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. 2020 May 24;20(1):459.
doi: 10.1186/s12885-020-06919-w.

LRIG1 gene copy number analysis by ddPCR and correlations to clinical factors in breast cancer

Mahmood Faraz  1 , Andreas Tellström  1 , Christina Edwinsdotter Ardnor  1 , Kjell Grankvist  2 , Lukasz Huminiecki  3   4 , Björn Tavelin  1 , Roger Henriksson  1 , Håkan Hedman  1 , Ingrid Ljuslinder  5
Affiliations

LRIG1 gene copy number analysis by ddPCR and correlations to clinical factors in breast cancer

Mahmood Faraz et al. BMC Cancer. .

Abstract

Background: Leucine-rich repeats and immunoglobulin-like domains 1 (LRIG1) copy number alterations and unbalanced gene recombination events have been reported to occur in breast cancer. Importantly, LRIG1 loss was recently shown to predict early and late relapse in stage I-II breast cancer.

Methods: We developed droplet digital PCR (ddPCR) assays for the determination of relative LRIG1 copy numbers and used these assays to analyze LRIG1 in twelve healthy individuals, 34 breast tumor samples previously analyzed by fluorescence in situ hybridization (FISH), and 423 breast tumor cytosols.

Results: Four of the LRIG1/reference gene assays were found to be precise and robust, showing copy number ratios close to 1 (mean, 0.984; standard deviation, +/- 0.031) among the healthy control population. The correlation between the ddPCR assays and previous FISH results was low, possibly because of the different normalization strategies used. One in 34 breast tumors (2.9%) showed an unbalanced LRIG1 recombination event. LRIG1 copy number ratios were associated with the breast cancer subtype, steroid receptor status, ERBB2 status, tumor grade, and nodal status. Both LRIG1 loss and gain were associated with unfavorable metastasis-free survival; however, they did not remain significant prognostic factors after adjustment for common risk factors in the Cox regression analysis. Furthermore, LRIG1 loss was not significantly associated with survival in stage I and II cases.

Conclusions: Although LRIG1 gene aberrations may be important determinants of breast cancer biology, and prognostic markers, the results of this study do not verify an important role for LRIG1 copy number analyses in predicting the risk of relapse in early-stage breast cancer.

Keywords: Breast cancer; Gene copy number; LRIG1; Prognosis; ddPCR.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Frequency distributions of LRIG1 and ERBB2 copy number ratios and ER levels and relationships between LRIG1 copy number ratios and breast cancer subtypes among 423 breast cancer cases. a Frequency distributions of LRIG1/CYP1B1 ratios determined by ddPCR. b Frequency distributions of ERBB2/CYP1B1 ratios determined by ddPCR (c) Frequency distributions of ER levels retrieved from clinical records. d Box plots of LRIG1/CYP1B1 ratios for each tumor subtype
Fig. 2
Fig. 2
Kaplan-Meier curves for OS and MFS according to LRIG1 status. Kaplan-Meier analyses were performed for OS (A-F) or MFS (G-L) for 423 breast cancer patients according to LRIG1 status (___ normal LRIG1,___LRIG1 loss, ___LRIG1 gain). Analyses are presented for the entire follow-up time (a, d, g, and j), five-year survival (b, e, h, and k), or ten-year survival (c, f, i, and l). Statistical significance was calculated using the log-rank test and is indicated in each graph
See this image and copyright information in PMC

References

    1. Ferlay J, Shin HR, Bray F, Forman D, Mathers C, Parkin DM. Estimates of worldwide burden of cancer in 2008: GLOBOCAN 2008. Int J Cancer. 2010;127(12):2893–2917. doi: 10.1002/ijc.25516. - DOI - PubMed
    1. DeSantis C, Ma J, Bryan L, Jemal A. Breast cancer statistics, 2013. CA Cancer J Clin. 2014;64(1):52–62. doi: 10.3322/caac.21203. - DOI - PubMed
    1. Perou CM, Sorlie T, Eisen MB, van de Rijn M, Jeffrey SS, Rees CA, Pollack JR, Ross DT, Johnsen H, Akslen LA, et al. Molecular portraits of human breast tumours. Nature. 2000;406(6797):747–752. doi: 10.1038/35021093. - DOI - PubMed
    1. Sorlie T, Perou CM, Tibshirani R, Aas T, Geisler S, Johnsen H, Hastie T, Eisen MB, van de Rijn M, Jeffrey SS, et al. Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc Natl Acad Sci U S A. 2001;98(19):10869–10874. doi: 10.1073/pnas.191367098. - DOI - PMC - PubMed
    1. Lehmann BD, Bauer JA, Chen X, Sanders ME, Chakravarthy AB, Shyr Y, Pietenpol JA. Identification of human triple-negative breast cancer subtypes and preclinical models for selection of targeted therapies. J Clin Invest. 2011;121(7):2750–2767. doi: 10.1172/JCI45014. - DOI - PMC - PubMed

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