Abstract 5029: Precision cancer medicine based on 3D drug profiling of patient-derived cancer cell spheroid models
| العنوان: | Abstract 5029: Precision cancer medicine based on 3D drug profiling of patient-derived cancer cell spheroid models |
|---|---|
| المؤلفون: | Olli Kallioniemi, Piia Mikkonen, Lassi Paavolainen, Laura Turunen, Lauri Paasonen, Astrid Murumägi, Swapnil Potdar, Vilja Pietiäinen |
| المصدر: | Cancer Research. 78:5029-5029 |
| بيانات النشر: | American Association for Cancer Research (AACR), 2018. |
| سنة النشر: | 2018 |
| مصطلحات موضوعية: | Drug, 0303 health sciences, Cancer Research, Tumor microenvironment, Matrigel, business.industry, media_common.quotation_subject, Cancer, medicine.disease, 3. Good health, 03 medical and health sciences, 0302 clinical medicine, Oncology, 030220 oncology & carcinogenesis, Cancer cell, medicine, Cancer research, Viability assay, Stem cell, Ovarian cancer, business, 030304 developmental biology, media_common |
| الوصف: | We have set up a precision medicine strategy for solid tumors to i) understand biological heterogeneity and driver signaling pathways in cancer, ii) identify new drug opportunities, iii) develop biomarkers for drug responses, and iv) eventually tailor effective treatments for individual patients. Fresh cancer tissue is obtained directly from clinics and processed to provide patient -derived cells (PDCs). PDCs and original tumor tissues are characterized using genetic profiling and image-based phenotyping, phenomics. Systematic drug sensitivity and resistance testing (DSRT) is carried out on the representative PDC models. The PDCs are plated in 384-well plates and treated with oncological compounds, each in five concentrations in 384-well plates. The cell viability and toxicity are measured as drug responses using plate readers. Alternatively, drug-treated cells are immunostained and subjected to automated high-content imaging and image analysis. Central to the success of this approach is to grow and test the patient derived cells in ex vivo conditions mimicking the tumor microenvironment. To this aim, we have developed 3D drug profiling for PDC spheroids from ovarian and renal cancers. For the 3D drug profiling, cells were cultivated either in Matrigel or in cellulose-based hydrogel, GrowDex, which has earlier been shown to support 3D growth of cell lines and stem cells. The pipetting robot Biomek FXp (Beckman Coulter) was utilized for transfer of matrices and cells to 384-well plates, and acoustic dispenser Echo 550 (Labcyte) for delivery of tailored drug library of 52 drugs to the spheroids. The drug sensitivities of cancer cell spheroids were scored using cell viability measurement as well as high-content confocal imaging. Our results with primary cells and cell lines suggest that both Matrigel and cellulose-based hydrogel are applicable in the 384-well plate drug profiling assay, and PDC spheroids are formed in both conditions. When the drug responses of ovarian cancer PDCs grown in different 2D and 3D conditions were systematically compared, we observed significant differences in sensitivity to several drugs. Here, we describe the individual drug effects in all conditions, such as some chemotherapeutics being less effective in 3D. As a conclusion, the comparison of results from 2D and 3D drug profiling increases our understanding of mechanisms of drugs, and may aid to select the most representative drugs for the patient. Citation Format: Piia Mikkonen, Laura Turunen, Lauri Paasonen, Swapnil Potdar, Lassi Paavolainen, Astrid Murumägi, Olli Kallioniemi, Vilja M. Pietiäinen. Precision cancer medicine based on 3D drug profiling of patient-derived cancer cell spheroid models [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 5029. |
| تدمد: | 1538-7445 0008-5472 |
| DOI: | 10.1158/1538-7445.am2018-5029 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_________::4f579770dd20caf2698cdf41f2373ef4 https://doi.org/10.1158/1538-7445.am2018-5029 |
| Rights: | OPEN |
| رقم الانضمام: | edsair.doi...........4f579770dd20caf2698cdf41f2373ef4 |
| قاعدة البيانات: | OpenAIRE |
| ResultId |
1 |
|---|---|
| Header |
edsair OpenAIRE edsair.doi...........4f579770dd20caf2698cdf41f2373ef4 793 3 unknown 793.4140625 |
| PLink |
https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&scope=site&db=edsair&AN=edsair.doi...........4f579770dd20caf2698cdf41f2373ef4&custid=s6537998&authtype=sso |
| FullText |
Array
(
[Availability] => 0
)
Array ( [0] => Array ( [Url] => https://explore.openaire.eu/search/publication?articleId=doi_________::4f579770dd20caf2698cdf41f2373ef4# [Name] => EDS - OpenAIRE [Category] => fullText [Text] => View record in OpenAIRE [MouseOverText] => View record in OpenAIRE ) ) |
| Items |
Array
(
[Name] => Title
[Label] => Title
[Group] => Ti
[Data] => Abstract 5029: Precision cancer medicine based on 3D drug profiling of patient-derived cancer cell spheroid models
)
Array ( [Name] => Author [Label] => Authors [Group] => Au [Data] => <searchLink fieldCode="AR" term="%22Olli+Kallioniemi%22">Olli Kallioniemi</searchLink><br /><searchLink fieldCode="AR" term="%22Piia+Mikkonen%22">Piia Mikkonen</searchLink><br /><searchLink fieldCode="AR" term="%22Lassi+Paavolainen%22">Lassi Paavolainen</searchLink><br /><searchLink fieldCode="AR" term="%22Laura+Turunen%22">Laura Turunen</searchLink><br /><searchLink fieldCode="AR" term="%22Lauri+Paasonen%22">Lauri Paasonen</searchLink><br /><searchLink fieldCode="AR" term="%22Astrid+Murumägi%22">Astrid Murumägi</searchLink><br /><searchLink fieldCode="AR" term="%22Swapnil+Potdar%22">Swapnil Potdar</searchLink><br /><searchLink fieldCode="AR" term="%22Vilja+Pietiäinen%22">Vilja Pietiäinen</searchLink> ) Array ( [Name] => TitleSource [Label] => Source [Group] => Src [Data] => <i>Cancer Research</i>. 78:5029-5029 ) Array ( [Name] => Publisher [Label] => Publisher Information [Group] => PubInfo [Data] => American Association for Cancer Research (AACR), 2018. ) Array ( [Name] => DatePubCY [Label] => Publication Year [Group] => Date [Data] => 2018 ) Array ( [Name] => Subject [Label] => Subject Terms [Group] => Su [Data] => <searchLink fieldCode="DE" term="%22Drug%22">Drug</searchLink><br /><searchLink fieldCode="DE" term="%220303+health+sciences%22">0303 health sciences</searchLink><br /><searchLink fieldCode="DE" term="%22Cancer+Research%22">Cancer Research</searchLink><br /><searchLink fieldCode="DE" term="%22Tumor+microenvironment%22">Tumor microenvironment</searchLink><br /><searchLink fieldCode="DE" term="%22Matrigel%22">Matrigel</searchLink><br /><searchLink fieldCode="DE" term="%22business%2Eindustry%22">business.industry</searchLink><br /><searchLink fieldCode="DE" term="%22media%5Fcommon%2Equotation%5Fsubject%22">media_common.quotation_subject</searchLink><br /><searchLink fieldCode="DE" term="%22Cancer%22">Cancer</searchLink><br /><searchLink fieldCode="DE" term="%22medicine%2Edisease%22">medicine.disease</searchLink><br /><searchLink fieldCode="DE" term="%223%2E+Good+health%22">3. Good health</searchLink><br /><searchLink fieldCode="DE" term="%2203+medical+and+health+sciences%22">03 medical and health sciences</searchLink><br /><searchLink fieldCode="DE" term="%220302+clinical+medicine%22">0302 clinical medicine</searchLink><br /><searchLink fieldCode="DE" term="%22Oncology%22">Oncology</searchLink><br /><searchLink fieldCode="DE" term="%22030220+oncology+%26+carcinogenesis%22">030220 oncology & carcinogenesis</searchLink><br /><searchLink fieldCode="DE" term="%22Cancer+cell%22">Cancer cell</searchLink><br /><searchLink fieldCode="DE" term="%22medicine%22">medicine</searchLink><br /><searchLink fieldCode="DE" term="%22Cancer+research%22">Cancer research</searchLink><br /><searchLink fieldCode="DE" term="%22Viability+assay%22">Viability assay</searchLink><br /><searchLink fieldCode="DE" term="%22Stem+cell%22">Stem cell</searchLink><br /><searchLink fieldCode="DE" term="%22Ovarian+cancer%22">Ovarian cancer</searchLink><br /><searchLink fieldCode="DE" term="%22business%22">business</searchLink><br /><searchLink fieldCode="DE" term="%22030304+developmental+biology%22">030304 developmental biology</searchLink><br /><searchLink fieldCode="DE" term="%22media%5Fcommon%22">media_common</searchLink> ) Array ( [Name] => Abstract [Label] => Description [Group] => Ab [Data] => We have set up a precision medicine strategy for solid tumors to i) understand biological heterogeneity and driver signaling pathways in cancer, ii) identify new drug opportunities, iii) develop biomarkers for drug responses, and iv) eventually tailor effective treatments for individual patients. Fresh cancer tissue is obtained directly from clinics and processed to provide patient -derived cells (PDCs). PDCs and original tumor tissues are characterized using genetic profiling and image-based phenotyping, phenomics. Systematic drug sensitivity and resistance testing (DSRT) is carried out on the representative PDC models. The PDCs are plated in 384-well plates and treated with oncological compounds, each in five concentrations in 384-well plates. The cell viability and toxicity are measured as drug responses using plate readers. Alternatively, drug-treated cells are immunostained and subjected to automated high-content imaging and image analysis. Central to the success of this approach is to grow and test the patient derived cells in ex vivo conditions mimicking the tumor microenvironment. To this aim, we have developed 3D drug profiling for PDC spheroids from ovarian and renal cancers. For the 3D drug profiling, cells were cultivated either in Matrigel or in cellulose-based hydrogel, GrowDex, which has earlier been shown to support 3D growth of cell lines and stem cells. The pipetting robot Biomek FXp (Beckman Coulter) was utilized for transfer of matrices and cells to 384-well plates, and acoustic dispenser Echo 550 (Labcyte) for delivery of tailored drug library of 52 drugs to the spheroids. The drug sensitivities of cancer cell spheroids were scored using cell viability measurement as well as high-content confocal imaging. Our results with primary cells and cell lines suggest that both Matrigel and cellulose-based hydrogel are applicable in the 384-well plate drug profiling assay, and PDC spheroids are formed in both conditions. When the drug responses of ovarian cancer PDCs grown in different 2D and 3D conditions were systematically compared, we observed significant differences in sensitivity to several drugs. Here, we describe the individual drug effects in all conditions, such as some chemotherapeutics being less effective in 3D. As a conclusion, the comparison of results from 2D and 3D drug profiling increases our understanding of mechanisms of drugs, and may aid to select the most representative drugs for the patient. Citation Format: Piia Mikkonen, Laura Turunen, Lauri Paasonen, Swapnil Potdar, Lassi Paavolainen, Astrid Murumägi, Olli Kallioniemi, Vilja M. Pietiäinen. Precision cancer medicine based on 3D drug profiling of patient-derived cancer cell spheroid models [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 5029. ) Array ( [Name] => ISSN [Label] => ISSN [Group] => ISSN [Data] => 1538-7445<br />0008-5472 ) Array ( [Name] => DOI [Label] => DOI [Group] => ID [Data] => 10.1158/1538-7445.am2018-5029 ) Array ( [Name] => URL [Label] => Access URL [Group] => URL [Data] => <link linkTarget="URL" linkTerm="https://explore.openaire.eu/search/publication?articleId=doi_________::4f579770dd20caf2698cdf41f2373ef4" linkWindow="_blank">https://explore.openaire.eu/search/publication?articleId=doi_________::4f579770dd20caf2698cdf41f2373ef4</link><br /><link linkTarget="URL" linkTerm="https://doi.org/10.1158/1538-7445.am2018-5029" linkWindow="_blank">https://doi.org/10.1158/1538-7445.am2018-5029</link> ) Array ( [Name] => Copyright [Label] => Rights [Group] => Cpyrght [Data] => OPEN ) Array ( [Name] => AN [Label] => Accession Number [Group] => ID [Data] => edsair.doi...........4f579770dd20caf2698cdf41f2373ef4 ) |
| RecordInfo |
Array
(
[BibEntity] => Array
(
[Identifiers] => Array
(
[0] => Array
(
[Type] => doi
[Value] => 10.1158/1538-7445.am2018-5029
)
)
[Languages] => Array
(
[0] => Array
(
[Text] => Undetermined
)
)
[PhysicalDescription] => Array
(
[Pagination] => Array
(
[PageCount] => 1
[StartPage] => 5029
)
)
[Subjects] => Array
(
[0] => Array
(
[SubjectFull] => Drug
[Type] => general
)
[1] => Array
(
[SubjectFull] => 0303 health sciences
[Type] => general
)
[2] => Array
(
[SubjectFull] => Cancer Research
[Type] => general
)
[3] => Array
(
[SubjectFull] => Tumor microenvironment
[Type] => general
)
[4] => Array
(
[SubjectFull] => Matrigel
[Type] => general
)
[5] => Array
(
[SubjectFull] => business.industry
[Type] => general
)
[6] => Array
(
[SubjectFull] => media_common.quotation_subject
[Type] => general
)
[7] => Array
(
[SubjectFull] => Cancer
[Type] => general
)
[8] => Array
(
[SubjectFull] => medicine.disease
[Type] => general
)
[9] => Array
(
[SubjectFull] => 3. Good health
[Type] => general
)
[10] => Array
(
[SubjectFull] => 03 medical and health sciences
[Type] => general
)
[11] => Array
(
[SubjectFull] => 0302 clinical medicine
[Type] => general
)
[12] => Array
(
[SubjectFull] => Oncology
[Type] => general
)
[13] => Array
(
[SubjectFull] => 030220 oncology & carcinogenesis
[Type] => general
)
[14] => Array
(
[SubjectFull] => Cancer cell
[Type] => general
)
[15] => Array
(
[SubjectFull] => medicine
[Type] => general
)
[16] => Array
(
[SubjectFull] => Cancer research
[Type] => general
)
[17] => Array
(
[SubjectFull] => Viability assay
[Type] => general
)
[18] => Array
(
[SubjectFull] => Stem cell
[Type] => general
)
[19] => Array
(
[SubjectFull] => Ovarian cancer
[Type] => general
)
[20] => Array
(
[SubjectFull] => business
[Type] => general
)
[21] => Array
(
[SubjectFull] => 030304 developmental biology
[Type] => general
)
[22] => Array
(
[SubjectFull] => media_common
[Type] => general
)
)
[Titles] => Array
(
[0] => Array
(
[TitleFull] => Abstract 5029: Precision cancer medicine based on 3D drug profiling of patient-derived cancer cell spheroid models
[Type] => main
)
)
)
[BibRelationships] => Array
(
[HasContributorRelationships] => Array
(
[0] => Array
(
[PersonEntity] => Array
(
[Name] => Array
(
[NameFull] => Olli Kallioniemi
)
)
)
[1] => Array
(
[PersonEntity] => Array
(
[Name] => Array
(
[NameFull] => Piia Mikkonen
)
)
)
[2] => Array
(
[PersonEntity] => Array
(
[Name] => Array
(
[NameFull] => Lassi Paavolainen
)
)
)
[3] => Array
(
[PersonEntity] => Array
(
[Name] => Array
(
[NameFull] => Laura Turunen
)
)
)
[4] => Array
(
[PersonEntity] => Array
(
[Name] => Array
(
[NameFull] => Lauri Paasonen
)
)
)
[5] => Array
(
[PersonEntity] => Array
(
[Name] => Array
(
[NameFull] => Astrid Murumägi
)
)
)
[6] => Array
(
[PersonEntity] => Array
(
[Name] => Array
(
[NameFull] => Swapnil Potdar
)
)
)
[7] => Array
(
[PersonEntity] => Array
(
[Name] => Array
(
[NameFull] => Vilja Pietiäinen
)
)
)
)
[IsPartOfRelationships] => Array
(
[0] => Array
(
[BibEntity] => Array
(
[Dates] => Array
(
[0] => Array
(
[D] => 01
[M] => 07
[Type] => published
[Y] => 2018
)
)
[Identifiers] => Array
(
[0] => Array
(
[Type] => issn-print
[Value] => 15387445
)
[1] => Array
(
[Type] => issn-print
[Value] => 00085472
)
[2] => Array
(
[Type] => issn-locals
[Value] => edsair
)
[3] => Array
(
[Type] => issn-locals
[Value] => edsairFT
)
)
[Numbering] => Array
(
[0] => Array
(
[Type] => volume
[Value] => 78
)
)
[Titles] => Array
(
[0] => Array
(
[TitleFull] => Cancer Research
[Type] => main
)
)
)
)
)
)
)
|
| IllustrationInfo |