Multigene Assays in Metastatic Colorectal Cancer

Authors:
Kristin K. Deeb From the Center for Human Genetics Laboratory, Case Western Reserve University, Cleveland, Ohio; Clinical Molecular Diagnostic Laboratory, Department of Pathology, City of Hope Comprehensive Cancer Center, Duarte, California; Fulgent Therapeutics Inc, Temple City, California; and Medical Oncology and Experimental Therapeutics, City of Hope Comprehensive Cancer Center, Duarte, California.

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Jakub P. Sram From the Center for Human Genetics Laboratory, Case Western Reserve University, Cleveland, Ohio; Clinical Molecular Diagnostic Laboratory, Department of Pathology, City of Hope Comprehensive Cancer Center, Duarte, California; Fulgent Therapeutics Inc, Temple City, California; and Medical Oncology and Experimental Therapeutics, City of Hope Comprehensive Cancer Center, Duarte, California.

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Hanlin Gao From the Center for Human Genetics Laboratory, Case Western Reserve University, Cleveland, Ohio; Clinical Molecular Diagnostic Laboratory, Department of Pathology, City of Hope Comprehensive Cancer Center, Duarte, California; Fulgent Therapeutics Inc, Temple City, California; and Medical Oncology and Experimental Therapeutics, City of Hope Comprehensive Cancer Center, Duarte, California.

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Marwan G. Fakih From the Center for Human Genetics Laboratory, Case Western Reserve University, Cleveland, Ohio; Clinical Molecular Diagnostic Laboratory, Department of Pathology, City of Hope Comprehensive Cancer Center, Duarte, California; Fulgent Therapeutics Inc, Temple City, California; and Medical Oncology and Experimental Therapeutics, City of Hope Comprehensive Cancer Center, Duarte, California.

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Specific genomic colorectal cancer alterations are increasingly linked to prognosis and/or response to specific anticancer agents. The identification of KRAS mutations as markers of resistance to epidermal growth factor receptor (EGFR) inhibitors has paved the way to the interrogation of numerous other markers of resistance to anti-EGFR therapy, such as NRAS, BRAF, and PI3KCA mutations. Other genomic and protein expression alterations have recently been identified as potential targets of treatment or as markers of chemotherapy or targeted-therapy resistance, including ERCC1 expression, c-Met expression, PTEN expression, HER2 amplification, HER3 expression, and rare KRAS mutations. As the number of distinct validated intratumor genomic assays increases, numerous molecular assays will need to be compiled into one multigene panel assay. Several companies and academic centers are now offering multigene assays to patients with metastatic colorectal cancer and other solid tumors. This article discusses the technology behind multigene assays, its limitations, its current advantages, and its potential in the clinical care of metastatic colorectal cancer.

With an estimated excess of 50,000 deaths per year, colorectal cancer continues to be the second leading cause of cancer death in the United States.1 Despite FDA approval of 10 different agents for the treatment of metastatic colorectal cancer, improvement in overall survival has been modest and the median survival is estimated at 20 to 30 months.2-6 One of the main obstacles toward significant improvements in overall survival is related to innate or acquired tumor resistance to chemotherapy and targeted agents. Furthermore, clinical studies and molecular genotyping have now clearly indicated the heterogeneity of colorectal cancer and the inadequacy of the concept of “one size fits all” in the systemic treatment of metastatic colorectal cancer.7,8 For example, one of the major advances in the past 5 to 7 years has been the interrogation of the epidermal growth factor receptor (EGFR) pathway vis-à-vis cetuximab-based therapy in colorectal cancer. Retrospective analyses of tissue obtained from prospective studies of cetuximab or panitumumab in colorectal cancer indicate a lack of clinical benefit from anti-EGFR therapy in colorectal cancer tumors with KRAS mutations.9-13 However, some debate continues regarding the degree of resistance to anti-EGFR therapy among the various KRAS mutations, specifically with KRAS codon 13 mutations.14-16 Furthermore, colorectal tumors with BRAF V600E mutation have been associated with a more aggressive clinical behavior and increased resistance to chemotherapy and refractoriness to anti-EGFR therapy.17,18 Other mutations, such as PIK3CA and PTEN mutations, have been linked to resistance to anti-EGFR therapy in some studies, but their predictive role is yet to be conclusively validated.19-21 Other molecular characteristics have been implicated in the prognosis and response of colorectal cancer to various therapeutics agents. C-met overexpression has been associated with resistance to cetuximab treatment in primary xenograft models.22 Insulin-like growth factor 1 (IGF-1) overexpression has been linked to a benefit from IGF receptor (IGFR) targeting.23 MTHFR methylation and silencing have been associated with an increased likelihood of response to temozolomide.24 Finally, ERCC-1 overexpression has been associated with resistance to platinum therapy.25 These are only a few examples of the changing landscape of target-directed therapy and patient selection in the management of metastatic colorectal cancer.

This increasingly complex treatment landscape highlights the need for a refined, molecularly characterized approach to treating metastatic colorectal cancer. Molecular diagnostics has now transcended beyond the application of mutation assays of a single genetic mutation, such as KRAS, to a more comprehensive assessment of genomic alterations. This review describes next-generation sequencing (NGS)-based multigene assays in clinical practice, their distinct advantages, and their limitations.

Application of Multigene Assays in Clinical Practice

The identification of multiple clinically relevant driver mutations in molecular and cellular mechanisms involved in tumor initiation, maintenance, and progression has improved the understanding of cancer pathogenesis and led to the discovery of novel drug targets and development of new treatment paradigms for patients with cancer. The standard of care for patients with metastatic colorectal cancer has shifted from selecting conventional chemotherapy based on the patient’s clinicopathologic features, to using biomarker-driven targeted treatment algorithms based on the molecular profile of a patient’s tumor, such as assessing the status of KRAS and BRAF.19 These genotype-based targeted therapies represent the first step toward personalizing the treatment of different types of cancers. Multiple commercial molecular laboratories and numerous academic centers have begun to develop multiplex mutational profiling assays in clinical molecular diagnostic laboratories to sequence and genotype tumors and use the mutational profiles for clinical decision-making (Table 1). Recent technological advances in high-throughput target gene panel sequencing and genomic profiling (exome capture or whole genome) using NGS technology offer the opportunity to broadly interrogate a patient’s cancer genome from tumor biopsies for determining personalized therapy (Figure 1). Therefore, molecularly defined subsets of patients with metastatic colorectal cancer will be given the opportunity to clinically explore, at least through appropriate clinical trials, a growing list of novel molecularly targeted therapeutics that are currently available.

NGS Platforms

Cancer molecular diagnosis and treatment have co-evolved through the decades from single gene-based assays toward genomic profiling of individual patients. Currently, clinicopathologic features of tumors remain the primary standard to select available drugs for an individual patient. Traditional single-gene molecular tests, using Sanger DNA sequencing, pyrosequencing, or allele-specific or melting curve real-time polymerase chain reaction (RT-PCR), offer varying degrees of sensitivity but also have major disadvantages: they 1) are time-consuming (Sanger); 2) have high costs and are labor-intensive; 3) shorten length read limits (pyrosequencing); 4) lack the ability to detect deletions, translocations, and copy number changes; and 5) have limited scalability and multiplex capability. Single gene-based tests for KRAS and BRAF mutations in colorectal cancer have been incorporated into oncology practice for cetuximab and panitumumab therapy since 2008.26 High-throughput technologies using NGS that enable massively parallel sequencing of nucleic acid (DNA and RNA) at a significantly lower cost per base, faster speed, higher sensitivity, and reduced error rate (Table 2) continue to be deployed in clinical laboratories.27 The 3 levels of analysis that can be performed by NGS, with increasing complexity, are targeted-gene panels, exome sequencing, and whole-genome sequencing. Disease-targeted gene panels focus on a limited set of genes known to be disease-associated and allow greater depth of coverage for increased analytical sensitivity and specificity, and better interpretation of findings in a clinical context.28 Exome sequencing covers all the coding regions of the genome with approximately 85% of recognized disease-causing mutations.29 Exome sequencing is often used to detect variants in known disease-associated genes and discovering new gene-disease associations.28 Whole-genome sequencing covers both coding and noncoding regions that may affect gene expression of disease-associated genes of the genome.28

The 3 major components in NGS involve sample preparation, sequencing, and data analysis. Because clinical samples available for molecular studies are from formalin-fixed paraffin-embedded (FFPE) tissue, in which DNA quality and amount can be limiting, minimal amount of input sample can be used for a massively parallel sequencing approach to detect tumor genomic alterations in FFPE tumor samples.30-32 Genomic DNA extracted from a patient sample is enriched for a subset of genomic targets (targeted-gene panels or exome sequencing). These targets, flanked by platform-specific adapters, are the required input for the currently available NGS platforms. Multiple NGS platforms have been developed by Roche, Illumina, and Life Technologies with the capacity to massively sequence millions of DNA fragments in parallel, which differ in sequencing chemistry and impact the differences in total sequence capacity, sequence read length, sequence run time, and quality and accuracy of the data (Table 2).27,33,34 Depending on the platform, a series of processing steps are required to convert the DNA sample into appropriate sequencing format. A brief overview and the relative strengths and disadvantages of the different NGS platforms are summarized in Table 2.

Table 1

Multigene Assays Using Different Methodologiesa

Table 1

Targeted Multigene DNA Sequencing

Targeted multigene panels use several targeted enrichment strategies: PCR-amplicon-based and hybridization capture approaches.35 PCR-based approaches, if adequately normalized before pooling and sequencing, are highly specific and generate more uniform coverage than capture hybridization approaches. Ion AmpliSeq technology (Life Technologies, Carlsbad, CA) is based on ultrahigh-multiplex PCR in a single PCR reaction, and provides simple and fast library construction for the targeted sequencing of a target set of genes with very little input DNA (10 ng). The TruSeq Amplicon (Illumina, San Diego, CA) allows amplification of multiple targets with minimum genomic DNA (150 ng) through hybridizing 2 independent 5′ and 3′ flanking oligonucleotides to a genomic DNA template, enabling polymerase extension and ligation, and incorporation of universal barcoded indices and Illumina sequencing adapters. However, multiplex PCR raises the challenge of uniform reads across targeted amplicons and may require rebalancing of oligonucleotide pools to achieve adequate and uniform coverage. In the target hybridization capture approach, adaptor-modified genomic DNA libraries are hybridized to target-specific probes either immobilized on a microarray surface or in solution.35 The sample quality, presence of variants within the capture region, and DNA fragment size (ie, shorter fragments being captured with higher specificity than longer ones) have a large influence over the outcome of target enrichment.36,37 Compared with PCR, hybridization capture may lack specificity because of cross-hybridization and GC content of target sequence. Genes high in GC content are difficult to capture or amplify and are poorly represented in NGS data; however, optimized methods attempt to overcome this obstacle.38-41 These target-enrichment methods aim to increase the scale of PCR, and minimize reagent use, technical labor, and amount of DNA template required. Reliability of targeted enrichment, albeit hybridization capture-based or multiplex PCR-based, is very important in obtaining adequate representation, coverage, and sequence depth for all targeted regions.

Figure 1
Figure 1

Technological advances in molecular diagnosis for personalized therapy. High-throughput targeted-gene sequencing panel and genomic (whole-genome and exome sequence) profiling by NGS technologies offer the opportunity to broadly interrogate the cancer genome of individual patients for personalized diagnosis and treatment.

Abbreviations: NGS, next-generation sequencing; RT-PCR, real-time polymerase chain reaction.

Citation: Journal of the National Comprehensive Cancer Network J Natl Compr Canc Netw 11, suppl_4; 10.6004/jnccn.2013.0221

Because biopsy samples and amount of genomic DNA from FFPE tissue are limiting, the concept of a single test and a single drug is becoming unsustainable, with a growing list of molecular targets and need for assessment of multiple genomic alterations. Several companies and academic institutions (Table 1) are offering pan-cancer NGS panels for solid tumors, comprising many prognostic and actionable genes across multiple tumor types, to simultaneously identify actionable driver mutations in an individual patient’s tumor. These pan-cancer NGS panels are not tumor-specific because of the overlap of many driver gene mutations in the various tumor types. The NGS gene panels differ in the number of targeted genes (ie, full-length and mutation hotspots), platform used, and target enrichment methods. Although bioinformatics for NGS analysis remains a significant challenge, most centers have analysis pipelines for targeted NGS panels, in which data analysis becomes more manageable compared with whole-genome or exome sequencing. The FoundationOne pan-cancer test (Foundation Medicine, Inc., Cambridge, MA) and UW-OncoPlex cancer gene panel (Washington University, St. Louis, MO) interrogate a comprehensive list of 236 and 194 cancer-related genes, respectively. Several academic centers, such as Knight Diagnostic Laboratories, Memorial Sloan-Kettering, and City of Hope, offer tests that target 38 to 50 genes. Moreover, Baylor College of Medicine, Ambry Genetics, and EdgeBio are offering NGS to interrogate the exome of the tumors. Most centers specify the depth of coverage (eg, 200x reads) for reportable gene mutations. The read depth or depth of coverage refers to the number of times a base is sequenced in a single run, and provides a level of certainty of the gene mutation required for that result. Several academic centers offer confirmation of mutations using other methodologies, such as fluorescence in situ hybridization, allele-specific RT-PCR, pyrosequencing, or Sanger sequencing. FoundationOne does not confirm NGS mutations. However, validation of low-level variants below a certain given mutant allele threshold from NGS data may be challenging with existing sequencing technologies, such as Sanger sequencing and pyrosequencing, because several studies have shown that NGS has superior sensitivity compared with these techniques.29

Table 2

Overview of Next-Generation Sequencing Platforms Commonly Used for Multigene Assays

Table 2

Discussion

NGS-based multigene panels are currently being integrated into clinical practice as a suitable platform to provide quantitative, sensitive, and accurate sequencing data on a constantly increasing number of molecular mutations. NGS provided sensitivity superior to Sanger sequencing and pyrosequencing in detecting EGFR and KRAS mutations in lung cancer specimens and clinical response to EGFR inhibitor.42 Challenges remain for clinical oncologists regarding how to select, interpret, and apply these new genetic and genomic assays for patient treatment to improve clinical outcome. The ultimate goal of NGS-based multigene cancer sequencing panels is to provide oncologists with timely information on potentially actionable mutations to help guide patient management. The term actionable remains elusive, because it is dependent on the definition by different providers of NGS-based cancer panels. Several academic centers defined actionable to include FDA-approved drugs for the patient’s cancer type, and off-label use of FDA-approved drugs.43 Foundation Medicine, Inc., provider of the 236-gene FoundationOne pan-cancer test, more broadly defined actionable as FDA-approved targeted therapy in the solid tumor under study or in another tumor type, and any clinical trial under investigation for a therapy targeting the alteration.43 Currently available multigene NGS-based cancer panels include genes for which the FDA has approved single-gene companion diagnostics, and also allow interrogation of a patient’s other cancer-related mutations in a single assay instead of a series of tests, which is much more affordable, and saves time and tissues. Recently, standards and professional practice guidelines were established for NGS for clinical applications to assist clinical laboratories with the validation of NGS methods and platforms, monitoring of NGS testing, and interpretation and reporting of variants found using these technologies.44,45 NCCN has not recommended the standard use of multigene assays in metastatic colorectal cancer. The only genetic mutation currently endorsed by NCCN and ASCO to guide anti-EGFR therapy in metastatic colorectal cancer is KRAS. Many other genetic alterations have been associated with chemotherapy or targeted therapy resistance, such as PIK3CA mutations, HER2 amplification, PTEN mutations, BRAF mutations, and NRAS and HRAS mutations, as the authors previously reviewed.46 Furthermore, increasing evidence suggests that KRAS mutations involving exons 3 and 4, which are not captured in most currently used KRAS assays (focused on six mutations in codon 12 and one mutation in codon 13 of exon 2), may also be associated with anti-EGFR resistance.47Although multigene assays can identify patients with these genetic alterations and direct them to specific clinical trials, no definitive clinical evidence at this point suggests that this strategy is beneficial to the individual patients. Early clinical trials do not support a robust clinical activity to PIK3CA or BRAF targeting in metastatic colorectal cancer, and clinical data continue to be lacking in the setting of HER2 amplification.

The understanding of molecular biomarkers that drive tumorigenesis and maintenance of malignancy has led to the rational utility of clinically targeted therapy, and patient relapse risk assessment and cancer prognosis. Although single targets for personalized therapy are currently exploited (KRAS and BRAF), future treatment strategies may depend on therapies directed toward multiple targets to avoid relapses and may allow monitoring of disease response to therapy (ie, emerging resistance and acquired mutations). Therefore, the utility of pan-cancer NGS multigene panels may be advantageous in building a comprehensive knowledge base of genes and mutations that may direct patients to future targeted therapies.

A genomic revolution is occurring that will transform medicine and how patients are treated. Multigene assays undoubtedly have the potential to provide a more efficient platform for personalized medicine in colorectal cancer. However, the reality is that the clinical data provided from these assays do not, to date, confirm an advantage over assays focusing on 2 genes in colorectal cancer (KRAS and BRAF). This will likely change as the utility of additional genomic alterations in the clinical management of patients with colorectal cancer is confirmed. Therefore, multigene assays, such as those developed by Foundation Medicine and CARIS, can be regarded as potential strategies to direct patients and providers to more patient-selective clinical trials, rather than a superior standard clinical practice at this time. These multigene assays are more consistent with experimental screening assays, rather than reflective of standard-of-care practice.

Some considerations exist for clinical NGS: sequencing performance, amplicon coverage and sensitivity, and variant detection. Challenges for variant detection in cancer result from inherent characteristics of tumor samples: DNA quality from FFPE tissues, aneuploidy, tumor heterogeneity, and contamination with normal tissue. Mutations can be observed in the range of 1% to 20%, which is below the currently accepted cutoff value of conventional Sanger-based sequencing and pyrosequencing. Sensitivities of well below 1% can be seen if tens of thousands of reads are generated. One can envision that a patient with a specific molecular mutation or a disease-relevant hot spot region can be targeted and sequenced with sufficient depth, so that monitoring minimal residual disease will be possible (eg, RUNX1 runt-related transcription factor 1 and CEBPA CCAAT/enhancer binding protein α).48,49

The real drive for NGS-based multigene panels will be the ability to rapidly and comprehensively assess tumors at the molecular level, in a cost-effective manner, with the short turnaround time that is required by individualized treatment regimens. Small molecule inhibitors and antibodies against druggable gene targets will continue to evolve, and these multigene panels need to be just as flexible. Pan-cancer multigene panels, with high representation of cancer-related genes, increase the complexity of analysis and interpretation: variants of unknown significance and function are overstated for clinical care given that not all genes have clear clinical value. Generic “cancer” multigene gene panels may evolve toward more disease-specific gene panels, wherein data are more manageable and relevant to cancer type in regard to actionable, targeted therapies, therapeutic implications (resistance), prognosis, and recurrent risks. This will allow multiplexing of multiple samples per run, thereby reducing cost and affordability of sequencing. NGS technology is evolving and progressing fast, allowing the possibility that this point will be reached in the foreseeable future.

The authors have disclosed that they have no financial interests, arrangements, affiliations, or commercial interests with the manufacturers of any products discussed in this article or their competitors.

References

  • 1.

    Jemal A, Bray F, Center MM et al.. Global cancer statistics. CA Cancer J Clin 2011;61:6990.

  • 2.

    Maughan TS, Adams RA, Smith CG et al.. Addition of cetuximab to oxaliplatin-based first-line combination chemotherapy for treatment of advanced colorectal cancer: results of the randomised phase 3 MRC COIN trial. Lancet 2011;377:21032114.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 3.

    Van Cutsem E, Kohne CH, Lang I et al.. Cetuximab plus irinotecan, fluorouracil, and leucovorin as first-line treatment for metastatic colorectal cancer: updated analysis of overall survival according to tumor KRAS and BRAF mutation status. J Clin Oncol 2011;29:20112019.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 4.

    Hecht JR, Mitchell E, Chidiac T et al.. A randomized phase IIIB trial of chemotherapy, bevacizumab, and panitumumab compared with chemotherapy and bevacizumab alone for metastatic colorectal cancer. J Clin Oncol 2009;27:672680.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 5.

    Saltz LB, Clarke S, Diaz-Rubio E et al.. Bevacizumab in combination with oxaliplatin-based chemotherapy as first-line therapy in metastatic colorectal cancer: a randomized phase III study. J Clin Oncol 2008;26:20132019.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 6.

    Kopetz S, Chang GJ, Overman MJ et al.. Improved survival in metastatic colorectal cancer is associated with adoption of hepatic resection and improved chemotherapy. J Clin Oncol 2009;27:36773683.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 7.

    Shen L, Toyota M, Kondo Y et al.. Integrated genetic and epigenetic analysis identifies three different subclasses of colon cancer. Proc Natl Acad Sci U S A 2007;104:1865418659.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 8.

    Cancer Genome Atlas Network. Comprehensive molecular characterization of human colon and rectal cancer. Nature 2012;487:330337.

  • 9.

    Bokemeyer C, Bondarenko I, Makhson A et al.. Fluorouracil, leucovorin, and oxaliplatin with and without cetuximab in the first-line treatment of metastatic colorectal cancer. J Clin Oncol 2009;27:663671.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 10.

    Di Fiore F, Blanchard F, Charbonnier F et al.. Clinical relevance of KRAS mutation detection in metastatic colorectal cancer treated by cetuximab plus chemotherapy. Br J Cancer 2007;96:11661169.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 11.

    Fakih M. The role of targeted therapy in the treatment of advanced colorectal cancer. Curr Treat Options Oncol 2008;9:357374.

  • 12.

    Fakih MM. KRAS mutation screening in colorectal cancer: from paper to practice. Clin Colorectal Cancer 2010;9:2230.

  • 13.

    Karapetis CS, Khambata-Ford S, Jonker DJ et al.. K-ras mutations and benefit from cetuximab in advanced colorectal cancer. N Engl J Med 2008;359:17571765.

  • 14.

    Tejpar S, Celik I, Schlichting M et al.. Association of KRAS G13D tumor mutations with outcome in patients with metastatic colorectal cancer treated with first-line chemotherapy with or without cetuximab. J Clin Oncol 2012;30:35703577.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 15.

    De Roock W, Jonker DJ, Di Nicolantonio F et al.. Association of KRAS p.G13D mutation with outcome in patients with chemotherapy-refractory metastatic colorectal cancer treated with cetuximab. JAMA 2010;304:18121820.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 16.

    Peeters M, Douillard JY, Van Cutsem E et al.. Mutant KRAS codon 12 and 13 alleles in patients with metastatic colorectal cancer: assessment as prognostic and predictive biomarkers of response to panitumumab. J Clin Oncol 2013;31:759765.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 17.

    De Roock W, Claes B, Bernasconi D et al.. Effects of KRAS, BRAF, NRAS, and PIK3CA mutations on the efficacy of cetuximab plus chemotherapy in chemotherapy-refractory metastatic colorectal cancer: a retrospective consortium analysis. Lancet Oncol 2010;11:753762.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 18.

    Di Nicolantonio F, Martini M, Molinari F et al.. Wild-type BRAF is required for response to panitumumab or cetuximab in metastatic colorectal cancer. J Clin Oncol 2008;26:57055712.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 19.

    De Roock W, De Vriendt V, Normanno N et al.. KRAS, BRAF, PIK3CA, and PTEN mutations: implications for targeted therapies in metastatic colorectal cancer. Lancet Oncol 2011;12:594603.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 20.

    Loupakis F, Pollina L, Stasi I et al.. PTEN expression and KRAS mutations on primary tumors and metastases in the prediction of benefit from cetuximab plus irinotecan for patients with metastatic colorectal cancer. J Clin Oncol 2009;27:26222629.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 21.

    Prenen H, De Schutter J, Jacobs B et al.. PIK3CA mutations are not a major determinant of resistance to the epidermal growth factor receptor inhibitor cetuximab in metastatic colorectal cancer. Clin Cancer Res 2009;15:31843188.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 22.

    Krumbach R, Schuler J, Hofmann M et al.. Primary resistance to cetuximab in a panel of patient-derived tumour xenograft models: activation of MET as one mechanism for drug resistance. Eur J Cancer 2011;47:12311243.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 23.

    Watkins D, Ayers M, Cunningham D et al.. Molecular analysis of the randomized phase II/III study of the anti-IGF-1R antibody dalotuzumab (MK-0646) in combination with cetuximab (Cx) and irinotecan (Ir) in the treatment of chemorefractory KRAS wild-type metastatic colorectal cancer (mCRC) [abstract]. J Clin Oncol 2012;30(Suppl):Abstract 3531.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 24.

    Shacham-Shmueli E, Beny A, Geva R et al.. Response to temozolomide in patients with metastatic colorectal cancer with loss of MGMT expression: a new approach in the era of personalized medicine? J Clin Oncol 2011;29:e262265.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 25.

    Shirota Y, Stoehlmacher J, Brabender J et al.. ERCC1 and thymidylate synthase mRNA levels predict survival for colorectal cancer patients receiving combination oxaliplatin and fluorouracil chemotherapy. J Clin Oncol 2001;19:42984304.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 26.

    Kelley RK, Van Bebber SL, Phillips KA, Venook AP. Personalized medicine and oncology practice guidelines: a case study of contemporary biomarkers in colorectal cancer. J Natl Compr Canc Netw 2011;9:1325.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 27.

    Metzker ML. Sequencing technologies - the next generation. Nat Rev Genet 2010;11:3146.

  • 28.

    Rehm HL, Bale SJ, Bayrak-Toydemir P et al.. ACMG clinical laboratory standards for next-generation sequencing. Genet Med 2013;15:733747.

  • 29.

    Majewski J, Schwartzentruber J, Lalonde E et al.. What can exome sequencing do for you? J Med Genet 2011;48:580589.

  • 30.

    Kerick M, Isau M, Timmermann B et al.. Targeted high throughput sequencing in clinical cancer settings: formaldehyde fixed-paraffin embedded (FFPE) tumor tissues, input amount and tumor heterogeneity. BMC Med Genomics 2011;4:68.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 31.

    Schweiger MR, Kerick M, Timmermann B et al.. Genome-wide massively parallel sequencing of formaldehyde fixed-paraffin embedded (FFPE) tumor tissues for copy-number- and mutation-analysis. PLoS One 2009;4:e5548.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 32.

    Wagle N, Berger MF, Davis MJ et al.. High-throughput detection of actionable genomic alterations in clinical tumor samples by targeted, massively parallel sequencing. Cancer Discov 2012;2:8293.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 33.

    Glenn TC. Field guide to next-generation DNA sequencers. Mol Ecol Resour 2011;11:759769.

  • 34.

    Meldrum C, Doyle MA, Tothill RW. Next-generation sequencing for cancer diagnostics: a practical perspective. Clin Biochem Rev 2011;32:177195.

  • 35.

    Mamanova L, Coffey AJ, Scott CE et al.. Target-enrichment strategies for next-generation sequencing. Nat Methods 2010;7:111118.

  • 36.

    Gnirke A, Melnikov A, Maguire J et al.. Solution hybrid selection with ultra-long oligonucleotides for massively parallel targeted sequencing. Nat Biotechnol 2009;27:182189.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 37.

    Hodges E, Rooks M, Xuan Z et al.. Hybrid selection of discrete genomic intervals on custom-designed microarrays for massively parallel sequencing. Nat Protoc 2009;4:960974.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 38.

    Fisher S, Barry A, Abreu J et al.. A scalable, fully automated process for construction of sequence-ready human exome targeted capture libraries. Genome Biol 2011;12:R1.

  • 39.

    Grossmann V, Schnittger S, Schindela S et al.. Strategy for robust detection of insertions, deletions, and point mutations in CEBPA, a GC-rich content gene, using 454 next-generation deep-sequencing technology. J Mol Diagn 2011;13:129136.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 40.

    Oyola SO, Otto TD, Gu Y et al.. Optimizing Illumina next-generation sequencing library preparation for extremely AT-biased genomes. BMC Genomics 2012;13:1.

  • 41.

    Quail MA, Smith M, Coupland P et al.. A tale of three next generation sequencing platforms: comparison of Ion Torrent, Pacific Biosciences and Illumina MiSeq sequencers. BMC Genomics 2012;13:341.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 42.

    Querings S, Altmuller J, Ansen S et al.. Benchmarking of mutation diagnostics in clinical lung cancer specimens. PLoS One 2011;6:e19601.

  • 43.

    Toner B. At AACR, early providers of NGS-based cancer panel debate ‘actionable’ mutations, regulatory issues. Available at: www.genomeweb.com. Acccessed September 6, 2013.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 44.

    Rehm HL, Bale SJ, Bayrak-Toydemir P et al.. ACMG clinical laboratory standards for next-generation sequencing. Genet Med 2013;15:733747.

  • 45.

    Gargis AS, Kalman L, Berry MW et al.. Assuring the quality of next-generation sequencing in clinical laboratory practice. Nat Biotechnol 2012,30:10331036.

  • 46.

    Fakih M. Targeting mechanisms of resistance to anti-EGF receptor therapy in KRAS wild-type colorectal cancer: the path to more personalized medicine. Future Oncol 2013;9:551560.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 47.

    Oliner K, Douillard J, Siena S et al.. Analysis of KRAS/NRAS and BRAF mutations in the phase III PRIME study of panitumumab (pmab) plus FOLFOX versus FOLFOX as first-line treatment (tx) for metastatic colorectal cancer (mCRC) [abstract]. J Clin Oncol 2013;31(Suppl):Abstract 3511.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 48.

    Kohlmann A, Grossmann V, Harbich S et al.. Next-generation deep-sequencing enables a quantitative monitoring of RUNX1 mutations in 534 patients with myelodysplastic/myeloproliferative neoplasms and myelodysplastic syndromes Blood 2011;118:2792.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 49.

    Kohlmann A, Grossmann V, Fasan A et al.. Sensitive monitoring of mnimal residual disease status in CEBPA-mutated acute myeloid leukemia using amplicon deep-sequencing Blood 2011;118:2517.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 50.

    Bentley DR, Balasubramanian S, Swerdlow HP et al.. Accurate whole human genome sequencing using reversible terminator chemistry. Nature 2008;456:5359.

  • 51.

    Ronaghi M, Uhlen M, Nyren P. A sequencing method based on real-time pyrophosphate. Science 1998;281:363.

  • 52.

    Dressman D, Yan H, Traverso G et al.. Transforming single DNA molecules into fluorescent magnetic particles for detection and enumeration of genetic variations. Proc Natl Acad Sci U S A 2003;100:88178822.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 53.

    Margulies M, Egholm M, Altman WE et al.. Genome sequencing in microfabricated high-density picolitre reactors. Nature 2005;437:376380.

  • 54.

    McKernan KJ, Peckham HE, Costa GL et al.. Sequence and structural variation in a human genome uncovered by short-read, massively parallel ligation sequencing using two-base encoding. Genome Res 2009;19:15271541.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 55.

    Loman NJ, Misra RV, Dallman TJ et al.. Performance comparison of benchtop high-throughput sequencing platforms. Nat Biotechnol 2012;30:434439.

  • 56.

    Junemann S, Sedlazeck FJ, Prior K et al.. Updating benchtop sequencing performance comparison. Nat Biotechnol 2013;31:294296.

  • 57.

    Rothberg JM, Hinz W, Rearick TM et al.. An integrated semiconductor device enabling non-optical genome sequencing. Nature 2011;475: 348352.

Correspondence: Marwan G. Fakih, MD, Medical Oncology and Experimental Therapeutics, Gastrointestinal Medical Oncology, City of Hope Comprehensive Cancer Center, 1500 East Duarte Street, Duarte, CA 91010. E-mail: mfakih@coh.org
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  • Technological advances in molecular diagnosis for personalized therapy. High-throughput targeted-gene sequencing panel and genomic (whole-genome and exome sequence) profiling by NGS technologies offer the opportunity to broadly interrogate the cancer genome of individual patients for personalized diagnosis and treatment.

    Abbreviations: NGS, next-generation sequencing; RT-PCR, real-time polymerase chain reaction.

  • 1.

    Jemal A, Bray F, Center MM et al.. Global cancer statistics. CA Cancer J Clin 2011;61:6990.

  • 2.

    Maughan TS, Adams RA, Smith CG et al.. Addition of cetuximab to oxaliplatin-based first-line combination chemotherapy for treatment of advanced colorectal cancer: results of the randomised phase 3 MRC COIN trial. Lancet 2011;377:21032114.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 3.

    Van Cutsem E, Kohne CH, Lang I et al.. Cetuximab plus irinotecan, fluorouracil, and leucovorin as first-line treatment for metastatic colorectal cancer: updated analysis of overall survival according to tumor KRAS and BRAF mutation status. J Clin Oncol 2011;29:20112019.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 4.

    Hecht JR, Mitchell E, Chidiac T et al.. A randomized phase IIIB trial of chemotherapy, bevacizumab, and panitumumab compared with chemotherapy and bevacizumab alone for metastatic colorectal cancer. J Clin Oncol 2009;27:672680.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 5.

    Saltz LB, Clarke S, Diaz-Rubio E et al.. Bevacizumab in combination with oxaliplatin-based chemotherapy as first-line therapy in metastatic colorectal cancer: a randomized phase III study. J Clin Oncol 2008;26:20132019.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 6.

    Kopetz S, Chang GJ, Overman MJ et al.. Improved survival in metastatic colorectal cancer is associated with adoption of hepatic resection and improved chemotherapy. J Clin Oncol 2009;27:36773683.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 7.

    Shen L, Toyota M, Kondo Y et al.. Integrated genetic and epigenetic analysis identifies three different subclasses of colon cancer. Proc Natl Acad Sci U S A 2007;104:1865418659.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 8.

    Cancer Genome Atlas Network. Comprehensive molecular characterization of human colon and rectal cancer. Nature 2012;487:330337.

  • 9.

    Bokemeyer C, Bondarenko I, Makhson A et al.. Fluorouracil, leucovorin, and oxaliplatin with and without cetuximab in the first-line treatment of metastatic colorectal cancer. J Clin Oncol 2009;27:663671.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 10.

    Di Fiore F, Blanchard F, Charbonnier F et al.. Clinical relevance of KRAS mutation detection in metastatic colorectal cancer treated by cetuximab plus chemotherapy. Br J Cancer 2007;96:11661169.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 11.

    Fakih M. The role of targeted therapy in the treatment of advanced colorectal cancer. Curr Treat Options Oncol 2008;9:357374.

  • 12.

    Fakih MM. KRAS mutation screening in colorectal cancer: from paper to practice. Clin Colorectal Cancer 2010;9:2230.

  • 13.

    Karapetis CS, Khambata-Ford S, Jonker DJ et al.. K-ras mutations and benefit from cetuximab in advanced colorectal cancer. N Engl J Med 2008;359:17571765.

  • 14.

    Tejpar S, Celik I, Schlichting M et al.. Association of KRAS G13D tumor mutations with outcome in patients with metastatic colorectal cancer treated with first-line chemotherapy with or without cetuximab. J Clin Oncol 2012;30:35703577.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 15.

    De Roock W, Jonker DJ, Di Nicolantonio F et al.. Association of KRAS p.G13D mutation with outcome in patients with chemotherapy-refractory metastatic colorectal cancer treated with cetuximab. JAMA 2010;304:18121820.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 16.

    Peeters M, Douillard JY, Van Cutsem E et al.. Mutant KRAS codon 12 and 13 alleles in patients with metastatic colorectal cancer: assessment as prognostic and predictive biomarkers of response to panitumumab. J Clin Oncol 2013;31:759765.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 17.

    De Roock W, Claes B, Bernasconi D et al.. Effects of KRAS, BRAF, NRAS, and PIK3CA mutations on the efficacy of cetuximab plus chemotherapy in chemotherapy-refractory metastatic colorectal cancer: a retrospective consortium analysis. Lancet Oncol 2010;11:753762.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 18.

    Di Nicolantonio F, Martini M, Molinari F et al.. Wild-type BRAF is required for response to panitumumab or cetuximab in metastatic colorectal cancer. J Clin Oncol 2008;26:57055712.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 19.

    De Roock W, De Vriendt V, Normanno N et al.. KRAS, BRAF, PIK3CA, and PTEN mutations: implications for targeted therapies in metastatic colorectal cancer. Lancet Oncol 2011;12:594603.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 20.

    Loupakis F, Pollina L, Stasi I et al.. PTEN expression and KRAS mutations on primary tumors and metastases in the prediction of benefit from cetuximab plus irinotecan for patients with metastatic colorectal cancer. J Clin Oncol 2009;27:26222629.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 21.

    Prenen H, De Schutter J, Jacobs B et al.. PIK3CA mutations are not a major determinant of resistance to the epidermal growth factor receptor inhibitor cetuximab in metastatic colorectal cancer. Clin Cancer Res 2009;15:31843188.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 22.

    Krumbach R, Schuler J, Hofmann M et al.. Primary resistance to cetuximab in a panel of patient-derived tumour xenograft models: activation of MET as one mechanism for drug resistance. Eur J Cancer 2011;47:12311243.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 23.

    Watkins D, Ayers M, Cunningham D et al.. Molecular analysis of the randomized phase II/III study of the anti-IGF-1R antibody dalotuzumab (MK-0646) in combination with cetuximab (Cx) and irinotecan (Ir) in the treatment of chemorefractory KRAS wild-type metastatic colorectal cancer (mCRC) [abstract]. J Clin Oncol 2012;30(Suppl):Abstract 3531.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 24.

    Shacham-Shmueli E, Beny A, Geva R et al.. Response to temozolomide in patients with metastatic colorectal cancer with loss of MGMT expression: a new approach in the era of personalized medicine? J Clin Oncol 2011;29:e262265.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 25.

    Shirota Y, Stoehlmacher J, Brabender J et al.. ERCC1 and thymidylate synthase mRNA levels predict survival for colorectal cancer patients receiving combination oxaliplatin and fluorouracil chemotherapy. J Clin Oncol 2001;19:42984304.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 26.

    Kelley RK, Van Bebber SL, Phillips KA, Venook AP. Personalized medicine and oncology practice guidelines: a case study of contemporary biomarkers in colorectal cancer. J Natl Compr Canc Netw 2011;9:1325.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 27.

    Metzker ML. Sequencing technologies - the next generation. Nat Rev Genet 2010;11:3146.

  • 28.

    Rehm HL, Bale SJ, Bayrak-Toydemir P et al.. ACMG clinical laboratory standards for next-generation sequencing. Genet Med 2013;15:733747.

  • 29.

    Majewski J, Schwartzentruber J, Lalonde E et al.. What can exome sequencing do for you? J Med Genet 2011;48:580589.

  • 30.

    Kerick M, Isau M, Timmermann B et al.. Targeted high throughput sequencing in clinical cancer settings: formaldehyde fixed-paraffin embedded (FFPE) tumor tissues, input amount and tumor heterogeneity. BMC Med Genomics 2011;4:68.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 31.

    Schweiger MR, Kerick M, Timmermann B et al.. Genome-wide massively parallel sequencing of formaldehyde fixed-paraffin embedded (FFPE) tumor tissues for copy-number- and mutation-analysis. PLoS One 2009;4:e5548.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 32.

    Wagle N, Berger MF, Davis MJ et al.. High-throughput detection of actionable genomic alterations in clinical tumor samples by targeted, massively parallel sequencing. Cancer Discov 2012;2:8293.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 33.

    Glenn TC. Field guide to next-generation DNA sequencers. Mol Ecol Resour 2011;11:759769.

  • 34.

    Meldrum C, Doyle MA, Tothill RW. Next-generation sequencing for cancer diagnostics: a practical perspective. Clin Biochem Rev 2011;32:177195.

  • 35.

    Mamanova L, Coffey AJ, Scott CE et al.. Target-enrichment strategies for next-generation sequencing. Nat Methods 2010;7:111118.

  • 36.

    Gnirke A, Melnikov A, Maguire J et al.. Solution hybrid selection with ultra-long oligonucleotides for massively parallel targeted sequencing. Nat Biotechnol 2009;27:182189.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 37.

    Hodges E, Rooks M, Xuan Z et al.. Hybrid selection of discrete genomic intervals on custom-designed microarrays for massively parallel sequencing. Nat Protoc 2009;4:960974.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 38.

    Fisher S, Barry A, Abreu J et al.. A scalable, fully automated process for construction of sequence-ready human exome targeted capture libraries. Genome Biol 2011;12:R1.

  • 39.

    Grossmann V, Schnittger S, Schindela S et al.. Strategy for robust detection of insertions, deletions, and point mutations in CEBPA, a GC-rich content gene, using 454 next-generation deep-sequencing technology. J Mol Diagn 2011;13:129136.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 40.

    Oyola SO, Otto TD, Gu Y et al.. Optimizing Illumina next-generation sequencing library preparation for extremely AT-biased genomes. BMC Genomics 2012;13:1.

  • 41.

    Quail MA, Smith M, Coupland P et al.. A tale of three next generation sequencing platforms: comparison of Ion Torrent, Pacific Biosciences and Illumina MiSeq sequencers. BMC Genomics 2012;13:341.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 42.

    Querings S, Altmuller J, Ansen S et al.. Benchmarking of mutation diagnostics in clinical lung cancer specimens. PLoS One 2011;6:e19601.

  • 43.

    Toner B. At AACR, early providers of NGS-based cancer panel debate ‘actionable’ mutations, regulatory issues. Available at: www.genomeweb.com. Acccessed September 6, 2013.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 44.

    Rehm HL, Bale SJ, Bayrak-Toydemir P et al.. ACMG clinical laboratory standards for next-generation sequencing. Genet Med 2013;15:733747.

  • 45.

    Gargis AS, Kalman L, Berry MW et al.. Assuring the quality of next-generation sequencing in clinical laboratory practice. Nat Biotechnol 2012,30:10331036.

  • 46.

    Fakih M. Targeting mechanisms of resistance to anti-EGF receptor therapy in KRAS wild-type colorectal cancer: the path to more personalized medicine. Future Oncol 2013;9:551560.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 47.

    Oliner K, Douillard J, Siena S et al.. Analysis of KRAS/NRAS and BRAF mutations in the phase III PRIME study of panitumumab (pmab) plus FOLFOX versus FOLFOX as first-line treatment (tx) for metastatic colorectal cancer (mCRC) [abstract]. J Clin Oncol 2013;31(Suppl):Abstract 3511.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 48.

    Kohlmann A, Grossmann V, Harbich S et al.. Next-generation deep-sequencing enables a quantitative monitoring of RUNX1 mutations in 534 patients with myelodysplastic/myeloproliferative neoplasms and myelodysplastic syndromes Blood 2011;118:2792.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 49.

    Kohlmann A, Grossmann V, Fasan A et al.. Sensitive monitoring of mnimal residual disease status in CEBPA-mutated acute myeloid leukemia using amplicon deep-sequencing Blood 2011;118:2517.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 50.

    Bentley DR, Balasubramanian S, Swerdlow HP et al.. Accurate whole human genome sequencing using reversible terminator chemistry. Nature 2008;456:5359.

  • 51.

    Ronaghi M, Uhlen M, Nyren P. A sequencing method based on real-time pyrophosphate. Science 1998;281:363.

  • 52.

    Dressman D, Yan H, Traverso G et al.. Transforming single DNA molecules into fluorescent magnetic particles for detection and enumeration of genetic variations. Proc Natl Acad Sci U S A 2003;100:88178822.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 53.

    Margulies M, Egholm M, Altman WE et al.. Genome sequencing in microfabricated high-density picolitre reactors. Nature 2005;437:376380.

  • 54.

    McKernan KJ, Peckham HE, Costa GL et al.. Sequence and structural variation in a human genome uncovered by short-read, massively parallel ligation sequencing using two-base encoding. Genome Res 2009;19:15271541.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 55.

    Loman NJ, Misra RV, Dallman TJ et al.. Performance comparison of benchtop high-throughput sequencing platforms. Nat Biotechnol 2012;30:434439.

  • 56.

    Junemann S, Sedlazeck FJ, Prior K et al.. Updating benchtop sequencing performance comparison. Nat Biotechnol 2013;31:294296.

  • 57.

    Rothberg JM, Hinz W, Rearick TM et al.. An integrated semiconductor device enabling non-optical genome sequencing. Nature 2011;475: 348352.

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