Proteomics-driven multi-omics classification of triple negative
breast cancer

a 2025

Proteomics-driven multi-omics classification of triple negative breast cancer

ŠIMONÍK, Jan; Pavla BOUCHALOVÁ; Petr LAPČÍK; Kateřina JURÁSKOVÁ; Ingrid KOVÁČOVÁ et al.

Základní údaje

Originální název

Proteomics-driven multi-omics classification of triple negative breast cancer

Autoři

Vydání

In Book of Abstracts of PhD Conference 2025, Brno, Česká republika 29.5. 2025, P16, 2025

Další údaje

Jazyk

angličtina

Typ výsledku

Konferenční abstrakt

Stát vydavatele

Česká republika

Utajení

není předmětem státního či obchodního tajemství

Označené pro přenos do RIV

Organizační jednotka

Přírodovědecká fakulta

Změněno: 3. 2. 2026 14:22, doc. Mgr. Pavel Bouchal, Ph.D.

Anotace

V originále

Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer lacking estrogen, progesterone, and HER2 receptors. It makes up 15% of all breast cancer cases and often affects women under 40. Due to its poor prognosis and the absence of targetable receptors, chemotherapy is the primary treatment. TNBC has been categorized into about four subgroups based on gene expression profiles, but we believe that next-generation proteomics could provide a more accurate classification, as proteins are the key molecular effectors in cells. This study investigated a well-characterized cohort of 105 fresh-frozen triple-negative breast cancer (TNBC) tissues obtained from the Masaryk Memorial Cancer Institute. Employing whole-exome sequencing (WES) and RNA sequencing via Illumina NovaSeq, alongside data-independent acquisition-based proteomics, we generated high-quality multi-omics data for 96 samples. The integration of RNA-Seq and proteomics datasets led to the identification of 1223 transcript-protein pairs exhibiting strong correlations. Utilizing these 1223 proteins, we classified the TNBC samples into 7 distinct clusters. For TNBC subtypes discrimination, we developed a classifier using the Random Forest algorithm containing 45 proteins with an overall accuracy of 78.6% when assessed using Leave-One-Out Cross-Validation. Furthermore, to elucidate the molecular underpinnings of these clusters, we conducted gene set enrichment analysis (GSEA) utilizing both proteomics and RNA-seq datasets. Top candidates were selected based on GSEA outcomes, as well as progression-free survival and overall survival analyses. Functional validation of top candidates is currently in process. The pivotal aim of this project is to pinpoint therapeutic targets that could enhance treatment strategies for TNBC. Acknowledgement Supported by the Ministry of Health of the Czech Republic in cooperation with the Czech Health Research Council under project No. NU22-08-00230. Supported by the project National Institute for Cancer Research (Programme EXCELES, ID Project No. LX22NPO5102)—Funded by the European Union—Next Generation EU.

Návaznosti

LX22NPO5102, projekt VaV

Název: Národní ústav pro výzkum rakoviny (Akronym: NÚVR)

Investor: Ministerstvo školství, mládeže a tělovýchovy ČR, Národní ústav pro výzkum rakoviny, 5.1 EXCELES

MUNI/A/1684/2024, interní kód MU

Název: Podpora biochemického výzkumu v roce 2025

Investor: Masarykova univerzita, Podpora biochemického výzkumu v roce 2025

NU22-08-00230, projekt VaV

Název: Proteogenomová klasifikace trojitě negativních nádorů prsu ve vztahu k prognóze a cílené terapii

Investor: Ministerstvo zdravotnictví ČR, Proteogenomová klasifikace trojitě negativních nádorů prsu ve vztahu k prognóze a cílené terapii, Podprogram 1 - standardní

Citovat

ŠIMONÍK, Jan; Pavla BOUCHALOVÁ; Petr LAPČÍK; Kateřina JURÁSKOVÁ; Ingrid KOVÁČOVÁ; David POTĚŠIL; Lucia JANÁČOVÁ; Miloš HOLÁNEK; Boris TICHÝ; Vojtěch BYSTRÝ; Rudolf NENUTIL; Roman HRSTKA a Pavel BOUCHAL. Proteomics-driven multi-omics classification of triple negative breast cancer. In In Book of Abstracts of PhD Conference 2025, Brno, Česká republika 29.5. 2025, P16. 2025.

@proceedings{2543420,
   author = {Šimoník, Jan and Bouchalová, Pavla and Lapčík, Petr and Jurásková, Kateřina and Kováčová, Ingrid and Potěšil, David and Janáčová, Lucia and Holánek, Miloš and Tichý, Boris and Bystrý, Vojtěch and Nenutil, Rudolf and Hrstka, Roman and Bouchal, Pavel},
   booktitle = {In Book of Abstracts of PhD Conference 2025, Brno, Česká republika 29.5. 2025, P16},
   language = {eng},
   title = {Proteomics-driven multi-omics classification of triple negative breast cancer},
   year = {2025}
}

TY  - CONF
ID  - 2543420
AU  - Šimoník, Jan - Bouchalová, Pavla - Lapčík, Petr - Jurásková, Kateřina - Kováčová, Ingrid - Potěšil, David - Janáčová, Lucia - Holánek, Miloš - Tichý, Boris - Bystrý, Vojtěch - Nenutil, Rudolf - Hrstka, Roman - Bouchal, Pavel
PY  - 2025
TI  - Proteomics-driven multi-omics classification of triple negative breast cancer
N2  - Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer lacking estrogen, progesterone, and HER2 receptors. It makes up 15% of all breast cancer cases and often affects women under 40. Due to its poor prognosis and the absence of targetable receptors, chemotherapy is the primary treatment. TNBC has been categorized into about four subgroups based on gene expression profiles, but we believe that next-generation proteomics could provide a more accurate classification, as proteins are the key molecular effectors in cells. This study investigated a well-characterized cohort of 105 fresh-frozen triple-negative breast cancer (TNBC) tissues obtained from the Masaryk Memorial Cancer Institute. Employing whole-exome sequencing (WES) and RNA sequencing via Illumina NovaSeq, alongside data-independent acquisition-based proteomics, we generated high-quality multi-omics data for 96 samples. The integration of RNA-Seq and proteomics datasets led to the identification of 1223 transcript-protein pairs exhibiting strong correlations. Utilizing these 1223 proteins, we classified the TNBC samples into 7 distinct clusters. For TNBC subtypes discrimination, we developed a classifier using the Random Forest algorithm containing 45 proteins with an overall accuracy of 78.6% when assessed using Leave-One-Out Cross-Validation. Furthermore, to elucidate the molecular underpinnings of these clusters, we conducted gene set enrichment analysis (GSEA) utilizing both proteomics and RNA-seq datasets. Top candidates were selected based on GSEA outcomes, as well as progression-free survival and overall survival analyses. Functional validation of top candidates is currently in process. The pivotal aim of this project is to pinpoint therapeutic targets that could enhance treatment strategies for TNBC. Acknowledgement Supported by the Ministry of Health of the Czech Republic in cooperation with the Czech Health Research Council under project No. NU22-08-00230. Supported by the project National Institute for Cancer Research (Programme EXCELES, ID Project No. LX22NPO5102)—Funded by the European Union—Next Generation EU.
ER  -

ŠIMONÍK, Jan; Pavla BOUCHALOVÁ; Petr LAPČÍK; Kateřina JURÁSKOVÁ; Ingrid KOVÁČOVÁ; David POTĚŠIL; Lucia JANÁČOVÁ; Miloš HOLÁNEK; Boris TICHÝ; Vojtěch BYSTRÝ; Rudolf NENUTIL; Roman HRSTKA a Pavel BOUCHAL. Proteomics-driven multi-omics classification of triple negative breast cancer. In \textit{In Book of Abstracts of PhD Conference 2025, Brno, Česká republika 29.5. 2025, P16}. 2025.

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