| Coverage Policy Manual | |
| Policy #: | 2017028 |
| Category: | Laboratory |
| Initiated: | August 2017 |
| Last Review: | July 2023 |
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| Genetic Test: Proteogenomic Testing for Patients with Cancer (GPS Cancer Test) | |
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| Description: |
PROTEOGENOMICS
The term proteome refers to the entire complement of proteins produced by an organism or cellular system, and proteomics refers to the large-scale comprehensive study of a specific proteome. Similarly, the term transcriptome refers to the entire complement of transcription products (messenger RNAs [mRNAs]), and transcriptomics refers to the study of a specific transcriptome. Proteogenomics refers to the integration of genomic information with proteomic and transcriptomic information to provide a more complete picture of the function of the genome.
A system’s proteome is related to its genome and genomic alterations. However, while the genome is relatively static over time, the proteome is more dynamic and may vary over time and/or in response to selected stressors (Office of Cancer Clinical Proteomics Research, 2016; Gregorich, 2014). Proteins undergo a number of modifications as part of normal physiologic processes. Following protein translation, modifications occur by splicing events, alternative folding mechanisms, and incorporation into larger complexes and signaling networks. These modifications are linked to protein function and result in functional differences that occur by location and over time (Gregorich, 2014).
Some of the main potential applications of proteogenomics in medicine include:
Proteogenomics is an extremely complex field due to the intricacies of protein architecture and function, the many potential proteomic targets that can be measured, and the numerous testing methods used. We discuss the types of targets currently being investigated and the testing methods used and under development next.
Proteomic Targets
A proteomic target can be any altered protein that results from a genetic variant (Subbannayya, 2016). Protein alterations can result from both germline and somatic genetic variants. Altered protein products include mutated proteins, fusion proteins, alternative splice variants, noncoding mRNAs, and posttranslational modifications (PTMs).
Sequence Alterations (Mutated Protein)
A mutated protein has an altered amino acid sequence that arises from a genetic variant. A single amino acid may be replaced in a protein or multiple amino acids in sequence may be affected (Subbannayya, 2016). Mutated proteins can arise from either germline or somatic genetic variants. Somatic variants can be differentiated from germline variants by comparison with normal and diseased tissue.
Fusion Proteins
Fusion proteins are the product of one or more genes that fuse together. Most fusion genes discovered to date have been oncogenic, and fusion genes have been shown to have clinical relevance in a variety of cancers.
Alternative Splice Events
Posttranslational enzymatic splicing of proteins results in numerous protein isoforms. Alternative splicing events can lead to abnormal protein isoforms with altered function. Some alternative splicing events have been associated with tumor-specific variants (Subbannayya, 2016).
Noncoding RNAs
Noncoding portions of the genome serve as the template for noncoding RNA (ncRNA), which plays various roles in the regulation of gene expression. There are 2 classes of ncRNA: shorter ncRNAs, which include microRNAs and related transcript products, and longer ncRNAs, which are thought to be involved in cancer progression (Subbannayya, 2016).
Posttranslational Modifications
PTMs of histone proteins occur in normal cells and are genetically regulated. Histone proteins are found in the nuclei and play a role in gene regulation by structuring the DNA into nucleosomes. A nucleosome is composed of a histone protein core surrounded by DNA. Nucleosomes are assembled into chromatin fibers composed of multiple nucleosomes assembled in a specific pattern. PTMs of histone proteins include a variety of mechanisms, including methylation, acetylation, phosphorylation, glycosylation, and related modifications (Hudler, 2015).
Proteogenomic Testing Methods
Proteogenomic testing involves isolating, separating, and characterizing proteins from biologic samples, followed by correlation with genomic and transcriptomic data (Office of Cancer Clinical Proteomics Research, 2016). Isolation of proteins is accomplished by trypsin digestion and solubilization. The soluble mix of protein isolates is then separated into individual proteins. This is generally done in multiple stages using high-performance liquid chromatography ionexchange chromatography, 2-dimensional gel electrophoresis, and related methods. Once individual proteins are obtained, they may be characterized using various methods and parameters, some of which we describe below.
Immunohistochemistry/Fluorescence in situ Hybridization
Immunohistochemistry (IHC) and fluorescence in situ hybridization are standard techniques for isolating and haracterizing proteins. IHC identifies proteins by using specific antibodies that bind to the protein. Therefore, this technique can only be used for known proteins and protein variants because it relies on the availability of a specific antibody. This technique also can only test a relatively small number of samples at once.
There are a number of reasons why IHC and fluorescence in situ hybridization are not well-suited for large-scale proteomic research. They are semiquantitative techniques and involve subjective interpretation. They are considered low-throughput assays that are time-consuming and expensive and require a relatively large tissue sample. Some advances in IHC and fluorescence in situ hybridization have addressed these limitations, including tissue microarray and reverse phase protein array.
Mass Spectrometry
Mass spectrometry (MS) separates molecules by their mass to charge ratio and has been used as a research tool for studying proteins for many years (Office of Cancer Clinical Proteomics Research, 2016). Development of technology that led to the application of MS to biologic samples has advanced the field of proteogenomics rapidly. However, the application of MS to clinical medicine is in its formative stages. There are currently several types of mass spectrometers and a lack of standardization in the testing methods.4 Additionally, MS equipment is expensive and currently largely restricted to tertiary research centers.
The potential utility of MS lies in its ability to provide a wide range of proteomic information in an efficient manner, including:
“Top-down” MS refers to identification and characterization of all proteins in a sample without prior knowledge of which proteins are present (Gregorich, 2014). This method provides a profile of all proteins in a system, including documentation of PMTs and other protein isoforms. This method, therefore, provides a protein “profile” or “map” of a specific system. Following initial analysis, intact proteins can be isolated and further analyzed to determine amino acid sequences and related information.
“Bottom-up” MS refers to the identification of known proteins in a sample. This method identifies peptide fragments that indicate the presence of a specific protein. This method depends on having peptide fragments that can reliably identify a specific protein. Selective reaction monitoring-MS is a bottom-up modification of MS that allows for direct quantification and specific identification of low-abundance proteins without the need for specific antibodies (Hudler, 2015). This method requires the selection of a peptide fragment or “signature” that is used to target the specific protein. Multiplex assays have also been developed to quantitate the epidermal growth factor receptor, human epidermal growth factor receptors 2 and 3, and insulin-like growth factor-1 receptor (Hembrough, 2013).
Bioinformatics
Due to the complexity of proteomic information, the multiple tests used, and the need to integrate this information with other genomic data, a bioinformatics approach is necessary to interpret proteogenomic data. Software programs are available that integrate and assist in the interpretation of the vast amounts of data generated by proteogenomics research. One software platform that integrates genomic and proteomic information is PARADIGM, which is used by The Cancer Genome Atlas (TCGA) project for data analysis (Cancer Genome Atlas Network, 2013). Other software tools currently available include (Subbannayya, 2016):
Ongoing Proteogenomic Database Projects
Numerous ongoing databases are being constructed for proteogenomic research. There are also networks of researchers coordinating their activities in this field. The Clinical Proteomic Tumor Analysis Consortium is a coordinated project among 8 analysis sites sponsored by the National Cancer Institute (Edwards, 2015). This project seeks to characterize the genomic and transcriptomic profiles of common cancers systematically. As of 2014, this consortium had cataloged proteomic information for breast, colon, and ovarian cancers. All data from this project are freely available.
Many existing genomic databases have begun to incorporate proteomic information. TCGA intends to profile changes in the genomes of 20 different cancers. As part of its analysis, mRNA expression is used to help define signaling pathways that are either upregulated or deregulated in conjunction with genetic variations. Currently, TCGA has published comprehensive molecular characterizations of breast (Brat, 2015), colorectal (Cancer Genome Atlas Network, 2012), lung (Cancer Genome Atlas Research Network, 2014), gliomas (Brat, 2015), renal (Linehan, 2016), and endometrial (Kandoth, 2013) cancers.
GPS CANCER TEST
The GPS Cancer test is a commercially available proteogenomic test intended for patients with cancer. The test includes whole-genome sequencing (20,000 genes, 3 billion base pairs), whole transcriptome (RNA) sequencing, and quantitative proteomics by mass spectrometry (NantHealth, 2016). The test is intended to inform personalized treatment decisions for cancer, and treatment options are listed when available, although treatment recommendations are not made. Treatment options may include U.S. Food and Drug Administration-approved targeted drugs with potential for clinical benefit, active clinical trials of drugs with potential for clinical benefit, and/or available drugs to which the cancer may be resistant.
REGULATORY STATUS
Clinical laboratories may develop and validate tests in-house and market them as a laboratory service; laboratory-developed tests must meet the general regulatory standards of the Clinical Laboratory Improvement Act. The GPS Cancer™ test (NantHealth, Culver City, CA) is available under the auspices of Clinical Laboratory Improvement Amendments. Laboratories that offer laboratory-developed tests must be licensed by Clinical Laboratory Improvement Amendments for high-complexity testing. To date, the U.S. Food and Drug Administration has chosen not to require any regulatory review of this test.
CODING
There is no specific CPT code for the GPS Cancer test. It would likely be reported with the unlisted molecular pathology procedure code 81479.
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Policy/ Coverage: |
Does Not Meet Primary Coverage Criteria Or Is Investigational For Contracts Without Primary Coverage Criteria
Proteogenomic testing of patients with cancer (including, but not limited to the GPS Cancer test) does not meet member benefit certificate primary coverage criteria that there be scientific evidence of effectiveness in improving health outcomes.
For members with contracts without primary coverage criteria, proteogenomic testing of patients with cancer (including, but not limited to the GPS Cancer test) is considered investigational for all indications. Investigational services are specific contract exclusions in most member benefit certificates of coverage.
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| Rationale: |
The evaluation of a genetic test focuses on 3 main principles: (1) analytic validity (technical accuracy of the test in detecting a variant that is present or in excluding a variant that is absent); (2) clinical validity (diagnostic performance of the test [sensitivity, specificity, positive and negative predictive values] in detecting clinical disease); and (3) clinical utility (ie, a demonstration that the diagnostic information can be used to improve patient health outcomes).
PROTEOGENOMIC TESTING
Clinical Context and Test Purpose
The purpose of proteogenomic testing in patients who have cancer is to detect cancer, improve evaluation of prognosis, select treatments, and monitor for treatment response or resistance.
The question addressed in this evidence review is: Does proteogenomic testing using the GPS Cancer test improve the net health outcome in individuals with cancer?
Analytic Validity
No published literature was identified on the analytic validity of the GPS Cancer test. Additionally, search of selected websites did not identify any data on analytic validity of the test.
Some general studies on the analytic validity of proteogenomics were identified. This literature includes the following types of studies that correlate results of different testing methods.
Catenacci et al (2014) published 2 studies that compared the performance of mass spectrometry (MS) with immunohistochemistry (IHC) and fluorescence in situ hybridization (FISH). In the first study, these 3 methods were used to quantitate the Met protein (hepatocyte growth factor receptor) (Catenacci, 2014). Overexpression of Met is associated with poor outcomes for a variety of gastrointestinal cancers, and Met levels are currently quantitated by IHC or FISH. This study described development of a selective reaction monitoring-MS (SRM-MS) assay for Met by selecting the optimal peptide sequence and setting technical aspects of the assay. The reliability and reproducibility of the assay were both reported to be high. Results of the SRM-MS assay were then compared with standard IHC and FISH in 130 tissue samples of gastroesophageal cancer, comprised of primary tumor resections, endoscopic primary tumor biopsies, and core needle biopsies of metastatic sites. Forty-four tissue samples had results for both SRM-MS and IHC; the correlation coefficient between these tests was 0.54. Thirty-one tissue samples had results for both SRM-MS and FISH; the correlation coefficient between these tests was 0.89.
In the second study, similar methods were used to quantitate human epidermal growth factor receptor 2 (HER2) levels in 139 tissue samples from gastrointestinal cancers (Catenacci, 20160. Reliability and reproducibility of the assay were high. Forty-two tissue samples had both SRM-MS and FISH results; the univariate correlation between the 2 tests was 0.36. When a multivariate model was used to control for expression of the Met protein, epidermal growth factor receptor, and HER3, the correlation between the 2 tests improved to 0.73. One hundred twenty-two samples had results for both SRM-MS and IHC; the correlation between tests was 0.04. Compared with SRM-MS as the criterion standard, IHC was sensitive (89.9%) but not specific (15.2%) in identifying samples with elevated HER2 levels.
Section Summary: Analytic Validity
There is no published evidence on the analytic validity of the GPS Cancer test and, therefore, the analytic validity of this test is undefined. For proteomic research in general, a few types of studies have provided information on analytic validity. The most common type is correlation between SRM-MS results and IHC and/or FISH results. These early studies involved assay development as well as assay validation, so it is not possible to compare results across different studies. These studies also lacked a true criterion standard to compare SRM-MS with IHC or FISH. Further research is needed to standardize and validate proteogenomic testing adequately for clinical use.
Clinical Validity
No published literature was identified on the clinical validity of the GPS Cancer test. In addition, searches of selected websites did not identify any data on clinical validity of the test.
The general published literature on the clinical validity of proteogenomics includes the following types of studies: proteomic biomarkers as prognostic markers, molecular characterization, and monitoring quantitative protein levels.
Proteomic Biomarkers as Prognostic Markers
Some research has compared the association of proteogenomic results with clinical outcomes and compared the strength of association between genomic and proteomic data. Yau et al (2015) published a report comparing whether proteogenomic and genomic data can predict metastatic outcomes in breast cancer (Yau, 2015). This study measured FOXM transcript mRNA levels and compared the prognostic ability with FOXM1 target genes and a gene proliferation score.
Zhang et al (2016) combined MS-based proteomic measurements with genomic data of 174 ovarian tumors previously analyzed by TCGA (Zhang, 2016). Copy number alterations having high correlation with protein abundance or mRNA were found on chromosomes 2, 7, 20, and 22. A lasso-based Cox proportional hazards model was used to model the association between these copy-number alterations and overall survival on a training set of 82 tumors and then used to predict survival in 87 nonoverlapping tumors. A consensus of the 4 signatures was created, using a voting method, as a binary indicator for signature relative level up versus down. The consensus indicator was highly associated with survival (hazard ratio not given; p<0.001). Comparison to genomic stratification was not given.
Defining Molecular Subtypes of Cancer
Comprehensive molecular characterization has been performed for various cancer types, and in some cases, these investigations have defined subtypes that differ from the standard histologic classification. Clinical validity can be demonstrated in this situation if the molecular subtypes are more homogeneous than the histologic class and correlate more closely with clinical outcomes.
An example of molecular subtyping of cancer by proteogenomics was published by TCGA network in 2015 (Brat, 2015). This study integrated data from multiple platforms, including exome sequencing, DNA copy-number profiling, DNA methylation, and protein profiling by MS. For each platform, clusters of similar cases were identified. Three distinct molecular subtypes were identified using a second-level cluster analysis. They were most concordant with isocitrate dehydrogenase enzyme, 1p/19q, and TP53 genetic variant status. The molecular subtypes showed differences in clinical characteristics, recurrence, and survival that could not be explained by histologic class.
Monitoring Quantitative Protein Levels Over Time
Quantification of protein levels over time may have applications for determining resistance to targeted therapy. Levels of protein markers may correlate with the presence of resistant tumor cells and may be an early marker of resistance that occurs before tumor progression. Clinical validity can be demonstrated if quantitative protein levels identify resistance more accurately or earlier than other surveillance methods. Currently, few studies have reported on monitoring protein levels over time. A case report, published in 2016, demonstrated that repeat quantitation of the HER2, HER3, and epidermal growth factor receptor proteins was feasible and that protein levels changed in response to different therapies and over time (Sellappan, 2016).
Section Summary: Clinical Validity
There is no published evidence on the clinical validity of the GPS Cancer test and, therefore, the clinical validity of this test is undefined. For proteomic research in general, a few types of studies in the literature provide information on clinical validity. A small number of studies use proteogenomic biomarkers for diagnosis or prognosis and compare these biomarkers with traditional genomic testing. Other studies have performed comprehensive molecular characterization of different tumors and, in some cases, have shown that molecular characterization correlates more strongly with clinical outcomes than with histologic classification. The third type of study in the literature quantitates and monitors protein markers over time for surveillance purposes, particularly for the emergence of resistance to targeted cancer therapies. This available research on clinical validity outlines some types of research that will be needed to establish clinical validity for a variety of clinical situations. However, the research is currently in its early stages, and no conclusions on clinical validity can be drawn at present from the evidence.
Clinical Utility
No direct evidence on clinical utility was identified. Therefore, the clinical utility of the GPS Cancer test is uncertain. For proteogenomic testing in general, there is no published literature on clinical utility. Furthermore, absent additional evidence establishing the analytic and clinical validity of proteogenomic testing, it will not be possible to determine whether clinical utility is present.
SUMMARY OF EVIDENCE
For individuals who have cancer and indications for genetic testing who receive proteogenomic testing (GPS Cancer test), the evidence includes cross-sectional studies that correlate results with standard testing and that report comprehensive molecular characterization of various cancers, and cohort studies that use proteogenomic markers to predict outcomes and that follow quantitative levels over time. Relevant outcomes are overall survival, disease-specific survival, test accuracy and validity, and treatment-related mortality and morbidity. There is no published evidence on the analytic validity or clinical utility of the GPS Cancer test. For proteogenomic testing in general, the research is at an early stage.. There is a lack of standardization of testing methods and uncertain accuracy for most proteogenomic technologies. A few studies have described assay development and validation for proteogenomic targets and correlation of proteogenomic testing results with standard testing methods. Other studies have used proteogenomic in conjunction with genomic testing to provide a more comprehensive molecular characterization of various cancers. Very few studies have used proteogenomic tumor markers for diagnosis or prognosis, and at least 1 study has reported following quantitative protein levels for surveillance purposes. Further research is needed to standardize and validate proteogenomic testing methods. When standardized and validated testing methods are available, the analytic validity and clinical utility of proteogenomic testing can be adequately evaluated. The evidence is insufficient to determine the effect of the technology on health outcomes.
2018 Update
Annual policy review completed with a literature search using the MEDLINE database through July 2018. No new literature was identified that would prompt a change in the coverage statement. The key identified literature is summarized below.
Monitoring Quantitative Protein Levels Over Time
Quantification of protein levels over time may have applications for determining resistance to targeted therapy. Levels of protein markers may correlate with the presence of resistant tumor cells and may be an early marker of resistance that occurs before tumor progression. Clinical validity can be demonstrated if quantitative protein levels identify resistance more accurately or earlier than other surveillance methods.
Currently, few studies have reported on monitoring protein levels over time. A case report, published in 2016, demonstrated that repeat quantitation of human epidermal growth factor receptors 2 and 3, as well as epidermal growth factor receptor proteins, was feasible and that protein levels changed in response to different therapies and over time (Sellappan, 2016).
More recently, Latonen et al generated distinct profiles from patient tissue samples of benign prostate hyperplasia (n=10), untreated prostate cancer (n=17), and locally recurrent castration-resistant prostate cancer (n=11), demonstrating changes in protein levels that may be associated with tumor progression (Latonen, 2018).
2019 Update
Annual policy review completed with a literature search using the MEDLINE database through July 2019. No new literature was 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 July 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 July 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 June 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 June 2023. No new literature was identified that would prompt a change in the coverage statement.
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| References: |
Cancer Genome Atlas Network.(2012) Comprehensive molecular characterization of human colon and rectal cancer. Jul 18 2012;487(7407):330-337. PMID 22810696 Cancer Genome Atlas Network.(2012) Comprehensive molecular portraits of human breast tumours. Nature. Oct 4 2012;490(7418):61-70. PMID 23000897 Cancer Genome Atlas Research Network, Brat DJ, Verhaak RG, et al.(2015) Comprehensive, integrative genomic analysis of diffuse lower-grade gliomas. N Engl J Med. Jun 25 2015;372(26):2481-2498. PMID 26061751 Cancer Genome Atlas Research Network, Kandoth C, Schultz N, et al.(2013) Integrated genomic characterization of endometrial carcinoma. Nature. May 2 2013;497(7447):67-73. PMID 23636398 Cancer Genome Atlas Research Network, Linehan WM, Spellman PT, et al.(2016) Comprehensive molecular characterization of papillary renal-cell carcinoma. N Engl J Med. Jan 14 2016;374(2):135-145. PMID 26536169 Cancer Genome Atlas Research Network.(2014) Comprehensive molecular profiling of lung adenocarcinoma. Jul 31 2014;511(7511):543-550. PMID 25079552 Catenacci DV, Liao WL, Thyparambil S, et al.(2014) Absolute quantitation of Met using mass spectrometry for clinical application: assay precision, stability, and correlation with MET gene amplification in FFPE tumor tissue. PLoS One. Jul 1 2014;9(7):e100586. PMID 24983965 Catenacci DV, Liao WL, Zhao L, et al.(2016) Mass-spectrometry-based quantitation of Her2 in gastroesophageal tumor tissue: comparison to IHC and FISH. Gastric Cancer. Oct 2016;19(4):1066-1079. PMID 26581548 Chang Gung Bioinformatics Center.(2016) Cancer Mutant Proteome Database. http://120.126.1.62/cmpd/. Accessed May 27, 2016. Clinical Proteomic Tumor Analysis Consortium (CPTAC).(2017) CPTAC Data Portal Overview. https://cptac-dataportal. georgetown.edu/cptacPublic/. Accessed April 28, 2017. Edwards NJ, Oberti M, Thangudu RR, et al.(2015) The CPTAC Data Portal: a resource for cancer proteomics research. J Proteome Res. Jun 5 2015;14(6):2707-2713. PMID 25873244 EWHA Research Center for Systems Biology.(2016) ChimerDB 2.0. http://biome.ewha.ac.kr:8080/FusionGene/. Accessed May 27, 2016. Geneffects.(2012) Peppy – proteogenomic, proteomic search tool. 2012; http://www.geneffects.com/peppy. Accessed June 5, 2017. Gregorich ZR, Ge Y.(2014) Top-down proteomics in health and disease: challenges and opportunities. May 2014;14(10):1195-1210. PMID 24723472 Hembrough T, Thyparambil S, Liao WL, et al.(2013) Application of selected reaction monitoring for multiplex quantification of clinically validated biomarkers in formalin-fixed, paraffin-embedded tumor tissue. J Mol Diagn. Jul 2013;15(4):454-465. PMID 23672976 Huang PJ, Lee CC, Tan BC, et al.(2015) CMPD: cancer mutant proteome database. Nucleic Acids Res. Jan 2015;43(D1):D849-855. PMID 25398898 Hudler P, Videtic Paska A, Komel R.(2015) Contemporary proteomic strategies for clinical epigenetic research and potential impact for the clinic. Expert Rev Proteomics. Apr 2015;12(2):197-212. PMID 25719543 Keshava Prasad TS, Goel R, Kandasamy K, et al.(2009) Human Protein Reference Database--2009 update. Jan 2009;37(Supp 1):D767-772. PMID 18988627 Latonen L, Afyounian E, Jylha A, et al.(2018) Integrative proteomics in prostate cancer uncovers robustness against genomic and transcriptomic aberrations during disease progression. Nat Commun. Mar 21 2018;9(1):1176. PMID 29563510 Li J, Duncan DT, Zhang B.(2010) CanProVar: a human cancer proteome variation database. Hum Mutat. Mar 2010;31(3):219-228. PMID 20052754 NantHealth.(2016) GPS CancerTM. http://nanthealth.com/gps-cancer/. Accessed May 27, 2016, 2016. NetWatch Science.(2016) NONCODE. IncRNAtor. http://lncrnator.ewha.ac.kr/index.htm. Accessed May 27, 2016. Office of Cancer Clinical Proteomics Research, National Cancer Institute.(2016) What is Cancer Proteomics? http://proteomics.cancer.gov/whatisproteomics. Accessed June 10, 2016. Pacific Northwest National Laboratory.(2017) VESPA. n.d.; http://cbb.pnnl.gov/portal/software/vespa.html. Accessed June 5, 2017. Pandey Lab and Institute of Bioinformatics.(2016) Human Protein Reference Database. http://www.hprd.org/. Accessed May 27, 2016. Rudnick PA, Markey SP, Roth J, et al.(2016) A description of the Clinical Proteomic Tumor Analysis Consortium (CPTAC) common data analysis pipeline. J Proteome Res. Mar 04 2016;15(3):1023-1032. PMID 26860878 Sanders WS, Wang N, Bridges SM, et al.(2011) The proteogenomic mapping tool. BMC Bioinformatics. BMC Bioinformatics. Apr 22 2011;12:115. PMID 21513508 Sellappan S, Blackler A, Liao WL, et al.(2016) Therapeutically induced changes in HER2, HER3, and EGFR protein expression for treatment guidance. J Natl Compr Canc Netw. May 2016;14(5):503-507. PMID 27160229 Specht M.(2012) Genomic Peptide Finder. 2012; http://specht.github.io/gpf/. Accessed June 5, 2017. Subbannayya Y, Pinto SM, Gowda H, et al.(2016) Proteogenomics for understanding oncology: recent advances and future prospects. Expert Rev Proteomics. Mar 2016;13(3):297-308. PMID 26697917 University of North Texas Health Science Center.(2017) Synthetic Alternative Splicing Database. http://bioinfo.hsc.unt.edu/sasd/. Accessed June 5, 2017. Vanderbilt University.(2016) Human Cancer Proteome Variation Database. http://bioinfo.vanderbilt.edu/canprovar/datadownload.php. Accessed May 27, 2016. Yau C, Meyer L, Benz S, et al.(2015) FOXM1 cistrome predicts breast cancer metastatic outcome better than FOXM1 expression levels or tumor proliferation index. Breast Cancer Res Treat. Nov 2015;154(1):23-32. PMID 26456572 Zhang H, Liu T, Zhang Z, et al.(2016) Integrated proteogenomic characterization of human high-grade serous ovarian cancer. Cell. Jul 28 2016;166(3):755-765. PMID 27372738 |
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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.
CPT Codes Copyright © 2024 American Medical Association. |
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