Targeted next generation sequencing assays using triplet samples of normal breast tissue, primary breast cancer, and recurrent/metastatic lesions

doi:10.21203/rs.2.16536/v2

Background: Next generation sequencing (NGS) has shown that recurrent/metastatic breast cancer lesions may have additional genetic changes compared with the primary tumour. These additional changes may be related to tumour progression and/or drug resistance. The breast cancer-targeted NGS, however, is not still widely used for comparing genomic profile of primary breast cancer and recurrent/metastatic lesions in clinical practice.

Methods: Genomic DNA was extracted from normal breast tissue, primary breast cancer, and recurrent/metastatic lesion(s) from the same patient. A DNA library was constructed using the QIAseq Human Breast Cancer Panel (93 genes, Qiagen) and then sequenced by a MiSeq (Illumina). The Qiagen web portal was utilized for data analysis.

Results: Of 35 breast cancer cases with recurrence/metastases, successful results for three or four samples (normal breast tissue, primary tumour, and at least one metastatic/recurrent lesion) were obtained for 11 patients (36 samples). We detected shared somatic mutations in all but one patient, who had germline mutation in TP53. Additional mutations were detected in recurrent/metastatic lesions compared with primary tumour in genes including TP53 (three patients) and one case each of ATR, BLM, CBFB, EP300, ERBB2, MUC16, PBRM1, and PIK3CA. Actionable mutations and/or copy number variations (CNVs) were detected in 82% (9/11) of recurrent/metastatic breast cancer cases.

Conclusions: The QIAseq Human Breast Cancer Panel assay could identify driver mutations in both primary breast tumour tissue and recurrent/metastatic lesions in almost all patients. This method can assist in identifying drug-targetable mutation and CNV in metastatic breast cancers.

Oncology

Breast cancer

Metastasis

Recurrence

Next generation sequencing

Studies using next generation sequencing (NGS) have demonstrated remodelling of genes in metastatic cancer compared with the primary tumour from the same individual, possibly as a result of subclonal diversification or mutational evolution [1-10]. Whole-exon or whole-genome analyses can yield valuable information but are costly and time-consuming. Whole-exon sequencing analysis of metastatic breast cancers showed drug-targetable mutations in genes such as ERBB4, NOTCH3, and ALK [11]. A targeted NGS assay using a general cancer panel of metastatic breast cancers and their associated primary tumour can also detect drug-targetable mutations. Vasan et al. reported that 84% (43/51) of metastatic breast cancers showed at least one genomic alteration that could be targeted by currently available drugs [12]. In 2016, Muller et al. reported that 45% (10/22) of metastatic breast cancers contained molecular targets for currently available therapies, including off-label use [13]. In these studies, cancer comprehensive panels that were not breast cancer specific were used. To our knowledge, there has been no study of breast cancer-targeted NGS using paired samples of primary breast cancer and recurrent/metastatic cancer.

The aim of the present study was to perform breast cancer-targeted NGS to compare gene mutations and copy number variation (CNV) in samples of primary breast cancer and recurrent/metastatic lesions from the same individual. Since sequencing analyses of matched tumour and normal tissue are essential [14], we performed an NGS study using three or more samples from each patient, consisting of normal breast tissue, primary breast cancer, and recurrent/metastatic lesion(s). This study will elucidate the practical possibility of breast cancer-targeted NGS and add better understanding of subclonal diversification or mutational evolution of metastatic breast cancers.

Patients and samples

One hundred and seven cases of distant metastasis or local recurrence of breast cancer were collected from the pathology archives of Kawasaki Medical School Hospital from 2010 to 2017. The microscopic evaluation of these cases found that normal breast tissue, sufficient (20% or more tumour content) primary breast cancer tissue, and tissue from at least one relapse site were available for 35 patients. For 66 patients, the tumour content was less than 20% in primary tumours and/or recurrent/metastatic tumours. For six patients, the primary breast cancers were operated in other institutions, so the histologic specimens were not available. The protocol of the present study was approved by the Ethics Committee of Kawasaki Medical School and Hospital (approval number: 2695).

DNA extraction and quality assessment

Formalin-fixed paraffin-embedded (FFPE) tissue blocks were obtained from the Department of Pathology at Kawasaki Medical School Hospital. DNA was extracted from the tumour and normal tissue at the primary site (breast) and from sites of metastasis and/or local recurrence. Samples of pleural or pericardial effusion were collected using the collodion bag method to generate cell blocks. Four 10-mm sections were cut from each paraffin block. Maxwell 16 FFPE Tissue LEV DNA purification kits (#AS1130, Promega, Madison, WI, USA) were employed for DNA extraction. DNA was quantified using a Qubit ® 2.0 Fluorometer (Thermo Fisher Scientific, Waltham, MA, USA) and Qubit ® dsDNA BR assay kits (#Q32850, Thermo Fisher Scientific). DNA quality was assessed by calculating the QC score (https://www.qiagen.com/us/resources/download.aspx?id=aae35658-5ef2-44b2-bd02-fbe73fe7737c&lang=en). DNA amplification for library construction was performed by quantitative polymerase chain reaction (qPCR) using QIAseq DNA QuantiMIZE Assay Kits (#DNQC-100Y-R, Qiagen, Hilden, Germany) [15].

Next generation sequencing

The QIAseq Human Breast Cancer Panel (93 genes, DHS-001Z, Qiagen) was used for library construction according to the manufacturer’s instructions. The libraries were assessed using a QIAseq Library Quant Assay Kit (#QSTF-ILZ-R, Qiagen) and applied to MiSeq sequencer (Illumina, San Diego, CA, USA). The Qiagen web portal (https://www.qiagen.com/us/shop/genes-and-pathways/data-analysis-center-overview-page/) was utilized for data analysis [16]. For alignment, GenomeBrowse (http://goldenhelix.com/products/GenomeBrowse/index.html) was used, and GRCH37 was used for the human genome reference. CNV was calculated using the cloud analysis pipeline of the Qiagen web portal, and corrected with the tumour content percentage. Four or more and one or less CNVs were regarded as significant.

Statistical analyses

Statistical analyses were performed using IBM SPSS Statistics for Windows (v 25; IBM Corp., Armonk, NY, USA) and P<0.05 was considered significant.

Clinicopathological findings

For the successfully analyzed 11 patients, the median age was 52.5 years at diagnosis of breast cancer. The median time to first relapse was 11 months, and the overall survival was 39 months. All but one patients died from breast cancer. The breast cancer subtypes were six triple-negative, four luminal, and one HER2-enhanced. All patients but one had received chemotherapy and/or hormonal therapy (Table 1, Supplementary Table 1).

Table 1 Clinicopathological factors of the breast cancer patients with recurrent/metastatic lesions

Case ID	Primary or relapse	Age	Primary systemic therapy	Adjuvant therapy	Histology	pT	pN	Grade	Ly	V	ER (%)	PgR (%)	HER2	Ki67 (%)	Follow-up (months)	Outcome
K18	breast primary	41	CPA, EPI, DTX, 5'DFUR	X	ICNOS	1a	0	2	0	1	0	0	0	76.0	23	DOD
	local recurrence	42									<1	0	1+	70.5
K20	breast primary	37	CPA, EPI	PTX, TRA, TAM	ICNOS	2	2a	3	3	1	60	0	3+	57.1	60	DOD
	pleural effusion	42									0	0	2 + (FISH +)	45.0
K21	breast primary	66	CPA, EPI, EXE	X, EXE	ICNOS	4b	1a	2	1	2	90	10	1+	39.5	63	DOD
	local recurrence	68									100	70	1+	22.3
	local recurrence	69									100	90	1+	47.6
K22	breast primary	63	no	TAM	ILC	1b	0	1	0	0	30	0	0	9.4	25	DOD
	pleural effusion	65									0	<1	0	24.8
K23	breast primary	65	no	no	ICNOS	1a	0	1	0	0	95	80	1+	11.8	92	LND
	local recurrence	68									95	90	1+	5.8
K25	breast primary	35	CPA, EPI, DTX, 5'DFUR	GEM	ICNOS	2	1a	3	1	1	0	0	0	55.4	21	DOD
	local recurrence	36									0	0	1+	83.0
K27	breast primary	55	CPA, EPI	X	ICNOS	3	0	3	3	0	5	2	1+	68.2	7	DOD
	local recurrence	55									0	0	0	87.0
K28	breast primary	50	CPA, EPI, DTX, 5'DFUR	CPA, EPI, X	ICNOS	3	1a	3	3	2	0	0	1+	86.8	50	DOD
	local recurrence	51									0	0	1+	57.4
K30	breast primary	68	no	5FU, CPA, EPI	ICNOS	1c	3b	3	1	0	0	0	0	80.2	12	DOD
	pleural effusion	68									0	0	0	87.0
	pleural effusion #2	69									0	0	0	84.0
K31	breast primary	47	CPA, EPI	CPA, X	ICNOS	1a	3a	3	1	0	0	0	0	65.4	28	DOD
	pericardial effusion	48									0	0	0	36.6
	pleural effusion	49									0	0	0	58.4
K32	breast primary	38	no	CPA, EPI, TAM	ICNOS	1c	1mi	2	2	1	80	2	1+	37.5	83	DOD
	lung metastasis	45									10	0	0	58.4
CPA: cyclophosphamide, EPI: epirubicin, DTX: docetaxel, 5'DFUR: doxifluridine, TAM: tamoxifen, X: capecitabine, PTX: paclitaxel, TRA: trastuzumab, EXE: exemestane,
GEM: gemcitabine, ICNOS: invasive carcinoma of no special type, ILC: invasive lobular carcinoma, na: not available, DOD: dead of disease, LND: living without disease.

DNA quality

The data for DNA quality are shown in Supplementary Table 2. The QC score was calculated as the difference between the CT values obtained by qPCR using the 100-bp and 200-bp primers of the QIAseq DNA QuantiMIZE assay kit. A high QC score indicates severe DNA fragmentation. A favourable QC score (£0.04) was obtained from 43 of 85 (50.6%) samples. The samples with good DNA quality were collected significantly more recently than those with poor DNA quality (p<0.0005, Fig. 1). The FFPE blocks used in this study were 6 months to 17 years old; 66.7% (38/57) of the blocks that were ³ 5 years old had a QC score > 0.04. We attempted to construct libraries from 14 high QC score samples; however, the reading depth for the molecular tagged sites in eight cases (57.2%) was <10, and failed to identify a mutation. The depth of molecular tagged sites and all examined sites were both significantly correlated with the storage years (P < 0.0005 and r=-0.586, P=0.004 and r=-0.412, Spearman's correlation).

QIAseq Human Breast Cancer Panel

Successful results were obtained from samples of 11 patients that comprised normal breast tissue, primary tumour, and at least one metastatic/recurrence lesion (36 samples, Table 1) using the QIAseq Human Breast Cancer Panel. We detected somatic driver mutations in all but one case (90.1%) (Table 2). The mutations shared between the primary tumour and a recurrent/metastatic lesion were in TP53 (five cases), PIK3CA (three cases), CDH1 (one case), ESR1 (one case), GATA3 (one case), and PTEN (one case). In five cases (45.4%), additional mutations were detected in the recurrent/metastatic lesions compared with the primary tumour. These additional mutations occurred in TP53 for three cases and in ATR, BLM, CBFB, EP300, ERBB2, MUC16, PBRM1, and PIK3CA in one case each (Table 2, Fig. 2). No additional mutation was found in one long-surviving patient (K23) at 92 months after partial mastectomy.

The results for CNV are shown in Table 2 and Supplementary Table 2. Significant CNV seems to be higher in distant metastasis than in local recurrence, but not statistically significant (P=0.091, Fig. 3).

Table 2. Mutations and copy number variations of primary breast cancers and recurrent/metastatic lesions
Primary or relapse	Tumour ratio† (%)	Mutation	Variant frequency (%)	Representative copy number alternation (CN)
breast primary	40	TP53 p.P152fs	17.95	CDKN2A (0.78) PTEN (0.8) CCND1 (8.25)
local recurrence	50	TP53 p.P152fs	19.2	CDKN2A (0) TP53 (0.76)
		PBRM1 c.996-4G>A	27.12
		ATR p.I774fs	16.74
breast primary	20	PIK3CA p.H1047R	9.3	PMS1 (0.03) PTEN (0) RB1 (0.3) MAP2K4 (0.47) ERBB2 (8.37)
pleural effusion	60	PIK3CA p.H1047R	24.07	PMS1 (0.87) PTEN (0.3) RB1 (0.92) MAP2K4 (0.85) ERBB2 (14.83)
		PIK3CA p.S6L	17.71
		TP53 p.X224_splice	33.3
		MUC16 p.Q5773E	18.75
		EP300 p.1706D	18.53
breast primary	70	PIK3CA p.H1047R	33.8
		GATA3 p.X308_splice	31.4
		ESR1 p.D538G	36.22
local recurrence	60	PIK3CA p.H1047R	28.06
		GATA3 p.X308_splice	20.65
		ESR1 p.D538G	31.05
local recurrence #2	70	PIK3CA p.H1047R	30.08
		GATA3 p.X308_splice	39.74
		ESR1 p.D538G	38.12
breast primary	20	CDH1 p.Q264*	8.46	PTEN (0.6) RB1 (0.97)
pleural effusion	50	CDH1 p.Q264*	20.69	TP53 (0.96)
		ERBB2 p.S310F	20.69
		CBFB p.X27_splice	18.31
		TP53 p.R248Q	18.3
breast primary	60	PTEN p.A328fs*15	52
local recurrence	40	PTEN p.A328fs*15	22.81	PTEN (0.95)
breast primary	70	TP53 p.P36fs*7	48.94	PTEN (0.44)
breast primary	70	KMT2C p.Y816fs* (VUS)	28.95
local recurrence	60	TP53 p.P36fs*7	54.07	MYC (5.07) PTEN (0.33)
local recurrence	60	KMT2C p.Y816fs* (VUS)	29.25
breast primary	30	TP53 p.X306_splice	32.22	MYC (6.9)
local recurrence	40	TP53 p.X306_splice	20.33	MYC (4.98)
breast primary	70	TP53 p.R213*	30.3	MYC (4.16) CCND1 (7.8)
local recurrence	30	TP53 p.R213*	29.7	PTEN (0.55) CCND1 (10.73)
breast primary	60	TP53 p.R342fs*3	21.25	PMS1 (0.73) MYC (6.85) PTEN (0.28) ATM (0.8) RB1 (0.67)
pleural effusion	80	TP53 p.R342fs*3	75.86	MYC (7.41)
pleural effusion #2	80	TP53 p.R342fs*3	71.11	FGFR1 (4.4) MYC (8.2) ATM (0.91)
breast primary	20	TP53 p.R146*	8.5	CDKN2A (0.95)
pericardial effusion	90	TP53 p.R146*	56.52	MYC (4.11)
pericardial effusion	90	TP53 p.I195T	32.29
pleural effusion	90	TP53 p.R146*	70	MYC (5.99)
pleural effusion	90	TP53 p.I195T	25
breast primary	70	PIK3CA p.H1047R	71.43
breast primary	70	TP53 p.R196*	34.09
lung metastasis	60	PIK3CA p.H1047R	62.62	EGFR (4.57) FGFR1 (11.67) CDKN2A (0.98) ATM (0.95)
		TP53 p.R196*	59.73
		BLM p.N525fs*16	11.03
†Tumour ratio was estimated by conventional haematoxylin–eosin staining.
‡Normal breast tissue of case K25 showed the same mutations in TP53 and KMT2C.
Red: “actionable” mutation.

1.Feasibility of QIAseq Human Breast Cancer Panel

We showed that the older FFPE materials often had lower DNA quality and could not be analyzed. The depth discrepancy of molecular tagged sites and all sites is interesting. The Spearman's correlation coefficients with age were -0.586 for tagged sites and -0.412 for all sites. Molecular tag might be designed to sensitive for aged DNA damage. PAXgene tissue fixation and/or low-temperature paraffin block storing at 4°C or −20°C could improve DNA quality [41].

For 66 of 107 cases (61.7%), the tumour content was less than 20% in primary tumours and/or recurrent/metastatic tumours. The cell blocks of fluid materials frequently included many inflammatory cells and reactive mesothelial cells together with cancer cells, therefore, the extraction of tumour DNA extraction from cell blocks was often inefficient. A primary breast cancer containing abundant stromal lymphoplasmacytic cell infiltration or a severe desmoplastic reaction would also inhibit effective cancer genome recovery. Thus, a more efficient microdissection system is desirable to be developed.

2. Mutation and CNV of primary breast cancer and recurrent/metastatic lesions

We demonstrated that targeted NGS using the QIAseq Human Breast Cancer Panel could detect the driver mutations in all cases of breast cancer examined, except for one case with germline TP53 mutation that did not meet the classic criteria for Li–Fraumeni syndrome [17] or the Chompret criteria [18].

The most frequently detected mutation shared between primary and metastatic lesions was in TP53. Breast cancer patients with a somatic TP53 mutation have a poor prognosis [19, 20]; unfortunately, TP53 mutations are not presently targetable. Phosphatidylinositide 3-kinase (PI3K) inhibitor could be a good therapeutic option for cases with PIK3CA mutations [21-23]. Alpelisib has been reported to improve the survival of PIK3CA-altered, ER-positive, HER2-negative breast cancer patients [24, 25]. Alpelisib is already approved by Food and Drug Administration of USA. The Lotus trial showed that ipatasertib, an oral AKT inhibitor, improved the progression-free survival of breast cancer patients with PIK3CA/AKT/PTEN mutations [26]. IPATunity130, a pivotal randomized phase III trial evaluating ipatasertib (IPAT) + paclitaxel for PIK3CA/AKT1/PTEN-altered advanced triple-negative or hormone receptor-positive HER2-negative breast cancer, is ongoing (http://ascopubs.org/doi/abs/10.1200/JCO.2018.36.15_suppl.TPS1117). Breast cancers with a PIK3CA mutation have a good prognosis [20]. CDH1-mutated breast cancer cells are sensitive to ROS1 tyrosine kinase inhibitor including foretinib or crizotinib [27]. ESR1 mutation of breast cancer is often reported after aromatase inhibitor and/or tamoxifen therapy [28]. The SoFEA (Study of Faslodex Versus Exemestane With or Without Arimidex) trial showed that cases with ESR1 mutations had better survival when treated with fulvestrant compared with exemestane [29].

Of the detected additional mutations, ERBB2 p.S310F is notable because it results in activation of HER2 without gene amplification or protein overexpression [30]. A tumour with this mutation is likely to be sensitive to neratinib, as are those with G660D, R678Q, E693K, and Q709 mutations [31]. The neratinib HER Mutation Basket Study (SUMMIT) has already started [32]. PBRM1 mutation may evoke immunotherapy resistance [33].

Cases with PTEN loss could be treated with PI3K/AKT inhibitor [34]. Case K18 and K28 (triple-negative cancers) with CCND1 gene amplification might be sensitive to CDK4/6 inhibitors [35]. Meanwhile, erdafitinib and cetuximab might be effective for case K30 and K32 in which the metastatic lesions contained FGFR and EGFR amplifications [36, 37]. Case K30 and K32 with decreased ATM might be sensitive to topotecan or the poly-(ADP-ribose) polymerase (PARP) inhibitor olaparib [38]. CDKN2A or RB1 down-regulation could be target of palbociclib [39].

The present study with only 93 genes analysed showed actionable mutations or CNVs in 82% (9/11) of recurrent/metastatic breast cancer cases. This is comparable to previous studies including MSK-IMPACT (61%), the study by Vasan et al. (84%), and the study by Muller et al. (45%) [12, 13, 40].

Small scale and subtype bias are major limitations of the present study. DNA availability problem is responsible. Luminal breast cancer patients often show late recurrence/metastasis, up to 18 years in our series. The primary tumour blocks of such cases are too old and rarely keep sufficient DNA quality. Meanwhile, triple negative breast cancer usually recurs shortly after the operation. Another limitation is the determination of the cut-off level for CNV. The relationship between drug sensitivity and CNV remains to be elucidated. Moreover, additional immunohistochemistry might be helpful for cases with altered CNVs of EGFR or FGFR. We could detect the somatic driver mutations or germline mutations in five triple-negative cancers; however, the genes covered by the QIAseq Human Breast Cancer Panel might be inadequate for analysis of triple-negative breast cancers because they are known to have largely variable mutations [41].

Our targeted NGS assay using the QIAseq Human Breast Cancer Panel could identify driver somatic mutations in both primary breast tumour and recurrent/metastatic lesions with the exception for a germline mutation case. This method assists the identification of drug-targetable mutations and changes in CNV levels. The performance of an expanded study including an analysis of drug sensitivity is to be encouraged.

CNV: Copy number variation

FFPE: Formalin-fixed paraffin-embedded

NGS: Next generation sequencing

Ethics approval and consent to participate

Written informed consent was obtained for use of retrospective tissue samples from the patients within this study. Ethnical approval was obtained from Institutional Review Board of Kawasaki Medical School ethics committee (approval number: 2695).

Consent for publication

Not applicable.

Availability of data and materials

All data generated or analysed during this study are included in this published article.

Competing interests

The authors declare that they have no competing interests.

Funding

This work was supported by Research Project Grants from Kawasaki Medical School (30-026) and JSPS KAKENHI (17K08750). All the grand supporters have no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.

Authors’ contributions

TA and AT were responsible for NGS, NK was responsible for histology and drafted the manuscript; OH contributed statistical analyses; TY and JK contributed clinical information. TM contributed data analysis and interpretation. TM also supervised NK. All authors read and approved the manuscript.

Acknowledgements

We thank Megumi Kuriyama for her technical assistance and Hiroko Murakami for her secretarial assistance. We also thank H. Nikki March, PhD, from Edanz Group (www.edanzediting.com/ac) for editing a draft of this manuscript.

Ding L, Ellis MJ, Li S, Larson DE, Chen K, Wallis JW, Harris CC, McLellan MD, Fulton RS, Fulton LL et al. Genome remodelling in a basal-like breast cancer metastasis and xenograft. Nature. 2010;464(7291):999-1005.
De Mattos-Arruda L, Weigelt B, Cortes J, Won HH, Ng CK, Nuciforo P, Bidard FC, Aura C, Saura C, Peg V et al. Capturing intra-tumor genetic heterogeneity by de novo mutation profiling of circulating cell-free tumor DNA: a proof-of-principle. Ann Oncol. 2014;25(9):1729-35.
Brastianos PK, Carter SL, Santagata S, Cahill DP, Taylor-Weiner A, Jones RT, Van Allen EM, Lawrence MS, Horowitz PM, Cibulskis K et al. Genomic Characterization of Brain Metastases Reveals Branched Evolution and Potential Therapeutic Targets. Cancer Discov. 2015;5(11):1164-77.
Juric D, Castel P, Griffith M, Griffith OL, Won HH, Ellis H, Ebbesen SH, Ainscough BJ, Ramu A, Iyer G et al. Convergent loss of PTEN leads to clinical resistance to a PI(3)Kalpha inhibitor. Nature. 2015;518(7538):240-4.
Yates LR, Gerstung M, Knappskog S, Desmedt C, Gundem G, Van Loo P, Aas T, Alexandrov LB, Larsimont D, Davies H et al. Subclonal diversification of primary breast cancer revealed by multiregion sequencing. Nat Med. 2015;21(7):751-9.
Hoadley KA, Siegel MB, Kanchi KL, Miller CA, Ding L, Zhao W, He X, Parker JS, Wendl MC, Fulton RS et al. Tumor Evolution in Two Patients with Basal-like Breast Cancer: A Retrospective Genomics Study of Multiple Metastases. PLoS Med. 2016;13(12):e1002174.
Savas P, Teo ZL, Lefevre C, Flensburg C, Caramia F, Alsop K, Mansour M, Francis PA, Thorne HA, Silva MJ et al. The Subclonal Architecture of Metastatic Breast Cancer: Results from a Prospective Community-Based Rapid Autopsy Program "CASCADE". PLoS Med. 2016;13(12):e1002204.
Yates LR, Knappskog S, Wedge D, Farmery JHR, Gonzalez S, Martincorena I, Alexandrov LB, Van Loo P, Haugland HK, Lilleng PK et al. Genomic Evolution of Breast Cancer Metastasis and Relapse. Cancer Cell. 2017;32(2):169-84 e7.
Siegel MB, He X, Hoadley KA, Hoyle A, Pearce JB, Garrett AL, Kumar S, Moylan VJ, Brady CM, Van Swearingen AE et al. Integrated RNA and DNA sequencing reveals early drivers of metastatic breast cancer. J Clin Invest. 2018;128(4):1371-83.
Bertucci F, Ng CKY, Patsouris A, Droin N, Piscuoglio S, Carbuccia N, Soria JC, Dien AT, Adnani Y, Kamal M et al. Genomic characterization of metastatic breast cancers. Nature. 2019;569(7757):560-4.
Lefebvre C, Bachelot T, Filleron T, Pedrero M, Campone M, Soria JC, Massard C, Levy C, Arnedos M, Lacroix-Triki M et al. Mutational Profile of Metastatic Breast Cancers: A Retrospective Analysis. PLoS Med. 2016;13(12):e1002201.
Vasan N, Yelensky R, Wang K, Moulder S, Dzimitrowicz H, Avritscher R, Wang B, Wu Y, Cronin MT, Palmer G et al. A targeted next-generation sequencing assay detects a high frequency of therapeutically targetable alterations in primary and metastatic breast cancers: implications for clinical practice. Oncologist. 2014;19(5):453-8.
Muller KE, Marotti JD, de Abreu FB, Peterson JD, Miller TW, Chamberlin MD, Tsongalis GJ, Tafe LJ. Targeted next-generation sequencing detects a high frequency of potentially actionable mutations in metastatic breast cancers. Exp Mol Pathol. 2016;100(3):421-5.
Jones S, Anagnostou V, Lytle K, Parpart-Li S, Nesselbush M, Riley DR, Shukla M, Chesnick B, Kadan M, Papp E et al. Personalized genomic analyses for cancer mutation discovery and interpretation. Sci Transl Med. 2015;7(283):283ra53.
Akahane T, Yamaguchi T, Kato Y, Yokoyama S, Hamada T, Nishida Y, Higashi M, Nishihara H, Suzuki S, Ueno S et al. Comprehensive validation of liquid-based cytology specimens for next-generation sequencing in cancer genome analysis. PLoS One. 2019;14(6):e0217724.
Bandoh N, Akahane T, Goto T, Kono M, Ichikawa H, Sawada T, Yamaguchi T, Nakano H, Kawase Y, Kato Y et al. Targeted next-generation sequencing of cancer-related genes in thyroid carcinoma: A single institution's experience. Oncol Lett. 2018;16(6):7278-86.
Li FP, Fraumeni JF, Jr., Mulvihill JJ, Blattner WA, Dreyfus MG, Tucker MA, Miller RW. A cancer family syndrome in twenty-four kindreds. Cancer Res. 1988;48(18):5358-62.
Chompret A, Abel A, Stoppa-Lyonnet D, Brugieres L, Pages S, Feunteun J, Bonaiti-Pellie C. Sensitivity and predictive value of criteria for p53 germline mutation screening. J Med Genet. 2001;38(1):43-7.
Silwal-Pandit L, Vollan HK, Chin SF, Rueda OM, McKinney S, Osako T, Quigley DA, Kristensen VN, Aparicio S, Borresen-Dale AL et al. TP53 mutation spectrum in breast cancer is subtype specific and has distinct prognostic relevance. Clin Cancer Res. 2014;20(13):3569-80.
Griffith OL, Spies NC, Anurag M, Griffith M, Luo J, Tu D, Yeo B, Kunisaki J, Miller CA, Krysiak K et al. The prognostic effects of somatic mutations in ER-positive breast cancer. Nat Commun. 2018;9(1):3476.
Baselga J, Im SA, Iwata H, Cortes J, De Laurentiis M, Jiang Z, Arteaga CL, Jonat W, Clemons M, Ito Y et al. Buparlisib plus fulvestrant versus placebo plus fulvestrant in postmenopausal, hormone receptor-positive, HER2-negative, advanced breast cancer (BELLE-2): a randomised, double-blind, placebo-controlled, phase 3 trial. Lancet Oncol. 2017;18(7):904-16.
Di Leo A, Johnston S, Lee KS, Ciruelos E, Lonning PE, Janni W, O'Regan R, Mouret-Reynier MA, Kalev D, Egle D et al. Buparlisib plus fulvestrant in postmenopausal women with hormone-receptor-positive, HER2-negative, advanced breast cancer progressing on or after mTOR inhibition (BELLE-3): a randomised, double-blind, placebo-controlled, phase 3 trial. Lancet Oncol. 2018;19(1):87-100.
Tamura K, Kodaira M, Shimizu C, Yonemori K, Yunokawa M, Shimomura A, Kobayashi T, Nakano K, Tomomatsu J, Ito Y et al. Phase I study of taselisib in Japanese patients with advanced solid tumors or hormone receptor-positive advanced breast cancer. Cancer Sci. 2018;109(5):1592-601.
Andre F, Ciruelos E, Rubovszky G, Campone M, Loibl S, Rugo HS, Iwata H, Conte P, Mayer IA, Kaufman B et al. Alpelisib for PIK3CA-Mutated, Hormone Receptor-Positive Advanced Breast Cancer. N Engl J Med. 2019;380(20):1929-40.
Juric D, Janku F, Rodon J, Burris HA, Mayer IA, Schuler M, Seggewiss-Bernhardt R, Gil-Martin M, Middleton MR, Baselga J et al. Alpelisib Plus Fulvestrant in PIK3CA-Altered and PIK3CA-Wild-Type Estrogen Receptor-Positive Advanced Breast Cancer: A Phase 1b Clinical Trial. JAMA Oncol. 2018:e184475.
Kim SB, Dent R, Im SA, Espie M, Blau S, Tan AR, Isakoff SJ, Oliveira M, Saura C, Wongchenko MJ et al. Ipatasertib plus paclitaxel versus placebo plus paclitaxel as first-line therapy for metastatic triple-negative breast cancer (LOTUS): a multicentre, randomised, double-blind, placebo-controlled, phase 2 trial. Lancet Oncol. 2017;18(10):1360-72.
Bajrami I, Marlow R, van de Ven M, Brough R, Pemberton HN, Frankum J, Song F, Rafiq R, Konde A, Krastev DB et al. E-Cadherin/ROS1 Inhibitor Synthetic Lethality in Breast Cancer. Cancer Discov. 2018;8(4):498-515.
Zundelevich A, Dadiani M, Kahana-Edwin S, Itay A, Sella T, Gadot M, Cesarkas K, Farage-Barhom S, Saar EG, Eyal E et al. ESR1 mutations are frequent in newly diagnosed metastatic and loco-regional recurrence of endocrine-treated breast cancer and carry worse prognosis. Breast Cancer Res. 2020;22(1):16.
Fribbens C, O'Leary B, Kilburn L, Hrebien S, Garcia-Murillas I, Beaney M, Cristofanilli M, Andre F, Loi S, Loibl S et al. Plasma ESR1 Mutations and the Treatment of Estrogen Receptor-Positive Advanced Breast Cancer. J Clin Oncol. 2016;34(25):2961-8.
Greulich H, Kaplan B, Mertins P, Chen TH, Tanaka KE, Yun CH, Zhang X, Lee SH, Cho J, Ambrogio L et al. Functional analysis of receptor tyrosine kinase mutations in lung cancer identifies oncogenic extracellular domain mutations of ERBB2. Proc Natl Acad Sci U S A. 2012;109(36):14476-81.
Pahuja KB, Nguyen TT, Jaiswal BS, Prabhash K, Thaker TM, Senger K, Chaudhuri S, Kljavin NM, Antony A, Phalke S et al. Actionable Activating Oncogenic ERBB2/HER2 Transmembrane and Juxtamembrane Domain Mutations. Cancer Cell. 2018;34(5):792-806 e5.
Hyman DM, Piha-Paul SA, Won H, Rodon J, Saura C, Shapiro GI, Juric D, Quinn DI, Moreno V, Doger B et al. HER kinase inhibition in patients with HER2- and HER3-mutant cancers. Nature. 2018;554(7691):189-94.
Otto G. Kidney cancer: PBRM1 loss promotes tumour response to immunotherapy. Nat Rev Clin Oncol. 2018;15(3):134-5.
Xing Y, Lin NU, Maurer MA, Chen H, Mahvash A, Sahin A, Akcakanat A, Li Y, Abramson V, Litton J et al. Phase II trial of AKT inhibitor MK-2206 in patients with advanced breast cancer who have tumors with PIK3CA or AKT mutations, and/or PTEN loss/PTEN mutation. Breast Cancer Res. 2019;21(1):78.
Hamilton E, Infante JR. Targeting CDK4/6 in patients with cancer. Cancer Treat Rev. 2016;45:129-38.
Roncato F, Rruga F, Porcu E, Casarin E, Ronca R, Maccarinelli F, Realdon N, Basso G, Alon R, Viola G et al. Improvement and extension of anti-EGFR targeting in breast cancer therapy by integration with the Avidin-Nucleic-Acid-Nano-Assemblies. Nat Commun. 2018;9(1):4070.
Karkera JD, Cardona GM, Bell K, Gaffney D, Portale JC, Santiago-Walker A, Moy CH, King P, Sharp M, Bahleda R et al. Oncogenic Characterization and Pharmacologic Sensitivity of Activating Fibroblast Growth Factor Receptor (FGFR) Genetic Alterations to the Selective FGFR Inhibitor Erdafitinib. Mol Cancer Ther. 2017;16(8):1717-26.
Balmus G, Pilger D, Coates J, Demir M, Sczaniecka-Clift M, Barros AC, Woods M, Fu B, Yang F, Chen E et al. ATM orchestrates the DNA-damage response to counter toxic non-homologous end-joining at broken replication forks. Nat Commun. 2019;10(1):87.
Frisone D, Charrier M, Clement S, Christinat Y, Thouvenin L, Homicsko K, Michielin O, Bodmer A, Chappuis PO, McKee TA et al. Durable response to palbociclib and letrozole in ovarian cancer with CDKN2A loss. Cancer Biol Ther. 2020;21(3):197-202.
Zehir A, Benayed R, Shah RH, Syed A, Middha S, Kim HR, Srinivasan P, Gao J, Chakravarty D, Devlin SM et al. Mutational landscape of metastatic cancer revealed from prospective clinical sequencing of 10,000 patients. Nat Med. 2017;23(6):703-13.
Lips EH, Michaut M, Hoogstraat M, Mulder L, Besselink NJ, Koudijs MJ, Cuppen E, Voest EE, Bernards R, Nederlof PM et al. Next generation sequencing of triple negative breast cancer to find predictors for chemotherapy response. Breast Cancer Res. 2015;17(1):134.

Targeted next generation sequencing assays using triplet samples of normal breast tissue, primary breast cancer, and recurrent/metastatic lesions

Status:

Journal Publication

Version 2

Abstract

Figures

Background

Methods

Results

Discussion

Conclusions

Abbreviations

Declarations

References

Supplementary Files

Status:

Journal Publication

Version 2