Detection of cultured breast cancer cells from human tumor-derived matrix by differential ion mobility spectrometry

Lydia Lindfors; Patrik Sioris; Anna Anttalainen; Katja Korelin; Anton Kontunen; Markus Karjalainen; Erika Naakka; Tuula Salo; Antti Vehkaoja; Niku Oksala; Vesa Hytönen; Antti Roine; Maiju Lepomäki

doi:10.1016/j.aca.2022.339659

Detection of cultured breast cancer cells from human tumor-derived matrix by differential ion mobility spectrometry

Lydia Lindfors
, Patrik Sioris^*
, Anna Anttalainen
, Katja Korelin
, Anton Kontunen
, Markus Karjalainen
, Erika Naakka
, Tuula Salo
, Antti Vehkaoja
, Niku Oksala
, Vesa Hytönen
, Antti Roine
, Maiju Lepomäki

^*Tämän työn vastaava kirjoittaja

Tutkimustuotos: Artikkeli › Tieteellinen › vertaisarvioitu

8 Sitaatiot (Scopus)

104 Lataukset (Pure)

Abstrakti

The primary treatment of breast cancer is the surgical removal of the tumor with an adequate healthy tissue margin. An intraoperative method for assessing surgical margins could optimize tumor resection. Differential ion mobility spectrometry (DMS) is applicable for tissue analysis and allows for the differentiation of malignant and benign tissues. However, the number of cancer cells necessary for detection remains unknown. We studied the detection threshold of DMS for cancer cell identification with a widely characterized breast cancer cell line (BT-474) dispersed in a human myoma-based tumor microenvironment mimicking matrix (Myogel). Predetermined, small numbers of cultured BT-474 cells were dispersed into Myogel. Pure Myogel was used as a zero sample. All samples were assessed with a DMS-based custom-built device described as “the automated tissue laser analysis system” (ATLAS). We used machine learning to determine the detection threshold for cancer cell densities by training binary classifiers to distinguish the reference level (zero sample) from single predetermined cancer cell density levels. Each classifier (sLDA, linear SVM, radial SVM, and CNN) was able to detect cell density of 3700 cells μL⁻¹ and above. These results suggest that DMS combined with laser desorption can detect low densities of breast cancer cells, at levels clinically relevant for margin detection, from Myogel samples in vitro.

Alkuperäiskieli	Englanti
Artikkeli	339659
Sivumäärä	11
Julkaisu	Analytica Chimica Acta
Vuosikerta	1202
DOI - pysyväislinkit	https://doi.org/10.1016/j.aca.2022.339659
Tila	Julkaistu - 15 huhtik. 2022
OKM-julkaisutyyppi	A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä

Rahoitus

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Antti Roine, Niku Oksala, Markus Karjalainen, and Anton Kontunen, are shareholders and employees of Olfactomics Ltd, a medical device company that develops novel technology for intraoperative surgical margin assessment. Anton Kontunen declares funding from the Doctoral School of Tampere University, The Finnish Foundation for Technology Promotion (Grant number 7671 ) and Emil Aaltonen Foundation (Grant number 210073 ). Markus Karjalainen declares funding from the Finnish Cultural Foundation , Pirkanmaa Regional Fund. Niku Oksala declares funding from Competitive State Research Financing of the Expert Responsibility area of Tampere University Hospital (Grant numbers 9s045 , 9T044 , 9U042 , 150618 , 9V044 , 9X040 , 9AA057 , 9ab052 and Mk301 ; from Competitive funding to streng t hten university research profiles funded by Academy of Finland , decision number 292377 ). Maiju Lepomäki declares funding from the Doctoral School of Tampere University, The Finnish Medical Foundation (grant numbers 2167 , 4038 ), and Cancer Foundation of Finland . The Myogel research has been funded by Sigrid Jusélius Foundation, the Cancer Society of Finland , the Oulu University Hospital MRC grant, the Helsinki University Central Hospital research funds, and Jane and Aatos Erkko Foundation . The Myogel research funding consisted of academic grants without any engagements considering the research project. The remaining authors declare that they have no conflicts of interest. This study has received funding from the ATTRACT project funded by the EC under Grant Agreement 777222 . The authors would like to thank Merja Jokela for assistance with the sample preparation, Artturi Vuorinen for participation in the ATLAS measurements and Meri Mäkelä for the proofreading of this paper. The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Antti Roine, Niku Oksala, Markus Karjalainen, and Anton Kontunen, are shareholders and employees of Olfactomics Ltd, a medical device company that develops novel technology for intraoperative surgical margin assessment. Anton Kontunen declares funding from the Doctoral School of Tampere University, The Finnish Foundation for Technology Promotion (Grant number 7671) and Emil Aaltonen Foundation (Grant number 210073). Markus Karjalainen declares funding from the Finnish Cultural Foundation, Pirkanmaa Regional Fund. Niku Oksala declares funding from Competitive State Research Financing of the Expert Responsibility area of Tampere University Hospital (Grant numbers 9s045, 9T044, 9U042, 150618, 9V044, 9X040, 9AA057, 9ab052 and Mk301; from Competitive funding to strengthten university research profiles funded by Academy of Finland, decision number 292377). Maiju Lepom?ki declares funding from the Doctoral School of Tampere University, The Finnish Medical Foundation (grant numbers 2167, 4038), and Cancer Foundation of Finland. The Myogel research has been funded by Sigrid Jus?lius Foundation, the Cancer Society of Finland, the Oulu University Hospital MRC grant, the Helsinki University Central Hospital research funds, and Jane and Aatos Erkko Foundation. The Myogel research funding consisted of academic grants without any engagements considering the research project. The remaining authors declare that they have no conflicts of interest.This study has received funding from the ATTRACT project funded by the EC under Grant Agreement 777222. The authors would like to thank Merja Jokela for assistance with the sample preparation, Artturi Vuorinen for participation in the ATLAS measurements and Meri M?kel? for the proofreading of this paper.

YK:n kestävän kehityksen tavoitteet

Tämä tuotos edistää seuraavia kestävän kehityksen tavoitteita:

SDG 3 – Hyvä terveys ja hyvinvointi

Julkaisufoorumi-taso

Jufo-taso 2

!!ASJC Scopus subject areas

Analytical Chemistry
Biochemistry
Environmental Chemistry
Spectroscopy

Pääsy asiakirjaan

10.1016/j.aca.2022.339659Lisenssi: CC BY

1-s2.0-S0003267022002306-mainFinal published version, 2,19 MBLisenssi: CC BY

https://urn.fi/URN:NBN:fi:tuni-202203252763Lisenssi: CC BY

Siteeraa tätä

Lindfors, L., Sioris, P., Anttalainen, A., Korelin, K., Kontunen, A., Karjalainen, M., Naakka, E., Salo, T., Vehkaoja, A., Oksala, N., Hytönen, V., Roine, A., & Lepomäki, M. (2022). Detection of cultured breast cancer cells from human tumor-derived matrix by differential ion mobility spectrometry. Analytica Chimica Acta, 1202, Artikkeli 339659. https://doi.org/10.1016/j.aca.2022.339659

@article{4bc0aff905304a0f8044d30f74312d6e,

title = "Detection of cultured breast cancer cells from human tumor-derived matrix by differential ion mobility spectrometry",

abstract = "The primary treatment of breast cancer is the surgical removal of the tumor with an adequate healthy tissue margin. An intraoperative method for assessing surgical margins could optimize tumor resection. Differential ion mobility spectrometry (DMS) is applicable for tissue analysis and allows for the differentiation of malignant and benign tissues. However, the number of cancer cells necessary for detection remains unknown. We studied the detection threshold of DMS for cancer cell identification with a widely characterized breast cancer cell line (BT-474) dispersed in a human myoma-based tumor microenvironment mimicking matrix (Myogel). Predetermined, small numbers of cultured BT-474 cells were dispersed into Myogel. Pure Myogel was used as a zero sample. All samples were assessed with a DMS-based custom-built device described as “the automated tissue laser analysis system” (ATLAS). We used machine learning to determine the detection threshold for cancer cell densities by training binary classifiers to distinguish the reference level (zero sample) from single predetermined cancer cell density levels. Each classifier (sLDA, linear SVM, radial SVM, and CNN) was able to detect cell density of 3700 cells μL−1 and above. These results suggest that DMS combined with laser desorption can detect low densities of breast cancer cells, at levels clinically relevant for margin detection, from Myogel samples in vitro.",

author = "Lydia Lindfors and Patrik Sioris and Anna Anttalainen and Katja Korelin and Anton Kontunen and Markus Karjalainen and Erika Naakka and Tuula Salo and Antti Vehkaoja and Niku Oksala and Vesa Hyt{\"o}nen and Antti Roine and Maiju Lepom{\"a}ki",

note = "Funding Information: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Antti Roine, Niku Oksala, Markus Karjalainen, and Anton Kontunen, are shareholders and employees of Olfactomics Ltd, a medical device company that develops novel technology for intraoperative surgical margin assessment. Anton Kontunen declares funding from the Doctoral School of Tampere University, The Finnish Foundation for Technology Promotion (Grant number 7671 ) and Emil Aaltonen Foundation (Grant number 210073 ). Markus Karjalainen declares funding from the Finnish Cultural Foundation , Pirkanmaa Regional Fund. Niku Oksala declares funding from Competitive State Research Financing of the Expert Responsibility area of Tampere University Hospital (Grant numbers 9s045 , 9T044 , 9U042 , 150618 , 9V044 , 9X040 , 9AA057 , 9ab052 and Mk301 ; from Competitive funding to streng t hten university research profiles funded by Academy of Finland , decision number 292377 ). Maiju Lepom{\"a}ki declares funding from the Doctoral School of Tampere University, The Finnish Medical Foundation (grant numbers 2167 , 4038 ), and Cancer Foundation of Finland . The Myogel research has been funded by Sigrid Jus{\'e}lius Foundation, the Cancer Society of Finland , the Oulu University Hospital MRC grant, the Helsinki University Central Hospital research funds, and Jane and Aatos Erkko Foundation . The Myogel research funding consisted of academic grants without any engagements considering the research project. The remaining authors declare that they have no conflicts of interest. Funding Information: This study has received funding from the ATTRACT project funded by the EC under Grant Agreement 777222 . The authors would like to thank Merja Jokela for assistance with the sample preparation, Artturi Vuorinen for participation in the ATLAS measurements and Meri M{\"a}kel{\"a} for the proofreading of this paper. Funding Information: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Antti Roine, Niku Oksala, Markus Karjalainen, and Anton Kontunen, are shareholders and employees of Olfactomics Ltd, a medical device company that develops novel technology for intraoperative surgical margin assessment. Anton Kontunen declares funding from the Doctoral School of Tampere University, The Finnish Foundation for Technology Promotion (Grant number 7671) and Emil Aaltonen Foundation (Grant number 210073). Markus Karjalainen declares funding from the Finnish Cultural Foundation, Pirkanmaa Regional Fund. Niku Oksala declares funding from Competitive State Research Financing of the Expert Responsibility area of Tampere University Hospital (Grant numbers 9s045, 9T044, 9U042, 150618, 9V044, 9X040, 9AA057, 9ab052 and Mk301; from Competitive funding to strengthten university research profiles funded by Academy of Finland, decision number 292377). Maiju Lepom?ki declares funding from the Doctoral School of Tampere University, The Finnish Medical Foundation (grant numbers 2167, 4038), and Cancer Foundation of Finland. The Myogel research has been funded by Sigrid Jus?lius Foundation, the Cancer Society of Finland, the Oulu University Hospital MRC grant, the Helsinki University Central Hospital research funds, and Jane and Aatos Erkko Foundation. The Myogel research funding consisted of academic grants without any engagements considering the research project. The remaining authors declare that they have no conflicts of interest.This study has received funding from the ATTRACT project funded by the EC under Grant Agreement 777222. The authors would like to thank Merja Jokela for assistance with the sample preparation, Artturi Vuorinen for participation in the ATLAS measurements and Meri M?kel? for the proofreading of this paper. Publisher Copyright: {\textcopyright} 2022 The Authors",

year = "2022",

month = apr,

day = "15",

doi = "10.1016/j.aca.2022.339659",

language = "English",

volume = "1202",

journal = "Analytica Chimica Acta",

issn = "0003-2670",

publisher = "Elsevier",

}

Lindfors, L, Sioris, P, Anttalainen, A, Korelin, K, Kontunen, A, Karjalainen, M, Naakka, E, Salo, T, Vehkaoja, A , Oksala, N , Hytönen, V, Roine, A & Lepomäki, M 2022, 'Detection of cultured breast cancer cells from human tumor-derived matrix by differential ion mobility spectrometry', Analytica Chimica Acta, Vuosikerta 1202, 339659. https://doi.org/10.1016/j.aca.2022.339659

TY - JOUR

T1 - Detection of cultured breast cancer cells from human tumor-derived matrix by differential ion mobility spectrometry

AU - Lindfors, Lydia

AU - Sioris, Patrik

AU - Anttalainen, Anna

AU - Korelin, Katja

AU - Kontunen, Anton

AU - Karjalainen, Markus

AU - Naakka, Erika

AU - Salo, Tuula

AU - Vehkaoja, Antti

AU - Oksala, Niku

AU - Hytönen, Vesa

AU - Roine, Antti

AU - Lepomäki, Maiju

N1 - Funding Information: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Antti Roine, Niku Oksala, Markus Karjalainen, and Anton Kontunen, are shareholders and employees of Olfactomics Ltd, a medical device company that develops novel technology for intraoperative surgical margin assessment. Anton Kontunen declares funding from the Doctoral School of Tampere University, The Finnish Foundation for Technology Promotion (Grant number 7671 ) and Emil Aaltonen Foundation (Grant number 210073 ). Markus Karjalainen declares funding from the Finnish Cultural Foundation , Pirkanmaa Regional Fund. Niku Oksala declares funding from Competitive State Research Financing of the Expert Responsibility area of Tampere University Hospital (Grant numbers 9s045 , 9T044 , 9U042 , 150618 , 9V044 , 9X040 , 9AA057 , 9ab052 and Mk301 ; from Competitive funding to streng t hten university research profiles funded by Academy of Finland , decision number 292377 ). Maiju Lepomäki declares funding from the Doctoral School of Tampere University, The Finnish Medical Foundation (grant numbers 2167 , 4038 ), and Cancer Foundation of Finland . The Myogel research has been funded by Sigrid Jusélius Foundation, the Cancer Society of Finland , the Oulu University Hospital MRC grant, the Helsinki University Central Hospital research funds, and Jane and Aatos Erkko Foundation . The Myogel research funding consisted of academic grants without any engagements considering the research project. The remaining authors declare that they have no conflicts of interest. Funding Information: This study has received funding from the ATTRACT project funded by the EC under Grant Agreement 777222 . The authors would like to thank Merja Jokela for assistance with the sample preparation, Artturi Vuorinen for participation in the ATLAS measurements and Meri Mäkelä for the proofreading of this paper. Funding Information: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Antti Roine, Niku Oksala, Markus Karjalainen, and Anton Kontunen, are shareholders and employees of Olfactomics Ltd, a medical device company that develops novel technology for intraoperative surgical margin assessment. Anton Kontunen declares funding from the Doctoral School of Tampere University, The Finnish Foundation for Technology Promotion (Grant number 7671) and Emil Aaltonen Foundation (Grant number 210073). Markus Karjalainen declares funding from the Finnish Cultural Foundation, Pirkanmaa Regional Fund. Niku Oksala declares funding from Competitive State Research Financing of the Expert Responsibility area of Tampere University Hospital (Grant numbers 9s045, 9T044, 9U042, 150618, 9V044, 9X040, 9AA057, 9ab052 and Mk301; from Competitive funding to strengthten university research profiles funded by Academy of Finland, decision number 292377). Maiju Lepom?ki declares funding from the Doctoral School of Tampere University, The Finnish Medical Foundation (grant numbers 2167, 4038), and Cancer Foundation of Finland. The Myogel research has been funded by Sigrid Jus?lius Foundation, the Cancer Society of Finland, the Oulu University Hospital MRC grant, the Helsinki University Central Hospital research funds, and Jane and Aatos Erkko Foundation. The Myogel research funding consisted of academic grants without any engagements considering the research project. The remaining authors declare that they have no conflicts of interest.This study has received funding from the ATTRACT project funded by the EC under Grant Agreement 777222. The authors would like to thank Merja Jokela for assistance with the sample preparation, Artturi Vuorinen for participation in the ATLAS measurements and Meri M?kel? for the proofreading of this paper. Publisher Copyright: © 2022 The Authors

PY - 2022/4/15

Y1 - 2022/4/15

N2 - The primary treatment of breast cancer is the surgical removal of the tumor with an adequate healthy tissue margin. An intraoperative method for assessing surgical margins could optimize tumor resection. Differential ion mobility spectrometry (DMS) is applicable for tissue analysis and allows for the differentiation of malignant and benign tissues. However, the number of cancer cells necessary for detection remains unknown. We studied the detection threshold of DMS for cancer cell identification with a widely characterized breast cancer cell line (BT-474) dispersed in a human myoma-based tumor microenvironment mimicking matrix (Myogel). Predetermined, small numbers of cultured BT-474 cells were dispersed into Myogel. Pure Myogel was used as a zero sample. All samples were assessed with a DMS-based custom-built device described as “the automated tissue laser analysis system” (ATLAS). We used machine learning to determine the detection threshold for cancer cell densities by training binary classifiers to distinguish the reference level (zero sample) from single predetermined cancer cell density levels. Each classifier (sLDA, linear SVM, radial SVM, and CNN) was able to detect cell density of 3700 cells μL−1 and above. These results suggest that DMS combined with laser desorption can detect low densities of breast cancer cells, at levels clinically relevant for margin detection, from Myogel samples in vitro.

AB - The primary treatment of breast cancer is the surgical removal of the tumor with an adequate healthy tissue margin. An intraoperative method for assessing surgical margins could optimize tumor resection. Differential ion mobility spectrometry (DMS) is applicable for tissue analysis and allows for the differentiation of malignant and benign tissues. However, the number of cancer cells necessary for detection remains unknown. We studied the detection threshold of DMS for cancer cell identification with a widely characterized breast cancer cell line (BT-474) dispersed in a human myoma-based tumor microenvironment mimicking matrix (Myogel). Predetermined, small numbers of cultured BT-474 cells were dispersed into Myogel. Pure Myogel was used as a zero sample. All samples were assessed with a DMS-based custom-built device described as “the automated tissue laser analysis system” (ATLAS). We used machine learning to determine the detection threshold for cancer cell densities by training binary classifiers to distinguish the reference level (zero sample) from single predetermined cancer cell density levels. Each classifier (sLDA, linear SVM, radial SVM, and CNN) was able to detect cell density of 3700 cells μL−1 and above. These results suggest that DMS combined with laser desorption can detect low densities of breast cancer cells, at levels clinically relevant for margin detection, from Myogel samples in vitro.

U2 - 10.1016/j.aca.2022.339659

DO - 10.1016/j.aca.2022.339659

M3 - Article

AN - SCOPUS:85125628883

SN - 0003-2670

VL - 1202

JO - Analytica Chimica Acta

JF - Analytica Chimica Acta

M1 - 339659

ER -

Detection of cultured breast cancer cells from human tumor-derived matrix by differential ion mobility spectrometry

Abstrakti

Rahoitus

YK:n kestävän kehityksen tavoitteet

Julkaisufoorumi-taso

!!ASJC Scopus subject areas

Pääsy asiakirjaan

Sormenjälki

Siteeraa tätä