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  • Research
  • Open Access

MET alterations detected in blood-derived circulating tumor DNA correlate with bone metastases and poor prognosis

Journal of Hematology & Oncology201811:76

https://doi.org/10.1186/s13045-018-0610-8

  • Received: 1 March 2018
  • Accepted: 26 April 2018
  • Published:

Abstract

Background

We analyzed clinical associations of MET alterations in the plasma of patients with diverse malignancies.

Methods

Digital sequencing of circulating tumor DNA (ctDNA) (54–70 genes) was performed in 438 patients; 263 patients also had tissue sequencing (182–315 genes). The most represented tumor types were gastrointestinal (28.1%), brain (24.9%), and lung (23.2%). Most patients (71.2%) had recurrent/metastatic disease.

Results

MET alterations were observed in 31 patients (7.1%) and correlated with bone metastasis (P = 0.007), with TP53 (P = 0.001) and PTEN (P = 0.003) abnormalities, and with an increased number of alterations (median, 4 vs 1, P = 0.001) (all multivariable analyses). Patients with MET alterations demonstrated a significantly shorter median time to metastasis/recurrence (1.0 vs 10.4 months, P = 0.044, multivariable) and a poorer survival (30.6 vs 58.4 months, P = 0.013, univariate only). Of the 31 patients with MET alterations, 18 also had tissue testing; only two also had tissue MET alterations (11.1%); MET alterations were detected at a lower frequency in tissue (1.14%) compared to ctDNA (7.1%), with P = 0.0002.

Conclusions

In conclusion, the detection of MET alterations by liquid biopsy is feasible. MET ctDNA alterations were associated with a poorer prognosis, higher numbers of genomic abnormalities, and bone metastases. The correlation with bone metastases may explain the higher frequency of MET alterations in blood ctDNA than in tissue (since bones are rarely biopsied) and the previous observations of bone-predominant responses to MET inhibitors. The high number of co-altered genes suggests that MET inhibitors may need to be combined with other agents to induce/optimize responses.

Keywords

  • MET
  • ctDNA
  • cfDNA
  • Liquid biopsies
  • Bone metastasis

Background

MET, also called c-MET or hepatocyte growth factor (HGF) receptor, is a receptor tyrosine kinase discovered as an oncogene in the 1980s [1, 2]. Independent research found that HGF or scatter factor (SF) was a ligand for MET [3]. Upon binding of HGF to MET, the kinase domain phosphorylates growth factor receptor-bound protein 2 (GRB2) and GRB2-associated binding protein 1 (GAB1) and activates diverse downstream signaling pathways important in cancer, including the ERK/MAPK, PI3K-Akt/PKB, Crk-Rap, and Rac-Pak pathways [4]. These pathways form distinct branches that interact to regulate cell proliferation, invasion, migration, angiogenesis, development, organ regeneration, and tumorigenesis [4].

Activating MET mutations are found in diverse human cancer [5]. For instance, activating mutations in the kinase domain are a feature of both hereditary and some non-hereditary forms of papillary renal cell carcinoma [6]. MET amplification is seen in 5 to 20% of non-small cell lung cancer (NSCLC) and gastric cancer [7]. MET overexpression correlates with poor treatment outcome in some malignancies [8].

Drugs that target MET include inhibitors of the HGF/-MET pathway, MET antibodies, and MET kinase inhibitors [8]. Cabozantinib and crizotinib are both Food and Drug Administration (FDA)-approved multikinase inhibitors that are also potent suppressors of MET [9, 10]. Crizotinib induced responses in some patients with highly MET-amplified lung cancer [11]. Studies with the multi-target MET inhibitor cabozantinib have shown significant activity against a variety of solid tumors including melanoma, as well as renal and non-small cell lung, liver, medullary thyroid, breast, and ovarian cancer, but this activity is likely due to other targets of cabozantinib, such as VEGFR or RET [9]. In the phase I setting, patients with MET amplification did not respond to MET inhibitors (but the number of treated patients was small) [5].

Because of their non-invasive nature, liquid biopsies are increasingly used in the clinical setting. Indeed, numerous studies showed a relatively good correlation with tissue sequencing and the potential to detect actionable alterations [1216]. In this study, we analyzed MET alterations in the plasma-derived circulating tumor DNA (ctDNA) of 438 patients with diverse malignancies and explored the relationship between MET alterations, demographics, as well as other molecular alterations and clinical outcomes.

Methods

Patients

We reviewed the clinicopathology and clinical outcomes of 438 consecutive patients with cancer for whom ctDNA testing had been performed and who were seen at the UC San Diego Moores Cancer Center from June 2014 to July 2016. Pathology was reviewed at UCSD. Data was abstracted from the electronic medical record. This study was performed and consents were obtained in accordance with the UCSD Institutional Review Board guidelines [PREDICT-UCSD (Profile Related Evidence Determining Individualized Cancer Therapy); NCT02478931].

Next-generation sequencing

Digital sequencing of ctDNA (DNA) in all patients was performed by Guardant Health, Inc. (Guardant360, Redwood City, California, http://www.guardanthealth.com/guardant360/), a Clinical Laboratory Improvement Amendment (CLIA)-certified and College of American Pathologists (CAP)-accredited clinical laboratory. The analytical and clinical validation of Guardant360 was conducted in accordance with evidentiary standards established by the Standards for Reporting of Diagnostic Accuracy (STARD), REporting of tumor MARKer Studies (REMARK), Evaluation of Genomic Applications in Practice and Prevention (EGAPP), and the recent Next-generation Sequencing: Standardization of Clinical Testing (Nex-StoCT) biomarker guidelines [17]. As described in Lanman et al. [17], 5–30 ng of ctDNA was isolated from plasma (two 10 ml Streck tubes drawn for each patient) and sequencing libraries were prepared with custom in-line barcode molecular tagging and complete sequencing at 15,000× read depth (~ 4000 unique double-stranded cfDNA fragments, each represented by 3–5 sequencing reads). The panels utilize hybrid capture followed by NGS of the critical exons in a panel of 54–70 genes (Additional file 1: Table S1 to S3) and report all four major types of genomic alterations (point mutations, indels, fusions, and copy number amplifications). Post-sequencing bioinformatics matches the complementary strands of each barcoded DNA fragment to remove false positive results [17]. The variant allele fraction (VAF) is computed as the number of mutated DNA molecules divided by the total number (mutated plus wild type) of DNA fragments at that allele; VAF is reported as a percentage. The majority of cell-free DNA is wild type (germline); thus, the median VAF of somatic alterations is < 0.5%. The analytic sensitivity reaches detection of one to two single-mutant fragments from a 10-ml blood sample (0.1% limit of detection), and the analytic specificity is greater than 99.9999% [17].

For 144 patients, a 54-gene panel was used, which identified potential tumor-related alterations in 54 cancer-related genes (Additional file 1: Table S1) including copy number amplifications in ERBB2, EGFR, and MET (indels and fusions were not detected as part of this panel). For 272 patients, a 68-gene version of the original panel (expanded to all four major alteration types) was used, and for 22 patients, the most recent 70-gene panel version (further expanded to amplifications in 18 genes and fusions in 6 genes) was applied (Additional file 1: Table S2 and S3). Only non-synonymous alterations were included in our analysis.

In addition, 263 patients (~ 60%) of the 438 patients with ctDNA test also had CLIA/CAP-accredited next-generation sequencing (NGS) performed on tumor tissue (FoundationOne™, Cambridge, Massachusetts, http://www.foundationone.com.) (N = 182 to 315 gene panels).

Statistical and outcome analysis

Patient characteristics were summarized using descriptive statistics. Medians and respective 95% confidence intervals and range were calculated, whenever possible. Associations between categorical variables were tested using a binary logistic regression model. Linear variables were tested using the Mann-Whitney U test for univariable analyses and a multiple linear regression model for multivariable analyses.

Time to metastasis/recurrence was defined as the time interval between diagnosis and first metastasis/recurrence (whichever came first) or last follow-up date (patients who had not recurred/developed metastases at last follow-up were censored on that date). Overall survival (OS) was defined as the time from diagnosis to death or last follow-up date for patients who were alive (patients still alive at the last follow-up were censored on that date). Estimations for the time to first metastasis/recurrence and OS were done using Kaplan-Meier analyses and were compared among subgroups by the log-rank test for univariable analysis or Cox regression models for multivariable analysis. All statistical analyses were performed by author MS with SPSS version 24.0.

Results

Patient demographic characteristics

The median age of patients at diagnosis was 57.5 years (CI 95%, 54.5–59.1). Women comprised 52.1% (N = 228) of the population. The majority of patients were Caucasian (69.2%, N = 303). The most represented tumor types were gastrointestinal (28.1%, N = 123), brain (24.9%, N = 109), lung (23.2%, N = 102), and breast (11.6%, N = 51) cancers. The majority of the patients had recurrent or metastatic disease at the time of blood draw used for testing (71.2%, N = 312) (Table 1).
Table 1

Demographics comparison of 438 patients with or without MET alterations

Characteristics

Total patients, N = 438 (100%)

MET wild type, N = 407 (92.9%)

MET alteration(s), N = 31 (7.1%)

P values*

Age at diagnosis (years) (median, CI 95%)

57.5 (54.5–59.1)

57.7 (55.3–59.2)

53.8 (49.0–62.2)

0.791

Gender

   

0.288

 Women

228 (52.1%)

209 (91.7%)

19 (8.3%)

 

 Men

210 (47.9%)

198 (94.3%)

12 (5.7%)

 

Ethnicity

 Caucasian

303 (69.2%)

284 (93.7%)

19 (6.3%)

0.326

 Asian

52 (11.9%)

46 (88.5%)

6 (11.5%)

0.188

 Hispanic

30 (6.8%)

28 (93.3%)

2 (6.7%)

0.928

 African American

9 (2.1%)

8 (88.9%)

1 (11.1%)

0.637

 Middle Eastern

5 (1.1%)

5 (100%)

0 (0%)

0.998

 Unknown

39 (8.9%)

36 (92.3%)

3 (7.7%)

0.875

Type of cancer

 Gastrointestinal

123 (28.1%)

118 (95.9%)

5 (4.1%)

0.132

 Brain

109 (24.9%)

107 (98.2%)

2 (1.8%)

0.026**

 Lung

102 (23.2%)

90 (88.2%)

12 (11.8%)

0.039**

 Breast

51 (11.6%)

45 (88.2%)

6 (11.8%)

0.172

 Genitourinary

18 (4.1%)

15 (83.3%)

3 (16.7%)

0.120

 Head and neck

10 (2.3%)

9 (90.0%)

1 (10.0%)

0.717

 Gynecologic

7 (1.6%)

6 (85.7%)

1 (14.3%)

0.465

 Melanoma

5 (1.1%)

4 (80.0%)

1 (20.0%)

0.285

 Hematologic

3 (0.7%)

3 (100%)

0 (0%)

0.998

 Othera

10 (2.3%)

10 (100%)

0 (0%)

0.998

Presence of metastasis or recurrence at the time of blood draw

 Yes

312 (71.2%)

284 (91.0%)

28 (9.0%)

0.024**

 No

126 (28.8%)

123 (97.6%)

3 (2.4%)

 

The percentages on the first column are expressed over the total number of patients (N = 438); for the second and third columns, percentages are expressed over the total number of patients for each variable

aLymphoma (n = 2), sarcoma (n = 2), thymoma (n = 2), desmoid tumor, neurofibromatosis, peripheral nerve sheath tumor, and carcinoma of unknown primary (each n = 1)

*P values were computed using the independent sample Mann-Whitney U test for linear variables (age at diagnostic) and the logistic binary regression analysis for categorical variables, as appropriate

**MET alterations were negatively associated with brain tumors but positively associated with lung tumors and metastasis or recurrence at the time of blood draw

MET alterations and associations with patient characteristics

Overall, MET alterations were observed in 31 of the 438 patients whose ctDNA was tested (7.1%). Sixteen patients had a MET amplification only; 13 had a somatic mutation only; and two had both an amplification and a somatic MET mutation (Fig. 1a).
Fig. 1
Fig. 1

a MET alterations representation. Pie chart representing the different types of MET alterations identified in our cohort comprising 438 patients. b Overall survival and time to first metastasis/recurrence. Kaplan-Meier curves depicting the overall survival (left panel) and the time to first metastasis/recurrence (both from diagnosis time). P values are from univariable analysis. For more details, refer to Tables 4 and 5

Univariate analyses

MET alterations were significantly (positively) associated with lung cancers (11.8%) and the presence of metastasis/recurrence at the time of blood draw and negatively associated with brain tumors 1.8% (all P < 0.05) (Table 1). We then examined the associations with other genomic alterations and found that MET alterations correlated in univariable analysis with alterations in TP53, EGFR, PIK3CA, BRAF, ARID1A, ALK, and PTEN genes (all P < 0.05, Additional file 1: Table S4). We also investigated the potential associations between MET alterations and the location of metastatic sites and showed a significant correlation with lymph nodes and bone metastasis (both P < 0.02, Additional file 1: Table S4). Of note, patients with MET alterations had a median of two metastatic sites versus a single site for patients without MET alterations (P = 0.001). The latter conclusions remained unchanged if the patients with brain tumors, lymphoma/leukemia, thymoma, and NF were excluded, with P values of < 0.05 for association between MET alterations and lymph node or bone metastases and increased number of metastases.

Multivariate analyses

In the multiple logistic regression model (that included any parameters that were significant (P ≤ 0.05) in univariate analysis), the only variables that remained statistically associated with MET alterations were aberrations in TP53 (P = 0.001) and PTEN (P = 0.003) genes, as well as an increased incidence of bone metastasis (P = 0.007) (Table 2). The univariate association between MET alterations and lung cancer, the negative association with brain tumors, and the correlation with metastases at the time of blood draw did not remain significant in multivariate analysis. Multivariable analysis also demonstrated that MET alterations correlated with an increased number of alterations (median of 4 alterations vs 1, P = 0.001) (Table 3). These conclusions remained valid if the patients with brain tumors, lymphoma/leukemia, thymoma, and NF were excluded, with P values of < 0.01 for association between MET alterations and TP53 or PTEN alterations, bone metastases, and increased number of metastases.
Table 2

Multivariable analysis of the variables associated with MET alterations*

 

MET wild type, N = 407

MET altered, N = 31

Univariable

Multivariable

Characteristics

  

Wald

Odds ratio (CI 95%)

P value

Wald

Odds ratio (CI 95%)

P value

Type of cancer

 Brain (n = 109)**

107 (26.3%)

2 (6.5%)

4.94

0.19 (0.05–0.83)

0.026**

 Lung (n = 102)

90 (22.1%)

12 (38.7%)

4.26

2.22 (1.04–4.76)

0.039

Presence of metastasis or recurrence at the time of blood draw (n = 312)

284 (69.8%)

28 (90.3%)

5.13

4.0 (1.2–14.3)

0.024

Genetic alteration type

TP53 (n = 149)

129 (31.7%)

20 (64.5%)

12.2

3.85 (1.82–8.33)

< 0.001

10.9

3.7 (1.7–8.3)

0.001

EGFR (n = 53)

43 (10.6%)

10 (32.4%)

11.2

4.0 (1.79–9.1)

0.001

PIK3CA (n = 42)

35 (8.6%)

7 (22.6%)

5.93

3.1 (1.25–7.7)

0.015

BRAF (n = 27)

22 (5.4%)

5 (16.1%)

5.14

3.3 (1.2–10.0)

0.023

ARID1A (n = 19)

15 (3.7%)

4 (12.9%)

5.14

3.85 (1.2–12.5)

0.023

ALK (n = 14)

11 (2.7%)

3 (9.7%)

3.94

3.85 (1.02–14.3)

0.047

PTEN (n = 9)

6 (1.5%)

3 (9.7%)

7.72

7.1 (1.7–33.3)

0.007

9.12

11.1 (2.3–50.0)

0.003

Metastatic sites

 Lymph node (n = 139)

123 (30.2%)

16 (51.6%)

5.77

2.4 (1.2–5.3)

0.016

 Bone (n = 102)

88 (21.6%)

14 (45.2%)

8.26

2.9 (1.4–6.3)

0.004

7.34

2.9 (1.35–6.25)

0.007

 No metastases (n = 147)**

145 (35.6%)

2 (6.5%)

7.96

0.13 (0.03–0.5)

0.005**

The Wald statistics test the unique contribution of each variable; the higher the Wald statistics, the higher the association/contribution in the model

*Only variables that were significant in the univariable models (logistic regression) were included in the multivariable analysis, with the final model containing only significant covariates in the multivariable analyses (forward stepwise selection model)

**“Brain tumors” and “no metastases” were negatively associated with MET alterations in univariate analysis. These variables were not significant in the final multivariate analysis model

Table 3

Association with the number of alterations*

Variables

Median N of alterations (95% CI) (range)

P value (univariable)

t statistic (multivariable)

P value (multivariable)

Overall

1 (1–1) (0–26)

   

Tumor type

 Gastrointestinal

2 (1–2) (0–26)

0.031

 Brain

0 (0–0) (0–5)

< 0.001

 Lung

2 (2–3) (0–21)

< 0.001

Genomic alterations

MET

4 (3–6) (1–21)

< 0.001

3.35

0.001

TP53

3 (3–3) (1–21)

< 0.001

6.86

< 0.001

EGFR

3 (3–4) (1–26)

< 0.001

PIK3CA

5 (4–6) (1–26)

< 0.001

7.31

< 0.001

BRAF

5 (4–6) (1–26)

< 0.001

6.54

< 0.001

KRAS

4 (3–4) (1–26)

< 0.001

6.01

< 0.001

MYC

5 (3–6) (2–7)

< 0.001

2.70

0.007

Metastatic/recurrence at the time of blood draw

2 (1–2) (0–26)

< 0.001

Metastatic sites

 Lymph node

2 (2–3) (0–21)

< 0.001

2.05

0.041

 Bone

2 (2–3) (0–26)

< 0.001

 Liver

2 (2–3) (0–26)

< 0.001

 Lung

2 (1–3) (0–19)

0.010

 Brain

2 (1–3) (0–21)

< 0.001

 Adrenal

2 (2–4) (0–20)

< 0.001

2.06

0.040

*Only variables with ≥ 20 patients in the overall population were tested. Only significant variables in the univariable analysis (non-parametric Mann-Whitney U test) were included in the multivariable model (multiple linear regression), with the final model containing only significant covariates (stepwise model selection). The t statistics test the unique contribution of each variable; the higher the t statistics, the higher the association/contribution in the model

The other variables that were significantly associated with a higher number of alterations in the multivariable analysis were alterations in TP53, PIK3CA, BRAF, KRAS, or MYC genes as well as the presence of metastasis in the lymph node or adrenal tissue (Table 3).

MET alterations and survival outcomes

Overall survival

Overall, 213 patients (213/438, 48.6%) had died at the time of our analysis (20/31 with MET alterations; 193/407 without MET alterations detected in their ctDNA). A log rank test demonstrated a poorer survival (time from diagnosis until death or last follow-up date) for patients bearing MET alterations (30.6 months vs 58.4, P = 0.013). In the multivariable analysis, only lung cancer as well as the presence of ARID1A, KRAS, ALK, and MYC alterations and liver metastasis remained significant predictors of a poorer survival (all P < 0.50, Fig. 1b) (Table 4).
Table 4

Overall survival analysis (N = 438 patients)*

 

Univariable

Multivariable

Characteristics

Median time (months) (std. error)

P value

Wald

Hazard ratio

(CI 95%)

P value

Type of cancer

 Gastrointestinal (n = 123) vs not

36.3 (4.5) vs 71.6 (6.5)

0.002

 Brain (n = 109) vs not**

95.0 (50.1) vs 51.5 (5.9)

0.003

 Lung (n = 102) vs not

31.8 (6.0) vs 61.0 (7.4)

0.002

9.59

1.7 (1.21–4.04)

0.002

 Breast (n = 51) vs not

104.8 (20.3) vs 50.0 (5.6)

0.027

   

Genetic alteration type

MET (n = 31) vs not

30.6 (6.2) vs 58.4 (6.3)

0.013

TP53 (n = 149) vs not

42.1 (6.1) vs 66.5 (10.3)

0.003

PIK3CA (n = 42) vs not

43.2 (10.6) vs (58.4 (6.3)

0.013

ARID1A (n = 19) vs not

24.8 (6.2) vs 58.4 (6.8)

0.002

7.98

2.7 (1.25–4.0)

0.005

KRAS (n = 50) vs not

18.3 (7.3) vs 59.7 (6.5)

< 0.001

15.68

2.1 (1.5–3.12)

< 0.001

ALK (n = 14) vs not

31.3 (9.0) vs 58.3 (6.7)

0.015

5.76

2.04 (1.14–3.7)

0.016

SMAD4 (n = 12) vs not

21.7 (8.2) vs 58.3 (6.2)

0.007

MYC (n = 20) vs not

22.2 (3.9) vs 59.1 (6.5)

< 0.001

7.59

2.17 (1.24–3.77)

0.006

Metastatic/recurrence at the time of blood draw vs not

53.6 (4.9) vs NR

0.011

Metastatic sites

 Liver (n = 111) vs not

49.3 (8.2) vs 59.1 (7.9)

0.023

4.85

1.4 (1.03–1.89)

0.028

 Lymph node (n = 139) vs not

39.9 (7.4) vs 74.1 (11.1)

< 0.001

Only variables that were significant in the univariable models (log rank test) were included in the multivariable analysis (Cox regression model), with the final model containing only significant covariates in the multivariable analyses (forward stepwise selection model). The Wald statistics test the unique contribution of each variable; the higher the Wald statistics, the higher the association/contribution in the model

NR not reached

*Overall survival was defined as the time from diagnosis to death or last follow-up date

**Brain tumors (N = 109) included n = 50 glioblastoma cases, while the rest of the tumors were lower grade astrocytomas or other lower grade brain tumors

Time to metastasis/recurrence

Patients with MET alterations had a significantly shorter median time to metastasis/recurrence, with a median of 1.0 months (95%CI could not be computed) versus 10.4 months (95%CI 6.9–14.0) (P = 0.003) (Table 5). MET alterations remained significantly associated with a shorter time to metastasis/recurrence in a multivariable analysis (P = 0.044), along with the presence of liver (P = 0.022) or lymph node metastases (P < 0.001). Inversely, breast cancer correlated with a longer time to metastasis/recurrence (P < 0.001) (Fig. 1b and Table 5).
Table 5

Time to metastasis/recurrence from diagnosis (N = 438 patients)*

 

Univariable

Multivariable

Characteristics

Median time (months) (95% CI)

P value

Wald

Hazard ratio

(CI 95%)

P value

Type of cancer

 Gastrointestinal (n = 123) vs not

0.67 (0–1.9) vs 14.1 (8.3–19.8)

< 0.001

 Brain (n = 109) vs not

42.8 (19.0–66.7) vs 2.5 (0.49–4.4)

< 0.001

 Lung (n = 102) vs not

0.6 (0–1.3) vs 14.0 (9.5–18.6)

< 0.001

 Breast (n = 51) vs not

30.4 (14.3–46.4) vs 7.0 (3.8–10.2)

0.001

40.03

0.33 (0.23–0.47)

< 0.001

 Genitourinary (n = 18) vs not

0 (−) vs 10.1 (6.8–13.4)

0.020

 Head and neck (n = 10) vs not

0 (−) vs 9.8 (6.2–13.4)

0.015

Genetic alteration type

MET (n = 31) vs not

1.0 (−) vs 10.4 (6.9–14.0)

0.003

4.05

1.47 (1.01–2.13)

0.044

TP53 (n = 149) vs not

4.1 (0.5–7.7) vs 12.7 (6.1–19.3)

0.001

EGFR (n = 53) vs not

3.6 (0–8.2) vs 11.4 (7.9–14.8)

0.003

ARID1A (n = 19) vs not

3.6 (0–10.1) vs 10.0 (6.3–13.7)

0.013

KRAS (n = 50) vs not

0 (−) vs 11.4 (8.3–13.7)

< 0.001

SMAD4 (n = 12) vs not

0 (−) vs 10.1 (6.7–13.4)

0.040

MYC (n = 20) vs not

0.2 (0–0.75) vs 10.4 (7.0–13.9)

< 0.001

Metastatic sites

 Adrenal (n = 29) vs not

0 (−--) vs 11.3 (8.0–14.6)

0.002

 Bone (n = 102) vs not

1.5 (0–6.7) vs 11.4 (8.2–14.5)

0.049

 Liver (n = 111) vs not

0.9 (0–2.6) vs 132.3 (9.7–16.9)

< 0.001

10.04

1.49 (1.16–1.92)

0.022

 Lymph node (n = 139) vs not

0.3 (−) vs 20.1 (14.3–25.9)

< 0.001

13.41

1.54 (1.22–1.92)

< 0.001

 Peritoneal (n = 49) vs not

0 (−) vs 11.3 (8.1–14.5)

< 0.001

 Brain (n = 63) vs not

3.6 (0–7.8) vs 10.4 (7.0–13.9)

0.045

 Lung (n = 90) vs not

1.2 (0–8.0) vs 11.3 (7.7–15.0)

0.003

The Wald statistics test the unique contribution of each variable; the higher the Wald statistics, the higher the association/contribution in the model

*Only variables that were significant in the univariable models (log rank test) were included in the multivariable analysis (Cox regression model), with the final model containing only significant covariates in the multivariable analyses (forward stepwise selection model). For some values, the 95% CI could not be computed (−)

Comparison with tissue testing

As noted, 438 patients had ctDNA testing; 263 of these patients also had tissue NGS performed.

Of the 31 patients with MET alterations in ctDNA, 18 also had tissue testing (Foundation Medicine see the “Methods” section). The median time interval between the blood draw and the tissue biopsy for these 18 patients was 6.1 months (95% CI (2–13.7); range (0.2–32.6)). In most of the patients with both types of testing, the ctDNA test was performed after the tissue testing (16/18 cases). Only two patients who had a MET alteration identified in their ctDNA also had a MET alteration found in their tissue testing (11.1%; MET amplification and MET Y501C, one patient each). In these two patients, the time interval between the ctDNA and tissue biopsies was 1.8 and 15.3 months, and both tissues used for the testing were from the primary tumor.

In only one patient, a MET amplification was detected in the tissue and not in ctDNA (1 of 263 total patients who had NGS tissue testing). The tissue test was performed on a pancreatic tumor that was surgically removed, and the ctDNA test was done more than 1 year later (recent scans showed appearance of new pulmonary and liver nodules 2 months prior the ctDNA testing).

Overall, MET alterations were detected at a significantly lower frequency in tissue (3/263 patients, 1.14%) compared to ctDNA (31/438, 7.1%), P = 0.0002. Further, of eight patients who harbored MET alterations in ctDNA, and had ctDNA and tissue testing within 2 months of each other, only one patient showed a similar MET alteration in the tissue. Six of the seven patients with only ctDNA positive for MET alterations had bone metastases.

Discussion

This is the largest study interrogating the feasibility and utility of MET alteration detection through blood-derived ctDNA. Liquid biopsy is a non-invasive method to find genomic aberrations and is increasingly utilized in the clinical setting as reflected by the non-small cell lung cancer National Comprehensive Cancer Network (NCCN) guidelines [18, 19]. Our study demonstrated that MET ctDNA alterations were detected in 7.1% of patients with solid tumors. This detection rate is higher compared to previous tissue studies [5, 20, 21]. For instance, a study of MET tissue amplification determined by fluorescent in situ hybridization (FISH) demonstrated that 2.6% of 1115 solid tumor specimens were positive [5]. Furthermore, the MSKCC-IMPACT study showed that 3% of patients had MET tissue alterations [20, 21]. The differences in rate of MET alterations between our study and the other studies could be due to following reasons: (i) our technology detected both single nucleotide substitutions and amplifications, while the previous investigation by Jardim et al. at MD Anderson Cancer Center [5] discerned only amplifications (though the MSKCC study would have discerned both single nucleotide substitutions and amplifications [21]); (ii) our study used blood-derived ctDNA, which could capture shed tumor DNA from multiple sites, while the previous reports used tissue-based testing, which would only detect aberrations in the piece of tissue biopsied. Consistent with the above observations, our tissue NGS testing also showed significantly lower rates of MET alterations than the ctDNA NGS: 3 of 263 patients (1.1%) (who also had ctDNA tests) were positive for MET alterations in tissue versus 31 of 438 ctDNA-tested patients (7%) being positive for MET alterations (P = 0.0002). Further, of 18 patients positive for MET ctDNA alterations who also had tissue NGS, only 2 (11.1%) were also positive for a MET alteration by tissue NGS. The biologic underpinnings of discordance between ctDNA and tissue NGS results have been previously documented and include spatial (intra-tumor and inter-tumor) and temporal heterogeneity in genomic anomalies in cancers along with the fact that ctDNA is comprised of DNA that has leaked into the circulation from diverse metastatic sites while tissue NGS reflects only the tissue specimen analyzed [14].

The question that arises is whether or not biological explanations can specifically account for the higher rate of MET alterations in ctDNA. Of interest in this regard, our study showed that bone metastases were independently correlated with MET alterations (Additional file 1: Table S2 and Table S4). Indeed, 14 of 31 patients (45%) positive for MET ctDNA alterations had bone metastases (Additional file 1: Table S4). Of possible relevance in this regard, MET inhibitors are known to show efficacy in bone lesions [22, 23]. In the COMET-1 trial, 682 patients with castrate-resistant prostate cancer who progressed after docetaxel and androgen modulators (abiraterone and/or enzalutamide) were randomly assigned to either cabozantinib (MET inhibitor) or prednisone. Although there was no difference in overall survival, 42% of cabozantinib-treated patients showed bone scan response compared to 3% of prednisone-treated patients (P < 0.001), albeit without prostate surface antigen (PSA) response [24]. In the METEOR trial, 658 patients with advanced renal cell carcinoma who progressed with at least one VEGFR small molecule inhibitor were randomized to the MET inhibitor cabozantinib or the mTOR inhibitor everolimus [25]. In a sub-group analysis, patients randomized to cabozantinib arm with bone metastasis were associated with better overall survival (OS) (hazard ratio (HR) 0.54, 95% confidence interval (CI) 0.34–0.84) compared with non-bone metastasis (HR 0.71, 95% CI 0.55–0.91) [25]. These data together with our results raise the possibility that cancer clones with MET alterations preferentially localize to bone and may therefore explain bone responses after MET inhibitor therapy. Tissue biopsies are rarely performed on bones (none of our 18 patients with MET alterations in ctDNA who also had tissue NGS had a bone biopsy). Of interest in this regard, MET is prominently expressed (as determined by immunohistochemistry) at the site of bone metastases in renal cell cancer [26]. It is therefore conceivable that the high rates of ctDNA positivity for MET alterations, which strongly and independently correlated with bone metastases in our study, reflect shed MET alteration-bearing DNA from bone lesions (Additional file 1: Table S2 and Table S4).

Our study also found that MET ctDNA alterations are associated with poor prognosis, including decreased survival and shorter time to recurrence/metastasis (Tables 4 and 5, Fig. 1b). These results are consistent with those found by correlating MET alterations found in tissue NGS with outcome in specific malignancies, such as astrocytomas [27], breast cancers [28], genitourinary malignancies [29], and ovarian [30] or gastric/esophageal cancers [31]. Specific genes that were co-altered with MET in multivariate analysis of our study participants include PTEN and TP53 (Table 2); previously, another study has also shown the association between MET and PTEN abnormalities [5]. Overall, MET alterations significantly correlated with a higher number of alterations, which may explain—at least in part—the relatively limited efficacy of MET inhibitors as single agents for the treatment of MET-altered advanced malignancies observed in prior studies [5, 2931]. Indeed, if patients with MET alterations generally have multiple genomic abnormalities, it is likely that combination therapy, rather than monotherapy with a MET inhibitor, may be necessary to achieve salutary effects.

Our study has several limitations. First, only 60% of our patients (N = 263) with ctDNA analyses also had tissue sequencing and the median time between the two tests in the patients with MET-altered ctDNA was about 6 months. Second, our patients did not have bone biopsies done, which would be of interest to determine if MET alterations are found in such biopsies, since ctDNA MET alterations correlate independently with the presence of bone metastases. Third, though our study with 438 patients is the largest to date, the rarity of MET alterations suggests that investigations of even greater numbers of patients may be worthwhile in order to best understand the biology and correlations of MET alterations.

Conclusions

In summary, our study demonstrated that assessment of MET genomic aberrations by liquid biopsy is feasible. We found that MET ctDNA anomalies were associated with bone metastases, multiple genomic alterations, and a poorer prognosis, including poorer overall survival and a shorter time to recurrence/metastases. Further studies are needed to better understand the biologic relationship between MET alterations and bone lesions, and next-generation trials with MET inhibitors may require combinations of drugs that address the genes such as PTEN that are frequently co-altered in these patients.

Abbreviations

95% CI: 

95% confidence interval

CAP: 

College of American Pathologists

ctDNA: 

Circulating tumor DNA

FDA: 

Food and Drug Administration

FISH: 

Fluorescent in situ hybridization

GAB1: 

GRB2-associated binding protein 1

GRB2: 

Growth factor receptor-bound protein 2

HGF: 

Hepatocyte growth factor

HR: 

Hazard ratio

NCCN: 

National Comprehensive Cancer Network

NGS: 

Next-generation sequencing

NSCLC: 

Non-small cell lung cancer

OS: 

Overall survival

PREDICT: 

Profile Related Evidence Determining Individualized Cancer Therapy

VAF: 

Variant allele fraction

Declarations

Acknowledgments

This work was supported in part by the Joan and Irwin Jacobs Fund philanthropic fund [no grant number is applicable—Razelle Kurzrock] and by the National Cancer Institute at the National Institutes of Health [grant P30 CA016672 (Razelle Kurzrock, rkurzrock@ucsd.edu)].

Availability of data and materials

All data generated or analyzed during this study are included in this published article [and its additional files].

Authors’ contributions

SI, RK, and MS designed the study. MS analyzed the data to generate the results and created the figures and tables. SI and MS drafted the manuscript. All authors edited and approved the final manuscript.

Ethics approval and consent to participate

Not applicable

Competing interests

Dr. Razelle Kurzrock has research funding from Incyte, Genentech, Merck Serono, Pfizer, Sequenom, Foundation Medicine, and Guardant as well as consultant fees from Sequenom, LOXO, and Actuate Therapeutics. Dr. Kurzrock is also a consultant for Genentech. She also has an ownership interest in CureMatch, Inc. The other authors declare that they have no competing interests.

Publisher’s Note

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Authors’ Affiliations

(1)
Department of Medicine, Center for Personalized Cancer Therapy, Division of Hematology/Oncology, University of California, San Diego, Moores Cancer Center, 3855 Health Sciences Drive, #0658, La Jolla, CA 92093-0987, USA
(2)
Tokyo Medical and Dental University, Tokyo, Japan
(3)
Department of Clinical Oncology, Hospital Sirio Libanes, Sao Paulo, Brazil

References

  1. Cooper CS, Park M, Blair DG, Tainsky MA, Huebner K, Croce CM, et al. Molecular cloning of a new transforming gene from a chemically transformed human cell line. Nature. 1984;311:b29–33.View ArticleGoogle Scholar
  2. Park M, Dean M, Kaul K, Braun MJ, Gonda MA, Vande WG. Sequence of MET protooncogene cDNA has features characteristic of the tyrosine kinase family of growth-factor receptors. Proc Natl Acad Sci U S A. 1987;84:6379–83.View ArticlePubMedPubMed CentralGoogle Scholar
  3. Bottaro DP, Rubin JS, Faletto DL, Chan AM, Kmiecik TE, Vande Woude GF, et al. Identification of the hepatocyte growth factor receptor as the c-met proto-oncogene product. Science. 1991;251:802–4.View ArticlePubMedGoogle Scholar
  4. Birchmeier C, Birchmeier W, Gherardi E, Vande Woude GF. Met, metastasis, motility and more. Nat Rev Mol Cell Biol. 2003;4:915–25.View ArticlePubMedGoogle Scholar
  5. Jardim DLF, Tang C, Gagliato DDM, Falchook GS, Hess K, Janku F, et al. Analysis of 1,115 patients tested for MET amplification and therapy response in the MD Anderson Phase I Clinic. Clin Cancer Res. 2014;20:6336–45.View ArticlePubMedGoogle Scholar
  6. Schmidt L, Duh FM, Chen F, Kishida T, Glenn G, Choyke P, et al. Germline and somatic mutations in the tyrosine kinase domain of the MET proto-oncogene in papillary renal carcinomas. Nat Genet. 1997;16:68–73.View ArticlePubMedGoogle Scholar
  7. Kawakami H, Okamoto I, Okamoto W, Tanizaki J, Nakagawa K, Nishio K. Targeting MET amplification as a new oncogenic driver. Cancers. 2014;6:1540–52.View ArticlePubMedPubMed CentralGoogle Scholar
  8. Madoz-Gúrpide J, Zazo S, Chamizo C, Casado V, Caramés C, Gavín E, et al. Activation of MET pathway predicts poor outcome to cetuximab in patients with recurrent or metastatic head and neck cancer. J Transl Med. 2015;13:282.View ArticlePubMedPubMed CentralGoogle Scholar
  9. Kurzrock R, Sherman SI, Ball DW, Forastiere AA, Cohen RB, Mehra R, et al. Activity of XL184 (cabozantinib), an oral tyrosine kinase inhibitor, in patients with medullary thyroid cancer. J Clin Oncol. 2011;29:2660–6.View ArticlePubMedPubMed CentralGoogle Scholar
  10. Shaw AT, Kim DW, Nakagawa K, Seto T, Crinó L, Ahn M-J, et al. Crizotinib versus chemotherapy in advanced ALK-positive lung cancer. N Engl J Med. 2013;368:2385–94.View ArticlePubMedGoogle Scholar
  11. Ross C, Sai-Hong IO, Shapiro G. Efficacy and safety of crizotinib in patients with advanced c MET-amplified non-small cell lung cancer (NSCLC). JCO. 2014;15:suppl 8001. Abstract. Available from: http://meetinglibrary.asco.org/record/92507/abstract.Google Scholar
  12. Schwaederle M, Patel SP, Husain H, Ikeda M, Lanman R, Banks KC, et al. Utility of genomic assessment of blood-derived circulating tumor DNA (ctDNA) in patients with advanced lung adenocarcinoma. Clin Cancer Res. 2017;23:5101–11.View ArticlePubMedGoogle Scholar
  13. Kato S, Krishnamurthy N, Banks KC, De P, Williams K, Williams C, et al. Utility of genomic analysis in circulating tumor DNA from patients with carcinoma of unknown primary. Cancer Res. 2017;77:4238–46.View ArticlePubMedGoogle Scholar
  14. Schwaederle M, Husain H, Fanta PT, Piccioni DE, Kesari S, Schwab RB, et al. Use of liquid biopsies in clinical oncology: pilot experience in patients. Clin Cancer Res. 2016;22:5497–505.View ArticlePubMedGoogle Scholar
  15. Bidard F-C, Madic J, Mariani P, Piperno-Neumann S, Rampanou A, Servois V, et al. Detection rate and prognostic value of circulating tumor cells and circulating tumor DNA in metastatic uveal melanoma. Int J Cancer. 2014;134:1207–13.View ArticlePubMedGoogle Scholar
  16. Schwaederle M, Husain H, Fanta PT, Piccioni DE, Kesari S, Schwab RB, et al. Detection rate of actionable mutations in diverse cancers using a biopsy-free (blood) circulating tumor cell DNA assay. Oncotarget. 2016;7:9707–17.View ArticlePubMedPubMed CentralGoogle Scholar
  17. Lanman RB, Mortimer SA, Zill OA, Sebisanovic D, Lopez R, Blau S, et al. Analytical and clinical validation of a digital sequencing panel for quantitative, highly accurate evaluation of cell-free circulating tumor DNA. PLoS One. 2015;10:e0140712.View ArticlePubMedPubMed CentralGoogle Scholar
  18. Kwapisz D. The first liquid biopsy test approved. Is it a new era of mutation testing for non small cell lung cancer? Ann Transl. 2017;Med5:46.View ArticleGoogle Scholar
  19. Chabon JJ, Simmons AD, Lovejoy AF, Esfahani MS, Newman AM, Haringsma HJ, et al. Circulating tumour DNA profiling reveals heterogeneity of EGFR inhibitor resistance mechanisms in lung cancer patients. Nat Commun. 2016;7:11815.View ArticlePubMedPubMed CentralGoogle Scholar
  20. Cheng DT, Mitchell TN, Zehir A, Shah RH, Benayed R, Syed A, et al. Memorial Sloan Kettering-integrated mutation profiling of actionable cancer targets (MSK-IMPACT): a hybridization capture-based next-generation sequencing clinical assay for solid tumor molecular oncology. J Mol Diagn. 2015;17:251–64.View ArticlePubMedPubMed CentralGoogle Scholar
  21. Zehir A, Benayed R, Shah RH, Syed A, Middha S, Kim HR, et al. Mutational landscape of metastatic cancer revealed from prospective clinical sequencing of 10,000 patients. Nat Med. 2017;23:703–13.View ArticlePubMedPubMed CentralGoogle Scholar
  22. Graham TJ, Box G, Tunariu N, Crespo M, Spinks TJ, Miranda S, et al. Preclinical evaluation of imaging biomarkers for prostate cancer bone metastasis and response to cabozantinib. J Natl Cancer Inst. 2014;106:dju033.View ArticlePubMedGoogle Scholar
  23. McKay RR, Kroeger N, Xie W, Lee J-L, Knox JJ, Bjarnason GA, et al. Impact of bone and liver metastases on patients with renal cell carcinoma treated with targeted therapy. Eur Urol. 2014;65:577–84.View ArticlePubMedGoogle Scholar
  24. Smith M, De Bono J, Sternberg C, Le Moulec S, Oudard S, De Giorgi U, et al. Phase III study of cabozantinib in previously treated metastatic castration-resistant prostate cancer: COMET 1. J Clin Oncol. 2016;34:3005–13.View ArticlePubMedGoogle Scholar
  25. Choueiri TK, Escudier B, Powles T, Tannir NM, Mainwaring PN, Rini BI, et al. Cabozantinib versus everolimus in advanced renal cell carcinoma (METEOR): final results from a randomised, open-label, phase 3 trial. Lancet Oncol. 2016;17:917–27.View ArticlePubMedGoogle Scholar
  26. Mukai S, Yorita K, Kawagoe Y, Katayama Y, Nakahara K, Kamibeppu T, et al. Matriptase and MET are prominently expressed at the site of bone metastasis in renal cell carcinoma: immunohistochemical analysis. Hum Cell. 2015;28:44–50.View ArticlePubMedGoogle Scholar
  27. Pierscianek D, Kim YH, Motomura K, Mittelbronn M, Paulus W, Brokinkel B, et al. MET gain in diffuse astrocytomas is associated with poorer outcome. Brain Pathol. 2013;23:13–8.View ArticlePubMedGoogle Scholar
  28. de Melo Gagliato D, Fontes Jardim DL, Falchook G, Tang C, Zinner R, et al. Analysis of MET genetic aberrations in patients with breast cancer at MD Anderson Phase I Unit. Clin Breast Cancer. 2014;14:468–74.View ArticlePubMedPubMed CentralGoogle Scholar
  29. Jardim DLF, de Melo Gagliato D, Falchook G, Zinner R, Wheler JJ, Janku F, et al. MET abnormalities in patients with genitourinary malignancies and outcomes with c-MET inhibitors. Clin Genitourin Cancer. 2015;13:e19–26.View ArticlePubMedGoogle Scholar
  30. Tang C, Fontes Jardim DL, Falchook GS, Hess K, Fu S, Wheler JJ, et al. MET nucleotide variations and amplification in advanced ovarian cancer: characteristics and outcomes with c-Met inhibitors. Oncoscience. 2013;1:5–13.View ArticlePubMedPubMed CentralGoogle Scholar
  31. Jardim DLF, Gagliato D de M, Falchook GS, Janku F, Zinner R, Wheler JJ, et al. MET aberrations and c-MET inhibitors in patients with gastric and esophageal cancers in a phase I unit. Oncotarget. 2014;5:1837–45.View ArticlePubMedPubMed CentralGoogle Scholar

Copyright

© The Author(s). 2018

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