Long non-coding RNA polymorphisms and prediction of response to chemotherapy based on irinotecan in patients with metastatic colorectal cancer

Abstract

BACKGROUND

: Colorectal cancer is the fourth cause of cancer related death. Drug resistance and toxicity remain major clinical issues. HOTAIR and MALAT1 are long non-coding RNAS that affect cellular proliferation, apoptosis and drug resistance; their up-regulation has been linked with a poor prognosis.

OBJECTIVE:

Investigation of the association between rs4759314 HOTAIR and rs3200401 MALAT1 polymorphisms and irinotecan-based chemotherapy in terms of drug efficacy and toxicity.

METHODS:

Samples from 98 patients receiving different regimens of irinotecan-based therapy were included. Efficacy and toxicity were evaluated. KRAS mutation, rs3200401 HOTAIR and rs4759314 MALAT1 polymorphisms genotyping in the tumors and peripheral blood respectively were performed with PCR.

RESULTS:

Neither rs3200401 MALAT1 nor rs4759314 HOTAIR polymorphism are associated with response to treatment regimens. Rs4759314 was also not associated with increased toxicity in patients receiving irinotecan-based regimens. CT genotype of rs3200401 was associated with significantly reduced overall survival. An association between KRAS mutation and AG/GG genotypes in the rs4759314 was detected.

CONCLUSIONS:

CT genotype of rs3200401 MALAT1 polymorphism could serve as a toxicity biomarker. Carriers of the G allele of the rs4759314 HOTAIR are more likely to be carriers of KRAS mutations too. However, further studies in larger patient populations are required.

Keywords

CRC lncRNA HOTAIR MALAT1 rs3200401 rs4759314 irinotecan drug resistance toxicity

1. Introduction

Colorectal cancer (CRC) represents a major cause of mortality and morbidity globally; CRC is the third most commonly diagnosed malignancy and the fourth cause of cancer-related death [1, 2]. Despite screening efforts, a significant proportion of CRC patients are diagnosed when metastasis is already present. Furthermore, targeted therapies have been developed and progress has been made in the field of cytotoxic drugs. However, drug resistance and the prediction of drug toxicity remain major clinical issues, especially at advanced disease stages [2, 3]. There are several mechanisms that are responsible for drug resistance: limitation of drug transport, dysregulation of important cellular pathways (including apoptotic pathways and DNA repair mechanisms) and epigenetic factors including DNA methylation, histone modification and changes in the expression of non-coding RNAs [4] Research has shown that long non-coding RNAs (lncRNAs) can alter tumor resistance to chemotherapy in many ways, including DNA damage repair mechanisms and inhibition of apoptosis [5]. HOTAIR and MALAT1 are two lncRNAs that have been extensively studied and that are both connected to drug resistance [6, 7]. Polymorphisms of these lncRNAs have also been found to increase gastrointestinal and hematological toxicity in platinum-based chemotherapy [8].

HOTAIR is a newly discovered lncRNA with multiple roles in important aspects of cancer including cellular proliferation, survival, genomic stability and drug resistance. This lncRNA has been connected to several solid malignancies including lung cancer, hepatocellular carcinoma, gastric and colorectal cancer [9]. Recently, a meta-analysis has found a correlation between HOTAIR polymorphisms and cancer risk [10]. In colon cancer, HOTAIR appears to promote oncogenesis and metastasis acting as an oncogene [11]. However, Tian et al. [12], in a meta-analysis reported that no significant associations between the HOTAIR polymorphisms (rs920778, rs4759314 and rs1899663) and cancer risk were observed. This difference may be explained by the fact that at this meta-analysis only one study on colorectal cancer in Asian population was included and it is known that many differences between different ethnic groups exist [13]. Luo et al. observed that tumors with increased HOTAIR expression tend to be chemotherapy-resistant; upregulation of HOTAIR indicates a poor prognosis [14]. Furthermore, three new studies showed that HOTAIR is involved in cisplatin, crizotinib and imatinib resistance in lung adenocarcinoma, in non-small cell lung cancer and in chronic myeloid leukemia respectively [15, 16, 17].

MALAT1 (also known as NEAT2) is a newly discovered lncRNA believed to be a promising marker for the metastasis of lung cancer [4]. Subsequent studies revealed that similarly to HOTAIR, MALAT1 is also involved in proliferation, tumor invasion and apoptosis. Its expression has been found to be upregulated in several malignancies, a fact that also indicates a poor prognosis. MALAT1 emerges as an appealing biomarker for several types of cancer as well as a promising therapeutic target [18]. In a 2012 study, a MALAT1 polymorphism was found to be corelated with CRC [19]. Three more studies showed that MALAT1 is linked to cisplatin, adriamycin and temozolomide resistance in non-small lung cell lung carcinoma, large B cell lymphoma and glioblastoma respectively [20, 21, 22]. MALAT1 upregulation results too in poor response to oxaliplatin (a chemotherapy drug) [23].

In the present study we aimed to investigate the connection between two lncRNA polymorphisms (rs47 59314 HOTAIR and rs3200401 MALAT1) and drug resistance as well as toxicity in two patient groups that were treated with irinotecan-based chemotherapy. Irinotecan is an analogue of camptothecin with anticancer properties. It is a first line antitumor agent used to treat metastatic CRC. However, it is highly toxic and sometimes even life-threatening [24, 25]; thus, approaches for personalized irinotecan doses need to be developed [26, 27]. Currently, therapeutic strategies are based on whether a patient is classified as “fit” or “unfit”, to determine treatment protocol (combination of cytotoxic drugs and/or a biological agent) [28]. Widely accepted and well-established guidelines have recommended mainly oxaliplatin and irinotecan-based regimens: FOLFOX (oxaliplatin, 5-fluorouracil, and leucovorin); FOLFIRI (irinotecan, 5-fluorouracil, and leucovorin); CAPOX (capecitabine and oxaliplatin); CAPIRI (capecitabine and irinotecan); and FOLFOXIRI (infusional5-FU/LV, oxaliplatin and irinotecan) [29]. Continuous infusion of 5-FU added to irinotecan (FOLFIRI) has been shown to be more effective and tolerable than bolus 5-FU. However, this regimen requires either hospitalization or the placement of central venous line. In contrast, the irinotecan-capecitabine combination (XELIRI) appears to be more convenient [30]. Moreover, a recent meta-analysis showed that although triplet chemotherapy (FOLFOXIRI) plus bevacizumab is more effective conversion therapy (converts unresectable metastatic colorectal cancer into resectable) than double chemo- therapy (FOLFOX/FOLFIRI) plus bevacizumab; the latter is associated with a higher risk of neutropenia and diarrhoea [31]. Several targeted therapies that inhibit the angiogenic process (bevacizumab, aflibercept) or the epidermal growth factor receptor (panitumumab, cetuximab) have currently been added to the treatment of mCRC, either as monotherapy or in combination with backbone chemotherapies [28, 29] resulting in further improvements in RR, PFS and OS [32, 33, 34, 35, 36].

2. Materials and methods

2.1 Experimental subjects

CRC adult patients ( $n=$ 98) were recruited during 2005–2017. All cases were confirmed histologically with bidimensionally measured metastatic and unresectable CRC. The patients had no previous exposure to chemotherapy and adequate bone marrow, renal and hepatic function. The study was carried out with local ethics committee approval and conforms with the Code of Ethics of the World Medical Association (Declaration of Helsinki). Treatment was administered until disease progression or until unacceptable toxicity, for a maximum of twelve cycles of chemotherapy. Additionally, only patients with performance status 0–2 (ECOG scale) were included in the study. All patients received an irinotecan-based therapy combined with aflibercept, cetuximab, bevacizumab or panitumumab, and capecitabine or 5-FU. More specifically, 38 patients received bevacizumab, irinotecan, and capecitabine; 12 patients received panitumumab, irinotecan, and capecitabine; 23 patients received bevacizumab, irinotecan, and 5-FU, 14 patients received panitumumab, irinotecan, and 5-FU, 6 patients received aflibercept, irinotecan, and 5-FU, 4 patients received cetuximab, irinotecan, and 5-FU, and 1 patient received cetuximab, irinotecan, and capecitabine. The treatment regimens used were based on the therapeutic protocols recommended by the Hellenic Society of Medical Oncology [37] and the initial doses were calculated based on age, renal function, performance status and the presence of other comorbidities. For regimens combining irinotecan and capecitabine the doses of the agents were: irinotecan 240 mg/mday1 and capecitabine 1000 mg/m ${}^{2}$ twice daily, on days 1–14 every 21 days or Irinotecan 210 mg/m ${}^{2}$ plus Capecitabine 850 mg/m ${}^{2}$ twice daily, on days 1–14 every 21 days. For the combination including irinotecan and 5-FU) the doses of the agents were: irinotecan 180 mg/m ${}^{2}$ on day1 plus leucovorin 400 mg/m ${}^{2}$ and 5-FU 400 mg/m ${}^{2}$ (bolus) and 1200 mg/m ${}^{2}$ on day 1, 2 (24 hour infusion), every 2 weeks. Doses were capped at 2 m ${}^{2}$ . Regarding the targeted therapeutic agents added to the chemotherapy protocols the schemes were as follows: Bevacizumab 7.5 mg/kg or 15 mg/kg every 21 days or 5 mg/kg or 15 mg/kg every 2 weeks, panitumumab 9 mg/kg day 1 every 21 days or 6 mg/kg on day 1 every 2 weeks, cetuximab 250 mg/m ${}^{2}$ weekly (after one loading dose of 400 mg/m ${}^{2}$ ) and aflibercept 4 mg/kg every two weeks. Dose Modifications, if needed were carried out to reduce the incidence of adverse events, according to recommended guidelines. Patient demographics and clinical data are shown on Table 1.

Table 1
Patient characteristics and therapeutic approaches

Characteristics		Number of patients
Age (years)	Mean	70.24
	Range	43–90
Gender	Male	54
	Female	44
Surgery on primary	Yes	87
	No	11
Primary location	Colon	29
	Sigmoid	27
	Rectum	23
	Orthosigmoid	19
Metastasis site	Liver	38
	Lung	22
	Peritoneum	5
	Adrenal gland	1
	Multiple sites	32
Toxicity effect (nausea,	Yes	76
neutropenia, etc.)	No	22

Table 2

Primer sequences

Polymorphism	Primer	Primer sequence
MALAT1	Forward	5’-AGTGAGTGTATGAGACCTTGC-3’
rs3200401	Reverse for C allele	5’-CTTGCCAACAGAACAGACAG-3’
	Reverse for T allele	5’-GCCAACAGAACAGACAAA-3’
HOTAIR	Forward	5’-GAGAGAAGCCAAATACACACA-3’
rs4759314	Reverse for A allele	5’-TTTATTAACTTGCATCCGCT-3’
	Reverse for G allele	5’-TTTATTAACTTGCATCCGCC-3’

2.2 Efficacy evaluation

The new Response Evaluation Criteria in Solid Tumors (RECIST v. 1.1) [38] were used to assess clinical response. This study included patients with measurable disease at baseline. CT and MRI imaging modalities are the best available for measuring response assessment.

The tumor burden was assessed based on target lesions (longest diameter $\geqslant$ 10 mm, number of lesions up to a maximum of five in total and a maximum of two lesions per organ) representative of all involved organs; these lesions were recorded and measured at baseline, along with the presence of non-target lesions. Calculation of the sum of the longest diameter for all target lesions was performed and was set as the baseline sum of target lesions. Pathological lymph nodes which were defined as target lesions met the criterion of a short axis of $\geqslant$ 15 mm by CT scan. All other lesions (or sites of disease) including pathological lymph nodes were identified as non-target lesions and were also recorded at baseline. Measurements for non-target lesions are not required neither at baseline nor during follow up assessments and these lesions were followed as ‘present’ or ‘absent’. The baseline sum LD was used as reference for characterizing the objective tumor response. The characterization of the disease as stable (SD) requires that the follow-up measurements meet the SD criteria at least once after study entry at a minimum interval (not less than 6–8 weeks) that is defined by the study protocol. Patients were grouped to SD, and PD. Time to progression and survival were calculated from day 1 of treatment.

2.3 Toxicity

The evaluation of toxicity was performed in every chemotherapy cycle based on Common Terminology Criteria for Adverse Events (CTCAE) Version 4.0 [39].

2.4 Pharmacogenomic analysis

DNA was extracted from frozen blood samples using a DNA extraction kit (Nucleospin Blood, Mache- rey-Nagel, Germany). HOTAIR (rs4759314) genotyping was performed by using allele specific PCR using the KAPA Taq PCR kit, (KAPA Biosystems Pty (Ltd)) with a set of primers for G and A alleles. The same procedure was followed for MALAT1 (rs3200401) for C and T alleles. Electrophoresis was performed on the PCR products using a 2% agarose-98% TAE (1% TAE) gel. All experiments were performed in duplicate and analyzed by two independent observers. Representative PCR products were subjected to sequencing for results verification. The sequences of primers used are shown on Table 2.

2.5 KRAS mutation analysis

The analysis was performed on formalin-fixed paraffin embedded tumor tissue removed from each patient. The percentage of tumor cells, in each sample was 30% at minimum. DNA extraction was performed using a commercial kit (NucleospinTissue, Macherey-Nagel, Germany). An enriched polymerase chain reaction followed by restriction fragment length polymorphism (PCR-RFLP) was performed as previously described [40]. Two mutations were detected, the first one at codon 12 and the other at codon 13 of KRAS.

2.6 Statistical analysis

Genotype frequencies were compared using the $\chi$ 2 test with Yate’s correction. Odds ratios (OR) and 95% confidence intervals (95% CI) were calculated with the corresponding $\chi^{2}$ distribution test. The $p$ -values were two tailed and $p<$ 0.05 was determined to be significant. Hardy-Weinberg equilibrium was verified by calculating the expected frequencies and numbers and was tested separately in patients and in controls using the goodness of fit $\chi^{2}$ test. Haplotype analysis was performed using the https://www.snpstats.net/preproc.php online platform. The survival curves were made using the Kaplan-Meier method and comparison was with the log-rank test. All the comparisons were performed using Graph Pad version 3.00 (GraphPad Software Inc., San Diego, CA, USA).

Table 3
The rs4759314 and rs3200410 genotypes distributions and the patients’ response status. No significant associations were observed

Genotype	CR $+$ PR	SD	PD
HOTAIR
rs4759314
AA	28	24	22
AG	5	7	8
GG	1	1	2
MALAT1
rs3200401
CC	21	10	14
CT	9	16	11
TT	4	6	7

Table 4

The rs4759314 and rs3200410 haplotype analysis and the patients’ response status. No significant associations were observed

rs4759314	rs3200410	Frequency	Difference (95% CI)	$P$
A	C	0.5361	1 [REFERENCE]
A	T	0.3211	0.17 ( $-$ 0.08–0.41)	0.18
G	C	0.1068	0.17 ( $-$ 0.23–0.57)	0.41
G	T	0.0361	0.55 ( $-$ 0.18–1.28)	0.14

Table 5

The rs4759314, and rs3200401 polymorphisms in association with KRAS status

Genotype	KRAS	KRAS	P; OR (CI95%)
	wild-type	mutated
HOTAIR
rs4759314
AA	40	34	1 [REFERENCE]
AG $+$ GG	5	19	0.005; 4.47 (1.51–13.25)
MALAT1
rs3200401
CC	25	20	1 [REFERENCE]
CT $+$ TT	26	27	0.55; 1.29 (0.58–2.88)

Table 6

The relationship between the rs4759314, and rs3200401 polymorphisms, and the toxicity status

Genotype	Toxicity/n	No toxicity/n	P; OR (CI95%)
HOTAIR
rs4759314
AA	49	25	1 [REFERENCE]
AG $+$ GG	16	8	1; 1.02 (0.38–2.71)
MALAT1
rs3200401
CC	26	19	1 [REFERENCE]
CT $+$ TT	43	10	0.015; 3.14 (1.27–7.79)

3. Results

A total of 98 CRC patients, were included in the analysis and successfully genotyped for HOTAIR (rs4759314) and MALAT1 (rs3200401) polymorphisms. The allelic frequencies observed for both polymorphisms were in accordance with probability limits of Hardy-Weinberg equilibrium. No statistical significance existed between the clinicopathological parameters and the examined polymorphisms. Until August 2018, 4 (4.08%) patients were characterized as CR, 30 (30.6%) as PR, 32 (32.6%) as SD, and 32 (32.6%) as PD. Regarding KRAS mutation status, 40 of all patients tested were carriers of codon 12 mutated allele, and 5 were carriers of the codon 13 mutated allele. We did not observe statistically significant differences between carriers of KRAS mutated alleles between CR, PR, SD, and PD patients’ group. As illustrated in Tables 3 and 4, no significant difference was found between response rates and both rs4759314 and rs3200401 genotypes and haplotypes. Additionally, as indicated in Table 5 KRAS status is statistically correlated with the carriers of rs4759314 AG and GG genotypes, whereas is not correlated with the host of rs3200401 genotypes.

The most frequent major toxicities observed were nausea (29 patients) and neutropenia (20 patients). Our findings did not indicate significant differences between the toxicity profile of the treatment combinations used. No statistically significant difference was observed between toxicity and rs4759314 genotypes. However, as indicated in Table 6 a significant association ( $p=$ 0.015) was observed between the carriers of rs3200401 CT/TT alleles and the toxicity development, indicating that the carriers of the mutated T allele is more likely to develop toxicity to irinotecan-based therapies ( $p=$ 0.0006). The haplotype analysis did not reveal a significant association of a specific haplotype with toxicity development (Table 7).

Table 7
The relationship between the rs4759314, and rs3200401 haplotypes, and the toxicity status

rs4759314	rs3200410	Frequency	Difference (95% CI)	P
A	C	0.5377	1 [REFERENCE]
A	T	0.3195	1.59 (0.82–3.09)	0.17
G	C	0.1052	0.51 (0.18–1.43)	0.2
G	T	0.0377	0.15 (0.02–1.35)	0.094

Table 8

Rs4759314, and rs3200401 genotypes in association with overall survival

	Overall survival
Genotype	Events (n)	13-yr	Hazard	$P$
		survival (%) ${}^{\rm a}$	ratio (95% CI)
rs4759314
AA	46	52.22	1.00 (Reference)
AG $+$ GG	14	61.05	0.96 (0.53–1.74)	0.95
rs3200401
CC	14	71.41	1.00 (Reference)
CT $+$ TT	46	45.94	2.02 (1.17–3.47)	0.01

${}^{\rm a}$ Proportion of survival derived from Kaplan-Meier analysis.

Table 8 shows the association of rs4759314 and rs3200401 genotypes with overall survival. During the study period, there were 60 deaths among subjects who were included in the study. The survival was calculated from the date of the first treatment with irinotecan-based combinations until death from any cause. Regarding the rs4759314 polymorphism no significant association with survival was found, whereas the rs3200401 CT $+$ TT genotypes was found to be related with a significantly lower overall survival in our study ( $p=$ 0.01) (Fig. 1).

Figure 1.

Association of overall survival with genotypes of HOTAIR rs4759314 (A) and MALAT1 rs3200401 (B) polymorphisms. Kaplan-Mayer curve represents the percentage of survival in months of patient’s different genotypes of the rs1561927 and rs3200401 polymorphisms.

4. Discussion

Irinotecan is currently one of most frequently prescribed anticancer drugs used in the mCRC treatment as monotherapy or in chemotherapy schemes (including FOLFIRI and XELIRI), which have significantly improved RR, PFS and OS [41, 42, 43, 44, 45]. However, irinotecan exhibits dose dependant and wide interindividual toxicity which is associated with the patient’s genetic profile [3, 24, 25, 26, 27]; genetic factors are investigated towards their role in the therapeutic outcome and in drug toxicity of irinotecan-based regimens [46, 47, 48, 49, 50].

The role of lncRNAs is also investigated. Li et al., demonstrated that HOTAIR contributes to 5-FU resistance and that high HOTAIR expression was corelated with shorter OS and RFS in CRC patients receiving 5FU-based therapy [51]. Until now, no previous study has investigated the correlation between HOTAIR expression or the polymorphism under study and irinotecan resistance.

Polymorphisms of MALAT1, which is also linked to cancer progression [52, 53], are investigated for the development of several clinical manifestations. A recent study showed that the rs3200401 CT/TT genotypes exhibited significantly lower levels of total cholesterol [54] and rs3200401 CT and CT/TT genotypes in MALAT1 had better prognosis than rs3200401 CC genotype, among lung cancer patients [55]. On the contrary, it has been suggested that carriers of the heterozygous rs3200401 CT genotype are more susceptible to Pb exposure than subjects with homozygous CC genotype, indicating that MALAT1 SNPs may play a critical role in Pb derived toxicity [56]. Given that cellular levels of MALAT1 were found to be increased during G1/S and M phases of the cell cycle suppressing apoptosis and/or increased DNA repair efficiency [57] and that irinotecan acts via inhibiting DNA topoisomerase I this could explain tumour resistance to irinotecan. Direct associations between MALAT1 SNPs and irinotecan resistance have not been investigated in previous studies.

Taken these into consideration, 98 samples were genotyped from patients with incurable, mCRC and the connection between germline polymorphisms in HOTAIR and MALAT1 and response to irinotecan-based regimens was investigated. More specifically, the impact of rs4759314 HOTAIR and rs3200401 MALAT1 polymorphisms were evaluated regarding clinical response and toxicity on irinotecan-based schemes. We found that both rs4759314 HOTAIR and rs3200401 MALAT1 polymorphisms were not associated with response to treatment regimens. Interestingly, although MALAT1 expression is associated with poor re- sponse to oxaliplatin-based regimens and chemore-sistance [23], our findings indicated that the SNP rs3200401 does not affect the response to irinotecan-based schemes. The second polymorphism of our study, rs4759314 HOTAIR, also showed no correlation with irinotecan induced toxicity, a finding which agrees with Gonk et al. who also observed no association between rs4759314 HOTAIR and platinum-based toxicity in Chinese patients with lung cancer [8].

RAS mutations have been implicated in CRC metastasis, and it has been reported that they are associated with increased risk of lung and ovarian metastases in patients treated with irinotecan-based regimens [53]. In our study, no significant association between KRAS mutation status and response to treatment was detected. This finding is in accordance with data supporting that KRAS mutations are not considered as independent predictors of survival and only a sub-group of patients benefit from drugs targeting EGRF, due to the complex aetiology of colorectal cancer and the resistance to these therapeutic agents [58, 59]. However, a statistically significant correlation between patients with AG/GG genotypes in the rs4759314 HOTAIR and KRAS mutation was detected, suggesting that carriers of the mutated rs4759314 HOTAIR G allele is more likely to be carriers of KRAS mutations too. This is the first study to have found this correlation.

Based on our data, there was a statistical significance between the CT/TT genotype in the rs3200401 MALAT1 and increased toxicity to irinotecan-based regimens; this finding was not associated with a better response to treatment, making it applicable as an independent toxicity biomarker for patients receiving irinotecan-based regimens. Our results are following Qian et al. observation that rs3200401 CT genotype is more susceptible to toxicity development [56]. Qian et al. suggested that the heterozygous CT genotype of MALAT1 rs3200401 polymorphism may influence lead-induced neurotoxicity via the MAPK or the P53 signalling pathway. However, it should be noted that irinotecan does not exhibit significant neurotoxic effects. In fact, only very few cases have been reported [60] and the exact cause of irinotecan-induced CNS toxicity remains unknown. Therefore, further studies are needed to clarify the role of this specific SNP on irinotecan toxicity mechanisms. To the best of our knowledge, this is the first study to investigate the association of this MALAT1 polymorphism and resistance or toxicity in irinotecan-based treatment of CRC.

Moreover, our results demonstrate that the rs3200401 CT $+$ TT genotypes are not only associated with increased toxicity but with a significantly lower overall survival. These findings provide evidence that the statistically significant shorter overall survival could be attributed to increased toxicity of irinotecan-based schemes in carriers of the mutated T allele of rs3200401. This is the first study to investigate the correlation between rs3200401 MALAT1 and survival in metastatic CRC patients. However, Wang et al. demonstrated a significant correlation between rs3200401T allele of MALAT1 and longer survival in advanced lung adenocarcinoma patients [55]. This inconsistency may be due different treating regimes used for colorectal cancer. Therefore, it should be noted that the patients included in Wang et al. study most likely received different chemotherapy regimens than those of our study.

However, our results need to be validated in future studies so as for larger populations of metastatic colorectal cancer to be included. Additionally, it should be noted that the precise biological function and mechanisms of the lncRNA SNPs in toxicity development and response remain unknown and need further investigation. We should also reckon that irinotecan-based chemotherapy toxicities may result from multiple genetic factors. Therefore, apart from the identification of novel biomarkers, multilevel studies need to be conducted, in order to better understand both the response and toxicity development in patients treated with irinotecan-based regimens.

In conclusion, our study provides the first evidence of the association between lncRNA MALAT1 rs3200401 polymorphism, toxicity development and effect in OS in mCRC patients treated with irinotecan-based regimens. Additionally, gene expression profiling analysis revealed that carriers of the mutated G allele of rs4759314 HOTAIR is more likely to be also carriers of KRAS mutations.

Footnotes

Acknowledgments

This study was supported by a non-profit organization of Greek Society of Cancer Biomarkers and Targeted Therapy.

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Long non-coding RNA polymorphisms and prediction of response to chemotherapy based on irinotecan in patients with metastatic colorectal cancer

Abstract

BACKGROUND

OBJECTIVE:

METHODS:

RESULTS:

CONCLUSIONS:

Keywords

1. Introduction

2. Materials and methods

2.1 Experimental subjects

Table 1 Patient characteristics and therapeutic approaches

2.3 Toxicity

2.4 Pharmacogenomic analysis

2.5 KRAS mutation analysis

2.6 Statistical analysis

Table 3 The rs4759314 and rs3200410 genotypes distributions and the patients’ response status. No significant associations were observed

Table 7 The relationship between the rs4759314, and rs3200401 haplotypes, and the toxicity status

Footnotes

Acknowledgments

References

Table 1
Patient characteristics and therapeutic approaches

Table 3
The rs4759314 and rs3200410 genotypes distributions and the patients’ response status. No significant associations were observed

Table 7
The relationship between the rs4759314, and rs3200401 haplotypes, and the toxicity status