There have been some indications that the response to treatment may also be improved in patients with the UGT1A1*28/*28 variant, but this has not been seen in all studies.[64] For example, in the study of 120 patients by Yu and colleagues, the presence of UGT1A1*28/*28 or elevated levels of total bilirubin and unconjugated bilirubin were actually associated with a lower response rate.[61] This could not be explained by decreases in doses associated with toxicity.
DPD
The cytotoxicity of 5-FU occurs via three principal mechanisms: 1) interference with the synthesis of thymidine monophosphate by the binding of fluorodeoxyuridine monophosphate (FdUMP) to TS; 2) incorporation of FdUTP into DNA, thereby interfering with DNA synthesis; and 3) incorporation of FUTP into RNA, leading to faulty translation of RNA and blocking the synthesis of multiple forms of RNA (ie, messenger, ribosomal, transfer, and small messenger RNAs).[65] The metabolic breakdown of 5-FU is primarily through the action of dihydropyrimidine dehydrogenase (DPD), which catabolizes 5-FU to 5-fluoro-5,6-dihydrouracil. DPYD, the gene coding for DPD, is present on chromosome 1, with 23 exons in a single copy. More than 80% of an administered dose of 5-FU is catabolized via this pathway, although some 5-FU is excreted unchanged in the urine.[49]
Loss of DPD activity has been well characterized as an autosomal recessive trait, with a prevalence of 0.5% and 5% for total and partial deficiencies, respectively.[66] When receiving a standard dose of 5-FU, individuals carrying certain variants of DPYD are at significantly greater risk for severe and potentially lethal adverse events (grade 3 or higher). Early studies indicated that almost one-third of the severe toxicities reported with 5-FU could be attributed to partial or total alteration in DPD activity.[49,66] More recent analyses have attributed at least 60% to 70% of cases of 5-FU toxicity to decreased levels of DPD. Among all variants studied, DPYD*2A (c.1905+1G>A; rs3918290) is the most common allele associated with loss of DPD activity. Many other alleles have also been associated with DPD loss and 5-FU toxicity, including DPYD c.2846A>T (D949V, rs67376798), c.1679T>G (I560S, DPYD*13, rs55886062), c.1236G>A/HapB3 (rs56038477/ rs75017182), and c.1601G>A (S534N, DPYD*4, rs1801158), but the results have been somewhat variable.[67]For example, a meta-analysis found that the variant c.1601G>A was not significantly associated with toxicity; a recent large study of stage III colon cancer showed that the variant c.1236G>A/HapB3 also was not associated with toxicity.[68] Work is ongoing to evaluate the individual genetic variant changes associated with 5-FU toxicity. It should be noted that other genetic alterations might also be associated with 5-FU toxicity, such as the loss of TYMS, which codes for TS.[69]
Most of these analyses were carried out using SNPs, and a variety of SNP genotyping assays are commercially available. Some pharmaceutical companies are only evaluating DPYD*2A by SNP analysis; others are focusing on four SNPs or conducting full nucleotide sequence analysis. The former approach of evaluating DPYD*2A alone has been found to be clinically practical and to decrease toxicity and its associated costs.[70] In a large clinical trial of more than 2,000 patients prospectively genotyped for DPYD*2A, variant carriers were treated with dose reductions of at least 50%, while wild-type patients were treated with standard dose.[70] The rate of grade 3 or higher toxicity was reduced from 73% to 28% in the variant carriers, similar to the rate of severe toxicity observed in noncarriers of *2A. Furthermore, it was estimated that average total treatment cost per patient was lower for screening than for nonscreening, outweighing the costs associated with screening. In contrast, the recent FDA approval of uridine triacetate has provided an emergency treatment option for patients who experience severe or life-threatening toxicity while receiving 5-FU, but administration of this antidote must begin within 96 hours, and a 5-day therapy course costs about $80,000. Though exciting, this large clinical trial considered only DPYD*2A variants, which only accounts for 25% of DPD-deficient patients, while more recently identified DPYD polymorphisms previously mentioned were not included; further, only 18 carriers received reduced-dose therapy. Nevertheless, the study shows that frontline genotyping is efficacious and cost-effective, and further large-scale prospective studies will bring the test closer to clinical practice.
Ongoing trials continue to employ a variety of assays. For example, a study in the Netherlands (ClinicalTrials.gov identifier: NCT02324452) is using SNPs to assess four different mutations to alter treatment, as well as cost and efficacy. In addition to SNP genotyping, the investigators are measuring the clearance of [2-13C]–labeled uracil in the breath, the serum level of uracil after an oral dose, and enzymatic DPD activity in peripheral blood mononuclear cells. Alterations in DPD activity are clearly associated with severe toxicity from 5-FU and other fluoropyrimidines in a relatively small number of patients. A variety of mutations can cause such a deficiency, and the optimal method for evaluation remains an area of ongoing research. A major challenge with genotyping for DPYD is that patients who do not carry any DPYD variant can still develop severe toxicity, while some patients carrying a DPYD variant do not develop any toxicity.[71] Some investigators suggest that at a minimum, genotyping of the DPYD*2A variant using SNP assays is worthwhile, but more sophisticated testing may predict greater numbers of patients likely to benefit from decreasing the treatment dosage of 5-FU.
The Consensus Molecular Subtypes of Colorectal Cancer
With the advancement of molecular technologies and the increasing capability of interrogating tumor tissue for various alterations, molecular subtyping of colorectal tumors has been attempted by multiple groups, generating a large amount of data; however, the results shared limited similarities. In order to resolve the inconsistencies and to establish a subtyping standard that can potentially be used to predict patient outcome and to tailor treatment, the international Colorectal Cancer Subtyping Consortium was formed and evaluated six independently developed colorectal cancer subtyping algorithms. These resultant CMS1–4 designations provided insight into the biological understanding of each subtype, as well as specific associated clinical and prognostic factors.[72]
CMS1, or the MSI immune group, is consistent with the established MSI phenotype.[73] It comprises 14% of the patient population; occurs more frequently in older, female patients; commonly presents as MSI and CIMP; and is associated with high rates of BRAF mutation and high levels of immune activation. CMS2, the canonical subgroup, is the most common subtype and comprises 37% of cases. These tumors are characterized by chromosomal instability, with marked activation of the WNT and MYC pathways, and high rates of TP53 mutation. These tumors are associated with the longest OS times and longest survival after relapse, and are found in more than half of tumors of the left colon and the rectum. CMS3, or the metabolic subtype, comprises 13% of tumors; similar to KRAS activating mutations, the tumor cells are enriched for multiple metabolism signatures. These tumors are associated with intermediate OS times compared with the other three subtypes. The CMS4 subtype, or mesenchymal tumors, comprise 23% of cases and exhibit prominent mesenchymal/transforming growth factor-β activation, stromal invasion, and angiogenesis. These patients are diagnosed at a younger age and have the worst OS outcomes of the four subtypes. Notably, none of the molecular subtypes were defined by an individual event, and no genetic aberration was limited to one particular subtype. The reliance on an integrative analysis of alterations obtained from multiple platforms emphasizes the molecular complexity of the subtypes. These consensus subtypes defined from various databases via a uniform algorithm established an important paradigm for a collaborative, community-based cancer subtyping strategy that can be adopted in the management of a variety of cancer types to utilize the large amount of emerging data and to resolve inconsistencies; however, further investigation is needed to clarify how to utilize these subtypes in the clinic to guide patient therapy.
Conclusion
Advances in the technology of tumor testing have enhanced our understanding of the molecular and genetic basis of a variety of cancer types, including colorectal cancer. This expanding body of knowledge has informed clinical trials that have led to an increased number of potential prognostic and predictive markers relevant to the management of patients with colorectal cancer. Personalized medicine, with treatment tailored based on the tumor’s molecular and genetic signature, remains a goal in the treatment of colorectal cancer. In this article we have reviewed some of the common markers used to guide therapy; however, for some of these putative markers there may be insufficient data or statistical power to establish their ability to affect treatment decisions. In patients with colorectal cancer, the use of markers that predict resistance to targeted therapies such as anti-EGFR has been proven to be cost-effective as well as clinically critical, and therefore has been incorporated into clinical guidelines. Similarly, MSI testing has been used to guide therapy in the setting of early disease and more recently to guide immunotherapy in patients with metastatic colorectal cancer. However, the use of other markers that predict response to targeted therapy is still lagging behind, perhaps due to the limited prevalence of these markers in colorectal cancer or the lack of strong prospective evidence to support their use. Hence, we recommend MSI testing (preferably IHC testing for MMR proteins) for all patients. We also recommend testing for mutations of KRAS, NRAS, and BRAF (any of the available approved methods) for patients with metastatic colorectal cancer only. Testing for HER2 gene amplification and HER2 protein overexpression should be limited to patients with metastatic colorectal cancer for whom standard treatment options have failed (Table 1).
The use of markers to predict the toxicity of treatment with cytotoxic agents is not yet widespread due to the limited prevalence of relevant markers; the lack of prospective evidence supporting their incorporation into routine patient care; and current limitations in testing sensitivity, specificity, and interpretation. Although these markers can guide individual therapy in selected patients (Table 2), at the general population level, collaborative work is needed in order to identify more markers, prospectively validate their prognostic and predictive value, and evaluate the cost-effectiveness of testing each marker. Hence, we recommend UGT1A1*28 testing in patients for whom treatment with irinotecan is planned at a dose higher than 250 mg/m2. On the other hand, testing for mutations of TS and DPYD can be considered in selected patients with colorectal cancer.
Finally, although this article has focused on reviewing the impact of single genetic markers associated with the response to single-treatment agents, it is possible that a multimarker modality may be required to achieve the promise of true personalized medicine in colorectal cancer. However, this approach inevitably leads to hypersegmentation of patient cohorts, a clinical challenge that can only be addressed by broad collaborative projects with established standards and requirements for biomarker testing.
Acknowledgment: The authors thank David Spetzler, PhD, MS, MBA, President and Chief Scientific Officer, Caris Life Sciences; and Joanne Xiu, PhD, Director of Medical Affairs, Caris Life Sciences, for their contributions to the editorial development of this article.
Financial Disclosure:Dr. Marshall has relationships with the following companies, for which he has served as an advisor, consultant, researcher, and speaker: Amgen, Bayer, Caris Life Sciences, Celgene, Genentech, and Taiho. Dr. Shields has received funding for research and travel from Caris Life Sciences. The other authors have no significant interest in or other relationship with the manufacturer of any product or provider of any service mentioned in this article.
References:
1. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2016. CA Cancer J Clin. 2016;66:7-30.
2. Fakih MG. Metastatic colorectal cancer: current state and future directions. J Clin Oncol. 2015;33:1809-24.
3. Setaffy L, Langner C. Microsatellite instability in colorectal cancer: clinicopathological significance. Pol J Pathol. 2015;66:203-18.
4. Boland CR, Koi M, Chang DK, Carethers JM. The biochemical basis of microsatellite instability and abnormal immunohistochemistry and clinical behavior in Lynch syndrome: from bench to bedside. Fam Cancer. 2008;7:41-52.
5. Vilar E, Gruber SB. Microsatellite instability in colorectal cancer-the stable evidence. Nat Rev Clin Oncol. 2010;7:153-62.
6. Zhang CM, Lv JF, Gong L, et al. Role of deficient mismatch repair in the personalized management of colorectal cancer. Int J Environ Res Public Health. 2016;13:E892.
7. Cicek MS, Lindor NM, Gallinger S, et al. Quality assessment and correlation of microsatellite instability and immunohistochemical markers among population- and clinic-based colorectal tumors results from the Colon Cancer Family Registry. J Mol Diagn. 2011;13:271-81.
8. Loughrey MB, Waring PM, Tan A, et al. Incorporation of somatic BRAF mutation testing into an algorithm for the investigation of hereditary non-polyposis colorectal cancer. Fam Cancer. 2007;6:301-10.
9. Lochhead P, Kuchiba A, Imamura Y, et al. Microsatellite instability and BRAF mutation testing in colorectal cancer prognostication. J Natl Cancer Inst. 2013;105:1151-6.
10. Ribic CM, Sargent DJ, Moore MJ, et al. Tumor microsatellite-instability status as a predictor of benefit from fluorouracil-based adjuvant chemotherapy for colon cancer. N Engl J Med. 2003;349:247-57.
11. Des Guetz G, Uzzan B, Nicolas P, et al. Microsatellite instability does not predict the efficacy of chemotherapy in metastatic colorectal cancer. A systematic review and meta-analysis. Anticancer Res. 2009;29:1615-20.
12. Yiu AJ, Yiu CY. Biomarkers in colorectal cancer. Anticancer Res. 2016;36:1093-102.
13. Le DT, Uram JN, Wang H, et al. PD-1 blockade in tumors with mismatch-repair deficiency. N Engl J Med. 2015;372:2509-20.
14. Smyrk TC, Watson P, Kaul K, Lynch HT. Tumor-infiltrating lymphocytes are a marker for microsatellite instability in colorectal carcinoma. Cancer. 2001;91:2417-22.
15. Llosa NJ, Cruise M, Tam A, et al. The vigorous immune microenvironment of microsatellite instable colon cancer is balanced by multiple counter-inhibitory checkpoints. Cancer Discov. 2015;5:43-51.
16. Rosenberg JE, Hoffman-Censits J, Powles T, et al. Atezolizumab in patients with locally advanced and metastatic urothelial carcinoma who have progressed following treatment with platinum-based chemotherapy: a single-arm, multicentre, phase 2 trial. Lancet. 2016;387:1909-20.
17. Rizvi NA, Hellman MD, Snyder A, et al. Cancer immunology: mutational landscape determines sensitivity to PD-1 blockade in non-small cell lung cancer. Science. 2015;348:124-8.
18. Snyder A, Makarov V, Merghoub T, et al. Genetic basis for clinical response to CTLA-4 blockade in melanoma. N Engl J Med. 2014;371:2189-99.
19. Stadler ZK, Battaglin F, Middha S, et al. Reliable detection of mismatch repair deficiency in colorectal cancers using mutational load in next-generation sequencing panels. J Clin Oncol. 2016;34:2141-7.
20. Mlecnik B, Bindea G, Angell HK, et al. Integrative analyses of colorectal cancer show immunoscore is a stronger predictor of patient survival than microsatellite instability. Immunity. 2016;44:698-711.
21. Mlecnik B, Bindea G, Kirilovsky A, et al. The tumor microenvironment and Immunoscore are critical determinants of dissemination to distant metastasis. Sci Transl Med. 2016;8:327ra26.
22. Galon J, Mlecnik B, Bindea G, et al. Towards the introduction of the ‘Immunoscore’ in the classification of malignant tumours. J Pathol. 2014;232:199-209.
23. Galon J, Mlecnik B, Marliot F, et al. Validation of the Immunoscore (IM) as a prognostic marker in stage I/II/III colon cancer: results of a worldwide consortium-based analysis of 1,336 patients. J Clin Oncol. 2016;34(suppl):abstr 3500.
24. Kassouf E, Tabchi S, Tehfe M. Anti-EGFR therapy for metastatic colorectal cancer in the era of extended RAS gene mutational analysis. BioDrugs. 2016;30:95-104.
25. Douillard JY, Oliner KS, Siena S, et al. Panitumumab-FOLFOX4 treatment and RAS mutations in colorectal cancer. N Engl J Med. 2013;369:1023-34.
26. Vaughn CP, Zobell SD, Furtado LV, et al. Frequency of KRAS, BRAF, and NRAS mutations in colorectal cancer. Genes Chromosomes Cancer. 2011;50:307-12.
27. Rui Y, Wang C, Zhou Z, et al. K-Ras mutation and prognosis of colorectal cancer: a meta-analysis. Hepatogastroenterology. 2015;62:19-24.
28. Roth AD, Tejpar S, Delorenzi M, et al. Prognostic role of KRAS and BRAF in stage II and III resected colon cancer: results of the translational study on the PETACC-3, EORTC 40993, SAKK 60-00 trial. J Clin Oncol. 2010;28:466-74.
29. Therkildsen C, Bergmann TK, Henrichsen-Schnack T, et al. The predictive value of KRAS, NRAS, BRAF, PIK3CA and PTEN for anti-EGFR treatment in metastatic colorectal cancer: a systematic review and meta-analysis. Acta Oncol. 2014;53:852-64.
30. Pietrantonio F, Petrelli F, Coinu A, et al. Predictive role of BRAF mutations in patients with advanced colorectal cancer receiving cetuximab and panitumumab: a meta-analysis. Eur J Cancer. 2015;51:587-94.
31. Cohen R, Cervera P, Svrcek M, et al. BRAF-mutated colorectal cancer: what is the optimal strategy for treatment? Curr Treat Options Oncol. 2017;18:9.
32. Santini D, Loupakis F, Vincenzi B, et al. High concordance of KRAS status between primary colorectal tumors and related metastatic sites: implications for clinical practice. Oncologist. 2008;13:1270-5.
33. Fedyanin M, Stroganova A, Senderovich A, et al. Concordance of KRAS, NRAS, BRAF, PIK3CA mutation status in the primary tumor (PT) and metachronous metastases in patients (pts) with colorectal cancer (CRC). J Clin Oncol. 2016;34(suppl):abstr e15026.
34. Misale S, Yaeger R, Hobor S, et al. Emergence of KRAS mutations and acquired resistance to anti-EGFR therapy in colorectal cancer. Nature. 2012;486:532-6.
35. Siravegna G, Mussolin B, Buscarino M, et al. Clonal evolution and resistance to EGFR blockade in the blood of colorectal cancer patients. Nat Med. 2015;21:795-801.
36. Taly V, Pekin D, Benhaim L, et al. Multiplex picodroplet digital PCR to detect KRAS mutations in circulating DNA from the plasma of colorectal cancer patients. Clin Chem. 2013;59:1722-31.
37. Bettegowda C, Sausen M, Leary RJ, et al. Detection of circulating tumor DNA in early- and late-stage human malignancies. Sci Transl Med. 2014;6:224ra24.
38. Lee MS, Kopetz S. Current and future approaches to target the epidermal growth factor receptor and its downstream signaling in metastatic colorectal cancer. Clin Colorectal Cancer. 2015;14:203-18.
39. Perkins G, Pilati C, Blons H, Laurent-Puig P. Beyond KRAS status and response to anti-EGFR therapy in metastatic colorectal cancer. Pharmacogenomics. 2014;15:1043-52.
40. Hagemann IS. Molecular testing in breast cancer: a guide to current practices. Arch Pathol Lab Med. 2016;140:815-24.
41. Raghav KPS, Overman MJ, Yu R, et al. HER2 amplification as a negative predictive biomarker for anti-epidermal growth factor receptor antibody therapy in metastatic colorectal cancer. J Clin Oncol. 2016;34(suppl):abstr 3517.
42. Clark JW, Niedzwiecki D, Hollis D, Mayer R. Phase II trial of 5-fluorouracil (5-FU), leucovorin (LV), oxaliplatin (Ox), and trastuzumab (T) for patients with metastatic colorectal cancer (CRC) refractory to initial therapy. Proc Am Soc Clin Oncol. 2003;22:abstr 3584.
43. Ramanathan RK, Hwang JJ, Zamboni WC, et al. Low overexpression of HER-2/neu in advanced colorectal cancer limits the usefulness of trastuzumab (Herceptin) and irinotecan as therapy. A phase II trial. Cancer Invest. 2004;22:858-65.
44. Leto SM, Sassi F, Catalano I, et al. Sustained inhibition of HER3 and EGFR is necessary to induce regression of HER2-amplified gastrointestinal carcinomas. Clin Cancer Res. 2015;21:5519-31.
45. Sartore-Bianchi A, Trusolino L, Martino C, et al. Dual-targeted therapy with trastuzumab and lapatinib in treatment-refractory, KRAS codon 12/13 wild-type, HER2-positive metastatic colorectal cancer (HERACLES): a proof-of-concept, multicentre, open-label, phase 2 trial. Lancet Oncol. 2016;17:738-46.
46. Longley DB, Harkin DP, Johnston PG. 5-fluorouracil: mechanisms of action and clinical strategies. Nat Rev Cancer. 2003;3:330-8.
47. Liu J, Schmitz JC, Lin X, et al. Thymidylate synthase as a translational regulator of cellular gene expression. Biochim Biophys Acta. 2002;1587:174-82.
48. Jason TL, Berg RW, Vincent MD, Koropatnick J. Antisense targeting of thymidylate synthase (TS) mRNA increases TS gene transcription and TS protein: effects on human tumor cell sensitivity to TS enzyme-inhibiting drugs. Gene Expr. 2007;13:227-39.
49. van Kuilenburg AB. Dihydropyrimidine dehydrogenase and the efficacy and toxicity of 5-fluorouracil. Eur J Cancer. 2004;40:939-50.
50. Chu E, Callender MA, Farrell MP, Schmitz JC. Thymidylate synthase inhibitors as anticancer agents: from bench to bedside. Cancer Chemother Pharmacol. 2003;52(suppl 1):S80-S89.
51. Atkin GK, Daley FM, Bourne S, et al. The impact of surgically induced ischaemia on protein levels in patients undergoing rectal cancer surgery. Br J Cancer. 2006;95:928-33.
52. Qiu LX, Tang QY, Bai JL, et al. Predictive value of thymidylate synthase expression in advanced colorectal cancer patients receiving fluoropyrimidine-based chemotherapy: evidence from 24 studies. Int J Cancer. 2008;123:2384-9.
53. Caris Life Sciences. CARIS database. http://www.carislifesciences.com/platforms/caris-research-institute/. Accessed March 18, 2017.
54. Choueiri MB, Shen JP, Gross AM, et al. ERCC1 and TS expression as prognostic and predictive biomarkers in metastatic colon cancer. PLoS One. 2015;10:e0126898.
55. Vincenzi B, Santini D, Tonini G. Thymidylate synthase expression in colorectal cancer: the never-ending story. J Clin Oncol. 2005;23:2108.
56. Butzke B, Oduncu FS, Severin F, et al. The cost-effectiveness of UGT1A1 genotyping before colorectal cancer treatment with irinotecan from the perspective of the German statutory health insurance. Acta Oncol. 2016;55:318-28.
57. Strassburg CP. Pharmacogenetics of Gilbert’s syndrome. Pharmacogenomics. 2008;9:703-15.
58. Etienne-Grimaldi MC, Boyer JC, Thomas F, et al. UGT1A1 genotype and irinotecan therapy: general review and implementation in routine practice. Fundam Clin Pharmacol. 2015;29:219-37.
59. Beutler E, Gelbart T, Demina A. Racial variability in the UDP-glucuronosyltransferase 1 (UGT1A1) promoter: a balanced polymorphism for regulation of bilirubin metabolism? Proc Natl Acad Sci USA. 1998;95:8170-4.
60. Chen S, Laverdiere I, Tourancheau A, et al. A novel UGT1 marker associated with better tolerance against irinotecan-induced severe neutropenia in metastatic colorectal cancer patients. Pharmacogenomics J. 2015;15:513-20.
61. Yu QQ, Qiu H, Zhang MS, et al. Predictive effects of bilirubin on response of colorectal cancer to irinotecan-based chemotherapy. World J Gastroenterol. 2016;22:4250-8.
62. Shulman K, Cohen I, Barnett-Griness O, et al. Clinical implications of UGT1A1*28 genotype testing in colorectal cancer patients. Cancer. 2011;117:3156-62.
63. Swen JJ, Nijenhuis M, de Boer A, et al. Pharmacogenetics: from bench to byte-an update of guidelines. Clin Pharmacol Ther. 2011;89:662-73.
64. Dias MM, McKinnon RA, Sorich MJ. Impact of the UGT1A1*28 allele on response to irinotecan: a systematic review and meta-analysis. Pharmacogenomics. 2012;13:889-99.
65. Parker WB, Cheng YC. Metabolism and mechanism of action of 5-fluorouracil. Pharmacol Ther. 1990;48:381-95.
66. Mercier C, Ciccolini J. Profiling dihydropyrimidine dehydrogenase deficiency in patients with cancer undergoing 5-fluorouracil/capecitabine therapy. Clin Colorectal Cancer. 2006;6:288-96.
67. Meulendijks D, Henricks LM, Sonke GS, et al. Clinical relevance of DPYD variants c.1679T>G, c.1236G>A/HapB3, and c.1601G>A as predictors of severe fluoropyrimidine-associated toxicity: a systematic review and meta-analysis of individual patient data. Lancet Oncol. 2015;16:1639-50.
68. Lee AM, Shi Q, Alberts SR, et al. Association between DPYD c.1129-5923 C>G/hapB3 and severe toxicity to 5-fluorouracil-based chemotherapy in stage III colon cancer patients: NCCTG N0147 (Alliance). Pharmacogenet Genomics. 2016;26:133-7.
69. Rosmarin D, Palles C, Church D, et al. Genetic markers of toxicity from capecitabine and other fluorouracil-based regimens: investigation in the QUASAR2 study, systematic review, and meta-analysis. J Clin Oncol. 2014;32:1031-9.
70. Deenen MJ, Meulendijks D, Cats A, et al. Upfront genotyping of DPYD*2A to individualize fluoropyrimidine therapy: a safety and cost analysis. J Clin Oncol. 2016;34:227-34.
71. Lunenburg CA, Henricks LM, Guchelaar HJ, et al. Prospective DPYD genotyping to reduce the risk of fluoropyrimidine-induced severe toxicity: ready for prime time. Eur J Cancer. 2016;54:40-8.
72. Guinney J, Dienstmann R, Wang X, et al. The consensus molecular subtypes of colorectal cancer. Nat Med. 2015;21:1350-6.
73. Des Guetz G, Schischmanoff O, Nicolas P, et al. Does microsatellite instability predict the efficacy of adjuvant chemotherapy in colorectal cancer? A systematic review with meta-analysis. Eur J Cancer. 2009;45:1890-6.