Integrative clustering reveals a novel split in the luminal A subtype of breast cancer with impact on outcome

Miriam Ragle Aure, Valeria Vitelli, Sandra Jernström, Surendra Kumar, Marit Krohn, Eldri U. Due, Tonje Husby Haukaas, Suvi Katri Leivonen, Hans Kristian Moen Vollan, Torben Lüders, Einar Rødland, Charles J. Vaske, Wei Zhao, Elen K. Møller, Silje Nord, Guro F. Giskeødegård, Tone Frost Bathen, Carlos Caldas, Trine Tramm, Jan AlsnerJens Overgaard, Jürgen Geisler, Ida R.K. Bukholm, Bjørn Naume, Ellen Schlichting, Torill Sauer, Gordon Mills, Rolf Kåresen, Gunhild M. Mælandsmo, Ole Christian Lingjærde, Arnoldo Frigessi, Vessela N. Kristensen, Anne Lise Børresen-Dale, Kristine K. Sahlberg, Elin Borgen, Olav Engebråten, Øystein Fodstad, Britt Fritzman, Øystein Garred, Gry A. Geitvik, Anita Langerød, Solveig Hofvind, Hege G. Russnes, Helle Kristine Skjerven, Therese Sørlie, OSBREAC

Research output: Contribution to journalArticle

19 Citations (Scopus)

Abstract

Background: Breast cancer is a heterogeneous disease at the clinical and molecular level. In this study we integrate classifications extracted from five different molecular levels in order to identify integrated subtypes. Methods: Tumor tissue from 425 patients with primary breast cancer from the Oslo2 study was cut and blended, and divided into fractions for DNA, RNA and protein isolation and metabolomics, allowing the acquisition of representative and comparable molecular data. Patients were stratified into groups based on their tumor characteristics from five different molecular levels, using various clustering methods. Finally, all previously identified and newly determined subgroups were combined in a multilevel classification using a "cluster-of-clusters" approach with consensus clustering. Results: Based on DNA copy number data, tumors were categorized into three groups according to the complex arm aberration index. mRNA expression profiles divided tumors into five molecular subgroups according to PAM50 subtyping, and clustering based on microRNA expression revealed four subgroups. Reverse-phase protein array data divided tumors into five subgroups. Hierarchical clustering of tumor metabolic profiles revealed three clusters. Combining DNA copy number and mRNA expression classified tumors into seven clusters based on pathway activity levels, and tumors were classified into ten subtypes using integrative clustering. The final consensus clustering that incorporated all aforementioned subtypes revealed six major groups. Five corresponded well with the mRNA subtypes, while a sixth group resulted from a split of the luminal A subtype; these tumors belonged to distinct microRNA clusters. Gain-of-function studies using MCF-7 cells showed that microRNAs differentially expressed between the luminal A clusters were important for cancer cell survival. These microRNAs were used to validate the split in luminal A tumors in four independent breast cancer cohorts. In two cohorts the microRNAs divided tumors into subgroups with significantly different outcomes, and in another a trend was observed. Conclusions: The six integrated subtypes identified confirm the heterogeneity of breast cancer and show that finer subdivisions of subtypes are evident. Increasing knowledge of the heterogeneity of the luminal A subtype may add pivotal information to guide therapeutic choices, evidently bringing us closer to improved treatment for this largest subgroup of breast cancer.

Original languageEnglish (US)
Article number44
JournalBreast Cancer Research
Volume19
Issue number1
DOIs
StatePublished - Mar 29 2017
Externally publishedYes

Fingerprint

Cluster Analysis
Breast Neoplasms
Neoplasms
MicroRNAs
Messenger RNA
DNA
Protein Array Analysis
Metabolomics
Metabolome
MCF-7 Cells
Cell Survival
RNA

Keywords

  • Breast cancer
  • Consensus clustering
  • Integration
  • Luminal A
  • MicroRNA

ASJC Scopus subject areas

  • Oncology
  • Cancer Research

Cite this

Integrative clustering reveals a novel split in the luminal A subtype of breast cancer with impact on outcome. / Aure, Miriam Ragle; Vitelli, Valeria; Jernström, Sandra; Kumar, Surendra; Krohn, Marit; Due, Eldri U.; Haukaas, Tonje Husby; Leivonen, Suvi Katri; Vollan, Hans Kristian Moen; Lüders, Torben; Rødland, Einar; Vaske, Charles J.; Zhao, Wei; Møller, Elen K.; Nord, Silje; Giskeødegård, Guro F.; Bathen, Tone Frost; Caldas, Carlos; Tramm, Trine; Alsner, Jan; Overgaard, Jens; Geisler, Jürgen; Bukholm, Ida R.K.; Naume, Bjørn; Schlichting, Ellen; Sauer, Torill; Mills, Gordon; Kåresen, Rolf; Mælandsmo, Gunhild M.; Lingjærde, Ole Christian; Frigessi, Arnoldo; Kristensen, Vessela N.; Børresen-Dale, Anne Lise; Sahlberg, Kristine K.; Borgen, Elin; Engebråten, Olav; Fodstad, Øystein; Fritzman, Britt; Garred, Øystein; Geitvik, Gry A.; Langerød, Anita; Hofvind, Solveig; Russnes, Hege G.; Skjerven, Helle Kristine; Sørlie, Therese; OSBREAC.

In: Breast Cancer Research, Vol. 19, No. 1, 44, 29.03.2017.

Research output: Contribution to journalArticle

Aure, MR, Vitelli, V, Jernström, S, Kumar, S, Krohn, M, Due, EU, Haukaas, TH, Leivonen, SK, Vollan, HKM, Lüders, T, Rødland, E, Vaske, CJ, Zhao, W, Møller, EK, Nord, S, Giskeødegård, GF, Bathen, TF, Caldas, C, Tramm, T, Alsner, J, Overgaard, J, Geisler, J, Bukholm, IRK, Naume, B, Schlichting, E, Sauer, T, Mills, G, Kåresen, R, Mælandsmo, GM, Lingjærde, OC, Frigessi, A, Kristensen, VN, Børresen-Dale, AL, Sahlberg, KK, Borgen, E, Engebråten, O, Fodstad, Ø, Fritzman, B, Garred, Ø, Geitvik, GA, Langerød, A, Hofvind, S, Russnes, HG, Skjerven, HK, Sørlie, T & OSBREAC 2017, 'Integrative clustering reveals a novel split in the luminal A subtype of breast cancer with impact on outcome', Breast Cancer Research, vol. 19, no. 1, 44. https://doi.org/10.1186/s13058-017-0812-y
Aure, Miriam Ragle ; Vitelli, Valeria ; Jernström, Sandra ; Kumar, Surendra ; Krohn, Marit ; Due, Eldri U. ; Haukaas, Tonje Husby ; Leivonen, Suvi Katri ; Vollan, Hans Kristian Moen ; Lüders, Torben ; Rødland, Einar ; Vaske, Charles J. ; Zhao, Wei ; Møller, Elen K. ; Nord, Silje ; Giskeødegård, Guro F. ; Bathen, Tone Frost ; Caldas, Carlos ; Tramm, Trine ; Alsner, Jan ; Overgaard, Jens ; Geisler, Jürgen ; Bukholm, Ida R.K. ; Naume, Bjørn ; Schlichting, Ellen ; Sauer, Torill ; Mills, Gordon ; Kåresen, Rolf ; Mælandsmo, Gunhild M. ; Lingjærde, Ole Christian ; Frigessi, Arnoldo ; Kristensen, Vessela N. ; Børresen-Dale, Anne Lise ; Sahlberg, Kristine K. ; Borgen, Elin ; Engebråten, Olav ; Fodstad, Øystein ; Fritzman, Britt ; Garred, Øystein ; Geitvik, Gry A. ; Langerød, Anita ; Hofvind, Solveig ; Russnes, Hege G. ; Skjerven, Helle Kristine ; Sørlie, Therese ; OSBREAC. / Integrative clustering reveals a novel split in the luminal A subtype of breast cancer with impact on outcome. In: Breast Cancer Research. 2017 ; Vol. 19, No. 1.
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TY - JOUR

T1 - Integrative clustering reveals a novel split in the luminal A subtype of breast cancer with impact on outcome

AU - Aure, Miriam Ragle

AU - Vitelli, Valeria

AU - Jernström, Sandra

AU - Kumar, Surendra

AU - Krohn, Marit

AU - Due, Eldri U.

AU - Haukaas, Tonje Husby

AU - Leivonen, Suvi Katri

AU - Vollan, Hans Kristian Moen

AU - Lüders, Torben

AU - Rødland, Einar

AU - Vaske, Charles J.

AU - Zhao, Wei

AU - Møller, Elen K.

AU - Nord, Silje

AU - Giskeødegård, Guro F.

AU - Bathen, Tone Frost

AU - Caldas, Carlos

AU - Tramm, Trine

AU - Alsner, Jan

AU - Overgaard, Jens

AU - Geisler, Jürgen

AU - Bukholm, Ida R.K.

AU - Naume, Bjørn

AU - Schlichting, Ellen

AU - Sauer, Torill

AU - Mills, Gordon

AU - Kåresen, Rolf

AU - Mælandsmo, Gunhild M.

AU - Lingjærde, Ole Christian

AU - Frigessi, Arnoldo

AU - Kristensen, Vessela N.

AU - Børresen-Dale, Anne Lise

AU - Sahlberg, Kristine K.

AU - Borgen, Elin

AU - Engebråten, Olav

AU - Fodstad, Øystein

AU - Fritzman, Britt

AU - Garred, Øystein

AU - Geitvik, Gry A.

AU - Langerød, Anita

AU - Hofvind, Solveig

AU - Russnes, Hege G.

AU - Skjerven, Helle Kristine

AU - Sørlie, Therese

AU - OSBREAC,

PY - 2017/3/29

Y1 - 2017/3/29

N2 - Background: Breast cancer is a heterogeneous disease at the clinical and molecular level. In this study we integrate classifications extracted from five different molecular levels in order to identify integrated subtypes. Methods: Tumor tissue from 425 patients with primary breast cancer from the Oslo2 study was cut and blended, and divided into fractions for DNA, RNA and protein isolation and metabolomics, allowing the acquisition of representative and comparable molecular data. Patients were stratified into groups based on their tumor characteristics from five different molecular levels, using various clustering methods. Finally, all previously identified and newly determined subgroups were combined in a multilevel classification using a "cluster-of-clusters" approach with consensus clustering. Results: Based on DNA copy number data, tumors were categorized into three groups according to the complex arm aberration index. mRNA expression profiles divided tumors into five molecular subgroups according to PAM50 subtyping, and clustering based on microRNA expression revealed four subgroups. Reverse-phase protein array data divided tumors into five subgroups. Hierarchical clustering of tumor metabolic profiles revealed three clusters. Combining DNA copy number and mRNA expression classified tumors into seven clusters based on pathway activity levels, and tumors were classified into ten subtypes using integrative clustering. The final consensus clustering that incorporated all aforementioned subtypes revealed six major groups. Five corresponded well with the mRNA subtypes, while a sixth group resulted from a split of the luminal A subtype; these tumors belonged to distinct microRNA clusters. Gain-of-function studies using MCF-7 cells showed that microRNAs differentially expressed between the luminal A clusters were important for cancer cell survival. These microRNAs were used to validate the split in luminal A tumors in four independent breast cancer cohorts. In two cohorts the microRNAs divided tumors into subgroups with significantly different outcomes, and in another a trend was observed. Conclusions: The six integrated subtypes identified confirm the heterogeneity of breast cancer and show that finer subdivisions of subtypes are evident. Increasing knowledge of the heterogeneity of the luminal A subtype may add pivotal information to guide therapeutic choices, evidently bringing us closer to improved treatment for this largest subgroup of breast cancer.

AB - Background: Breast cancer is a heterogeneous disease at the clinical and molecular level. In this study we integrate classifications extracted from five different molecular levels in order to identify integrated subtypes. Methods: Tumor tissue from 425 patients with primary breast cancer from the Oslo2 study was cut and blended, and divided into fractions for DNA, RNA and protein isolation and metabolomics, allowing the acquisition of representative and comparable molecular data. Patients were stratified into groups based on their tumor characteristics from five different molecular levels, using various clustering methods. Finally, all previously identified and newly determined subgroups were combined in a multilevel classification using a "cluster-of-clusters" approach with consensus clustering. Results: Based on DNA copy number data, tumors were categorized into three groups according to the complex arm aberration index. mRNA expression profiles divided tumors into five molecular subgroups according to PAM50 subtyping, and clustering based on microRNA expression revealed four subgroups. Reverse-phase protein array data divided tumors into five subgroups. Hierarchical clustering of tumor metabolic profiles revealed three clusters. Combining DNA copy number and mRNA expression classified tumors into seven clusters based on pathway activity levels, and tumors were classified into ten subtypes using integrative clustering. The final consensus clustering that incorporated all aforementioned subtypes revealed six major groups. Five corresponded well with the mRNA subtypes, while a sixth group resulted from a split of the luminal A subtype; these tumors belonged to distinct microRNA clusters. Gain-of-function studies using MCF-7 cells showed that microRNAs differentially expressed between the luminal A clusters were important for cancer cell survival. These microRNAs were used to validate the split in luminal A tumors in four independent breast cancer cohorts. In two cohorts the microRNAs divided tumors into subgroups with significantly different outcomes, and in another a trend was observed. Conclusions: The six integrated subtypes identified confirm the heterogeneity of breast cancer and show that finer subdivisions of subtypes are evident. Increasing knowledge of the heterogeneity of the luminal A subtype may add pivotal information to guide therapeutic choices, evidently bringing us closer to improved treatment for this largest subgroup of breast cancer.

KW - Breast cancer

KW - Consensus clustering

KW - Integration

KW - Luminal A

KW - MicroRNA

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