- Email: [email protected]
MassARRAY determination of somatic oncogenic mutations in solid tumors: Moving forward to personalized medicine
Accepted Manuscript MassARRAY determination of somatic oncogenic mutations in solid tumors: moving forward to personalized medicine Tania Fleitas, Maider Ibarrola-Villava, Gloria Ribas, Andrés Cervantes PII: DOI: Reference:
S0305-7372(16)30063-9 http://dx.doi.org/10.1016/j.ctrv.2016.07.007 YCTRV 1527
To appear in:
Cancer Treatment Reviews Cancer Treatment Reviews
Received Date: Accepted Date:
20 July 2016 22 July 2016
Please cite this article as: Fleitas, T., Ibarrola-Villava, M., Ribas, G., Cervantes, A., MassARRAY determination of somatic oncogenic mutations in solid tumors: moving forward to personalized medicine, Cancer Treatment Reviews Cancer Treatment Reviews (2016), doi: http://dx.doi.org/10.1016/j.ctrv.2016.07.007
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MassARRAY determination of somatic oncogenic mutations in solid tumors: moving forward to personalized medicine
Tania Fleitas a,*, Maider Ibarrola-Villava a,*, Gloria Ribasa,¥ and Andrés Cervantesa,¥¥
a
Department of Hematology and Medical Oncology, Biomedical Research Institute-
INCLIVA, University of Valencia, Av. Blasco Ibañez 17, 46010, Valencia, Spain.
*Both authors contributed equally
To whom correspondence should be addressed: ¥
Dr. Gloria Ribas, Department Medical Oncology, Biomedical Research Institute -
INCLIVA, University of Valencia, Av. Blasco Ibañez 17, 46010, Valencia, Spain. Telephone number: 0034 963864402 FAX: 0034 963 987860 Email: [email protected]
¥¥
Pr. Andres Cervantes, Department Medical Oncology, Biomedical Research Institute
- INCLIVA, University of Valencia, 46010, Valencia, Spain. Telephone number: 0034 961973531 FAX: 0034 963 987860 Email: [email protected]
MassARRAY determination of somatic oncogenic mutations in solid tumors: moving forward to personalized medicine
Abstract This article will review the impact of the recently developed MassARRAY technology on our understanding of cancer biology and treatment. Analysis of somatic mutations is a useful tool in selecting personalized therapy, and for predicting the outcome of many solid tumors. Here, we review the literature on the application of MassARRAY technology (Sequenom Hamburg, Germany) to determine the mutation profile of solid tumors from patients. We summarize the use of commercially available panels of mutations - such as OncoCartaTM or other combinations - and their concordance with results obtained by using other technologies, such as next generation sequencing.
Keywords MassARRAY technology; OncoCarta panel v1.0; somatic mutation profile; solid tumors.
Introduction Over the last decade, significant advances have been made in identifying oncology biomarkers, which have yielded greater insight into the molecular and cellular mechanisms that drive the initiation, maintenance and progression of tumors. Therapeutic approaches have also shifted substantially during this period, as new sequencing technologies have increased our understanding of the molecular and genetic make-up of cancers. As a result, classic chemotherapy treatments are being gradually displaced by targeted drug therapies, which interfere with specific molecules and thereby block cancer cell growth. This new approach has improved the overall survival of cancer patients, as seen with use of trastuzumab for the treatment of breast cancers that overexpresses HER2, or vemurafenib as a targeted therapy for melanomas in which the BRAF gene is mutated [1, 2]. In other words, the analysis of key cancer-driving mutations has become enormously useful in selecting personalized medicine. The current gold standard technique for identifying somatic mutations is nextgeneration sequencing (NGS); however, other technologies, such as mass spectrometry, may also be used for this purpose [3]. The mass spectrometry technique, based on matrix-assisted laser desorption/ionization-time of flight, detects known genetic variations with target therapies available and is widely used to assess point mutations across different solid tumors to treat patients with known response or to identify resistant clones. The Sequenom MassARRAY technology (Sequenom, San Diego, CA) is a mass spectrometry technique that when is used in combination with the commercial kit OncoCartaTM v1.0 (http://agenabio.com/oncocarta-panel), screens for up to 238 somatic mutations across 19 oncogenes in 24 multiplexed assays. Further versions, v2.0 and v3.0, include additional oncogenes and tumor suppressor gene mutations. Custom assays, such us the ColoCartaTM, GyneCartaTM, LungCartaTM and MelaCartaTM panels have also been incorporated in the overall design to permit detection of specific target genes as needed by different research groups.
Mass spectrometry is cost-effective, but its usefulness in clinical care is still being debated [3-5]. This review provides a systematic overview of all available data from studies that have used this technology to determine the mutational profile of tumors. We also highlight the clinical value of this methodology, in the context of the experience of research groups that have applied this technology across different panels and across a wide range of tumors.
Methods Search strategy and study identification Articles
were
selected
from
the
PubMed
database
(http://www.ncbi.nlm.nih.gov/pubmed), with use of key search terms, or aliases, for: “OncoCarta”, “OncoCarta Sequenom”, “Somatic mutation analysis Sequenom”, “ColoCarta”, “GyneCarta”, ”LungCarta”, “MelaCarta”, “Ultraseek” and “OncoMap”. Publication library available at Agena Bioscience website was also explored for the same
key
search
terms
(http://agenabio.com/oncology;
http://agenabio.com/resources/publication-library). In order to increase the sensibility of the search results, reference lists of the retrieved articles were manually screened and necessary citations were included into the review.
Literature search results The initial database search included 160 articles, 28 of them in both searches. Among the remaining 132 publications, the eligibility criteria included studies in patients with clinical and histological diagnosis of solid tumors that were molecularly characterized by the Sequenom technology as the principal tool. Forty one articles were excluded because of different reasons: (a) Sequenom technology was used for the genotyping of specific polymorphisms (9 reports) or used as a validation technique (5 papers); (b) lack of specific data analysis (2 reports ); (c) studies not focused on solid tumors (9 reports); (d) studies performed on cell lines or mice models (3 reports);
(e) studies involving pediatric populations (6 report) and (f) not mutation profile determined in the study (4 reports). Finally, 3 manuscripts were not a research article and were excluded (Figure 1).
Somatic mutation analysis using MassARRAY technology Ninety one articles, published between January 2009 to April 2016, described the use of the Sequenom MassARRAY technology in order to detect somatic mutations among different tumor types and were included in this review. Among them, 45 works were performed using the OncoCartaTM panel v1.0 for mutation profiling, whereas the other 46 studies used a customized-panel (See Tables 1 and 2, respectively). Regarding tumor types, 20 studies (22.0%) were conducted in patients with lung cancer, 11 (12.1%) in cervix and other gynecologic tumors, 10 (11.0%) in individuals with breast cancer, 8 (8.8%) in colorectal cancer patients (CRC), 8 (8.8%) in several solid tumors, 8 (8.8%) in melanoma tumors, 4 (4.4%) in head and neck tumors, 3 (3.3%) in sarcomas and 19 (20.9%) in other tumor types including adenoid cystic, adrenal, cholangiocarcinoma, central
nervous system, urothelial, germ cells,
gastrointestinal stromal tumor (GIST), thyroid, kidney, esophageal, gastric skin, myofibroblastic, salivary gland and penile carcinomas (See Figure 2) [1, 3-93]. Positive results were reported in fresh tissue, cell lines and plasma samples; however, most of the studies were done in formalin-fixed paraffin-embedded (FFPE) tissues. Results of 52 studies (57.1%) were validated with the use of different techniques, including NGS, Sanger sequencing, pyrosequencing, real-time PCR (RTPCR), Droplet Digital PCR (dd-PCR) or Affymetrix (Santa Clara, CA). Concordance rate was 100.0% in 31 (59.6%) articles whereas concordances higher than 85.0% were reported in 15 (28.8%) papers (Tables 1 and 2).
OncoCarta panels to determine the mutation profile in solid tumors
The forty five studies that accomplished the molecular characterization of solid tumors using the OncoCartaTM panel v1.0, used FFPE, frozen or blood tissues. Additionally, three of the works used the OncoCarta panels v2.0 or v3.0. Moreover, 23 of them used an extra panel or technology to validate their results. Samples sizes varied from 2 to more than 2200 individuals. Among the 45 studies, 10 (22.2%) were conducted in patients with lung cancer, 6 (13.3%) in those with varied solid tumors, 5 (11.1%) in those with breast cancer, 3 (6.7%) in those with CRC, 4 (8.9%) in those with melanoma, 3 (6.7%) in those with endometrium cancer, and 14 (31.1%) in those with other tumor types including ovary, cholangiocarcinoma, sarcoma, oral cavity, GIST, myofibroblastic, nasopharyngeal, adenoid cystic, thyroid, penile, salivary gland and adrenal carcinomas. Furthermore, for all studies, the accuracy between any sequencing result and the OncoCartaTM panel v1.0 output was high, with independence of the type of sample analyzed (FFPE, fresh tissue, cell lines or blood). Visualizing the mass spectra and determining the frequency of mutant and wild type alleles is done by the MassARRAY software called Typer Viewer. A wide range of thresholds have been used for considering alleles “mutated” or “non-mutated”. Information regarding the cut-off used is available in 10 studies and the cutoffs varies from 1.0% (in 3 studies) [8, 22, 52] to 10.0% (in 5 studies) [5, 7, 16, 17, 49, 51, 56]. Beadling and colleagues published one of the most comprehensive studies in 2011, in which they molecularly characterized 820 different FFPE solid tumors, using Sequenom OncoCartaTM Panels v1.0 and v2.0: they screened up to 390 mutations across 30 cancer-related genes [7]. The use of this platform in combination with the two commercial panels allowed the identification of those genes that are more or less frequently mutated across different cancer types. Results were confirmed with high accuracy using Sanger sequencing which supports the usefulness of this approach. The largest series of patients published (239, 254, 2299 patients included) [4, 22, 23] were focused on CRC. Mutation rates obtained using the Sequenom OncoCartaTM Panel technology were similar to those published in the COSMIC
database, or described with the use of different sequencing technologies. Notably, Fumagalli et al. (2010) [22] analyzed metastatic lymph nodes and their corresponding primary tumors mutation profiles using this technology and showed an 89.7% of concordance with COSMIC database. An important application of generating mutational profiles in oncology is the possible inclusion of patients in phase I trials of experimental target-therapies. Such implementation entails different challenges, such as a highly heterogeneous population with a mixture of tumor types, a heavy pre-treatment burden and multiple sites of disease, which are likely to be molecularly heterogeneous. Dienstmann et al. (2012) included data on therapeutic decisions made on the basis of the mutational profile of patients diagnosed with CRC: they report their experience with use of different sequencing technologies (RT-PCR, Sanger sequencing and the OncoCartaTM Panel v1.0) to determine the mutational profile of 254 patients with solid tumors, who were candidates for experimental therapies. They report that 68 of these patients received 82 different matched target therapies [4]. Finally, our group has just described the applicability of the Sequenom technology in relation to target-therapy decisions. We used OncoCartaTM Panel v1.0 to analyze 238 mutations across 19 oncogenes in 197 FFPE samples of different tumors and found that 97 (49.2%) of the specimens presented at least one mutation. In particular, 49 different oncogenic mutations were detected in KRAS, PIK3CA, NRAS, KIT, EGFR, BRAF, RET, CDK4, MET, GNAS, ABL1, AKT1, PDGFRA, IDH1, ERBB2 and ERBB3. Mutation profiles were validated using both mass spectrometry with a customized panel and NGS (GS-Junior 454, Roche) with high concordance. According to their molecular characterization, 28 patients (27.7%) either participated in early clinical trials or received specific treatments [5]. Additional data presented by studies using the OncoCartaTM panel v1.0 are summarized in Table 1 [4, 5, 7, 8, 11, 12, 16-20, 22, 23, 25-28, 31-34, 36-38, 40, 43, 45, 49-57, 60, 61, 63, 66, 69, 70, 73, 76, 92].
Customized panels aimed at characterizing specific tumors types One limitation of the OncoCartaTM Panels is the predefined board of hotspot mutations across different oncogenes. However, Sequenom technology allows the design of customized panels, to identify mutations within a specific tumor. Concrete mutations in different oncogenes, or in tumor suppressor genes, may also be designed by the company, to satisfy customers’ needs. In fact, the company already has panels for melanoma (MelaCartaTM, which includes 70 mutations in 20 different genes), lung (LungCartaTM, which characterizes 250 mutations in 26 genes), gynecological cancer (GyneCartaTM, which describes 168 mutations in 13 genes), and CRC (ColoCartaTM, which interrogates 32 mutations in 6 genes). In 2009, Mac Conaill and colleagues queried different databases for somatic oncogene and tumor suppressor gene mutations, and designed and implemented OncoMap, a customized assay for Sequenom technology that predicts response or resistance to targeted therapies [3]. They selected 396 different mutations (single-base substitutions, insertions and deletions) across 33 genes, based on several criteria including prior knowledge about the association of non-synonymous coding mutations with human cancer, the frequency of these genes across different cancer subtypes, and their potential functionality as drug-target gene. Forty six articles describe the use of designed panels. Twenty of them used the OncoMap panel or modified versions of it [3, 15, 29, 30, 35, 39, 42, 64, 71, 79, 83-86, 88-91, 93], whereas 7 articles employed the LungCartaTM panel v1.0 [21, 65, 68, 72, 75, 77, 87]. Moreover, one work used the GyneCartaTM panel v1.0 and v2.0 [80], one the MelaCartaTM panel v1.0 [82] and one used the UltraseekTM panel [74]. The remaining 16 works used customized panels to determine specific mutations profiles within specific genes. These customized panels mainly focus on a low number of genes, with 11 of the papers based on less than 10 genes (Table 2) [6, 9, 10, 13, 14, 24, 47, 48, 58, 59, 62]. Moreover, in these low number genes customized panels, 6
genes were the most commonly studied (PIK3CA in 12 articles, BRAF in 11, KRAS in 10, NRAS in 6, EGFR in 5, AKT1 in 5, and RET in 2 articles). Tumor types in these articles are diverse, and although positive results are reported in fresh tissue, cell lines and plasma samples by some of the researchers, most of the analyses were done in FFPE samples (31 of the 35, or 88.6%). Twenty nine studies used a second validation technique or a different panel. The concordance between different sequencing methods and these customized panels is higher than 85.0% (Table 2). Information regarding mutation threshold is provided in 10 of these studies varying from 1.5% to 15.0% (data not shown). Tan et al. (2014) [68] and Fallet et al. (2015) [21] used the LungCartaTM v1.0 panel to explore potential therapeutic targets in tongue carcinoma and lung sarcomatoid carcinoma, respectively. They found that 30.3% of the tongue carcinoma patients had at least one mutation whereas 70% of the lung sarcomatoid carcinoma patients harbored at least one mutation. Additionally, 5 studies employed the LungCartaTM panel with different purposes, including the determination of the mutation profile of Asiatic female no smoker patients [87], the mutation profile of a series of nonsmall cell lung cancer (NSCLC) patients included in a clinical trial [65], the use of the LungCartaTM panel to test mutations in biopsies with low tissue [75], to stratify patients both sensitive or resistant to erlotinib [72], and to correlate image techniques with the mutation profile determined by Sequenom [77]. All the authors found the strategy useful for the aim of their works. Barbour et al. (2014) used the MelaCartaTM panel to test the mutation profile of 134 stage III lymph nodes patient samples to guide the adjuvant setting decisions. The MelaCartaTM panel resulted suitable to detect mutations in genomic DNA obtained from FFPE tissues with an accuracy of 93% in their dataset [82]. Spaans and colleagues (2014) described the used of the GyneCartaTM panel design in gynecological tumors. Based on the COSMIC database, they selected 171 somatic hotspot mutations in the 13 most important genes for gynecological cancers,
being BRAF, CDKN2A, CTNNB1, FBXW7, FGFR2, FGFR3, FOXL2, HRAS, KRAS, NRAS, PIK3CA, PPP2R1A and PTEN. A total of 546 tumors (205 cervical, 227 endometrial, 89 ovarian, and 25 vulvar carcinomas) were used to test and validate their panel, and the results were validated by testing duplicate samples and by allelespecific qPCR. They found that the panel was reproducible and high-throughput, useful in FFPE material of low quality and quantity [80]. Finally, Brevet et al. (2011) studied the feasibility of studying EGFR mutations in lung cancer plasma samples (ctDNA), in order to guide clinical decisions for patients with insufficient or unavailable tumor specimens. Deletions of EGFR exon 19 and mutations of EGFR Exon 21 L858R were analyzed by a mass spectrometry genotyping assay combined with a mutant-enriched PCR (ME-PCR) technique. Results showed that, in 61.0% of the patients, the EGFR mutational status in ctDNA was identical to that in the primary tumor [10]. The need for customized panels for each type of tumor may have led to the design of new and more specific panels by the Sequenom Company. In term with this idea, we consider that the implementation of specific panels for each type of tumor in order to confirm the presence of key driving mutations will be a useful tool at the clinic.
Discussion and Conclusions In sum there is an intense interest in rapid, reliable and accurate methods for mutation screening. The Sequenom mass spectrometry platform has the advantage of being able to detect multiple mutations in the same assay – this means that smaller samples and less time are needed to screen for DNA mutations. Sequenom allows medium-size laboratories to analyze key hotspot mutations rapidly, with high accuracy, and without the need for complex tools for bioinformatics analysis. Overview of the mutation screening using MassARRAY technology is represented in Figure 3. The Sequenom MassARRAY technology, when used in combination with the OncoCartaTM panels, as well as other customized panels, is cost-effective in terms of
screening for somatic mutations in solid tumors. The paucity of rare or novel mutations found across different tumors suggests that the profiling of specific hotspot mutations in solid tumors would benefit from a better design and a more customized approach. Therefore, mass spectrometry approach would allow a rapid decision making which would improve patient clinical trial inclusion. MassARRAY technique becomes a robust and easy implemented technology in clinical settings, avoiding complex NGS designs, library constructions and labor intensive bioinformatics analyses. Mass spectrometry technology remains a good validation technology and is optimal when only hotspots are pursued. This review gathers satisfactory experiences among diverse research groups that use this method. In summary we could highlight the highly concordant rates among different sequencing technologies. Finally, we can conclude that mass spectrometry is an excellent tool for the study of somatic mutations in patients who are potential candidates for targeted therapies, which should facilitate in the near future its approval for clinical testing.
Acknowledgements This work was supported in part by grants from the Spanish government Ministerio de Salud Carlos III [PI15/2180; PI13/00606]; the Generalitat Valenciana Prometeo [Prometeo/2013/005]; and Fondos FEDER. TF was funded by the Ministerio de Salud Carlos III under a Rio Hortega contract [CM13/00193]. MI-V is funded by the Ministerio de Salud Carlos III under a Sara Borrell contract [CD15/00153]. GR is funded by the Ministerio de Salud Carlos III under a Miquel Servet II contract [CPII1400013].
Conflict of interest There is no conflict of interest.
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Figure Legends Figure 1. Flowchart of the eligible studies focused on the somatic mutation analysis using MassARRAY technology. Figure 2. Representation of the different tumor types studied by MassARRAY technology. X axis represents the number of publications among each tumor type. Dark grey bars indicate the number of studies using the OncoCartaTM panel whereas light grey bars are those using customized panels. GIST: gastrointestinal tumors; CNS: central nervous system. Figure 3. Overview of the mutation screening process from DNA extraction to MassARRAY repot generation. Summary of both advantages and disadvantages of the technology are disclosed.
TM
Table 1. Publications describing the use of the OncoCarta Panel v1.0 in order to determine somatic mutations across different tumor types Type of Validation Concordance First Author Year N Sample tumor Technology rate (%) Wetterskog D. 2013 AC 13 P; FT Yes 50.0 Ragazzon B. a 2014 Adrenal 26 P Yes 100.0 Hernandez L. 2012 Breast 13 FT Yes NR Tilch E. 2014 Breast 107 P; FT Yes >90.0 Duprez R. 2012 Breast 49 P NR NR Natrajan R. 2013 Breast 16 P Yes 100.0 Tan SH. 2015 Breast 40 FT NR NR Fumagalli D. 2010 CRC 239 P Yes 100.0 Gavin P. 2012 CRC 2299 P Yes 100.0 Dienstmann R. 2012 CRC 254 P NR NR Voss J.S. 2013 C 94 P Yes 86.2 Liu C. 2014 CCS 2 P NR NR Cote M.L. 2012 Endometrium 150 P NR NR Mackay H.J. 2013 Endometrium 94 P Yes 100.0 Biscuola M. 2013 Endometrium 34 P NR NR b Kang G. 2012 GIST 22 FT Yes 73.0 Yanagawa N. 2011 Lung 152 P Yes 100.0 Yip P.Y. 2013 Lung 204 P NR NR Rusell P.A. 2013 Lung 69 P NR NR John T. 2013 Lung 199 P NR NR John T. 2011 Lung 139 P Yes 100.0 Cote M.L. 2011 Lung 144 P NR NR Wen Y.S. 2014 Lung 156 P Yes >90.0 Dabir S. 2014 Lung 6 P Yes 100.0 Luk P.P. 2015 Lung 273 P NR NR Wang S 2015 Lung 142 P,C NR NR Dutton-Regester K. 2012 Melanoma 271 CL; FT Yes 100.0 Niu H. 2013 Melanoma 58 P NR NR Carlino M.S. 2014 Melanoma 193 P NR NR Lyle M. 2016 Melanoma 223 P NR NR Li C. 2014 MIT 2 P NR NR Jiang N 2014 N 160 P, CL, B Yes 100.0 Zhang Z-Ch. 2014 N 123 P NR NR Zanaruddin S.N. 2013 OSCC 107 FT Yes 100.0 Carden C. 2012 Ovary 88 P NR NR Ferrandiz-Pulido C. 2015 Penile 75 P Yes 100.0 Luk P.P. 2015 SG 9 P NR NR b Da Silva L. 2010 Solid tumor 47 P Yes 100.0 Beadling C. 2011 Solid tumor 820 P Yes 85.8 Monsma D.J. 2012 Solid tumor 182 FT NR NR Tran B. 2013 Solid tumor 51 P; FT Yes >95.0 Ibarrola-Villava M. 2016 Solid tumor 197 P Yes >87.0 Perkins G. 2012 Solid tumor 105 P; B Yes 90.0 Schechter R.B. 2015 Thyroid 28 P NR NR Li B. 2015 UPS 1 P NR NR N = number of samples analyzed; NR= Not reported AC = Adenoid cystic; CRC = Colorectal cancer; C = Cholangiocarcinomas; CCS = Clear cell sarcoma ; GIST = Gastrointestinal Stromal Tumors; MIT = Myofibroblastic inflammatory tumor; N = Nasopharyngeal; OSCC = Oral squamous cell carcinoma; SG = Sallivary Gland; UPS = Undifferentiated Pleomorphic Sarcoma B = Blood; CL = Cell Lines; FT = Fresh Tissue; M = Mice; P = Paraffin; C= cytology a TM In this study additional OncoCarta panel v3.0 was used b TM In this study additional OncoCarta panel v2.0 was used
Table 2. Publications describing the use of customized panels for MassARRAY technology in order to determine somatic mutations across different tumor types First Author
Year
Type of tumor
N
Sample
Chandarlapaty S. Santarpia L. Pommier S. Azim H.A. López-Knowles E. Wright A.A Ramkissoon S.H. Bond C. Smith C.G. Arcila M. Hanna M.C. Seol H.S. Krakstad C. Maeng C.H. Lee J. Feldman D.R.
2012 2012 2012 2014 2014 2013 2015 2012 2013 2011 2013 2013 2012 2012 2012 2014
Breast Breast Breast Breast Breast Cervix CNS CRC CRC CRC CRC CRC Endometrium Esophageal Gastric Germ Cells
63 267 11 167 85 80 86 694 1976 331 427 4 80 80 237 70
FT FT FT P P P P FT P P P; FT P;CL;M FT;CL P P FT;P
Spaans V.M.
2014
Gynecologic
548
FT;P
Qin W. Brevet M. Chaft J. Su K. Seol H.S. Okamoto I. Ulivi P. Weiss G.J. Quinn A.M. Fallet V. Ha SY. Daniels A.B. Greaves W. Barbour A. Mosko M. Choy E. Matulonis U.A. Stemke-Hale K. Kim Y.M. Despierre E Mac Conaill L.E. Portier B.P. Oberholzer P. Rivera M.
2011 2011 2012 2012 2013 2014 2014 2014 2015 2015 2015 2012 2013 2014 2016 2012 2011 2013 2014 2014 2009 2014 2012 2010
Kidney Lung Lung Lung Lung Lung Lung Lung Lung Lung Lung Melanom a Melanom a Melanom a Melanom a Osteosarcoma Ovary Ovary Ovary Ovary Solid tumors Solid tumors SSC Thyroid
9 31 1125 192 4 275 68 48 90 81 198 134 1112 134 122 98 203 52 46 262 903 46 237 47
FT;P B P P P;CL;M P P P P;C P P P;FT P P P;CL P;FT;CL FT;P FT P P;FT P;FT P P P
Pitt S.C.
2015
Thyroid
239
P;FT
Tan D.S.
2014
Tongue
66
FT
Guancial E.A.
2014
Urothelial
231
P
Number of genes analyzed and panel 2 – Customized 28 – Customized 30 – Customized 19 - Customized 1 - Customized 139 - OncoMap v4.0 41-OncoMap v4.0 2 – Customized 4 – Customized 2 – Customized 33 – OncoMap 41- OncoMap v4.0 28 – OncoMap 41 – OncoMap v4.0 41 – OncoMap v4.0 7 – Customized TM 12 - GyneCarta v1.0 and v2.0 115 – OncoMap v3.0 1 – Customized 8 – Customized 1 – Customized 41- OncoMap v4.0 TM 26 - LungCarta v1.0 TM 26 - LungCarta v1.0 TM 26 - LungCarta v1.0 TM 26 - LungCarta v1.0 TM 26 - LungCarta v1.0 26 – LungCartaTM v1.0 120 - OncoMap v3.0 1 – Customized TM 26-MelaCarta 12-Ultraseek 103 – OncoMap v2.0 112 - OncoMap v3.0 33 - Customized 41 - OncoMap v4.4 9 – Customized 33 – OncoMap 11 - Customized 33 – OncoMap 16 – Customized 33-OncoMap and OncoPanel* TM 26 – LungCarta v1.0 33-OncoMap v1.0 and 120-OncoMapv3.0 120-OncoMap v3.0 120-OncoMap v3.0
NR Yes NR NR Yes Yes NR Yes Yes Yes Yes NR Yes NR NR Yes
Concor dance rate (%) NR 100.0 NR NR 91.0 NR NR 100.0 99.0 99.0 100.0 NR 100.0 NR NR 100.0
Yes
99.4
NR Yes Yes Yes NR NR Yes NR Yes NR Yes Yes Yes NR Yes Yes Yes Yes Yes NR Yes Yes NR NR
NR 100.0 100.0 100.0 NR NR NR NR 100.0 NR 100.0 97.0 99.3 NR 95.0 100.0 100.0 100.0 88.6 NR >75.0 100.0 NR NR
Yes
100.0
Validation
NR
NR
Yes
100.0
Bellmunt J. 2014 Urothelial 96 P;FT Yes >90.0 Bellmunt J. 2015 Urothelial 103 P Yes 100.0 N = Number of patients analyzed CRC = Colorectal cancer; CNS= Central Nervous System; SSC = Skin Squam ous Cell B = Plasma samples; C= cytology samples ; CL = Cell lines samples; FT = Fresh tissue samples; P = Paraffin embedded samples *OncoPanel analyzes exonic DNA sequences of 275 cancer genes and 91 introns across 30 genes for rearrangement detection
Highlights of the manuscript entitled “MassARRAY determination of somatic oncogenic mutations in solid tumors: moving forward to personalized medicine”: • • • • •
We review those articles using MassARRAY technology for somatic mutation analysis. We describe commercial available panels and their applicability. We highlight the importance of designing customized panels for each tumor type. We reinforce the role of this technique in selecting target-drug therapies. Validation and concordance rates among different technologies are described.
S0305-7372(16)30063-9 http://dx.doi.org/10.1016/j.ctrv.2016.07.007 YCTRV 1527
To appear in:
Cancer Treatment Reviews Cancer Treatment Reviews
Received Date: Accepted Date:
20 July 2016 22 July 2016
Please cite this article as: Fleitas, T., Ibarrola-Villava, M., Ribas, G., Cervantes, A., MassARRAY determination of somatic oncogenic mutations in solid tumors: moving forward to personalized medicine, Cancer Treatment Reviews Cancer Treatment Reviews (2016), doi: http://dx.doi.org/10.1016/j.ctrv.2016.07.007
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MassARRAY determination of somatic oncogenic mutations in solid tumors: moving forward to personalized medicine
Tania Fleitas a,*, Maider Ibarrola-Villava a,*, Gloria Ribasa,¥ and Andrés Cervantesa,¥¥
a
Department of Hematology and Medical Oncology, Biomedical Research Institute-
INCLIVA, University of Valencia, Av. Blasco Ibañez 17, 46010, Valencia, Spain.
*Both authors contributed equally
To whom correspondence should be addressed: ¥
Dr. Gloria Ribas, Department Medical Oncology, Biomedical Research Institute -
INCLIVA, University of Valencia, Av. Blasco Ibañez 17, 46010, Valencia, Spain. Telephone number: 0034 963864402 FAX: 0034 963 987860 Email: [email protected]
¥¥
Pr. Andres Cervantes, Department Medical Oncology, Biomedical Research Institute
- INCLIVA, University of Valencia, 46010, Valencia, Spain. Telephone number: 0034 961973531 FAX: 0034 963 987860 Email: [email protected]
MassARRAY determination of somatic oncogenic mutations in solid tumors: moving forward to personalized medicine
Abstract This article will review the impact of the recently developed MassARRAY technology on our understanding of cancer biology and treatment. Analysis of somatic mutations is a useful tool in selecting personalized therapy, and for predicting the outcome of many solid tumors. Here, we review the literature on the application of MassARRAY technology (Sequenom Hamburg, Germany) to determine the mutation profile of solid tumors from patients. We summarize the use of commercially available panels of mutations - such as OncoCartaTM or other combinations - and their concordance with results obtained by using other technologies, such as next generation sequencing.
Keywords MassARRAY technology; OncoCarta panel v1.0; somatic mutation profile; solid tumors.
Introduction Over the last decade, significant advances have been made in identifying oncology biomarkers, which have yielded greater insight into the molecular and cellular mechanisms that drive the initiation, maintenance and progression of tumors. Therapeutic approaches have also shifted substantially during this period, as new sequencing technologies have increased our understanding of the molecular and genetic make-up of cancers. As a result, classic chemotherapy treatments are being gradually displaced by targeted drug therapies, which interfere with specific molecules and thereby block cancer cell growth. This new approach has improved the overall survival of cancer patients, as seen with use of trastuzumab for the treatment of breast cancers that overexpresses HER2, or vemurafenib as a targeted therapy for melanomas in which the BRAF gene is mutated [1, 2]. In other words, the analysis of key cancer-driving mutations has become enormously useful in selecting personalized medicine. The current gold standard technique for identifying somatic mutations is nextgeneration sequencing (NGS); however, other technologies, such as mass spectrometry, may also be used for this purpose [3]. The mass spectrometry technique, based on matrix-assisted laser desorption/ionization-time of flight, detects known genetic variations with target therapies available and is widely used to assess point mutations across different solid tumors to treat patients with known response or to identify resistant clones. The Sequenom MassARRAY technology (Sequenom, San Diego, CA) is a mass spectrometry technique that when is used in combination with the commercial kit OncoCartaTM v1.0 (http://agenabio.com/oncocarta-panel), screens for up to 238 somatic mutations across 19 oncogenes in 24 multiplexed assays. Further versions, v2.0 and v3.0, include additional oncogenes and tumor suppressor gene mutations. Custom assays, such us the ColoCartaTM, GyneCartaTM, LungCartaTM and MelaCartaTM panels have also been incorporated in the overall design to permit detection of specific target genes as needed by different research groups.
Mass spectrometry is cost-effective, but its usefulness in clinical care is still being debated [3-5]. This review provides a systematic overview of all available data from studies that have used this technology to determine the mutational profile of tumors. We also highlight the clinical value of this methodology, in the context of the experience of research groups that have applied this technology across different panels and across a wide range of tumors.
Methods Search strategy and study identification Articles
were
selected
from
the
PubMed
database
(http://www.ncbi.nlm.nih.gov/pubmed), with use of key search terms, or aliases, for: “OncoCarta”, “OncoCarta Sequenom”, “Somatic mutation analysis Sequenom”, “ColoCarta”, “GyneCarta”, ”LungCarta”, “MelaCarta”, “Ultraseek” and “OncoMap”. Publication library available at Agena Bioscience website was also explored for the same
key
search
terms
(http://agenabio.com/oncology;
http://agenabio.com/resources/publication-library). In order to increase the sensibility of the search results, reference lists of the retrieved articles were manually screened and necessary citations were included into the review.
Literature search results The initial database search included 160 articles, 28 of them in both searches. Among the remaining 132 publications, the eligibility criteria included studies in patients with clinical and histological diagnosis of solid tumors that were molecularly characterized by the Sequenom technology as the principal tool. Forty one articles were excluded because of different reasons: (a) Sequenom technology was used for the genotyping of specific polymorphisms (9 reports) or used as a validation technique (5 papers); (b) lack of specific data analysis (2 reports ); (c) studies not focused on solid tumors (9 reports); (d) studies performed on cell lines or mice models (3 reports);
(e) studies involving pediatric populations (6 report) and (f) not mutation profile determined in the study (4 reports). Finally, 3 manuscripts were not a research article and were excluded (Figure 1).
Somatic mutation analysis using MassARRAY technology Ninety one articles, published between January 2009 to April 2016, described the use of the Sequenom MassARRAY technology in order to detect somatic mutations among different tumor types and were included in this review. Among them, 45 works were performed using the OncoCartaTM panel v1.0 for mutation profiling, whereas the other 46 studies used a customized-panel (See Tables 1 and 2, respectively). Regarding tumor types, 20 studies (22.0%) were conducted in patients with lung cancer, 11 (12.1%) in cervix and other gynecologic tumors, 10 (11.0%) in individuals with breast cancer, 8 (8.8%) in colorectal cancer patients (CRC), 8 (8.8%) in several solid tumors, 8 (8.8%) in melanoma tumors, 4 (4.4%) in head and neck tumors, 3 (3.3%) in sarcomas and 19 (20.9%) in other tumor types including adenoid cystic, adrenal, cholangiocarcinoma, central
nervous system, urothelial, germ cells,
gastrointestinal stromal tumor (GIST), thyroid, kidney, esophageal, gastric skin, myofibroblastic, salivary gland and penile carcinomas (See Figure 2) [1, 3-93]. Positive results were reported in fresh tissue, cell lines and plasma samples; however, most of the studies were done in formalin-fixed paraffin-embedded (FFPE) tissues. Results of 52 studies (57.1%) were validated with the use of different techniques, including NGS, Sanger sequencing, pyrosequencing, real-time PCR (RTPCR), Droplet Digital PCR (dd-PCR) or Affymetrix (Santa Clara, CA). Concordance rate was 100.0% in 31 (59.6%) articles whereas concordances higher than 85.0% were reported in 15 (28.8%) papers (Tables 1 and 2).
OncoCarta panels to determine the mutation profile in solid tumors
The forty five studies that accomplished the molecular characterization of solid tumors using the OncoCartaTM panel v1.0, used FFPE, frozen or blood tissues. Additionally, three of the works used the OncoCarta panels v2.0 or v3.0. Moreover, 23 of them used an extra panel or technology to validate their results. Samples sizes varied from 2 to more than 2200 individuals. Among the 45 studies, 10 (22.2%) were conducted in patients with lung cancer, 6 (13.3%) in those with varied solid tumors, 5 (11.1%) in those with breast cancer, 3 (6.7%) in those with CRC, 4 (8.9%) in those with melanoma, 3 (6.7%) in those with endometrium cancer, and 14 (31.1%) in those with other tumor types including ovary, cholangiocarcinoma, sarcoma, oral cavity, GIST, myofibroblastic, nasopharyngeal, adenoid cystic, thyroid, penile, salivary gland and adrenal carcinomas. Furthermore, for all studies, the accuracy between any sequencing result and the OncoCartaTM panel v1.0 output was high, with independence of the type of sample analyzed (FFPE, fresh tissue, cell lines or blood). Visualizing the mass spectra and determining the frequency of mutant and wild type alleles is done by the MassARRAY software called Typer Viewer. A wide range of thresholds have been used for considering alleles “mutated” or “non-mutated”. Information regarding the cut-off used is available in 10 studies and the cutoffs varies from 1.0% (in 3 studies) [8, 22, 52] to 10.0% (in 5 studies) [5, 7, 16, 17, 49, 51, 56]. Beadling and colleagues published one of the most comprehensive studies in 2011, in which they molecularly characterized 820 different FFPE solid tumors, using Sequenom OncoCartaTM Panels v1.0 and v2.0: they screened up to 390 mutations across 30 cancer-related genes [7]. The use of this platform in combination with the two commercial panels allowed the identification of those genes that are more or less frequently mutated across different cancer types. Results were confirmed with high accuracy using Sanger sequencing which supports the usefulness of this approach. The largest series of patients published (239, 254, 2299 patients included) [4, 22, 23] were focused on CRC. Mutation rates obtained using the Sequenom OncoCartaTM Panel technology were similar to those published in the COSMIC
database, or described with the use of different sequencing technologies. Notably, Fumagalli et al. (2010) [22] analyzed metastatic lymph nodes and their corresponding primary tumors mutation profiles using this technology and showed an 89.7% of concordance with COSMIC database. An important application of generating mutational profiles in oncology is the possible inclusion of patients in phase I trials of experimental target-therapies. Such implementation entails different challenges, such as a highly heterogeneous population with a mixture of tumor types, a heavy pre-treatment burden and multiple sites of disease, which are likely to be molecularly heterogeneous. Dienstmann et al. (2012) included data on therapeutic decisions made on the basis of the mutational profile of patients diagnosed with CRC: they report their experience with use of different sequencing technologies (RT-PCR, Sanger sequencing and the OncoCartaTM Panel v1.0) to determine the mutational profile of 254 patients with solid tumors, who were candidates for experimental therapies. They report that 68 of these patients received 82 different matched target therapies [4]. Finally, our group has just described the applicability of the Sequenom technology in relation to target-therapy decisions. We used OncoCartaTM Panel v1.0 to analyze 238 mutations across 19 oncogenes in 197 FFPE samples of different tumors and found that 97 (49.2%) of the specimens presented at least one mutation. In particular, 49 different oncogenic mutations were detected in KRAS, PIK3CA, NRAS, KIT, EGFR, BRAF, RET, CDK4, MET, GNAS, ABL1, AKT1, PDGFRA, IDH1, ERBB2 and ERBB3. Mutation profiles were validated using both mass spectrometry with a customized panel and NGS (GS-Junior 454, Roche) with high concordance. According to their molecular characterization, 28 patients (27.7%) either participated in early clinical trials or received specific treatments [5]. Additional data presented by studies using the OncoCartaTM panel v1.0 are summarized in Table 1 [4, 5, 7, 8, 11, 12, 16-20, 22, 23, 25-28, 31-34, 36-38, 40, 43, 45, 49-57, 60, 61, 63, 66, 69, 70, 73, 76, 92].
Customized panels aimed at characterizing specific tumors types One limitation of the OncoCartaTM Panels is the predefined board of hotspot mutations across different oncogenes. However, Sequenom technology allows the design of customized panels, to identify mutations within a specific tumor. Concrete mutations in different oncogenes, or in tumor suppressor genes, may also be designed by the company, to satisfy customers’ needs. In fact, the company already has panels for melanoma (MelaCartaTM, which includes 70 mutations in 20 different genes), lung (LungCartaTM, which characterizes 250 mutations in 26 genes), gynecological cancer (GyneCartaTM, which describes 168 mutations in 13 genes), and CRC (ColoCartaTM, which interrogates 32 mutations in 6 genes). In 2009, Mac Conaill and colleagues queried different databases for somatic oncogene and tumor suppressor gene mutations, and designed and implemented OncoMap, a customized assay for Sequenom technology that predicts response or resistance to targeted therapies [3]. They selected 396 different mutations (single-base substitutions, insertions and deletions) across 33 genes, based on several criteria including prior knowledge about the association of non-synonymous coding mutations with human cancer, the frequency of these genes across different cancer subtypes, and their potential functionality as drug-target gene. Forty six articles describe the use of designed panels. Twenty of them used the OncoMap panel or modified versions of it [3, 15, 29, 30, 35, 39, 42, 64, 71, 79, 83-86, 88-91, 93], whereas 7 articles employed the LungCartaTM panel v1.0 [21, 65, 68, 72, 75, 77, 87]. Moreover, one work used the GyneCartaTM panel v1.0 and v2.0 [80], one the MelaCartaTM panel v1.0 [82] and one used the UltraseekTM panel [74]. The remaining 16 works used customized panels to determine specific mutations profiles within specific genes. These customized panels mainly focus on a low number of genes, with 11 of the papers based on less than 10 genes (Table 2) [6, 9, 10, 13, 14, 24, 47, 48, 58, 59, 62]. Moreover, in these low number genes customized panels, 6
genes were the most commonly studied (PIK3CA in 12 articles, BRAF in 11, KRAS in 10, NRAS in 6, EGFR in 5, AKT1 in 5, and RET in 2 articles). Tumor types in these articles are diverse, and although positive results are reported in fresh tissue, cell lines and plasma samples by some of the researchers, most of the analyses were done in FFPE samples (31 of the 35, or 88.6%). Twenty nine studies used a second validation technique or a different panel. The concordance between different sequencing methods and these customized panels is higher than 85.0% (Table 2). Information regarding mutation threshold is provided in 10 of these studies varying from 1.5% to 15.0% (data not shown). Tan et al. (2014) [68] and Fallet et al. (2015) [21] used the LungCartaTM v1.0 panel to explore potential therapeutic targets in tongue carcinoma and lung sarcomatoid carcinoma, respectively. They found that 30.3% of the tongue carcinoma patients had at least one mutation whereas 70% of the lung sarcomatoid carcinoma patients harbored at least one mutation. Additionally, 5 studies employed the LungCartaTM panel with different purposes, including the determination of the mutation profile of Asiatic female no smoker patients [87], the mutation profile of a series of nonsmall cell lung cancer (NSCLC) patients included in a clinical trial [65], the use of the LungCartaTM panel to test mutations in biopsies with low tissue [75], to stratify patients both sensitive or resistant to erlotinib [72], and to correlate image techniques with the mutation profile determined by Sequenom [77]. All the authors found the strategy useful for the aim of their works. Barbour et al. (2014) used the MelaCartaTM panel to test the mutation profile of 134 stage III lymph nodes patient samples to guide the adjuvant setting decisions. The MelaCartaTM panel resulted suitable to detect mutations in genomic DNA obtained from FFPE tissues with an accuracy of 93% in their dataset [82]. Spaans and colleagues (2014) described the used of the GyneCartaTM panel design in gynecological tumors. Based on the COSMIC database, they selected 171 somatic hotspot mutations in the 13 most important genes for gynecological cancers,
being BRAF, CDKN2A, CTNNB1, FBXW7, FGFR2, FGFR3, FOXL2, HRAS, KRAS, NRAS, PIK3CA, PPP2R1A and PTEN. A total of 546 tumors (205 cervical, 227 endometrial, 89 ovarian, and 25 vulvar carcinomas) were used to test and validate their panel, and the results were validated by testing duplicate samples and by allelespecific qPCR. They found that the panel was reproducible and high-throughput, useful in FFPE material of low quality and quantity [80]. Finally, Brevet et al. (2011) studied the feasibility of studying EGFR mutations in lung cancer plasma samples (ctDNA), in order to guide clinical decisions for patients with insufficient or unavailable tumor specimens. Deletions of EGFR exon 19 and mutations of EGFR Exon 21 L858R were analyzed by a mass spectrometry genotyping assay combined with a mutant-enriched PCR (ME-PCR) technique. Results showed that, in 61.0% of the patients, the EGFR mutational status in ctDNA was identical to that in the primary tumor [10]. The need for customized panels for each type of tumor may have led to the design of new and more specific panels by the Sequenom Company. In term with this idea, we consider that the implementation of specific panels for each type of tumor in order to confirm the presence of key driving mutations will be a useful tool at the clinic.
Discussion and Conclusions In sum there is an intense interest in rapid, reliable and accurate methods for mutation screening. The Sequenom mass spectrometry platform has the advantage of being able to detect multiple mutations in the same assay – this means that smaller samples and less time are needed to screen for DNA mutations. Sequenom allows medium-size laboratories to analyze key hotspot mutations rapidly, with high accuracy, and without the need for complex tools for bioinformatics analysis. Overview of the mutation screening using MassARRAY technology is represented in Figure 3. The Sequenom MassARRAY technology, when used in combination with the OncoCartaTM panels, as well as other customized panels, is cost-effective in terms of
screening for somatic mutations in solid tumors. The paucity of rare or novel mutations found across different tumors suggests that the profiling of specific hotspot mutations in solid tumors would benefit from a better design and a more customized approach. Therefore, mass spectrometry approach would allow a rapid decision making which would improve patient clinical trial inclusion. MassARRAY technique becomes a robust and easy implemented technology in clinical settings, avoiding complex NGS designs, library constructions and labor intensive bioinformatics analyses. Mass spectrometry technology remains a good validation technology and is optimal when only hotspots are pursued. This review gathers satisfactory experiences among diverse research groups that use this method. In summary we could highlight the highly concordant rates among different sequencing technologies. Finally, we can conclude that mass spectrometry is an excellent tool for the study of somatic mutations in patients who are potential candidates for targeted therapies, which should facilitate in the near future its approval for clinical testing.
Acknowledgements This work was supported in part by grants from the Spanish government Ministerio de Salud Carlos III [PI15/2180; PI13/00606]; the Generalitat Valenciana Prometeo [Prometeo/2013/005]; and Fondos FEDER. TF was funded by the Ministerio de Salud Carlos III under a Rio Hortega contract [CM13/00193]. MI-V is funded by the Ministerio de Salud Carlos III under a Sara Borrell contract [CD15/00153]. GR is funded by the Ministerio de Salud Carlos III under a Miquel Servet II contract [CPII1400013].
Conflict of interest There is no conflict of interest.
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Figure Legends Figure 1. Flowchart of the eligible studies focused on the somatic mutation analysis using MassARRAY technology. Figure 2. Representation of the different tumor types studied by MassARRAY technology. X axis represents the number of publications among each tumor type. Dark grey bars indicate the number of studies using the OncoCartaTM panel whereas light grey bars are those using customized panels. GIST: gastrointestinal tumors; CNS: central nervous system. Figure 3. Overview of the mutation screening process from DNA extraction to MassARRAY repot generation. Summary of both advantages and disadvantages of the technology are disclosed.
TM
Table 1. Publications describing the use of the OncoCarta Panel v1.0 in order to determine somatic mutations across different tumor types Type of Validation Concordance First Author Year N Sample tumor Technology rate (%) Wetterskog D. 2013 AC 13 P; FT Yes 50.0 Ragazzon B. a 2014 Adrenal 26 P Yes 100.0 Hernandez L. 2012 Breast 13 FT Yes NR Tilch E. 2014 Breast 107 P; FT Yes >90.0 Duprez R. 2012 Breast 49 P NR NR Natrajan R. 2013 Breast 16 P Yes 100.0 Tan SH. 2015 Breast 40 FT NR NR Fumagalli D. 2010 CRC 239 P Yes 100.0 Gavin P. 2012 CRC 2299 P Yes 100.0 Dienstmann R. 2012 CRC 254 P NR NR Voss J.S. 2013 C 94 P Yes 86.2 Liu C. 2014 CCS 2 P NR NR Cote M.L. 2012 Endometrium 150 P NR NR Mackay H.J. 2013 Endometrium 94 P Yes 100.0 Biscuola M. 2013 Endometrium 34 P NR NR b Kang G. 2012 GIST 22 FT Yes 73.0 Yanagawa N. 2011 Lung 152 P Yes 100.0 Yip P.Y. 2013 Lung 204 P NR NR Rusell P.A. 2013 Lung 69 P NR NR John T. 2013 Lung 199 P NR NR John T. 2011 Lung 139 P Yes 100.0 Cote M.L. 2011 Lung 144 P NR NR Wen Y.S. 2014 Lung 156 P Yes >90.0 Dabir S. 2014 Lung 6 P Yes 100.0 Luk P.P. 2015 Lung 273 P NR NR Wang S 2015 Lung 142 P,C NR NR Dutton-Regester K. 2012 Melanoma 271 CL; FT Yes 100.0 Niu H. 2013 Melanoma 58 P NR NR Carlino M.S. 2014 Melanoma 193 P NR NR Lyle M. 2016 Melanoma 223 P NR NR Li C. 2014 MIT 2 P NR NR Jiang N 2014 N 160 P, CL, B Yes 100.0 Zhang Z-Ch. 2014 N 123 P NR NR Zanaruddin S.N. 2013 OSCC 107 FT Yes 100.0 Carden C. 2012 Ovary 88 P NR NR Ferrandiz-Pulido C. 2015 Penile 75 P Yes 100.0 Luk P.P. 2015 SG 9 P NR NR b Da Silva L. 2010 Solid tumor 47 P Yes 100.0 Beadling C. 2011 Solid tumor 820 P Yes 85.8 Monsma D.J. 2012 Solid tumor 182 FT NR NR Tran B. 2013 Solid tumor 51 P; FT Yes >95.0 Ibarrola-Villava M. 2016 Solid tumor 197 P Yes >87.0 Perkins G. 2012 Solid tumor 105 P; B Yes 90.0 Schechter R.B. 2015 Thyroid 28 P NR NR Li B. 2015 UPS 1 P NR NR N = number of samples analyzed; NR= Not reported AC = Adenoid cystic; CRC = Colorectal cancer; C = Cholangiocarcinomas; CCS = Clear cell sarcoma ; GIST = Gastrointestinal Stromal Tumors; MIT = Myofibroblastic inflammatory tumor; N = Nasopharyngeal; OSCC = Oral squamous cell carcinoma; SG = Sallivary Gland; UPS = Undifferentiated Pleomorphic Sarcoma B = Blood; CL = Cell Lines; FT = Fresh Tissue; M = Mice; P = Paraffin; C= cytology a TM In this study additional OncoCarta panel v3.0 was used b TM In this study additional OncoCarta panel v2.0 was used
Table 2. Publications describing the use of customized panels for MassARRAY technology in order to determine somatic mutations across different tumor types First Author
Year
Type of tumor
N
Sample
Chandarlapaty S. Santarpia L. Pommier S. Azim H.A. López-Knowles E. Wright A.A Ramkissoon S.H. Bond C. Smith C.G. Arcila M. Hanna M.C. Seol H.S. Krakstad C. Maeng C.H. Lee J. Feldman D.R.
2012 2012 2012 2014 2014 2013 2015 2012 2013 2011 2013 2013 2012 2012 2012 2014
Breast Breast Breast Breast Breast Cervix CNS CRC CRC CRC CRC CRC Endometrium Esophageal Gastric Germ Cells
63 267 11 167 85 80 86 694 1976 331 427 4 80 80 237 70
FT FT FT P P P P FT P P P; FT P;CL;M FT;CL P P FT;P
Spaans V.M.
2014
Gynecologic
548
FT;P
Qin W. Brevet M. Chaft J. Su K. Seol H.S. Okamoto I. Ulivi P. Weiss G.J. Quinn A.M. Fallet V. Ha SY. Daniels A.B. Greaves W. Barbour A. Mosko M. Choy E. Matulonis U.A. Stemke-Hale K. Kim Y.M. Despierre E Mac Conaill L.E. Portier B.P. Oberholzer P. Rivera M.
2011 2011 2012 2012 2013 2014 2014 2014 2015 2015 2015 2012 2013 2014 2016 2012 2011 2013 2014 2014 2009 2014 2012 2010
Kidney Lung Lung Lung Lung Lung Lung Lung Lung Lung Lung Melanom a Melanom a Melanom a Melanom a Osteosarcoma Ovary Ovary Ovary Ovary Solid tumors Solid tumors SSC Thyroid
9 31 1125 192 4 275 68 48 90 81 198 134 1112 134 122 98 203 52 46 262 903 46 237 47
FT;P B P P P;CL;M P P P P;C P P P;FT P P P;CL P;FT;CL FT;P FT P P;FT P;FT P P P
Pitt S.C.
2015
Thyroid
239
P;FT
Tan D.S.
2014
Tongue
66
FT
Guancial E.A.
2014
Urothelial
231
P
Number of genes analyzed and panel 2 – Customized 28 – Customized 30 – Customized 19 - Customized 1 - Customized 139 - OncoMap v4.0 41-OncoMap v4.0 2 – Customized 4 – Customized 2 – Customized 33 – OncoMap 41- OncoMap v4.0 28 – OncoMap 41 – OncoMap v4.0 41 – OncoMap v4.0 7 – Customized TM 12 - GyneCarta v1.0 and v2.0 115 – OncoMap v3.0 1 – Customized 8 – Customized 1 – Customized 41- OncoMap v4.0 TM 26 - LungCarta v1.0 TM 26 - LungCarta v1.0 TM 26 - LungCarta v1.0 TM 26 - LungCarta v1.0 TM 26 - LungCarta v1.0 26 – LungCartaTM v1.0 120 - OncoMap v3.0 1 – Customized TM 26-MelaCarta 12-Ultraseek 103 – OncoMap v2.0 112 - OncoMap v3.0 33 - Customized 41 - OncoMap v4.4 9 – Customized 33 – OncoMap 11 - Customized 33 – OncoMap 16 – Customized 33-OncoMap and OncoPanel* TM 26 – LungCarta v1.0 33-OncoMap v1.0 and 120-OncoMapv3.0 120-OncoMap v3.0 120-OncoMap v3.0
NR Yes NR NR Yes Yes NR Yes Yes Yes Yes NR Yes NR NR Yes
Concor dance rate (%) NR 100.0 NR NR 91.0 NR NR 100.0 99.0 99.0 100.0 NR 100.0 NR NR 100.0
Yes
99.4
NR Yes Yes Yes NR NR Yes NR Yes NR Yes Yes Yes NR Yes Yes Yes Yes Yes NR Yes Yes NR NR
NR 100.0 100.0 100.0 NR NR NR NR 100.0 NR 100.0 97.0 99.3 NR 95.0 100.0 100.0 100.0 88.6 NR >75.0 100.0 NR NR
Yes
100.0
Validation
NR
NR
Yes
100.0
Bellmunt J. 2014 Urothelial 96 P;FT Yes >90.0 Bellmunt J. 2015 Urothelial 103 P Yes 100.0 N = Number of patients analyzed CRC = Colorectal cancer; CNS= Central Nervous System; SSC = Skin Squam ous Cell B = Plasma samples; C= cytology samples ; CL = Cell lines samples; FT = Fresh tissue samples; P = Paraffin embedded samples *OncoPanel analyzes exonic DNA sequences of 275 cancer genes and 91 introns across 30 genes for rearrangement detection
Highlights of the manuscript entitled “MassARRAY determination of somatic oncogenic mutations in solid tumors: moving forward to personalized medicine”: • • • • •
We review those articles using MassARRAY technology for somatic mutation analysis. We describe commercial available panels and their applicability. We highlight the importance of designing customized panels for each tumor type. We reinforce the role of this technique in selecting target-drug therapies. Validation and concordance rates among different technologies are described.