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Abstract

BACKGROUND:

The advancement of cancer genomics has allowed for multiplex gene assays using next-generation sequencing (NGS) to be practically implemented, however, a clinical practice system remains to be established.

OBJECTIVE:

We evaluated the feasibility of clinical sequencing using NGS-based multiplex gene assays between cooperating medical institutions in patients with advanced cancers.

METHODS:

In this observational study, DNA and RNA samples prepared from existing tumor tissues were subjected to comprehensive genomic profiling using targeted sequencing.

RESULTS:

From January 2017 to March 2019, 36 samples from 33 patients were assessed. Of all patients, 27 (82%) had lung cancer, with the median age of 50 years (range 38–83). Multiplex gene panel tests were successfully carried out on 35/36 (97%) samples. Potentially actionable gene alterations were identified in 10/30 (33%) samples (3 HER2, 2 KRAS, 2 ALK, 1 PIK3CA, 1 RET, and 1 CDKN2A). In the 6 samples examined for resistant mechanisms, ALK I1171N mutation and MET copy number gain were detected in 2 patients with ALK rearrangement-positive lung cancer.

CONCLUSIONS:

Clinical sequencing using NGS-based multiplex gene assays between collaborating domestic medical institutions was feasible, with a success rate of ${>}$ 97%. Overall, clinical sequencing benefits therapeutic decision-making in patients with advanced cancer.

Keywords

Clinical sequencing multiplex gene assay next-generation sequencing driver mutation resistant mechanism

1. Background

Cancer is thought to arise through the accumulation of acquired gene alterations [1, 2]. Progress in the field of molecular biology has facilitated the identification of multiple gene abnormalities that are associated with malignant tumors. The identification of therapeutic targetable driver mutations, such as the epidermal growth factor receptor (EGFR), anaplastic lymphoma kinase (ALK), c-ros oncogene 1 (ROS1), and v-raf murine sarcoma viral oncogene homolog B1 (BRAF) has been successfully translated into clinical practice for cancer patients [3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19]. The clinical application of driver mutations and their inhibitors are expanding.

Tumor genotyping to identify actionable oncogenic driver mutations and mechanisms of resistance to targeted drugs has become increasingly important in the management of cancer patients [20, 21, 22, 23]. Advances in next-generation sequencing (NGS) technologies allow for high-throughput multiplexing of therapeutic targetable gene alterations [24, 25, 26, 27, 28]. While cancer genomic profiling using NGS-based assays has been rapidly adopted in clinical practice, there is an urgent need for the implementation of an established system in a clinical setting [29, 30]. As the expanding number of potentially actionable mutations in cancer patients necessitates tumor genotyping, tissue biopsies are often assessed by NGS-based assays [21, 24]. Thus, we evaluated the feasibility of clinical sequencing using NGS-based multiplex gene assays between cooperating medical institutions for genotype-directed therapy in clinical practice [31].

2. Methods

Before the NGS-based panel tests, FoundationOne CDx Cancer Genomic Profile (Chugai Pharmaceutical Co., LTD.) and OncoGuide NCC Oncopanel System (Sysmex), received insurance coverage for cancer genome profiling in Japan in February 2019, solid cancer patients, considered eligible by the attending physician, were enrolled in this study and underwent comprehensive genomic profiling for detecting possible molecular targets. No restrictions on tumor, disease stage, subsequent or previous treatment, performance status, or other factors were imposed. All patients provided written informed consent to participate in this study.

After obtaining the consent, 10 sliced, undyed specimen slides were prepared from existing formalin fixed paraffin embedded (FFPE) samples, which were originally fixed for routine pathological diagnosis of patients at Kitasato University Hospital; samples were fixed in 10% neutral buffered formalin within 24–48 hours of being obtained. Tumor slide specimens were sent from Kitasato University Hospital, Kanagawa, to Kindai University, Osaka, for the multiplex NGS-based panel tests.

At the clinical laboratory of Kindai University, DNA and RNA were extracted after confirming if ${\geqslant}$ 10% of the tissue area in the guide hematoxylin-eosin slide was occupied by invasive malignant cells, as evaluated by a pathologist. The quality and quantity of the nucleic acid was evaluated using NanoDrop 2000, PicoGreen dsDNA reagent, and RiboGreen RNA reagent (all from Thermo Fisher Scientific, Wilmington, DE). Samples with nucleic acid yields lesser than 10 ng were excluded from further processing. After confirming the quality and quantity of the nucleic acids, selected NGS-based multiplex panel tests (Ion AmpliSeq Colon and Lung Cancer Research Panel v2 [CLv2] (Thermo Fisher Scientific), detects hotspots and targeted regions for 22 known genes and Ion AmpliSeq RNA Lung Fusion Cancer Research Panel [LF] (Thermo Fisher Scientific), targets over 70 fusion transcripts relevant to lung cancer research, or Ion Ampliseq cancer hotspot panel v2 [CHPv2] (Thermo Fisher Scientific), detects hotspot mutations of 50 cancer related genes associated with colon and lung tumor tissue, and Archer FusionPlex CTL panel [Archer CTL] (ArcherDx, Boulder, CO), detects fusions, SNVs and splicing variants of 30 cancer related genes) were performed to identify targeted gene alterations.

Mutation detection was performed on 10 ng of DNA, through multiplex PCR amplification using CLv2 or CHPv2. The Ion Xpress Barcode Adapters (Thermo Fisher Scientific) were used for sample identification. Fusion detection with LF was performed on 10 ng of RNA, using reverse transcription followed by library preparation. The Ion Xpress Barcode Adapters were used for sample identification. Fusion detection with Archer CTL was performed on 50 ng of RNA, using reverse transcription followed by library preparation. The MBC Adapters (ArcherDx) were used for sample identification. Purified libraries were pooled and sequenced on an Ion Torrent Proton system (Thermo Fisher Scientific). The panels used for each sample are shown in Supplemental Table 1.

DNA sequencing data were accessed through the latest version of Torrent Suite (5.0. to 5.10) (Thermo Fisher Scientific). Reads were aligned against the hg19 human reference genome, and variants were called using the latest version of variant caller (5.0. to 5.10) (Thermo Fisher Scientific). Raw variant calls were filtered using a quality score of ${<}$ 100 and depth of coverage of ${<}$ 10 and were manually checked using the integrative genomics viewer (IGV; Broad Institute, Cambridge, MA). Germline mutations were excluded using the Human Genetic Variation Database (http://www.genome.med.kyoto-u.ac.jp/SnpDB). RNA sequencing data were accessed through the latest version of Torrent Suite (5.0. to 5.10). Unaligned bam files from LF were analyzed for fusion detection using Ion Reporter Software (Thermo Fisher Scientific). Unaligned bam files from Archer CTL were analyzed for fusion detection using Archer? Analysis Software (ArcherDX). Genomic alterations were defined as actionable if they were associated with susceptibility to an approved targeted therapy, or if they could serve as a basis for direction of patients toward registered clinical trials.

This study was approved by the Kitasato University Medical Ethics Organization (study number: B16-55) and the ethics committee of Kindai University Faculty of Medicine (study number: 27-169).

3. Results

3.1 Patient characteristics

Between July 2017 and December 2018, written informed consent was obtained from 33 advanced-cancer patients at Kitasato University Hospital. Thirty-six tumor samples from these patients were submitted to Kindai University for genomic testing. The patient characteristics are summarized in Table 1. The median age was 50 years (range, 38–83), and 24 (72%) patients were under 60 years. Fifteen (45%) were female and 10 (30%) were never smokers. Most patients ( $n=$ 27, 82%) had non-small cell lung cancer, and the remainder had cancers of unknown primary site ( $n=$ 3; 6%), pancreatic cancer ( $n=$ 1; 3%), gall bladder cancer ( $n=$ 1; 3%), and leiomyosarcoma ( $n=$ 1; 3%). There were two types of objectives for genomic profiling: 1) identification of potentially actionable mutations ( $n=$ 30 samples from 29 patients) and 2) search for resistant mechanisms to molecularly targeted therapy ( $n=$ 6 samples from 4 patients including 1 EGFR mutation and 3 ALK rearrangements) (Table 2).

Table 1
Characteristics of the patients subjected to molecular analysis ( $n=$ 33)

	No.	%
Age, years, median (range)	50 (38–83)	–
${<}$ 60	24	73
${\geqslant}$ 60	8	27
Sex
Male	18	55
Female	15	45
Smoking status
Never	10	30
Former	8	24
Current	15	45
Type of tumor (known gene alterations)
Lung
Adenocarcinoma (EGFR/ALK)	18 (1/1)	55 (3/3)
Carcinoma, not otherwise specified	4	12
Squamous cell carcinoma (ALK)	2 (2)	6 (6)
Small cell carcinoma	2	6
Clear cell carcinoma	1	3
Primary unknown
Carcinoma	2	6
Adenocarcinoma	1	3
Pancreas
Poorly differentiated carcinoma	1	3
Gall bladder
Neuroendocrine carcinoma	1	3
Leiomyosarcoma
Sarcoma	1	3

EGFR, epidermal growth factor receptor; ALK, anaplastic lymphoma kinase.

Figure 1.

Distribution of actionable gene alterations based on the NGS-based assay for the 36 tumor specimens. Fig. 1-1. Genotyping to identify actionable gene alterations. Fig. 1-2. Genotyping to search for resistant mechanisms of molecular targeted therapies. The pie chart shows the number and frequency of patients with potentially actionable gene alterations (dark gray), without specific gene mutations (gray), and those not subjected to NGS analysis (light gray).

Table 2

Genotyping results for the 36 tumor specimens

Table 2-1. To identify therapeutic targeted mutations ( $n=$ 30)
#	Primary organ	Tissue analyzed	Gene alterations	COSMIC annotation
2	Lung	Lung biopsy	TP53 p.Arg248Trp	10656
7	Lung	TBLB	TP53 p.His179Tyr	10768
10	Lung	TBLB	TP53 p.Arg342*	11073
19	Lung	TBB	TP53 p.lle232Phe	43550
20	Lung	Pleural biopsy	TP53 p.Arg196Gly	5635597
22	Lung	TBLB	TP53 p.Met237Val	44525
28	Lung	Brain resection	TP53 p.Arg213*	10654
24	Leiomyosarcoma	Retroperitoneal tumor resection	TP53 p.Arg306*	10663
25	Primary unknown	LN biopsy	TP53 p.Ala307Ser	45485
26	Primary unknown	LN biopsy	TP53 p.Arg175Gly	10870
18-1	Lung	LN biopsy	TP53 p.Tyr236Cys	10731
18-2	Lung	Skin biopsy	EML4-ALK fusion/TP53 p.Tyr236Cys	-/ 10731
16	Lung	TBLB	EML4-ALK fusion/TP53 p.Arg249Trp	-/ 43629
6	Lung	TBLB	CCDC6-RET fusion/DDR2 p.Met441Val/ALK p.Leu1169Gln/FBXW7 p.Lys266Glu/-	-/ -/ -/ -
1	Lung	Pleural effusion	HER2 p.Ala775_Gly776insTyrValMetAla	133608
9	Lung	TBB	HER2 p.Pro780_Tyr781insGlySerPro	21607
29	Lung	Lung resection	HER2 p.Ala745_Gly746insTryValMetAla/TP53 p.Ser240Arg	-/ 45804
23	Lung	Lung biopsy	ERBB4 p.Cys262*/CDKN2A p.Gly89Val/RB1 p.Thr197 TP53 Ser/pThr152Pro	-/ 13472/ -/ -
8	Lung	LN biopsy	PIK3CA p.Glu545Gln	27133
14	Lung	Lung resection	KRAS p.Gly12Asp/TP53 p.Gln165fs	521/ -
30	Pancreas	Liver resection	KRAS p.Gly12Asp/TP53 p.Gly245Arg	521/10957
4	Lung	Brain resection	NRAS p.Gln61Arg/TP53 p.Arg273Cys	584/10659
5	Lung	TBLB	MAP2K1 p.Gln56Pro/TP53 p.Gly245Arg	1235481/10957
15	Lung	LN biopsy	FBXW7 p.Gln275*	6662584
27	Lung	TBLB	VHL p.Trp117Cys	14328
21	Primary unknown	LN biopsy	SLC3A2-NRG1	-
31	Gall bladder	Skin biopsy	APC p.Gly13655Ser	-
13	Lung	Skin biopsy	(None of detected)
32	Lung	TBLB	(None of detected)
17	Lung	Pleural biopsy	(Not evaluated)
Table 2-2. To search for resistance mechanisms ( $n=$ 6)
#	Primary organ	Tissue analyzed	Gene alterations	COSMIC annotation
11-1	Lung	Pleural effusion	EGFR p.Glu746_Ala750del/MAP2K1 p.Gln56Pro/TP53 p.Phe109Ser	6223/1235481/45169
11-2	Lung	TBLB	EGFR p.Glu746_Ala750del	6223
3-1	Lung	TBLB	EML4-ALK fusion	-
3-2	Lung	Pericardial effusion	EML4-ALK fusion	-
12	Lung	Adrenal gland biopsy	EML4-ALK fusion/ALK p.lle1171Asn	-/ 28498
33	Lung	LN biopsy	EML4-ALK fusion/MET amp (CN 6.4)/RB1 amp (CN 6.5)	-/ -/ -

TBLB, transbronchial lung biopsy; TBB, transbronchial biopsy; LN, lymph node; amp. amplification, CN, copy number.

Figure 2.

Treatment progress and timing of the multiplex NGS-based assay in the 10 patients with potentially actionable alterations. Each bar represents the time course from the first-line chemotherapy for individual patients with potentially actionable alterations. All patients with lung cancer except those with pancreatic cancer patient indicated as number 30. The color of each bar indicates the line of chemotherapy (dark gray: first, gray: second, light gray: third, gray vertical stripes: fourth, gray horizontal stripes: fifth, white: sixth line chemotherapy). Black circles indicate when the consent was obtained for the multiplex gene assay in this study.

3.2 Feasibility of the NGS-based multiplex gene assay

Archived FFPE tumor tissues were available, and DNA and RNA extracted from these tissues were used for genomic profiling using an NGS-based assay. Sequence reads were trimmed based on the quality score (Phred score ${\geqslant}$ Q20). The median of the total reads from CLv2, FL, CHPv2, and Archer CTL were 550,471 (range 89,298 to 3,626,542), 216,505 (range 141,176 to 1,209,987), 2,604,767 (range 1,208,649 to 3,697,202), and 2,774,273 (range 260 to 8,653,908), respectively. For fusion detection, one sample (#31) contained less total reads (260 reads); and therefore, was excluded from data analysis. The median read depth of CLv2 and CHPv2 were 3,912 (range 209 to 37,191) and 11,923 (range 5,502 to 17,070), respectively. Among patients with each of the mutations, the median allele frequency was 32.4% (range, 3.1% to 75.3%).

The success rate of the NGS assay was 97% ( $n=$ 35 of 36 samples); in one case, the quality of nucleic acid was poor and could not be tested. The turn-around time from obtaining consent to reporting results of genomic profiling including expert comments was a median of 42 days (range 21–153). The time from sample preparation in our hospital to confirm the malignant cells in the collaborative laboratory took a median of 19.5 days (range 4–121, including sample retake). Subsequent NGS-based assays could be performed in a median of 11.5 days (range 9–31).

3.3 Prevalence of genomic alterations

Of the 33 patients tested for molecular alterations in this study, 29 patients were investigated for actionable molecular therapeutic targets. Potentially actionable genomic alterations, including HER2 in 3 (11%), KRAS in 2 (7%), ALK in 2 (7%), and PIK3CA, CDKN2A, and RET in 1 each (3%), were identified in 10 (33%) of them (Fig. 1-1, Table 2-1). One of the two patients in whom ALK rearrangement was detected was treated with an ALK inhibitor under their health insurance. However, ALK inhibitor was not administered to the other patient, because the companion diagnostic, ALK immunohistochemistry (IHC), was negative; therefore, the treatment was not approved by the insurance company. The patients for whom the target genes were other than the ALK rearrangements could not be enrolled in the ongoing clinical trials for the experimental drugs based on the genomic information. Deterioration of the general condition of the patient during the course of treatment was the main reason for non-participation in clinical trials, even if the NGS-based panel test was performed early in the course of treatment (Fig. 2).

When the 4 patients were assessed for resistant mechanisms against EGFR tyrosine kinase inhibitor or ALK inhibitors, ALK mutation (p.lle1171Asn) and MET amplification were detected as reported resistant mechanisms of ALK inhibitors (Fig. 1-2, Table 2-2). Based on these results, the patient with the ALK p.lle1171Asn resistant mutation could receive the ALK inhibitor ceritinib for three months as a 7th line chemotherapy.

There was no incidental finding for germline variants to be disclosed, pointed out by experts in this study.

4. Discussion

Clinical sequencing using multiplex NGS-based gene assays was successful between collaborative domestic medical institutions, using archived FFPE samples from patients with advanced solid cancers. Potential actionable genomic alterations were detected in the 10 of 30 (33%) samples from 29 patients, who could not be enrolled in ongoing clinical trials except for a patient with ALK rearrangement treated with an ALK inhibitor. In this study, EGFR and ALK alternations had been single-gene tested in clinical practice, and twenty-four (72%) of patients under the age of 60 were enrolled, to actively perform genomic profiling for younger patients with advanced cancer. Although previous studies conducted on lung cancer patients at Kindai University identified 22% of actionable gene alterations other than EGFR mutations and ALK fusion, the frequency with which the therapeutic targeted genes can be identified may increase if the patients are selected by the attending physician based on their background [21]. However, cancer genomic profiling in clinical tissue specimens affects detection frequency due to various factors, such as variable mutational burden in different tumors, intra-tumor heterogeneity, and number of tumor cells. In addition, treatment selection after identification of actionable gene alterations poses major challenges, as ongoing clinical trials are only open in limited institutions and selected patients.

In one lung cancer patient, where ALK rearrangement was identified by the multiplex gene panel test, ALK IHC, performed as a companion diagnostic for insurance-approval of the drug use, was negative. The discrepancy between the panel test based on NGS and the IHC results could be due to the amount of sample and the number of tumor cells, intratumor heterogeneity, and test sensitivities. In clinical practice, there are problems with the quantity and quality of the tissue biopsy samples obtained for gene panel tests for cancer diagnosis. Sample management is one of the issues in cancer genomic profiling for targeted inhibitor use in clinical settings.

In order to identify resistance against EGFR or ALK inhibitors, re-biopsy samples after disease progression of the targeted therapy were submitted, and the mechanism for ALK resistance was found in 2 cases [32, 33, 34, 35, 36]. In 1 of the 2 patients, sequential treatment with an ALK inhibitor was administered, based on the genomic profiling results. Almost all patients with metastatic cancers who initially benefit from targeted therapies eventually develop resistance. It is important to elucidate the mechanisms of resistance, and it is hoped that the mechanism-based therapeutic strategy will be widespread in the future with the routine implementation of NGS-based multiplex gene assays.

At present, it is possible to perform multiplex gene panel tests that show the comprehensive genomic characteristics of cancer, and gene profiling tests are being performed in cooperation between facilities that perform cancer medical care. Based on the results of cancer genomic profiling, an expert panel comprising multidisciplinary cancer specialists is needed to ensure meaningful interpretation and accurate reporting of results to each institution to address the matched therapeutic opportunity. The system of precision medicine directed by cancer genomic profiling is currently being developed by medical institutions, from sample operation and gene analysis to the disclosure of results. This study assessed an NGS-based approach between related medical institutions, performed before the insurance approval of multiplex gene panel tests, and demonstrated that this strategy is feasible. In the future, it will be necessary to expand the gene analysis that leads to the determination of appropriate treatment options to provide more clinical benefits for the patients.

This study had certain limitations, including that this was an observational study conducted at two cooperating institutions with a small sample size; hence, the results cannot be considered definitive. Second, in this study, several biases, such as patient selection, should be considered because the target patients for genomic profiling are not limited and patients in various situations have been enrolled. Moreover, it remains unclear whether the genotype-directed therapy of patients positive for actionable gene alterations other than EGFR mutations or ALK rearrangement were affected by tyrosine kinase inhibitors. Although no definitive conclusions can be drawn, genomic profiling using NGS-based assays may be feasible in clinical practice, and could be considered an early test of treatment eligibility based on identified potentially actionable gene alterations. In order to select subsequent treatments based on clinical sequencing results, it is necessary to understand genomic abnormalities, including information on target inhibitors, before the disease progresses.

5. Conclusions

Clinical sequencing using NGS-based assays is feasible between collaborative medical institutions that cooperate with cancer genomics, and is useful for identifying targeted gene alterations and elucidating resistant mechanisms. While an improvement of the system in clinical practice is urgently warranted, there are several factors that must be considered, including appropriate patient selection for comprehensive genomic profiling, examination time, decision-making for treatment, sequencing costs, and the development of therapeutic drugs. The challenge is to carry out multiplex gene panel tests early and administer effective drugs to patients before their general condition deteriorates.

Author contributions

Conception: T. Fukui, J. Sasaki, K. Nishio

Interpretation or analysis of data: T. Fukui, K. Sakai, J. Sasaki, M. Takeda, T. Takahama, K. Nakagawa, K. Nishio

Preparation of the manuscript: T. Fukui, J. Sasaki, MI Kakegawa, S Igawa, H Mitsufuji, K. Naoki

Revision for important intellectual content: T. Fukui, K. Sakai, J. Sasaki, K. Nishio, K. Naoki

Supervision: J. Sasaki, K. Nakagawa, K. Naoki

Supplementary data

The supplementary files are available to download from http://dx.doi.org/10.3233/CBM-200781.

sj-docx-1-cbm-10.3233_CBM-200781.docx - Supplemental material

Supplemental material, sj-docx-1-cbm-10.3233_CBM-200781.docx

Footnotes

Acknowledgments

The authors would like to thank Editage (www.editage.jp) for English language editing.

Conflict of interest

The authors have no conflicts of interest.

References

Greenman

Stephens

Smith

Dalgliesh

G.L.

Hunter

Bignell

Davies

Teague

Butler

Stevens

Edkins

O’Meara

Vastrik

Schmidt

E.E.

Avis

Barthorpe

Bhamra

Buck

Choudhury

Clements

Cole

Dicks

Forbes

Gray

Halliday

Harrison

Hills

Hinton

Jenkinson

Jones

Menzies

Mironenko

Perry

Raine

Richardson

Shepherd

Small

Tofts

Varian

Webb

West

Widaa

Yates

Cahill

D.P.

Louis

D.N.

Goldstraw

Nicholson

A.G.

Brasseur

Looijenga

Weber

B.L.

Chiew

Y.E.

DeFazio

Greaves

M.F.

Green

A.R.

Campbell

Birney

Easton

D.F.

Chenevix-Trench

Tan

M.H.

Khoo

S.K.

Teh

B.T.

Yuen

S.T.

Leung

S.Y.

Wooster

Futreal

P.A.

and Stratton

M.R.

, Patterns of somatic mutation in human cancer genomes, Nature 446 (2007), 153–158.

Stratton

M.R.

, Journeys into the genome of cancer cells, EMBO Mol Med 5 (2013), 169–172.

Lawrence

M.S.

Stojanov

Polak

Kryukov

G.V.

Cibulskis

Sivachenko

Carter

S.L.

Stewart

Mermel

C.H.

Roberts

S.A.

Kiezun

Hammerman

P.S.

McKenna

Drier

Zou

Ramos

A.H.

Pugh

T.J.

Stransky

Helman

Kim

Sougnez

Ambrogio

Nickerson

Shefler

Cortés

M.L.

Auclair

Saksena

Voet

Noble

DiCara

Lin

Lichtenstein

Heiman

D.I.

Fennell

Imielinski

Hernandez

Hodis

Baca

Dulak

A.M.

Lohr

Landau

D.A.

C.J.

Melendez-Zajgla

Hidalgo-Miranda

Koren

McCarroll

S.A.

Mora

Crompton

Onofrio

Parkin

Winckler

Ardlie

Gabriel

S.B.

Roberts

C.W.M.

Biegel

J.A.

Stegmaier

Bass

A.J.

Garraway

L.A.

Meyerson

Golub

T.R.

Gordenin

D.A.

Sunyaev

Lander

E.S.

and Getz

, Mutational heterogeneity in cancer and the search for new cancer-associated genes, Nature 499 (2013), 214–218.

Sunami

Takahashi

Tsuchihara

Takeda

Suzuki

Naito

Sakai

Dosaka-Akita

Ishioka

Kodera

Muto

Wakai

Yamazaki

Yasui

Bando

Fujimoto

Fukuoka

Harano

Kawazoe

Kimura

Koganemaru

Kogawa

Kotani

Kuboki

Matsumoto

Mishima

Nakamura

Sawada

Shingaki

Shitara

Umemoto

Umemura

Yasuda

Yoshino

Yamamoto

and Nishio

, Clinical practice guidance for next-generation sequencing in cancer diagnosis and treatment (Edition 1.0), Cancer Sci 109 (2018), 2980–2985.

Paez

J.G.

Jänne

P.A.

Lee

J.C.

Tracy

Greulich

Gabriel

Herman

Kaye

F.J.

Lindeman

Boggon

T.J.

Naoki

Sasaki

Fujii

Eck

M.J.

Sellers

W.R.

Johnson

B.E.

and Meyerson

, EGFR mutations in lung cancer: correlation with clinical response to gefitinib therapy, Science 304 (2004), 1497–1500.

Lynch

T.J.

Bell

D.W.

Sordella

Gurubhagavatula

Okimoto

R.A.

Brannigan

B.W.

Harris

P.L.

Haserlat

S.M.

Supko

J.G.

Haluska

F.G.

Louis

D.N.

Christiani

D.C.

Settleman

and Haber

D.A.

, Activating mutations in the epidermal growth factor receptor underlying responsiveness of non-small-cell lung cancer to gefitinib, N Engl J Med 350 (2004), 2129–2139.

Pao

Miller

Zakowski

Doherty

Politi

Sarkaria

Singh

Heelan

Rusch

Fulton

Mardis

Kupfer

Wilson

Kris

and Varmus

, EGF receptor gene mutations are common in lung cancers from “never smokers” and are associated with sensitivity of tumors to gefitinib and erlotinib, Proc Natl Acad Sci U S A 101 (2004), 13306–13311.

Soda

Choi

Y.L.

Enomoto

Takada

Yamashita

Ishikawa

Fujiwara

Watanabe

Kurashina

Hatanaka

Bando

Ohno

Ishikawa

Aburatani

Niki

Sohara

Sugiyama

and Mano

, Identification of the transforming EML4-ALK fusion gene in non-small-cell lung cancer, Nature 448 (2007), 561–566.

Shaw

A.T.

Kim

D.W.

Nakagawa

Seto

Crinó

Ahn

M.J.

De Pas

Besse

Solomon

B.J.

Blackhall

Y.L.

Thomas

O’Byrne

K.J.

Moro-Sibilot

Camidge

D.R.

Mok

Hirsh

Riely

G.J.

Iyer

Tassell

Polli

Wilner

K.D.

and Jänne

P.A.

, Crizotinib versus chemotherapy in advanced ALK-positive lung cancer, N Engl J Med 368 (2013), 2385–2394.

10.

Hida

Nokihara

Kondo

Kim

Y.H.

Azuma

Seto

Takiguchi

Nishio

Yoshioka

Imamura

Hotta

Watanabe

Goto

Satouchi

Kozuki

Shukuya

Nakagawa

Mitsudomi

Yamamoto

Asakawa

Asabe

Tanaka

and Tamura

, Alectinib versus crizotinib in patients with ALK-positive non-small-cell lung cancer (J-ALEX): an open-label, randomised phase 3 trial, Lancet 390 (2017), 29–39.

11.

Bergethon

Shaw

A.T.

S.H.

Katayama

Lovly

C.M.

McDonald

N.T.

Massion

P.P.

Siwak-Tapp

Gonzalez

Fang

Mark

E.J.

Batten

J.M.

Chen

Wilner

K.D.

Kwak

E.L.

Clark

J.W.

Carbone

D.P.

Engelman

J.A.

Mino-Kenudson

Pao

and Iafrate

A.J.

, ROS1 rearrangements define a unique molecular class of lung cancers, J Clin Oncol 30 (2012), 863–870.

12.

Y.L.

Yang

J.C.

Kim

D.W.

Zhou

Seto

Yang

J.J.

Yamamoto

Ahn

M.J.

Takahashi

Yamanaka

Kemner

Roychowdhury

Paolini

Usari

Wilner

K.D.

and Goto

, Phase II study of crizotinib in east asian patients with ROS1-positive advanced non-small-cell lung cancer, J Clin Oncol 36 (2018), 1405–1411.

13.

Davies

Bignell

G.R.

Cox

Stephens

Edkins

Clegg

Teague

Woffendin

Garnett

M.J.

Bottomley

Davis

Dicks

Ewing

Floyd

Gray

Hall

Hawes

Hughes

Kosmidou

Menzies

Mould

Parker

Stevens

Watt

Hooper

Wilson

Jayatilake

Gusterson

B.A.

Cooper

Shipley

Hargrave

Pritchard-Jones

Maitland

Chenevix-Trench

Riggins

G.J.

Bigner

D.D.

Palmieri

Cossu

Flanagan

Nicholson

J.W.

Leung

S.Y.

Yuen

S.T.

Weber

B.L.

Seigler

H.F.

Darrow

T.L.

Paterson

Marais

Marshall

C.J.

Wooster

Stratton

M.R.

and Futreal

P.A.

, Mutations of the BRAF gene in human cancer, Nature 417 (2002), 949–954.

14.

Planchard

Besse

Groen

H.J.M.

Souquet

P.J.

Quoix

Baik

C.S.

Barlesi

Kim

T.M.

Mazieres

Novello

Rigas

J.R.

Upalawanna

D’Amelio

A.M.

, Jr. Zhang

Mookerjee

and Johnson

B.E.

, Dabrafenib plus trametinib in patients with previously treated BRAF(V600E)-mutant metastatic non-small cell lung cancer: an open-label, multicentre phase 2 trial, Lancet Oncol 17 (2016), 984–993.

15.

Planchard

Smit

E.F.

Groen

H.J.M.

Mazieres

Besse

Helland

Å.

Giannone

D’Amelio

A.M.

, Jr. Zhang

Mookerjee

and Johnson

B.E.

, Dabrafenib plus trametinib in patients with previously untreated BRAF(V600E)-mutant metastatic non-small-cell lung cancer: an open-label, phase 2 trial, Lancet Oncol 18 (2017), 1307–1316.

16.

Takeda

Sakai

Takahama

Fukuoka

Nakagawa

and Nishio

, New era for next-generation sequencing in Japan, Cancers (Basel) 11 (2019).

17.

Pao

and Girard

, New driver mutations in non-small-cell lung cancer, Lancet Oncol 12 (2011), 175–180.

18.

Zehir

Benayed

Shah

R.H.

Syed

Middha

Kim

H.R.

Srinivasan

Gao

Chakravarty

Devlin

S.M.

Hellmann

M.D.

Barron

D.A.

Schram

A.M.

Hameed

Dogan

Ross

D.S.

Hechtman

J.F.

DeLair

D.F.

Yao

Mandelker

D.L.

Cheng

D.T.

Chandramohan

Mohanty

A.S.

Ptashkin

R.N.

Jayakumaran

Prasad

Syed

M.H.

Rema

A.B.

Liu

Z.Y.

Nafa

Borsu

Sadowska

Casanova

Bacares

Kiecka

I.J.

Razumova

Son

J.B.

Stewart

Baldi

Mullaney

K.A.

Al-Ahmadie

Vakiani

Abeshouse

A.A.

Penson

A.V.

Jonsson

Camacho

Chang

M.T.

Won

H.H.

Gross

B.E.

Kundra

Heins

Z.J.

Chen

H.W.

Phillips

Zhang

Wang

Ochoa

Wills

Eubank

Thomas

S.B.

Gardos

S.M.

Reales

D.N.

Galle

Durany

Cambria

Abida

Cercek

Feldman

D.R.

Gounder

M.M.

Hakimi

A.A.

Harding

J.J.

Iyer

Janjigian

Y.Y.

Jordan

E.J.

Kelly

C.M.

Lowery

M.A.

Morris

L.G.T.

Omuro

A.M.

Raj

Razavi

Shoushtari

A.N.

Shukla

Soumerai

T.E.

Varghese

A.M.

Yaeger

Coleman

Bochner

Riely

G.J.

Saltz

L.B.

Scher

H.I.

Sabbatini

P.J.

Robson

M.E.

Klimstra

D.S.

Taylor

B.S.

Baselga

Schultz

Hyman

D.M.

Arcila

M.E.

Solit

D.B.

Ladanyi

and Berger

M.F.

, Mutational landscape of metastatic cancer revealed from prospective clinical sequencing of 10,000 patients, Nat Med 23 (2017), 703–713.

19.

Jameson

J.L.

and Longo

D.L.

, Precision medicine-personalized, problematic, and promising, N Engl J Med 372 (2015), 2229–2234.

20.

Schwaederle

Zhao

Lee

J.J.

Lazar

Leyland-Jones

Schilsky

R.L.

Mendelsohn

and Kurzrock

, Association of biomarker-based treatment strategies with response rates and progression-free survival in refractory malignant neoplasms: a meta-analysis, JAMA Oncol 2 (2016), 1452–1459.

21.

Takeda

Sakai

Terashima

Kaneda

Hayashi

Tanaka

Okamoto

Takahama

Yoshida

Iwasa

Shimizu

Nonagase

Kudo

Tomida

Mitsudomi

Saigo

Ito

Nakagawa

and Nishio

, Clinical application of amplicon-based next-generation sequencing to therapeutic decision making in lung cancer, Ann Oncol 26 (2015), 2477–2482.

22.

Hagemann

I.S.

Devarakonda

Lockwood

C.M.

Spencer

D.H.

Guebert

Bredemeyer

A.J.

Al-Kateb

Nguyen

T.T.

Duncavage

E.J.

Cottrell

C.E.

Kulkarni

Nagarajan

Seibert

Baggstrom

Waqar

S.N.

Pfeifer

J.D.

Morgensztern

and Govindan

, Clinical next-generation sequencing in patients with non-small cell lung cancer, Cancer 121 (2015), 631–639.

23.

Zugazagoitia

Rueda

Carrizo

Enguita

A.B.

Gómez-Sánchez

Díaz-Serrano

Jiménez

Mérida

Calero

Lujan

De Miguel

Gámez

Díaz-Hellín

Nuñez

J.A.

Iglesias

Ferrer

Paz-Ares

and Ponce-Aix

, Prospective clinical integration of an amplicon-based next-generation sequencing method to select advanced non-small-cell lung cancer patients for genotype-tailored treatments, Clin Lung Cancer 19 (2018), 65–73.e7.

24.

Kou

Kanai

Yamamoto

Kamada

Nakatsui

Sakuma

Mochizuki

Hiroshima

Sugiyama

Nakamura

Miyake

Minamiguchi

Takaori

Matsumoto

Haga

Seno

Kosugi

Okuno

and Muto

, Clinical sequencing using a next-generation sequencing-based multiplex gene assay in patients with advanced solid tumors, Cancer Sci 108 (2017), 1440–1446.

25.

Sholl

, Molecular diagnostics of lung cancer in the clinic, Transl Lung Cancer Res 6 (2017), 560–569.

26.

Moreira

A.L.

and Eng

, Personalized therapy for lung cancer, Chest 146 (2014), 1649–1657.

27.

Thompson

J.C.

Yee

S.S.

Troxel

A.B.

Savitch

S.L.

Fan

Balli

Lieberman

D.B.

Morrissette

J.D.

Evans

T.L.

Bauml

Aggarwal

Kosteva

J.A.

Alley

Ciunci

Cohen

R.B.

Bagley

Stonehouse-Lee

Sherry

V.E.

Gilbert

Langer

Vachani

and Carpenter

E.L.

, Detection of therapeutically targetable driver and resistance mutations in lung cancer patients by next-generation sequencing of cell-free circulating tumor DNA, Clin Cancer Res 22 (2016), 5772–5782.

28.

Metzker

M.L.

, Sequencing technologies – the next generation, Nat Rev Genet 11 (2010), 31–46.

29.

Yohe

and Thyagarajan

, Review of clinical next-generation sequencing, Arch Pathol Lab Med 141 (2017), 1544–1557.

30.

Jennings

L.J.

Arcila

M.E.

Corless

Kamel-Reid

Lubin

I.M.

Pfeifer

Temple-Smolkin

R.L.

Voelkerding

K.V.

and Nikiforova

M.N.

, Guidelines for validation of next-generation sequencing-based oncology panels: a joint consensus recommendation of the association for molecular pathology and college of american pathologists, J Mol Diagn 19 (2017), 341–365.

31.

Biesecker

L.G.

and Green

R.C.

, Diagnostic clinical genome and exome sequencing, N Engl J Med 370 (2014), 2418–2425.

32.

Chong

C.R.

and Jänne

P.A.

, The quest to overcome resistance to EGFR-targeted therapies in cancer, Nat Med 19 (2013), 1389–1400.

33.

Cross

D.A.

Ashton

S.E.

Ghiorghiu

Eberlein

Nebhan

C.A.

Spitzler

P.J.

Orme

J.P.

Finlay

M.R.

Ward

R.A.

Mellor

M.J.

Hughes

Rahi

Jacobs

V.N.

Red Brewer

Ichihara

Sun

Jin

Ballard

Al-Kadhimi

Rowlinson

Klinowska

Richmond

G.H.

Cantarini

Kim

D.W.

Ranson

M.R.

and Pao

, AZD9291, an irreversible EGFR TKI, overcomes T790M-mediated resistance to EGFR inhibitors in lung cancer, Cancer Discov 4 (2014), 1046–1061.

34.

Yang

Xiang

Jiang

Bao

Tong

Wang

Shao

Y.W.

Liu

Wang

and Zhou

, Investigating novel resistance mechanisms to third-generation EGFR tyrosine kinase inhibitor osimertinib in non-small cell lung cancer patients, Clin Cancer Res 24 (2018), 3097–3107.

35.

Toyokawa

and Seto

, Updated evidence on the mechanisms of resistance to ALK inhibitors and strategies to overcome such resistance: clinical and preclinical data, Oncol Res Treat 38 (2015), 291–298.

36.

Gainor

J.F.

Dardaei

Yoda

Friboulet

Leshchiner

Katayama

Dagogo-Jack

Gadgeel

Schultz

Singh

Chin

Parks

Lee

DiCecca

R.H.

Lockerman

Huynh

Logan

Ritterhouse

L.L.

L.P.

Muniappan

Digumarthy

Channick

Keyes

Getz

Dias-Santagata

Heist

R.S.

Lennerz

Sequist

L.V.

Benes

C.H.

Iafrate

A.J.

Mino-Kenudson

Engelman

J.A.

and Shaw

A.T.

, Molecular mechanisms of resistance to first- and second-generation ALK inhibitors in ALK-rearranged lung cancer, Cancer Discov 6 (2016), 1118–1133.

Implementation of clinical sequencing for molecular profiling in patients with advanced cancer

Abstract

BACKGROUND:

OBJECTIVE:

METHODS:

RESULTS:

CONCLUSIONS:

Keywords

1. Background

2. Methods

3. Results

3.1 Patient characteristics

Table 1 Characteristics of the patients subjected to molecular analysis ( n = 33)

3.3 Prevalence of genomic alterations

4. Discussion

5. Conclusions

Author contributions

Supplementary data

sj-docx-1-cbm-10.3233_CBM-200781.docx - Supplemental material

Footnotes

Acknowledgments

Conflict of interest

References

Table 1
Characteristics of the patients subjected to molecular analysis ( $n=$ 33)