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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptNIH Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
 
Ann Surg. Author manuscript; available in PMC Sep 1, 2012.
Published in final edited form as:
PMCID: PMC3202983
NIHMSID: NIHMS315219
Identification of a biomarker profile associated with resistance to neoadjuvant chemoradiation therapy in rectal cancer
Julio Garcia-Aguilar, MD, PhD,1* Zhenbin Chen, PhD,1 David D. Smith, PhD,2 Wenyan Li, BA,1 Robert D. Madoff, MD,3 Peter Cataldo, MD,4 Jorge Marcet, MD,5 and Carlos Pastor, MD6
1Department of Surgery, City of Hope, Duarte, CA, 91010
2Division of Biostatistics, City of Hope, Duarte, CA, 91010
3Department of Surgery, University of Minnesota, Minneapolis, MN, 55455
4Department of Surgery, University of Vermont, Burlington, VT, 05401
5Tampa General Hospital, Tampa, FL, 33606
6Department of General Surgery, Clinica Universidad de Navarra, University of Navarra, Pamplona 31008, Spain
* Corresponding author: Julio Garcia-Aguilar, M.D., Ph.D. Department of Surgery, City of Hope, 1500 E. Duarte Rd. Duarte, CA 91010; Tel: (626) 471-9309; Fax: (626) 301-8113; jgarcia-aguilar/at/coh.org
Objective
To identify a biomarker profile associated with tumor response to chemoradiation (CRT) in locally advanced rectal cancer.
Background
Rectal cancer response to neoadjuvant CRT is variable. While some patients have a minimal response, others achieve a pathologic compete response (pCR) and have no viable cancer cells in their surgical specimens. Identifying biomarkers of response will help select patients more likely to benefit from CRT.
Methods
This study includes 132 patients with locally advanced rectal cancer treated with neoadjuvant CRT followed by surgery. Tumor DNA from pre-treatment tumor biopsies and control DNA from paired normal surgical specimens was screened for mutations and polymorphisms in 23 genes. Genetic biomarkers were correlated with tumor response to CRT (pCR versus non-pCR), and the association of single or combined biomarkers with tumor response was determined.
Results
Thirty-three out of 132 (25%) patients achieved a pCR and 99 (75%) patients had non-pCR. Three individual markers were associated with non-pCR; KRAS mutation (p = 0.0145), CCND1 G870A (AA) polymorphism (p = 0.0138), and MTHFR C677T (TT) polymorphism (p = 0.0120). Analysis of biomarker combinations revealed that none of the 27 patients with both p53 and KRAS mutations had a pCR. Further, in patients with both p53 and KRAS mutations or the CCND1 G870A (AA) polymorphism or the MTHFR C677T (TT) polymorphism (n = 52) the association with non-pCR was further strengthened; 51 out of 52 (98%) of patients were non-pCR. These biomarker combinations had a validity of >70% and a positive predictive value of 97%–100%, predicting that patients harboring these mutation/polymorphism profiles will not achieve a pCR.
Conclusions
A specific biomarker profile is strongly associated with non-pCR to CRT and could be used to select optimal oncologic therapy in rectal cancer patients.
In recent years combined chemotherapy and radiation therapy (CRT) before total mesorectal excision (TME) has become the standard treatment for patients with locally advanced rectal cancer. This approach provides excellent tumor control and long-term survival14 but it is associated with measurable mortality, significant morbidity, and long-lasting sequelae that may permanently impair quality of life.58 It is now evident that preoperative CRT is not equally beneficial for all rectal cancer patients. Some patients have a minimal response to CRT, whereas others have no detectable cancer cells in the primary tumor location or in regional lymph nodes in the surgical specimen. Patients with such a pathologic complete response (pCR) have a better prognosis compared to non-pCR patients.915 If tumor response could be predicted before surgery, patients with resistant tumors could be spared CRT-related toxicity and expense. Furthermore, patients likely to achieve a pCR could potentially avoid the morbidity and functional consequences of TME. The benefit for these patients in terms of quality of life would be significant. Unfortunately, identifying responders and non-responders to CRT before surgery remains a challenge.
While tumor response to CRT depends on treatment-related factors, such as radiation dose and the type of chemotherapy administered, tumor biology appears to play the most important role in governing rectal cancer response to CRT.16,17 The search for molecular predictors of rectal cancer response to CRT has been an active area of research because such biomarkers could profoundly affect the clinical management of rectal cancer patients and influence the use of organ-preserving treatment strategies such as local excision or observation. Many studies have reported biomarkers of response to CRT, focusing on gene expression, mutations, and polymorphisms;1622 and although select genes or gene combinations have been identified as potential surrogates of response, none have been validated and incorporated into clinical practice.
In this study we screened a series of 132 patients who were treated in a prospective rectal cancer trial for mutations and polymorphisms in 23 genes with previously reported roles in the pathogenesis of colorectal cancer. Our objective was to determine whether these molecular alterations alone or in combination were associated with response to CRT.
Patient eligibility
Patients with clinical American Joint Committee on Cancer (AJCC)23 stage II (T3-4, N0) or stage III (any T, N1-2) invasive adenocarcinoma of the rectum with a distal tumor border within 12 cm of the anal verge were enrolled in the Timing of Rectal Cancer Response to Chemoradiation study, a multi-institutional prospective clinical trial investigating the effect of increasing the CRT-to-surgery time interval, and adding modified FOLFOX-6 chemotherapy (mFOLFOX-6) during the interval period (ClinicalTrials.org Identifier: NCT00335816). This trial was designed as a series of sequential Phase II trials or study groups (SGs), each with a progressively longer CRT-to-surgery interval and increasing cycles of preoperative mFOLFOX-6. This study was approved by an Institutional Review Board (IRB) at each participating institution as well as a central IRB, and informed written consent was obtained from each patient prior to enrollment in the trial. Patients included in the present study were pooled from SG1 (n = 52), SG2 (n = 58) and SG3 (n = 22). Further details of patient eligibility for this trial are presented elsewhere.24
Treatment protocol
Patients in all SGs were treated with CRT; 5-Fluorouracil (FU) 225 mg/m2/day for 7 days in continuous infusion, and a total of 50.4Gy radiation. Patients in SG1 underwent TME an average of 6 weeks after completing CRT (standard of care). Following CRT, patients in SG2 and SG3 with signs of stable disease or disease progression compared with baseline staging had surgery without further delay. All other patients received 2 and 4 cycles of additional chemotherapy (mFOLFOX-6), respectively; leucovorin 200 mg/m2 or 400 mg/m2 plus oxaliplatin 85 mg/m2 by 2h infusion, followed by bolus of 5-FU 400 mg/m2 and a 46h infusion of 5-FU 2,400 mg/m2. Patients in SG2 and SG3 underwent TME an average of 11 and 16 weeks after completing CRT. The clinical outcomes for these patients are presented elsewhere.24
Tumor response to CRT
Pathologic complete response was defined as the complete absence of tumor cells from the rectal wall and regional lymph nodes by hematoxylin and eosin staining under microscopy. Tumor pathology was assessed by two independent pathologists and graded according to the recommendations of the AJCC.23 For the purposes of the study, response was classified as either pCR or non-pCR based on the above criteria.
Sample preparation and molecular analysis
Tumor DNA from pre-treatment tumor biopsies and control DNA from paired normal surgical specimens for all patients was extracted as follows: 10–20 slides per patient sample from formalin-fixed paraffin-embedded (FFPE) tumor biopsies and normal tissues were de-paraffinized, hydrated, and stained with 0.2% methylene blue. A 27.5 gauge needle was then used to manually micro-dissect cells under inverted microscopy. Genomic DNA was extracted using the QIAamp DNA FFPE Tissue kit (Qiagen Inc., Valencia, CA) according to manufacturer’s instructions with the following modifications; an extension of digestion time at 56°C from 1 hour to 48 hours and the addition of three 20 μl aliquots of Proteinase-K at 4, 20, and 28 hours during digestion. DNA was then quantified by measuring absorbance at 260nm.
PCR analysis and Sanger sequencing
Gene mutations and polymorphisms were screened by standard polymerase chain reaction (PCR) followed by Sanger sequencing (primer sequences shown in Supplementary Table 1). PCR reactions consisted of 10 mM Tris-HCl (pH: 8.3), 50 mM KCl, 1.5 mM MgCl2, 5 mM primers, 200 mM dNTP, 0.1 μg/ml BSA, 0.5 U Amplitaq Gold (Applied Biosystems, Foster City, CA) plus 2 μl of tumor or control DNA in a total volume of 25 μl. Cycling conditions were 94°C for 30 seconds, annealing for 30 seconds (specific temperatures shown in Supplementary Table 1), and 72°C for 1 minute, for a total of 40–45 cycles. An initial denaturation at 94°C for 10 minutes and a final extension at 72°C for 7 minutes were used. Two independently extracted DNA samples for each patient biopsy or surgical specimen were simultaneously amplified with a negative control (H20). 2 μl of each PCR reaction was analyzed in a 2% agarose gel to verify the presence of the expected amplified product. All sequencing reactions were carried out in both sense and antisense directions with PCR primers by Sanger sequencing and all mutations and polymorphisms were confirmed by sequencing two independently-derived PCR products. Somatic mutations and polymorphisms were verified by comparison to paired normal surgical-specimen controls.
Statistical analysis
Patient characteristics
To determine differences in clinical and pathological features between pCR and non-pCR patients, the Mann-Whitney U test was used for comparing means of continuous variables between groups and the two sided Fisher’s Exact test and chi-square test were used for testing the significance of differences in the distributions of categorical variables.
Characterizing biomarkers
For genes which could be classified as wild-type or mutant, 2 × 2 analysis tables were constructed and genes were tested for association with tumor response using Fisher’s Exact test. For genes with multiple polymorphisms, each polymorphism was partitioned into three separate binary variables comparing a single allelic variation to the remaining combined alleles.
An exhaustive combinatorial analysis was performed to determine the association of each marker or combinations of markers with tumor response. All logical combinations were used (i.e., logical operator AND, logical operator OR) among the binary variables including 1, 2, 3 or 4 markers at a time. Over 3 × 106 combinations were tested. This combinatorial approach allowed us to determine whether patients fail to respond if they carry at least one biomarker (OR) or whether the presence of numerous simultaneous biomarkers (AND/OR) are required to predict non-pCR.
False discovery rate (FDR) was used to control for multiple comparisons.25 Markers that remained significant after FDR adjustment were internally cross-validated using 100 iterations, randomly selecting 70% of the subjects for “training” and 30% of the subjects for “testing”. The Monte Carlo-based CVLR in SAS cross-validation algorithm was used.26 Finally the 2 × 2 tables and logistic regression scores were used to calculate the area under the curve (AUC) of the receiver operating characteristic (ROC) curve, and the sensitivity, specificity, and positive and negative predictive values of the molecular markers.
Patient characteristics and tumor response
A total of 132 patients (58 in SG1, 52 in SG2, and 22 in SG3) were included in the analysis. Patient demographics stratified by pCR status for all patients are shown in Table 1. Overall, 33 out of 132 (25%) patients achieved a pCR. The 99 remaining non-pCR patients (75%) showed either a pathologic partial response (pPR) or had stable disease. No patients had disease progression. Seventeen (29%) patients in SG1 achieved a pCR, 10 (19%) in SG2 and 6 (27%) in SG3 (Table 1). There were no significant differences in tumor response between SGs, and there were no significant differences in clinical or pathological factors between pCR and non-pCR patients, or between SGs.
Table 1
Table 1
Patient demographics and tumor characteristics overall and stratified by study group
Individual biomarker analysis
Tumor DNA from pre-treatment tumor biopsies and control DNA from paired normal surgical specimens for all patients was screened for mutations and polymorphisms in 23 genes (Supplementary Table 1). Following screening and statistical analysis, 3 biomarkers were found to associate individually with non-pCR (Table 2). v-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog (KRAS) mutations were more common in non-pCR patients compared to patients with a pCR (49% versus 24%, p = 0.0145), while the cyclin D1 (CCND1) G870A and methylenetetrahydrofolate reductase (NAD(P)H) (MTHFR) C677T polymorphisms were also associated with non-pCR. Specifically, patients with the AA polymorphism at the 870 locus of the CCND1 gene were significantly less likely to achieve a pCR compared to patients who carried either the GA or GG alleles at the same location (p = 0.0138). Of the total number of patients who carried the AA polymorphism in our patient population (19 out of 132), 18 of 19 (95%) did not achieve a pCR. None of the patients that carried the TT polymorphism at the 677 locus of the MTHFR gene (14 out of 132) achieved a pCR (p = 0.0120).
Table 2
Table 2
Gene mutations and polymorphisms individually associated with non-PCR
Of interest, mutations in tumor protein p53 (p53), v-raf murine sarcoma viral oncogene homolog B1 (BRAF), catenin (cadherin-associated protein), beta 1, 88kDa (CTNNB1) and phosphoinositide-3-kinase, catalytic, alpha polypeptide (PIK3CA), each previously reported to play a role in the pathogenesis of colorectal cancer, were not significantly associated with pCR in our patient population as stand-alone biomarkers (Table 2). No other single gene mutation or polymorphism was significantly associated with tumor response (Supplementary Table 2).
Combination biomarker analysis
Next, mutation/polymorphism combinations for all molecular alterations were tested up to the order of 4 simultaneous markers. Approximately 3-million different combinations were tested and screened with FDR correction to the p-value. Following analysis, 6 different biomarker combinations were identified as both sufficiently prevalent in our patient population for meaningful validation and statistically associated with tumor response (pCR versus non-pCR) in a synergistic fashion (Table 3). The majority of these biomarker combinations included mutations in both p53 and KRAS (5 out of 6). The significance of this molecular profile is reflected in our patient population, where 27 out of 132 patients had concurrent KRAS and p53 mutations and none had a pCR to CRT. Further, in patients with both p53 and KRAS mutations or the CCND1 G870A (AA) polymorphism (n = 43) or the MTHFR C677T (TT) polymorphism (n = 52) the association with non-pCR was further strengthened (Table 3). When either polymorphism was added to the molecular profile, 51 out of 52 (98%) patients failed to achieve a pCR to CRT.
Table 3
Table 3
Significant biomarker combinations associated with non-pCR
Validation of predictive biomarker profiles
The four most prevalent biomarkers (KRAS mutation; p53 mutation; CCND1 G870A [AA] polymorphism; MHTFR C677T [TT] polymorphism) based on a significant association with non-pCR individually or in combination were chosen for further validation to determine their value for predicting non-pCR. Following internal validation, each biomarker combination analyzed had a validity of over 70% (Table 4). All combination sets had similar specificity and positive predictive values (97%–100%). The combination of all four markers - p53 and KRAS mutation, or the CCND1 G870A (AA) polymorphism, or the MTHFR C677T (TT) polymorphism - resulted in the highest AUC of the ROC curve (AUC = 0.74) and the highest sensitivity (52%) predicting that patients with this mutation/polymorphism profile will not achieve a pCR to CRT.
Table 4
Table 4
Cross-validation for biomarker combinations associated with non-pCR
In the current study we identified gene mutations and polymorphisms that are individually associated with failure to achieve a pCR to CRT. We also found that when combined, these mutations and polymorphisms synergistically identify a subset of rectal cancer patients who do not develop a pCR in response to CRT with a high degree of accuracy.
Our results have immediate clinical relevance given that achieving a complete clinical response to CRT may be followed by an organ preservation approach such as local excision or observation in select patients with rectal cancer. Indeed a number of collaborative groups are already exploring the feasibility of these approaches. The American College of Surgeons Oncology Group (ACOSOG) recently completed a trial of CRT before local excision for T2N0 rectal cancer patients27 and a wait-and watch approach has been attempted at a small number of institutions.28 The deferral of surgery for rectal cancer patients who develop a clinical complete response (cCR) to CRT is also being studied prospectively by the National Cancer Research Network in collaboration with the Pelican Cancer Foundation in the United Kingdom. However, a cCR does not always correlate with a pCR and patients with a cCR after CRT may still have cancer cells in their surgical specimens. Our results show that biomarker profiling could help identify a subset of patients highly unlikely to develop a pCR to CRT and consequently help direct these patients away from organ preservation treatment strategies and towards clinically beneficial therapies such as TME.
KRAS is a key component of the mitogen activated kinase (MAPK) pathway that is activated by cell surface receptors such as epidermal growth factor receptor (EGFR). These signals are then transduced to the nucleus where they phosphorylate and activate transcription factors leading to changes in gene expression.29,30 Mutant variants in the KRAS gene result in constitutive activation of its encoded protein, resulting in persistent activation of the MAPK pathway.29,30 KRAS is mutated in over a third of colorectal cancers and experimental evidence has shown that KRAS mutations can be found in the earliest tumor stage, and that once acquired, these mutations are preserved throughout the natural history of the tumor.31,32 Although previous studies on the prognostic value of KRAS mutation in patients with colorectal cancer have reported different results,33 the recent discovery that KRAS mutation is a strong predictor of colorectal cancer response to the anti-EGFR monoclonal antibodies Cetuximab and Panitumumab34,35 has further established this gene as an important biomarker in colorectal cancer. Indeed KRAS mutation status is now routinely checked in every rectal cancer patient for potential candidacy of anti-EGFR therapy.36
Few studies have evaluated KRAS as a biomarker for tumor response in rectal cancer patients treated with CRT and TME. Luna-Perez, et al. described a series of rectal cancer patients treated with preoperative CRT and reported that tumors with wild-type KRAS were more likely to respond to CRT than tumors with mutant KRAS.37 However, these results should be interpreted with caution due to their small sample size, low rate of pCR, sub-standard radiotherapy, and the use of radiated cancer tissue for KRAS analysis. Bengala, et al. also found that tumors with wild-type KRAS were more likely to respond to CRT compared to mutant KRAS (37% versus 11%) in 39 patients treated with Cetuximab and CRT.38 Our data confirm these observations indicating that rectal cancers with wild-type KRAS are more likely to develop a pCR to 5-FU based CRT compared to tumors with mutant KRAS. Mutant KRAS may therefore be a biomarker for non-pCR in rectal cancer patients treated with CRT, with or without EGFR-inhibitors. However, other studies have reported contradictory results.39,40 Erben, et al. found no correlation between KRAS mutation and tumor down-staging in 57 rectal cancer patients treated with Cetuximab, Irinotecan and Capecitabine in combination with pelvic irradiation.39 Similarly, Gaedcke, et al. found no correlation between KRAS mutation and tumor down-staging in rectal cancer patients treated with 5-FU and Oxaliplatin during radiation.40 However, these were smaller series, utilizing different radio-sensitizing drugs, and using different definitions of tumor response.
The CCND1 gene encodes the cyclin D1 protein which is a key regulator of the cell cycle, promoting the transition from G1 to S phase and committing the cell to division and proliferation.41 CCND1 expression is elevated in many types of cancers and its expression is regulated at multiple levels including transcription, translation and protein stability and degradation.41,42 Polymorphisms within CCND1 contribute to its regulation and possibly to its oncogenic potential. Of over 100 reported CCND1 polymorphisms, the GA polymorphism at locus 870 has received the most attention. This polymorphism is located at the exon 4/intron 4 boundary and has been linked to alternative gene splicing. The G allele codes for the optimal splicing which produces the canonical form, termed cyclin D1a, while the A allele constrains exon 4 excision and allows translation into intron 4 resulting in a truncated cyclin D1b transcript that lacks the sequences required for degradation. The increase in the half-life in this variant form of CCND1 is consistent with an increase in cell proliferation, and the A allele has been associated with increased risk and advanced tumor stage in colorectal cancer, and a poor prognosis in a variety of cancers.43 Li, et al. recently reported that cyclin D1a is also important to elicit the DNA damage response (DDR) that may result in DNA repair.44 Our findings, that patients homozygous for the A allele are less likely to respond to CRT, are consistent with the increased cell proliferation and reduced contribution to DDR associated with cyclin D1b. However, other series have reported contradictory results. Ho-Pun-Cheung, et al. reported better tumor response and lower risk of local recurrence associated with the AA allele among 65 patients with rectal cancer treated with preoperative radiotherapy.45 However in this smaller series, patients did not receive sensitizing chemotherapy, and response was based on histological tumor regression rather than on pCR.
Exposure of cells to DNA-damaging agents, such as ionizing radiation and chemotherapeutic drugs, elicits a complex set of acute cellular responses that involve the coupling of cell cycle arrest, DNA repair, and apoptosis. The central component of these responses is the product of the p53 gene, which modulates transcription of responsive genes involved in temporary or permanent growth arrest or apoptosis. Inactivation of p53 contributes to cellular resistance to DNA-damaging agents in vitro and in vivo.46,47 Over 50% of colorectal cancers harbor p53 mutations. Most of them are missense mutations, leading to the synthesis of a stable but inactive protein that accumulates in the nucleus of tumor cells. A number of studies have evaluated p53 mutations as predictors of response in rectal cancer patients treated with neoadjuvant therapy.4850 While some studies found an association between mutant p53 and tumor response to radiation,48,49 other studies have not confirmed this association.50 Our results concur with those series which found that mutant p53 alone is not associated with tumor response to CRT. However, we found that mutant p53 becomes a predictive biomarker of non-pCR when present in cancer cells harboring a KRAS mutation. In these cells, the combined mutation/polymorphism profile of these biomarkers may synergistically promote increased proliferation (KRAS mutation) coupled with tumor resistance to radiation and chemotherapy (p53 mutation) resulting in non-pCR.
The MTHFR gene codes for an enzyme that catalyzes conversion of 5,10-MTHF to 5-MTHF, the dominant circulating form of folate. 5,10-MTHF is used for the thymidylase synthase (TS)-catalyzed conversion of deoxyuridylate to deoxythymidylate, important for DNA synthesis. A C-to-T transition in codon 677 of the MTHFR gene results in a genotypic variant associated with decreased enzyme activity.51 Reduced MTHFR activity may increase the amount of 5,10-MTHF available for the TS enzyme, and increase the effect of TS inhibitors such as 5-FU, in individuals carrying this polymorphism. While there are mixed results from studies investigating the effect of the MTHFR C677T polymorphism on colorectal cancer response to 5-FU,5254 two studies suggest this polymorphism does affect rectal cancer response.53,54 Terrazzino, et al. found that the likelihood of response for 122 patients treated with fluoropyrimidine-based CRT and surgery was higher among patients homozygous for the C allele compared to carriers of the T allele.53 Similarly, Cecchin, et al. found that the T allele was associated with lower response rates in a series of 238 rectal cancer patients treated with fluoropyrimidine-based chemotherapy.54 Our study found similar resistance to CRT associated with MTHFR C677T. Despite methodological differences between these two studies and our trial (both used radio-sensitizers in addition to fluoropyrimidines, defined response using tumor regression grade (TRG) rather than pCR, and used different CRT-to-surgery intervals), their combined results suggest that the MTHFR C677T (TT) polymorphism is associated with non-pCR to CRT.
There are a number of limitations to our study that warrant consideration. First, the sample size is relatively small and the study endpoint, pCR, occurred in only 25% of patients. Furthermore, while we used a common statistical cross-validation method, which has been applied extensively to biomarker-validation studies,55 a larger independent series with more pCR and non-pCR patients will be important to validate these results. To address this we are continuing to collect specimens from additional patients in SG1–SG3 to further validate our results as well as extending our studies to an independent patient cohort. Second, although this is a prospective study with a homogenous patient population, the treatment regimen varied between the three SGs in both the use of adjuvant chemotherapy and the CRT-to-surgery interval. Third, the tissue used to extract normal control DNA was obtained from the proximal resection margin of the surgical specimens. This tissue is usually outside the radiation field and it is unlikely that it received the full dose of radiation, but it was exposed to chemotherapy. While we have recently shown that mutations in KRAS and p53 remain largely unchanged in rectal cancer after CRT,56 the possibility of mutations arising due to treatment can not be totally excluded.
In conclusion, we have identified a biomarker profile that is associated with tumor resistance to CRT, evidenced by a lack of pCR. These findings are important because they help identify a subset of patients with rectal cancers who most likely will not respond to CRT and therefore should not be considered as candidates for organ preservation following CRT.
Supplementary Material
Acknowledgments
The authors thank Nicola Solomon, PhD, for assistance in writing and editing the manuscript and Karin Avila for specimen collection and study management. This study was supported by the National Institutes of Health (NIH), National Cancer Institute (NCI) R01 Grant CA090559 (JGA). ClinicalTrials.org Identifier: NCT00335816.
Footnotes
Study Description: Rectal cancer response to neoadjuvant chemoradiation (CRT) is variable. Some patients have a minimal response while others have a pathologic complete response (pCR) and have no viable cancer cells in their surgical specimens. Identifying biomarkers of response will help select patients more likely to benefit from CRT. Here, we identify a biomarker profile strongly associated with non-pCR to CRT in locally advanced rectal cancer.
The authors declare no conflicts of interest associated with this manuscript.
Disclosures: This study was supported by the National Institutes of Health (NIH), National Cancer Institute (NCI) R01 Grant CA090559 (JGA). ClinicalTrials.org Identifier: NCT00335816.
1. Peeters KC, Marijnen CA, Nagtegaal ID, et al. The TME trial after a median follow-up of 6 years: increased local control but no survival benefit in irradiated patients with resectable rectal carcinoma. Ann Surg. 2007;246(5):693–701. [PubMed]
2. Merchant NB, Guillem JG, Paty PB, et al. T3N0 rectal cancer: results following sharp mesorectal excision and no adjuvant therapy. J Gastrointest Surg. 1999;3(6):642–7. [PubMed]
3. Leo E, Belli F, Andreola S, et al. Total rectal resection, mesorectum excision, and coloendoanal anastomosis: a therapeutic option for the treatment of low rectal cancer. Ann Surg Oncol. 1996;3(4):336–43. [PubMed]
4. Heald RJ, Husband EM, Ryall RD. The mesorectum in rectal cancer surgery-the clue to pelvic recurrence? Br J Surg. 1982;69(10):613–6. [PubMed]
5. Chessin DB, Guillem JG. Abdominoperineal resection for rectal cancer: historic perspective and current issues. Surg Oncol Clin N Am. 2005;14(3):569–86. [PubMed]
6. Luna-Perez P, Rodríguez-Ramírez S, Vega J, et al. Morbidity and mortality following abdominoperineal resection for low rectal adenocarcinoma. Rev Invest Clin. 2001;53(5):388–95. [PubMed]
7. Camilleri-Brennan J, Steele RJ. Objective assessment of morbidity and quality of life after surgery for low rectal cancer. Colorectal Dis. 2001;4(1):61–66. [PubMed]
8. Nesbakken A, Nygaard K, Bull-Njaa T, et al. Bladder and sexual dysfunction after mesorectal excision for rectal cancer. Br J Surg. 2000;87(2):206–10. [PubMed]
9. Chari RS, Tyler DS, Anscher MS, et al. Preoperative radiation and chemotherapy in the treatment of adenocarcinoma of the rectum. Ann Surg. 1995;221:778–86. [PubMed]
10. Diaz-Gonzalez JA, Calvo FA, Cortes J, et al. Prognostic factors for disease-free survival in patients with T3-4 or N+ rectal cancer treated with preoperative chemoradiation therapy, surgery, and intraoperative irradiation. Int J Radiat Oncol Biol Phys. 2006;64:1122–8. [PubMed]
11. Rodel C, Martus P, Papadoupolos T, et al. Prognostic significance of tumor regression after preoperative chemoradiotherapy for rectal cancer. J Clin Oncol. 2005;23:8688–96. [PubMed]
12. Valentini V, Coco C, Picciocchi A, et al. Does downstaging predict improved outcome after preoperative chemoradiation for extraperitoneal locally advanced rectal cancer? A long-term analysis of 165 patients. Int J Radiat Oncol Biol Phys. 2002;53:664–74. [PubMed]
13. Vecchio FM, Valentini V, Minsky BD, et al. The relationship of pathologic tumor regression grade (TRG) and outcomes after preoperative therapy in rectal cancer. Int J Radiat Oncol Biol Phys. 2005;62:752–60. [PubMed]
14. Borschitz T, Wachtlin D, Mohler M, et al. Neoadjuvant chemoradiation and local excision for T2-3 rectal cancer. Ann Surg Oncol. 2008;15:712–20. [PubMed]
15. Maas M, Nelemans PJ, Valentini V, et al. Long-term outcome in patients with a pathologic complete response after chemoradiation for rectal cancer: A pooled analysis of individual patient data. Lancet Oncol. 2010;11:835–44. [PubMed]
16. Kuremsky JG, Tepper JE, McLeod HL. Biomarkers for response to neoadjuvant chemoradiation for rectal cancer. Int J Radiat Oncol Biol Phys. 2009;74:673–688. [PubMed]
17. Smith FM, Reynolds JV, Miller N, et al. Pathological and molecular predictors of the response of rectal cancer to neoadjuvant radiochemotherapy. Eur J Surg Oncol. 2006;32:55–64. [PubMed]
18. Lurje G, Manegold PC, Ning Y, et al. Thymidylate synthase gene variations: predictive and prognostic markers. Mol Cancer Ther. 2009;8(5):1000–7. [PubMed]
19. Spindler KL, Nielsen JN, Lindebjerg J, et al. Prediction of response to chemoradiation in rectal cancer by a gene polymorphism in the epidermal growth factor receptor promoter region. Int J Radiat Oncol Biol Phys. 2006;66:500–4. [PubMed]
20. Kim IJ, Lim SB, Kang HC, et al. Microarray gene expression profiling for predicting complete response to preoperative chemoradiotherapy in patients with advanced rectal cancer. Dis Colon Rectum. 2007;50:1342–53. [PubMed]
21. Ghadimi BM, Grade M, Difilippantonio MJ, et al. Effectiveness of gene expression profiling for response prediction of rectal adenocarcinomas to preoperative chemoradiotherapy. J Clin Oncol. 2005;23:1826–38. [PubMed]
22. Watanabe T, Komuro Y, Kiyomatsu T, et al. Prediction of sensitivity of rectal cancer cells in response to preoperative radiotherapy by DNA microarray analysis of gene expression profiles. Cancer Res. 2006;66:3370–4. [PubMed]
23. Edge SB, Compton CC, Fritz AG, et al. American Joint Committee on Cancer (AJCC) cancer staging manual. 7. Springer, Inc; Chicago: 2010.
24. Garcia-Aguilar J, Smith DD, Avila K, et al. Optimal Timing of Surgery after Chemoradiation for Advanced Rectal Cancer: Preliminary Results of a Prospective Trial. Ann Surg. 2011 Apr 13; [Epub ahead of print] [PMC free article] [PubMed]
25. Benjamini YH, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B Statistical Methodology. 1995;57:289–300.
26. Monte Carlo Cross-Validation for Logistic Regression [computer program] Clint Moore; 2000.
27. Garcia-Aguilar J, Shi Q, Thomas CR, et al. A Phase II Trial of Neoadjuvant Chemoradiation and Local Excision for T2N0 Rectal Cancer: Preliminary Results of the ACOSOG Z6041 Trial. Ann Surg Oncol. 2011 In press. [PubMed]
28. Habr-Gama A, Perez RO, Nadalin W, et al. Operative versus nonoperative treatment for stage 0 distal rectal cancer following chemoradiation therapy: long-term results. Ann Surg. 2004;240:711–7. discussion 717–8. [PubMed]
29. Diaz-Flores E, Shannon K. Targeting oncogenic Ras. Genes Dev. 2007;21:1989–92. [PubMed]
30. Roberts PJ, Der CJ. Targeting the Raf-MEK-ERK mitogen-activated protein kinase cascade for the treatment of cancer. Oncogene. 2007;26:3291–310. [PubMed]
31. Rajagopalan H, Bardelli A, Lengauer C, et al. Tumorigenesis: RAF/RAS oncogenes and mismatch-repair status. Nature. 2002;418:934. [PubMed]
32. Takayama T, Ohi M, Hayashi T, et al. Analysis of K-ras, APC, and beta-catenin in aberrant crypt foci in sporadic adenoma, cancer, and familial adenomatous polyposis. Gastroenterology. 2001;121:599–611. [PubMed]
33. Anwar S, Frayling IM, Scott NA, et al. Systematic review of genetic influences on the prognosis of colorectal cancer. Br J Surg. 2004;91:1275–91. [PubMed]
34. Lievre A, Laurent-Puig P. Genetics: Predictive value of KRAS mutations in chemoresistant CRC. Nat Rev Clin Oncol. 2009;6:306–7. [PubMed]
35. Lievre A, Bachet JB, Boige V, et al. KRAS mutations as an independent prognostic factor in patients with advanced colorectal cancer treated with cetuximab. J Clin Oncol. 2008;26:374–9. [PubMed]
36. van Krieken JH, Jung A, Kirchner T, et al. KRAS mutation testing for predicting response to anti-EGFR therapy for colorectal carcinoma: proposal for a European quality assurance program. Virchows Arch. 2008;453:417–31. [PubMed]
37. Luna-Perez P, Segura J, Alvarado I, et al. Specific c-K-ras gene mutations as a tumor-response marker in locally advanced rectal cancer treated with preoperative chemoradiotherapy. Ann Surg Oncol. 2000;7:727–31. [PubMed]
38. Bengala C, Bettelli S, Bertolini F, et al. Epidermal growth factor receptor gene copy number, K-ras mutation and pathological response to preoperative cetuximab, 5-FU and radiation therapy in locally advanced rectal cancer. Ann Oncol. 2009;20:469–74. [PubMed]
39. Erben P, Ströbel P, Horisberger K, et al. KRAS and BRAF Mutations and PTEN Expression Do Not Predict Efficacy of Cetuximab-Based Chemoradiotherapy in Locally Advanced Rectal Cancer. Int J Radiat Oncol Biol Phys. 2010 Oct 13; [Epub ahead of print] [PubMed]
40. Gaedcke J, Grade M, Jung K, et al. KR+AS and BRAF mutations in patients with rectal cancer treated with preoperative chemoradiotherapy. Radiother Oncol. 2010;94(1):76–81. [PubMed]
41. Kim JK, Diehl JA. Nuclear cyclin D1: an oncogenic driver in human cancer. J Cell Physiol. 2009;220(2):292–6. [PMC free article] [PubMed]
42. Witzel II, Koh LF, Perkins ND. Regulation of cyclin D1 gene expression. Biochem Soc Trans. 2010;38(Pt 1):217–22. [PubMed]
43. Le Marchand L, Seifried A, Lum-Jones A, et al. Association of the cyclin D1 A870G polymorphism with advanced colorectal cancer. JAMA. 2003;290(21):2843–8. [PubMed]
44. Li Z, Jiao X, Wang C, et al. Alternative cyclin D1 splice forms differentially regulate the DNA damage response. Cancer Res. 2010;70(21):8802–11. [PMC free article] [PubMed]
45. Ho-Pun-Cheung A, Assenat E, Thezenas S, et al. Cyclin D1 gene G870A polymorphism predicts response to neoadjuvant radiotherapy and prognosis in rectal cancer. Int J Radiat Oncol Biol Phys. 2007;68(4):1094–101. [PubMed]
46. Saw RP, Morgan M, Koorey D, et al. p53, deleted in colorectal cancer gene, and thymidylate synthase as predictors of histopathologic response and survival in low, locally advanced rectal cancer treated with preoperative adjuvant therapy. Dis Colon Rectum. 2003;46:192–202. [PubMed]
47. Rau B, Sturm I, Lage H, et al. Dynamic expression profile of p21WAF1/CIP1 and Ki-67 predicts survival in rectal carcinoma treated with preoperative radiochemotherapy. J Clin Oncol. 2003;21:3391–401. [PubMed]
48. Kandioler D, Zwrtek R, Ludwig C, et al. TP53 genotype but not p53 immunohistochemical result predicts response to preoperative short-term radiotherapy in rectal cancer. Ann Surg. 2002;235:493–8. [PubMed]
49. Rebischung C, Gerard JP, Gayet J, et al. Prognostic value of P53 mutations in rectal carcinoma. Int J Cancer. 2002;100:131–5. [PubMed]
50. Lopez-Crapez E, Bibeau F, Thezenas S, et al. p53 status and response to radiotherapy in rectal cancer: a prospective multilevel analysis. Br J Cancer. 2005;92:2114–21. [PMC free article] [PubMed]
51. Lievers KJ, Boers GH, Verhoef P, et al. A second common variant in the methylenetetrahydrofolate reductase (MTHFR) gene and its relationship to MTHFR enzyme activity, homocysteine, and cardiovascular disease risk. J Mol Med. 2001;79(9):522–8. [PubMed]
52. De Mattia E, Toffoli G. C677T and A1298C MTHFR polymorphisms, a challenge for antifolate and fluoropyrimidine-based therapy personalisation. Eur J Cancer. 2009;45(8):1333–51. [PubMed]
53. Terrazzino S, Agostini M, Pucciarelli S, et al. A haplotype of the methylenetetrahydrofolate reductase gene predicts poor tumor response in rectal cancer patients receiving preoperative chemoradiation. Pharmacogenet Genomics. 2006;16(11):817–24. [PubMed]
54. Cecchin E, Agostini M, Pucciarelli S, et al. Tumor response is predicted by patient genetic profile in rectal cancer patients treated with neo-adjuvant chemo-radiotherapy. Pharmacogenomics J. 2011;11(3):214–26. [PubMed]
55. Molinaro AM, Simon R, Pfeiffer RM. Prediction error estimation: a comparison of resampling methods. Bioinformatics. 2005;21(15):3301–7. [PubMed]
56. Chen Z, Duldulao MP, Li W, Lee W, et al. Molecular Diagnosis of Response to Neoadjuvant Chemoradiation Therapy in Patients with Locally Advanced Rectal Cancer. J Am Coll Surg. 2011;212(6):1008–1017.e1. [PMC free article] [PubMed]