Skip to content

Advertisement

BMC Cancer

Open Access
Open Peer Review

This article has Open Peer Review reports available.

How does Open Peer Review work?

Next-generation sequencing of tyrosine kinase inhibitor-resistant non-small-cell lung cancers in patients harboring epidermal growth factor-activating mutations

  • Katsuhiro Masago1Email author,
  • Shiro Fujita1,
  • Miho Muraki2,
  • Akito Hata1,
  • Chiyuki Okuda1,
  • Kyoko Otsuka1,
  • Reiko Kaji1,
  • Jumpei Takeshita1,
  • Ryoji Kato1,
  • Nobuyuki Katakami1 and
  • Yukio Hirata1
BMC Cancer201515:908

https://doi.org/10.1186/s12885-015-1925-2

Received: 10 February 2015

Accepted: 11 November 2015

Published: 16 November 2015

Abstract

Background

The aim of this study was to detect the epidermal growth factor receptor (EGFR)-activating mutations and other oncogene alterations in patients with non-small-cell lung cancers (NSCLC) who experienced a treatment failure in response to EGFR-tyrosine kinase inhibitors (TKIs) with a next generation sequencer.

Methods

Fifteen patients with advanced NSCLC previously treated with EGFR-TKIs were examined between August 2005 and October 2014. For each case, new biopsies were performed, followed by DNA sequencing on an Ion Torrent Personal Genome Machine (PGM) system using the Ion AmpliSeq Cancer Hotspot Panel version 2.

Results

All 15 patients were diagnosed with NSCLC harboring EGFR-activating mutations (seven cases of exon 19 deletion, seven cases of L858R in exon 21, and one case of L861Q in exon 21). Of the 15 cases, acquired T790M resistance mutations were detected in 9 (60.0 %) patients. In addition, other mutations were identified outside of EGFR, including 13 cases (86.7 %) exhibiting TP53 P72R mutations, 5 cases (33.3 %) of KDR Q472H, and 2 cases (13.3 %) of KIT M541L.

Conclusions

Here, we showed that next-generation sequencing (NGS) is able to detect EGFR T790M mutations in cases not readily diagnosed by other conventional methods. Significant differences in the degree of EGFR T790M and other EGFR-activating mutations may be indicative of the heterogeneity of disease phenotype evident within these patients. The co-existence of known oncogenic mutations within each of these patients may play a role in acquired EGFR-TKIs resistance, suggesting the need for alternative treatment strategies, with PCR-based NGS playing an important role in disease diagnosis.

Keywords

Acquired resistanceEpidermal growth factorNext-generation sequencingTyrosine kinase inhibitor

Background

Recent advances in biomedical research have provided a greater understanding of the molecular basis of disease, with significant implications for therapeutic intervention. Somatic mutations, such as epidermal growth factor receptor (EGFR) mutations and anaplastic lymphoma kinase (ALK) gene rearrangements, play a significant role in the pathogenesis of non-small-cell lung cancer (NSCLC), with treatment decisions often based upon the outcome of these genetic tests [15].

Both EGFR and ALK function as a receptor tyrosine kinase, which are readily inhibited by a series of tyrosine kinase inhibitors (TKI), including gefitinib [6], erlotinib [7], and crizotinib [2]. Despite the initial treatment efficacy of these TKIs for the treatment of NSCLC, acquired resistance was found to develop in almost all cases. The well-known mechanism of acquired EGFR-TKIs resistance include second site mutations within the EGFR kinase domain [8, 9], up-regulation of alternative signaling pathways, such as MET [10], histologic transformation, epithelial to mesenchymal transition, and small cell transformation [11]. Although many resistance mechanisms have been clarified, the EGFR kinase domain mutation T790M in exon 20 accounts for nearly half of all acquired resistance, making testing for this mutation a key factor in determining following treatment strategies in the era of second- and third-generation EGFR-TKIs [12, 13].

The recent development of next-generation sequencing (NGS) as a diagnostic tool in the clinical setting has enabled us to determine rapid, targeted sequencing of tumors for causative mutations. When combined with various selective capture approaches, NGS has allowed for the efficient simultaneous genetic analysis of a large number of candidate genes. Here, we applied a polymerase chain reaction (PCR) based NGS in determining oncogene alternations in the state of disease progression.

PCR based next-generation sequencing is an outstanding tool to provide a comprehensive genomic diagnosis in patients with recurrent NSCLC [14]. The primary aim of this study was to evaluate EFGR T790M secondary mutations, along with other oncogenic alterations, in NSCLC patients previously diagnosed with EGFR activating mutations who experienced disease recurrence after treatment with first-generation EGFR-TKIs.

Methods

Patients and treatment regimens

Fifteen patients with NSCLC previously treated with EGFR-TKIs were examined between August 2005 and October 2014 at the Institute of Biomedical Research and Innovation in Kobe City, Japan. Patients were treated with either of erlotinib or gefitinib daily, at initial daily doses of 150 (erlotinib) and 250 (gefitinib) mg/day. Standard Response Evaluation Criteria in Solid Tumors (RECIST 1.0) was used to evaluate treatment response. Toxicities were graded according to the Common Terminology Criteria for Adverse Events (CTCAE) version 4.0. We obtained written informed consents from all the participants. This study was approved by the Research Ethics Committee of the Institute of Biomedical Research and Innovation.

EGFR mutational analysis

A quantity of cancer cells sufficient for a pathologic diagnosis (i.e., several hundred cells) were obtained from formalin-fixed paraffin-embedded (FFPE) biopsy specimens by manual micro-dissection. Similar biopsy specimens were used to analyze EGFR somatic mutations in exons 18–21 [15, 16].

MET gene amplification

For each patient, DNA was extracted, and the concentration measured using a Nanodrop ND-1000 spectrophotometer (Nanodrop Technologies, Rockland, DE). MET copy number gains (CNG) analysis was performed using the One-Step Real Time PCR System (Thermo Fisher Scientific, Foster City, CA) under the following conditions: one cycle of 95 °C for 10 min followed by 40 cycles of 95 °C for 15 s and 60 °C for 1 min. The qPCR reaction mixture contained 10 μL of 2X TaqMan genotyping master mix, 1 μL of the TaqMan copy number target assay, 1 μL of the TaqMan copy number reference assay (RNase P, which is known to exist only in two copies in a diploid genome), 4 μL of nuclease-free water, and 4 μL of DNA (diluted to a concentration of 5 ng/μL). Each sample was run in a minimum of four replicates. Amplification results were then analyzed using the CopyCaller Software (Thermo Fisher Scientific) for post-PCR data analysis. To accurately detect MET CNG, we analyzed the previous reported region of MET [17], a region spanning the intron 20–exon 21 boundary (TaqMan copy number assay Hs02884964_cn).

Ion torrent PGM library preparation and sequencing

An Ion Torrent adapter-ligated library was generated using an Ion AmpliSeq Library Kit 2.0 according to the manufacturer’s protocol (Thermo Fisher Scientific, Rev. 5; MAN0006735). Briefly, 50 ng of pooled amplicons and the Ion AmpliSeq Cancer Hotspot Panel version 2 (Thermo Fisher Scientific) were end-repaired, and Ion Torrent adapters P1 and A were ligated using DNA ligase. Following AMPure bead (Beckman Coulter, Brea, CA, USA) purification, the concentration and size of the library were determined using the Life Technologies StepOne system (Thermo Fisher Scientific) and Ion Library TaqMan quantitation assay kit (Thermo Fisher Scientific). Sample emulsion PCR, emulsion breaking, and enrichment were performed using the Ion PGM IC 200 Kit (Thermo Fisher Scientific), according to the manufacturer’s instructions. Briefly, an input concentration of one DNA template copy/Ion Sphere Particle (ISP) was added to the emulsion PCR master mix, and the emulsion was generated using the Ion Chef (Thermo Fisher Scientific). Next, ISPs were recovered and template-positive ISPs enriched using Dynabeads MyOne Streptavidin C1 beads (Thermo Fisher Scientific). Sequencing was undertaken using 314 BC chips on the Ion Torrent PGM for 65 cycles using barcoded samples. The totally turnaround time from library preparation to the end of sequencing is about 2 days.

Variant calling

After sequencing, data were processed using the Ion Torrent platform-specific pipeline software Torrent Suite to generate sequence reads, trim adapter sequences, and remove poor signal-profile reads. Initial variant calling was generated using Torrent Suite Software v4.0 using the variant caller plug-in. To eliminate erroneous base calling, three filtering steps were used. The first filter was set at an average total coverage depth of >100, variant coverage of >20, and P values <0.01. The second filter was employed by visually examining mutations using the Integrative Genomics Viewer (http//www.broadinstitute.org/igv) or CLC Genomics Workbench version 7.04 (Qiagen) software. Finally, possible strand-specific errors, such as mutation only detected in only the plus or minus strand were removed.

Results

A summary of patient characteristics can be found in Table 1. All patients were Japanese, consisting of 10 females (76.7 %) and 5 males (33.3 %). Nine patients (60.0 %) were never smokers, and the remaining six patients (40.0 %) were former smokers. All patients had stage IV adenocarcinoma, as defined based upon TNM classification criteria (7th edition) [18]. Eight patients received erlotinib, and four patients were treated with gefitinib. The remaining patient was treated first with gefitinib, then switched to erlotinib. The median duration of EGFR-TKI therapy was 510 days (range: 122–1912 days; Table 1).
Table 1

Patient characteristics

Patient characteristics

(%)

Age (years)

 Range

54–79

Gender

 Male

5 (33.3)

 Female

10 (76.7)

Smoking status

 Non-smoker

9 (60.0)

 Former Smoker

6 (40.0)

Stage

 IV

14 (93.4)

 rIVa

1 (6.6)

1st line

5 (33.3)

2nd line

7 (46.7)

3rd line

2 (13.4)

Subsequent therapy

1 (6.6)

arIV recurrent stage IV

EGFR sequence variations are listed in Table 2. All patients were diagnosed with adenocarcinomas harboring EGFR activating mutations (seven cases of exon 19 deletion, seven cases of L858R in exon 21, and one case of L861Q in exon 21). Of the 15 cases, acquired EGFR T790M resistance mutations in exon 20 were detected in 9 (60.0 %) patients. Of particular interest were cases 7, 8, and 10, in which T790M mutations were not detected by high-sensitivity conventional PCR-based methods, such as peptide nucleic acid-locked nucleic acid (PNA-LNA) PCR clamp [16], or Cycleave real-time PCR [15].
Table 2

Clinical characteristics and next-generation sequencing results

 

Histology

EGFR Sequence Variants

Frequency (%)

Allele Call

Exon 20 T790M

Frequency (%)

Conversion to SCLC

Prior TKIs

Duration (days)

Case 1

Adenocarcinoma

Exon 19

44.3

Heterozygous

Yes

7.2

No

Erlotinib

681

E746_T750 del

Case 2

Adenocarcinoma

Exon 19

59.4

Heterozygous

No

-

No

Gefitinib

537

E746_T751 del > A

Case 3

Adenocarcinoma

Exon 21 L858R

46.1

Heterozygous

No

-

No

Gefitinib

195

Exon 18 T725R

30.6

Heterozygous

Case 4

Adenocarcinoma

Exon 21 L858R

23.3

Heterozygous

No

-

No

Erlotinib

217

Exon 20 S768I

10.0

Heterozygous

Case 5

Adenocarcinoma

Exon21 L858R

56.9

Heterozygous

No

-

No

Gefitinib

1105

Exon 18 E709G

54.5

Heterozygous

Case 6

Adenocarcinoma

Exon 19

97.2

Homozygous

Yes

21.8

No

Erlotinib

693

E746_T750 del

Case 7

Adenocarcinoma

Exon 21 L858R

13.8

Heterozygous

Yes

5.2

No

Erlotinib

537

Case 8

Squamous cell carcinoma

Exon 19

86.9

Heterozygous

Yes

7.3

No

Erlotinib

315

E746_T750 del

Case 9

Adenocarcinoma

Exon 19

65.3

Heterozygous

Yes

41.3

No

Erlotinib

1555

E746_T750 del

Case 10

Adenocarcinoma

Exon21 L858R

11.2

Heterozygous

Yes

4.8

No

Gefitinib

1912

Case 11

Adenocarcinoma

Exon 19

46.4

Heterozygous

Yes

11.0

No

Erlotinib

256

E746_T750 del

Case 12

Adenocarcinoma

Exon21 L858R

22.2

Heterozygous

No

-

No

Erlotinib

924

Exon 21 G873R

10.8

Heterozygous

Case 13

Adenocarcinoma

Exon 21 L861Q

59.9

Heterozygous

No

-

No

Gefitinib Erlotinib

1304

122

Exon 20 P772S

10.2

Heterozygous

Exon19 L747S

11.8

Heterozygous

Exon2 A289V

12.3

Heterozygous

Case 14

Adenocarcinoma

Exon 19

80.82

Heterozygous

Yes

14.8

No

Erlotinib

392

E746_T750 del

  

Case 15

Adenocarcinoma

Exon21 L858R

76.7

Heterozygous

Yes

10.3

No

Erlotinib

339

In addition to T790M mutations, a large number of activating mutations were identified outside of EGFR. MET amplification, another common mutation associated with EGFR-TKI resistance, was not seen (Fig. 1), which is also confirmed by copy number analysis of NGS sequencing data (data not shown). Further screening of an additional 50 known oncogenes revealed a quite number of mutations in at least 32 genes (Table 3), including 13 cases (86.7 %) of TP53 P72R mutations, 5cases (33.3 %) of KDR Q472H, and 2 cases (13.3 %) of KIT M541L. A full list of genes analyzed in this study is shown in Table 4.
Fig. 1

Quantitaive polymerase chain reaction (qPCR) MET copy number gain (CNG) analysis for 15 cases

Table 3

Coexisting somatic mutations resulting in amino-acid changes identified using the Ion AmpliSeq Hotspot Panel version 2

  

Frequency (%)

 

Frequency (%)

 

Frequency (%)

 

Frequency (%)

 

Frequency (%)

Case 1

KIT M541L (COSM 28026)

70.9

TP53 P72R

53.2

---

---

---

---

---

---

Case 2

PTEN L57W (COSM 5253)

21.2

---

---

---

---

---

---

---

---

Case 3

TP53 P72R

57.0

CTNNB1 D32N (COSM 5672)

34.5

TP53 V73 del

29.1

CDH1 Q346* (COSM 19524)

25.1

---

---

Case 4

TP53 P72R

60.3

TP53 R337C (COSM 11071)

18.0

---

---

---

---

---

---

Case 5

TP53 P72R

46.9

KDR Q472H

46.9

KIT G534C

46.3

APC S1463fs

42.5

---

---

Case 6

PDGFRA P567Q

100

TP53 V73W

72.6

TP53 P151S

57.5

KDR Q472H

42.4

ERBB4 C614Y

38.2

 

SMAD4 R189H

29.0

PTEN R233Q

18.5

APC D1591N

18.4

HRAS T64*

17.9

AKT1 T21I

16.4

 

KIT L647F

16.2

SKT11 D352N

15.1

PTEN H123Y (COSM 5078)

7.3

PTEN R130Q (COSM 5033)

7.2

---

---

Case 7

TP53 P72R

96.7

SKT11 F345L

53.5

SKT11 P281L

53.3

KDR Q472H

42.6

---

---

Case 8

TP53 P72R

98.4

KIT M541L (COSM 28026)

59.8

TP53 V154G (COSM 43903)

35.4

KDR Q472H

26.7

SMAD4 G423R

14.7

 

ABL1 I347fs

11.1

ERBB4 C759T

8.8

FBXW7 M467I

8.0

MLH1 A169V

8.0

KDR G1284R

7.9

 

APC P1433L

6.7

TP53 F338L

6.5

SMO P610S

6.4

MET D340A

5.8

NOTCH1 V1575M

5.7

 

PTEN A328E

5.6

APC G1374K (COSM 18737)

5.1

MLH1 R148W

5.0

---

---

---

---

Case 9

APC E1464fs

59.2

TP53 P72R

48.2

BRAF G442D

6.1

MET G1102D

5.5

SMO T223I

5.0

Case 10

MET N375K

55.7

TP53 P72R

42.0

CTNNB1 G34V

6.5

---

---

---

---

Case 11

TP53 P72R

68.5

PTEN N329fs (COSM 4932)

39.5

TP53 K132R (COSM 11582)

29.7

---

---

---

---

Case 12

TP53 P72R

98.1

KDR Q472H

96.4

TP53 V272fs

21.0

RB1 I682T

12.6

APC P1433L

9.6

 

RET E884V

9.1

SMAD4 V354L

8.0

---

---

---

---

---

---

Case 13

TP53 P72R

99.1

CDKN2 G155S

51.6

FLT3 W603*

45.2

KRAS E37K

33.3

SMO P641L

23.7

 

IDH1 L103M

20.0

TP53 R267Q (COSM 43923)

18.8

GNA11 D205N

16.2

SMARCB1 P165S

14.0

RB1 M761T

13.9

 

SMARCB1 V145L

12.4

TP53 G245R (COSM 10957)

10.8

NOTCH1 H1591T

10.7

ERBB4 G240V

10.0

KIT S715N

9.9

 

FBXW7 R505H (COSM 25812)

9.8

FBXW7 M498I

9.2

MET S186L

8.8

IDH1 A111V

8.8

JAC3 V133I

8.5

 

KIT V825I (COSM 19110)

8.1

TP53 G112S

6.5

TP53 K132E (COSM 10813)

6.3

HNF1A A193V

6.3

VHL K171T

5.7

 

ALK P1191A

5.6

HNF1A T204I

5.3

---

---

---

---

---

---

Case 14

PTEN H1047L

62.9

FGFR3 R765S

7.2

IDH1 P118L

5.7

---

---

---

---

Case 15

TP53 P72R

100

MET A179M

5.1

---

---

---

---

---

---

Table 4

Target genes in the Ion AmpliSeq Hotspot Panel version 2

ABL1

EZH2

JAK3

PTEN

AKT1

FBXW7

IDH2

PTPN11

ALK

FGFR1

KDR

RB1

APC

FGFR2

KIT

RET

ATM

FGFR3

KRAS

SMAD4

BRAF

FLT3

MET

SMARCB1

CDH1

GNA11

MLH1

SMO

CDKN2A

GNAS

MPL

SRC

CSF1R

GNAQ

NOTCH1

STK11

CTNNB1

HNF1A

NPM1

TP53

EGFR

HRAS

NRAS

VHL

ERBB2

IDH1

PDGFRA

 

ERBB4

JAK2

PIK3CA

 

Discussion

In this study we analyzed biopsy specimens of patients who underwent second biopsy after treatment failure with the first generation EGFR-TKIs. There was a significant difference between the frequency of EGFR T790M and other EGFR-activating mutations, with significant variability among cases (4.8–41.3 %). The existence of EGFR and other mutations within the same tumor sample identified by NGS highlights the importance of this type of analysis in guiding appropriate cancer therapy.

High-throughput sequencing was able to detect T790M mutation in a number of cases with the same accuracy of conventional highly sensitive conventional PCR methods, such as PNA-LNA PCR clamp [16] and Cycleave real-time PCR [15]. While high sensitivity and specificity of these methods is well established [1927], the use of NGS provides important advantages with clarifying activating mutation rate in tumor sample as well as greater detection of rare mutations outside of target areas [2831]. In addition, to emphasize the power of NGS in clinical practice, we should also try to develop its applications and usages such as challenging specimens or testing processes, such as peripheral blood in the future.

NGS is also able to overcome issue of germ-line DNA contamination, similar to that of new PCR methods, such as digital PCR [32]. This tolerance of germ-line DNA contamination allows for more streamlined sample preparation techniques, without need for time-consuming procedures such as macro- or micro-dissection. In this study, all samples were extracted from FFPE biopsy specimens, highlighting both versatility and potential use of NGS in clinical settings. Furthermore NGS is able to quantify gene mutations within a tumor sample. Due to the unpredictablity of PCR amplification and germ line DNA contamination, observed mutations does not always reflect the penetrance of a mutation within a sample. While most highly sensitive detection methods provide only categorical results such as positive and negative, our analysis was able to identify the degree of EGFR T790M and other EGFR-activating mutations within a sample that could not be explained by germ-line DNA contamination and/or PCR efficacy. These results are consistent with previous reports detailing T790M allelic frequency in terms of both intra-tumor heterogeneity in localized lung adenocarcinomas [33] and allelic imbalances [34]. Our analysis was able to identify the degree of EGFR T790M and other EGFR-activating mutations within a sample that could not be explained by germ-line DNA contamination and/or PCR efficacy. Future treatment with next-generation EGFR-TKIs targeting T790M is likely to be informed by such analyses, as patients should be treated based upon their EGFR acquired mutation [35].

In addition to EGFR mutations, we also evaluated another 50 oncogenes thought to have an important role in cancer pathogenesis (Table 4). A large number of mutations were identified in this analysis. However, how much extent these genes affect tumorigenicity, tumor progression, and resistance to EGFR-TKIs is difficult to assess, as some mutations may represent only passive alterations (passenger mutations). Although many of these mutations were identified in a single patient, a series of mutations including TP53 P72R, KDR Q472R, and KIT M541L were detected in more than two cases, suggesting a role in disease progression.

TP53 P72R was the most common mutation, detected in 13 of 15 cases (86.7 %). In human populations, TP53 codon 72 is encoded by the nucleotide sequence CCC, which encodes proline, or CGC, which encodes arginine. While proline is the most common amino acid found at this residue, comparative sequence analyses have detected a high degree (>50 %) of TP53-R72 variants among certain populations [36]. The current understanding of TP53 biology is that TP53-R72 is more effective at inducing apoptosis and protecting stressed cells from neoplastic development than the more common TP53-P72 [37]. However, it is not yet understood how these functional differences might translate between in vitro and in vivo settings [38, 39], making it difficult to assess the role of this sequence variant of EGFR-TKI resistance.

KDR (kinase insert domain receptor, also known as VEGFR2) is an important factor in tumor development and progression due to its pro-angiogenic effects [40]. KDR Q472H mutations were detected in 5 of 15 cases (33.3 %), making it the second most common gene variant observed outside of EGFR. In human populations, codon 472 of KDR is encoded by the nucleotide sequence CAA, which encodes glutamine, or CAT, which encodes histidine. The Q472H variant is thought to affect protein function due to increased phosphorylation after vascular endothelial growth factor (VEGF)-A stimulation, along with increased binding efficiency for VEGF-A165 [41]. The effect of Q472H on microvessel density is thought to occur as a result of increased phosphorylation of VEGFR2 [42]. Here, increased microvessel density may have contributed to EGFR-TKI resistance, suggesting that VEGFR2 inhibition may inhibition may become an important therapeutic option in patients with documented EGFR-TKI resistance.

V-Kit Hardy-Zuckerman 4 Feline Sarcoma Viral Oncogene Homolog (KIT) M541L substitutions were detected in 2 of 15 cases (13.3 %). c-KIT is one of the primary targets of imatinib, and mutations in KIT are predictive of the efficacy of the drug in gastrointestinal stromal tumors (GIST) [43]. Several case reports have suggested a potential role of the KIT M541L variant in the sensitivity of Imatinib for aggressive fibromatosis [4446]. Furthermore, a wide array of in vitro analyses support a role for the L541 variant in tumorigenesis. FDC-P1 cells transfected with KIT-L541 showed an enhanced proliferative response, while KIT-L541 cells were more sensitive to imatinib than those expressing wild-type KIT [47]. Inokuchi, et al. observed a higher frequency of L541 variants among patients with chronic myelogenous leukemia (CML), which is consistent with increased tyrosine kinase activation and proliferative responses in KIT-L541 cells relative to wild-type controls [48]. From the view point of EGFR-TKI resistance, these data suggest a causative role for the KIT L541 variant in recurrence and drug resistance of NSCLC. Suppression of KIT with drugs like Imatinib may be a useful therapeutic choice in patients with KIT-variant tumors.

Five (cases 3, 4, 5, 12 and 13) out of six NSCLC patients that are negative for EGFR-T790M mutation harbored “compound mutations” (a rare EGFR mutation in combination with a more frequent activating mutation). On the other hand, all T790M-positive tumors (cases 1, 6, 7, 8, 9, 10 and 11) lack an additional rare mutation apart from the presence of a frequent inhibitor-sensitive EGFR mutation. Among these compound mutations (specifically rare mutations), tumors harboring S768I in exon 20 is known as resistant to EGFR-TKIs. On the contrary, tumors harboring point mutations in exon 18 and dual mutation of exon 19 deletion and S768I are reported to possible response to EGFR-TKIs. There have been limited data in other compounds mutations. So a role of these mutations in causing drug resistance in T790M-negative patients is uncertain and need to be evaluated [49].

This study has its limitations. The strongest limitations include a small sample size, and the retrospective nature of the study preventing the comparison of our findings to non-lesional or pre-treatment results. With this limitation of not having pre-treatment results, the role of activating mutations in additional oncogenes in TKI-resistance may be the primary cause for TKI resistance especially in the case of KDR Q472H mutations. A larger prospective study with strict enrollment criteria is definitely needed to overcome these limitations.

Conclusion

In conclusion, our study showed that NGS could be useful to detect EGFR T790M variants in patients not otherwise found with other conventional PCR based methods. Furthermore, our results highlight the difference of the extent of EGFR T790M and other EGFR-activating mutations among tumor samples, which may indicate the heterogeneity of acquired mutations. Identification of additional sequence variations in potential oncogenes that may affect EGFR-TKI resistance would suggest a series of new therapeutic agents targeting on a patient’s underlying genetic profile.

Declarations

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Division of Integrated Oncology, Institute of Biomedical Research and Innovation, Kobe City, Japan
(2)
Thermo Fisher Scientific, Tokyo, Japan

References

  1. Shigematsu H, Lin L, Takahashi T, Nomura M, Suzuki M, Wistuba II, et al. Clinical and biological features associated with epidermal growth factor receptor gene mutations in lung cancers. J Natl Cancer Inst. 2005;97(5):339–46.View ArticlePubMedGoogle Scholar
  2. Kwak EL, Bang YJ, Camidge DR, Shaw AT, Solomon B, Maki RG, et al. Anaplastic lymphoma kinase inhibition in non-small-cell lung cancer. N Engl J Med. 2010;363(18):1693–703.View ArticlePubMedPubMed CentralGoogle Scholar
  3. Gridelli C, de Marinis F, Cappuzzo F, Di Maio M, Hirsch FR, Mok T, et al. Treatment of advanced non-small-cell lung cancer with Epidermal Growth Factor Receptor (EGFR) mutation or ALK gene rearrangement: results of an international expert panel meeting of the Italian Association of Thoracic Oncology. Clin Lung Cancer. 2014;15(3):173–81.View ArticlePubMedGoogle Scholar
  4. Ulivi P, Zoli W, Capelli L, Chiadini E, Calistri D, Amadori D. Target therapy in NSCLC patients: relevant clinical agents and tumour molecular characterisation. Mol Clin Oncol. 2013;1(4):575–81.PubMedPubMed CentralGoogle Scholar
  5. Sandler A, Gray R, Perry MC, Brahmer J, Schiller JH, Dowlati A, et al. Paclitaxel-carboplatin alone or with bevacizumab for non-small-cell lung cancer. N Engl J Med. 2006;355(24):2542–50.View ArticlePubMedGoogle Scholar
  6. Thatcher N, Chang A, Parikh P, Rodrigues Pereira J, Ciuleanu T, von Pawel J, et al. Gefitinib plus best supportive care in previously treated patients with refractory advanced non-small-cell lung cancer: results from a randomised, placebo-controlled, multicentre study (Iressa Survival Evaluation in Lung Cancer). Lancet. 2005;366(9496):1527–37.View ArticlePubMedGoogle Scholar
  7. Shepherd FA, Rodrigues Pereira J, Ciuleanu T, Tan EH, Hirsh V, Thongprasert S, et al. Erlotinib in previously treated non-small-cell lung cancer. N Engl J Med. 2005;353(2):123–32.View ArticlePubMedGoogle Scholar
  8. Balak MN, Gong Y, Riely GJ, Somwar R, Li AR, Zakowski MF, et al. Novel D761Y and common secondary T790M mutations in epidermal growth factor receptor-mutant lung adenocarcinomas with acquired resistance to kinase inhibitors. Clin Cancer Res. 2006;12(21):6494–501.View ArticlePubMedGoogle Scholar
  9. Bean J, Riely GJ, Balak M, Marks JL, Ladanyi M, Miller VA, et al. Acquired resistance to epidermal growth factor receptor kinase inhibitors associated with a novel T854A mutation in a patient with EGFR-mutant lung adenocarcinoma. Clin Cancer Res. 2008;14(22):7519–25.View ArticlePubMedPubMed CentralGoogle Scholar
  10. Engelman JA, Zejnullahu K, Mitsudomi T, Song Y, Hyland C, Park JO, et al. MET amplification leads to gefitinib resistance in lung cancer by activating ERBB3 signaling. Science. 2007;316(5827):1039–43.View ArticlePubMedGoogle Scholar
  11. Sequist LV, Waltman BA, Dias-Santagata D, Digumarthy S, Turke AB, Fidias P, et al. Genotypic and histological evolution of lung cancers acquiring resistance to EGFR inhibitors. Sci Transl Med. 2011;3(75):75ra26.View ArticlePubMedPubMed CentralGoogle Scholar
  12. Yu HA, Riely GJ, Lovly CM. Therapeutic strategies utilized in the setting of acquired resistance to EGFR tyrosine kinase inhibitors. Clin Cancer Res. 2014;20:5898.View ArticlePubMedPubMed CentralGoogle Scholar
  13. Stinchcombe TE. Novel agents in development for advanced non-small cell lung cancer. Ther Adv Med Oncol. 2014;6(5):240–53.View ArticlePubMedPubMed CentralGoogle Scholar
  14. Yatabe Y, Thomas RK. Era of comprehensive cancer genome analyses. J Clin Oncol. 2014;32:4029.View ArticlePubMedGoogle Scholar
  15. Kosaka T, Yatabe Y, Onozato R, Kuwano H, Mitsudomi T. Prognostic implication of EGFR, KRAS, and TP53 gene mutations in a large cohort of Japanese patients with surgically treated lung adenocarcinoma. J Thorac Oncol. 2009;4(1):22–9.View ArticlePubMedGoogle Scholar
  16. Nagai Y, Miyazawa H, Huqun, Tanaka T, Udagawa K, Kato M, et al. Genetic heterogeneity of the epidermal growth factor receptor in non-small cell lung cancer cell lines revealed by a rapid and sensitive detection system, the peptide nucleic acid-locked nucleic acid PCR clamp. Cancer Res. 2005;65(16):7276–82.View ArticlePubMedGoogle Scholar
  17. Graziano F, Galluccio N, Lorenzini P, Ruzzo A, Canestrari E, D’Emidio S, et al. Genetic activation of the MET pathway and prognosis of patients with high-risk, radically resected gastric cancer. J Clin Oncol. 2011;29(36):4789–95.View ArticlePubMedGoogle Scholar
  18. Goldstraw P, Crowley J, Chansky K, Giroux DJ, Groome PA, Rami-Porta R, et al. The IASLC Lung Cancer Staging Project: proposals for the revision of the TNM stage groupings in the forthcoming (seventh) edition of the TNM Classification of malignant tumours. J Thorac Oncol. 2007;2(8):706–14.View ArticlePubMedGoogle Scholar
  19. Ellison G, Zhu G, Moulis A, Dearden S, Speake G, McCormack R. EGFR mutation testing in lung cancer: a review of available methods and their use for analysis of tumour tissue and cytology samples. J Clin Pathol. 2013;66(2):79–89.View ArticlePubMedGoogle Scholar
  20. Ohashi K, Maruvka YE, Michor F, Pao W. Epidermal growth factor receptor tyrosine kinase inhibitor-resistant disease. J Clin Oncol. 2013;31(8):1070–80.View ArticlePubMedPubMed CentralGoogle Scholar
  21. Watanabe M, Kawaguchi T, Isa SI, Ando M, Tamiya A, Kubo A, et al. Ultra-sensitive detection of the pretreatment EGFR T790M mutation in non-small cell lung cancer patients with an EGFR-activating mutation using droplet digital PCR. Clin Cancer Res. 2015;21:3552.View ArticlePubMedGoogle Scholar
  22. Rosell R, Molina MA, Costa C, Simonetti S, Gimenez-Capitan A, Bertran-Alamillo J, et al. Pretreatment EGFR T790M mutation and BRCA1 mRNA expression in erlotinib-treated advanced non-small-cell lung cancer patients with EGFR mutations. Clin Cancer Res. 2011;17(5):1160–8.View ArticlePubMedGoogle Scholar
  23. Su KY, Chen HY, Li KC, Kuo ML, Yang JC, Chan WK, et al. Pretreatment epidermal growth factor receptor (EGFR) T790M mutation predicts shorter EGFR tyrosine kinase inhibitor response duration in patients with non-small-cell lung cancer. J Clin Oncol. 2012;30(4):433–40.View ArticlePubMedGoogle Scholar
  24. He Y, Li S, Ren S, Cai W, Li X, Zhao C, et al. Impact of family history of cancer on the incidence of mutation in epidermal growth factor receptor gene in non-small cell lung cancer patients. Lung Cancer. 2013;81(2):162–6.View ArticlePubMedGoogle Scholar
  25. Hashida S, Soh J, Toyooka S, Tanaka T, Furukawa M, Shien K, et al. Presence of the minor EGFR T790M mutation is associated with drug-sensitive EGFR mutations in lung adenocarcinoma patients. Oncol Rep. 2014;32(1):145–52.PubMedGoogle Scholar
  26. Lee Y, Lee GK, Hwang JA, Yun T, Kim HT, Lee JS. Clinical likelihood of sporadic primary EGFR T790M mutation in EGFR-mutant lung cancer. Clin Lung Cancer. 2015;16(1):46–50.View ArticlePubMedGoogle Scholar
  27. Fujita Y, Suda K, Kimura H, Matsumoto K, Arao T, Nagai T, et al. Highly sensitive detection of EGFR T790M mutation using colony hybridization predicts favorable prognosis of patients with lung cancer harboring activating EGFR mutation. J Thorac Oncol. 2012;7(11):1640–4.View ArticlePubMedGoogle Scholar
  28. Richer AL, Friel JM, Carson VM, Inge LJ, Whitsett TG. Genomic profiling toward precision medicine in non-small cell lung cancer: getting beyond EGFR. Pharmgenomics Pers Med. 2015;8:63–79.PubMedPubMed CentralGoogle Scholar
  29. Zheng Z, Liebers M, Zhelyazkova B, Cao Y, Panditi D, Lynch KD, et al. Anchored multiplex PCR for targeted next-generation sequencing. Nat Med. 2014;20(12):1479–84.View ArticlePubMedGoogle Scholar
  30. Han JY, Kim SH, Lee YS, Lee SY, Hwang JA, Kim JY, et al. Comparison of targeted next-generation sequencing with conventional sequencing for predicting the responsiveness to epidermal growth factor receptor-tyrosine kinase inhibitor (EGFR-TKI) therapy in never-smokers with lung adenocarcinoma. Lung Cancer. 2014;85(2):161–7.View ArticlePubMedGoogle Scholar
  31. Kim HS, Sung JS, Yang SJ, Kwon NJ, Jin L, Kim ST, et al. Predictive efficacy of low burden EGFR mutation detected by next-generation sequencing on response to EGFR tyrosine kinase inhibitors in non-small-cell lung carcinoma. PLoS One. 2013;8(12):e81975.View ArticlePubMedPubMed CentralGoogle Scholar
  32. Wang Z, Chen R, Wang S, Zhong J, Wu M, Zhao J, et al. Quantification and dynamic monitoring of EGFR T790M in plasma cell-free DNA by digital PCR for prognosis of EGFR-TKI treatment in advanced NSCLC. PLoS One. 2014;9(11):e110780.View ArticlePubMedPubMed CentralGoogle Scholar
  33. Zhang J, Fujimoto J, Zhang J, Wedge DC, Song X, Zhang J, et al. Intratumor heterogeneity in localized lung adenocarcinomas delineated by multiregion sequencing. Science. 2014;346(6206):256–9.View ArticlePubMedPubMed CentralGoogle Scholar
  34. Yatabe Y, Matsuo K, Mitsudomi T. Heterogeneous distribution of EGFR mutations is extremely rare in lung adenocarcinoma. J Clin Oncol. 2011;29(22):2972–7.View ArticlePubMedGoogle Scholar
  35. Melosky B. Review of EGFR TKIs in metastatic NSCLC, including ongoing trials. Front Oncol. 2014;4:244.PubMedPubMed CentralGoogle Scholar
  36. Puente XS, Velasco G, Gutierrez-Fernandez A, Bertranpetit J, King MC, Lopez-Otin C. Comparative analysis of cancer genes in the human and chimpanzee genomes. BMC Genomics. 2006;7:15.View ArticlePubMedPubMed CentralGoogle Scholar
  37. Dumont P, Leu JI, Della Pietra 3rd AC, George DL, Murphy M. The codon 72 polymorphic variants of p53 have markedly different apoptotic potential. Nat Genet. 2003;33(3):357–65.View ArticlePubMedGoogle Scholar
  38. Sullivan A, Syed N, Gasco M, Bergamaschi D, Trigiante G, Attard M, et al. Polymorphism in wild-type p53 modulates response to chemotherapy in vitro and in vivo. Oncogene. 2004;23(19):3328–37.View ArticlePubMedGoogle Scholar
  39. Pim D, Banks L. p53 polymorphic variants at codon 72 exert different effects on cell cycle progression. Int J Cancer. 2004;108(2):196–9.View ArticlePubMedGoogle Scholar
  40. Ferrara N, Gerber HP, LeCouter J. The biology of VEGF and its receptors. Nat Med. 2003;9(6):669–76.View ArticlePubMedGoogle Scholar
  41. Wang Y, Zheng Y, Zhang W, Yu H, Lou K, Zhang Y, et al. Polymorphisms of KDR gene are associated with coronary heart disease. J Am Coll Cardiol. 2007;50(8):760–7.View ArticlePubMedGoogle Scholar
  42. Glubb DM, Cerri E, Giese A, Zhang W, Mirza O, Thompson EE, et al. Novel functional germline variants in the VEGF receptor 2 gene and their effect on gene expression and microvessel density in lung cancer. Clin Cancer Res. 2011;17(16):5257–67.View ArticlePubMedPubMed CentralGoogle Scholar
  43. Heinrich MC, Corless CL, Demetri GD, Blanke CD, von Mehren M, Joensuu H, et al. Kinase mutations and imatinib response in patients with metastatic gastrointestinal stromal tumor. J Clin Oncol. 2003;21(23):4342–9.View ArticlePubMedGoogle Scholar
  44. Goncalves A, Monges G, Yang Y, Palmerini F, Dubreuil P, Noguchi T, et al. Response of a KIT-positive extra-abdominal fibromatosis to imatinib mesylate and KIT genetic analysis. J Natl Cancer Inst. 2006;98(8):562–3.View ArticlePubMedGoogle Scholar
  45. Seinfeld J, Kleinschmidt-Demasters BK, Tayal S, Lillehei KO. Desmoid-type fibromatoses involving the brachial plexus: treatment options and assessment of c-KIT mutational status. J Neurosurg. 2006;104(5):749–56.View ArticlePubMedGoogle Scholar
  46. Dufresne A, Alberti L, Brahmi M, Kabani S, Philippon H, Perol D, et al. Impact of KIT exon 10 M541L allelic variant on the response to imatinib in aggressive fibromatosis: analysis of the desminib series by competitive allele specific Taqman PCR technology. BMC Cancer. 2014;14:632.View ArticlePubMedPubMed CentralGoogle Scholar
  47. Foster R, Byrnes E, Meldrum C, Griffith R, Ross G, Upjohn E, et al. Association of paediatric mastocytosis with a polymorphism resulting in an amino acid substitution (M541L) in the transmembrane domain of c-KIT. Br J Dermatol. 2008;159(5):1160–9.PubMedGoogle Scholar
  48. Inokuchi K, Yamaguchi H, Tarusawa M, Futaki M, Hanawa H, Tanosaki S, et al. Abnormality of c-kit oncoprotein in certain patients with chronic myelogenous leukemia--potential clinical significance. Leukemia. 2002;16(2):170–7.View ArticlePubMedGoogle Scholar
  49. Siegelin MD, Borczuk AC. Epidermal growth factor receptor mutations in lung adenocarcinoma. Lab Invest. 2014;94(2):129–37.View ArticlePubMedGoogle Scholar

Copyright

© Masago et al. 2015

Advertisement