6.

Dziadziuszko R, Hirsch FR.(2008) Advances in geomic and proteomic studies of non small-cell lung cancer: clinical and translational research perspective. Clin Lung Cancer, 2008; 9(2):78-84.

Garris VM, Abbate I, et al.(2008) SELDI-TOF serum proteomics and breast cancer: which perspective? Expert Rev Proteomics, 2008; 5(6):779-85.

Leman ES, Schoen RE, et al.(2007) Initial analysis of colon cancer-specific antigen (CCSA)-3 and CCSA-4 as colorectal cancer-associated serum markers. Cancer Res, 2007; 67(12):5600-5.

Li J, Zhuang Z, et al.(2006) Proteomic profiling distinguishes astrocytomas and identifies differential tumor markers. Neurology, 2006; 66(5):733-6.

Lin Y, Dynan WS, et al.(2009) The current state of proteomics in GI oncology. Dig Dis Sci, 2009; 54(3):431-57.

McLerran D, Grizzle WE, et al.(2008) SELDI-TOF MS whole serum proteomic profiling with IMAC surface does not reliably detect prostate cancer. Clin Chem, 2008; 54(1):53-60.

Nishizuka S, Chen ST, et al.(2003) Diagnostic markers that distinguish colon and ovarian adenocarcinomas: identification by genomic, proteomic, and tissue array profiling. Cancer Res 2003; 63:5243-50.

Ornstein DK, Rayford W, et al.(2004) Serum proteomic profiling can discriminate prostate cancer from benign prostates in men with total prostate specific antigen levels between 2.5 and 15.0 ng/ml. J Urol, 2004; 172(4 Pt 1):1302-5.

Petricoin EF, Ardekani AM, et al.(2002) Use of proteomic patterns in serum to identify ovarian cancer. Lancet 2002; 359:572-7.

Proteomic-based testing (OvaCheck) for the dectection of ovarian cancer. Hayes Alert 2006; 9;#4:April.

Reymond MA, Schiegel W.(2007) Proteomics in cancer. Adv Clin Chem, 2007; 44:103-42.

Rockhill B. Pearl D. Elwood M. Diamandis EP.(2002) Proteomic patterns in serum and identification of ovarian cancer. Lancet, 2002; 360:169-71.

Rosenblatt KP, Bryant-Greenwood P, et al.(2004) Serum proteomics in cancer diagnosis and management. Annu Rev Med 2004; 55:97-112.

Society of Gynecologists.(2004) Society of Gynecologic Oncologists Statement Regarding OvaCheck.

Sun L, Li L, Li Z, et al.(2014) Alterations in the serum proteome profile during the development of ovarian cancer. Int J Oncol. 2014 Dec;45(6):2495-501.

Taguchi F, Solomon B, Gregorc V, et al.(2007) Mass spectrometry to classify non-small-cell lung cancer patients for clinical outcome after treatment with epidermal growth factor receptor tyrosine kninase inhibitors: a multicohort cross-institutional study. J Natl Cancer Inst 2007;99:838-46.

The NCI/FDA Proteomics Research Program, Its Research, and Diagnostic Tests by Private Industry (e.g., OvaCheck™): Fact Sheet.

Veenstra TD, Conrads TP.(2003) Serum protein fingerprinting. Curr Opin Mol Ther 2003; 5:584-93.

Wu W, Hu W, Kavanagh JJ.(2002) Proteomics in cancer research. Int J Gynecol Cancer 2002; 12:409-23.

Zhu W, Wang X, et al.(2003) Detection of cancer-specific markers amid massive mass spectral data. Proc Natl Acad Sci U S A 2003; 1000:14666-71.

Group specific policy will supersede this policy when applicable. This policy does not apply to the Wal-Mart Associates Group Health Plan participants or to the Tyson Group Health Plan participants.
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