nach oben

Erschienen in:

2023 | OriginalPaper | Buchkapitel

Deep Learning Reveals Biological Basis of Racial Disparities in Quadruple-Negative Breast Cancer

verfasst von : Bikram Sahoo, Zandra Pinnix, Alex Zelikovsky

Erschienen in: Bioinformatics Research and Applications

Verlag: Springer Nature Singapore

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Triple-negative breast cancer (TNBC) lacks crucial receptors. More aggressive is quadruple-negative (QNBC), which lacks androgen receptors. Racial disparities emerge, with African Americans facing worse QNBC outcomes. Our study deploys deep neural networks to identify QNBC ancestral biomarkers. Achieving 0.85 accuracy and 0.928 AUC, the model displays robust learning, optimized through hyperparameter tuning. Top genes are chosen via ANOVA rankings and hypothesis testing, highlighting ABCD1 as significant post-correction. Effect sizes suggest important shifts in other genes. This approach enhances QNBC understanding, particularly racial aspects, potentially guiding targeted treatments.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel Exploring Racial Disparities in Triple-Negative Breast Cancer: Insights from Feature Selection Algorithms

Nächstes Kapitel CSA-MEM: Enhancing Circular DNA Multiple Alignment Through Text Indexing Algorithms

Anders, S., Huber, W.: Differential expression analysis for sequence count data. Genome Biol. 11 (2010). https://doi.org/10.1186/gb-2010-11-10-r106. https://genomebiology.biomedcentral.com/articles/10.1186/gb-2010-11-10-r106

Angajala, A., et al.: Quadruple negative breast cancers (QNBC) demonstrate subtype consistency among primary and recurrent or metastatic breast cancer. Transl. Oncol. 12, 493–501 (2019). https://doi.org/10.1016/j.tranon.2018.11.008

Cho, B., et al.: Evaluation of racial/ethnic differences in treatment and mortality among women with triple-negative breast cancer. JAMA Oncol. 7, 1016 (2021). https://doi.org/10.1001/jamaoncol.2021.1254

Davis, M., et al.: AR negative triple negative or “quadruple negative” breast cancers in African American women have an enriched basal and immune signature. PloS One 13, e0196909 (2018)

Gasparini, P., et al.: Androgen receptor status is a prognostic marker in non-basal triple negative breast cancers and determines novel therapeutic options. PLoS ONE 9, e88525 (2014). https://doi.org/10.1371/journal.pone.0088525

geo: Home - geo - NCBI (2019). https://www.ncbi.nlm.nih.gov/geo/

Hon, J.: Breast cancer molecular subtypes: from TNBC to QNBC (2016)

Huang, M., Wu, J., Ling, R., Li, N.: Quadruple negative breast cancer. Breast Cancer 27(4), 527–533 (2020). https://doi.org/10.1007/s12282-020-01047-6CrossRefPubMed

National Cancer Institute: The cancer genome atlas program (TCGA) - NCI (2022). https://www.cancer.gov/ccg/research/genome-sequencing/tcga

10.

Jinna, N., et al.: Racial disparity in quadruple negative breast cancer: aggressive biology and potential therapeutic targeting and prevention. Cancers 14, 4484 (2022). https://doi.org/10.3390/cancers14184484. https://www.mdpi.com/2072-6694/14/18/4484

11.

Jovanović, B., et al.: A randomized phase II neoadjuvant study of cisplatin, paclitaxel with or without everolimus in patients with stage II/III triple-negative breast cancer (TNBC): responses and long-term outcome correlated with increased frequency of DNA damage response gene mutations, TNBC subtype, AR status, and KI67. Clin. Cancer Res. Official J. Am. Assoc. Cancer Res. 23, 4035–4045 (2017). https://doi.org/10.1158/1078-0432.CCR-16-3055. https://pubmed.ncbi.nlm.nih.gov/28270498/

12.

Kakati, T., Bhattacharyya, D.K., Kalita, J.K., Norden-Krichmar, T.M.: DEGnext: classification of differentially expressed genes from RNA-seq data using a convolutional neural network with transfer learning. BMC Bioinformatics 23 (2022). https://doi.org/10.1186/s12859-021-04527-4

13.

Law, C.W., Chen, Y., Shi, W., Smyth, G.K.: voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol. 15, R29 (2014). https://doi.org/10.1186/gb-2014-15-2-r29CrossRefPubMedPubMedCentral

14.

Liu, S., et al.: Feature selection of gene expression data for cancer classification using double RBF-kernels. BMC Bioinformatics 19 (2018). https://doi.org/10.1186/s12859-018-2400-2

15.

Love, M.I., Huber, W., Anders, S.: Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15 (2014). https://doi.org/10.1186/s13059-014-0550-8

16.

Mahendran, N., Durai Raj Vincent, P.M., Srinivasan, K., Chang, C.Y.: Machine learning based computational gene selection models: a survey, performance evaluation, open issues, and future research directions. Frontiers Genet. 11 (2020). https://doi.org/10.3389/fgene.2020.603808

17.

Mori, Y., et al.: Deep learning-based gene selection in comprehensive gene analysis in pancreatic cancer. Sci. Rep. 11 (2021). https://doi.org/10.1038/s41598-021-95969-6

18.

Muhammad, A., et al.: Potential epigenetic modifications implicated in triple- to quadruple-negative breast cancer transition: a review. Epigenomics 14, 711–726 (2022). https://doi.org/10.2217/epi-2022-0033

19.

Newman, L.A., et al.: Hereditary susceptibility for triple negative breast cancer associated with western sub-Saharan African ancestry. Ann. Surg. 270, 484–492 (2019). https://doi.org/10.1097/sla.0000000000003459

20.

Robinson, M.D., McCarthy, D.J., Smyth, G.K.: edger: a bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2009). https://doi.org/10.1093/bioinformatics/btp616

21.

Sahoo, B., Pinnix, Z., Sims, S., Zelikovsky, A.: Identifying biomarkers using support vector machine to understand the racial disparity in triple-negative breast cancer. J. Comput. Biol. (2023). https://doi.org/10.1089/cmb.2022.0422

22.

Sahoo, B., Sims, S., Zelikovsky, A.: An SVM based approach to study the racial disparity in triple-negative breast cancer. In: Bansal, M.S., et al. (eds.) ICCABS 2021. LNB, vol. 13254, pp. 163–175. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-17531-2.13

23.

Tadist, K., Najah, S., Nikolov, N.S., Mrabti, F., Zahi, A.: Feature selection methods and genomic big data: a systematic review. J. Big Data 6(1), 1–24 (2019). https://doi.org/10.1186/s40537-019-0241-0CrossRef

Titel: Deep Learning Reveals Biological Basis of Racial Disparities in Quadruple-Negative Breast Cancer
verfasst von: Bikram Sahoo
Zandra Pinnix
Alex Zelikovsky
Verlag: Springer Nature Singapore
Buch: Bioinformatics Research and Applications
Print ISBN: 978-981-9970-73-5

Electronic ISBN: 978-981-9970-74-2

Copyright-Jahr: 2023
DOI: https://doi.org/10.1007/978-981-99-7074-2_40

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Premium Partner