Breast CancerPhase 1QUESTNCT05177432
Quadratic Phenotypic Optimisation Platform (QPOP) Utilisation to Enhance Selection of Patient Therapy Through Patient Derived Organoids in Breast Cancer
A Phase 1 study testing QPOP in people with Breast Cancer, run by National University Hospital, Singapore at 1 site in Singapore.
Trial summary
Based on proof-of-concept study, the investigators hypothesise that the QPOP prediction model can be further extended into use in solid tumors. Using breast cancer as a model, the investigators intend to investigate the feasibility of QPOP as a clinical decision support platform to identify patient-specific drug… Read more about this study
- Who is it for?
- Ages 21 to 99 years, women only
- Conditions the study is for
- Breast Cancer
- What is being tested?
- QPOP
- Type of treatment
- Medical device
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 26 participants
- Where
- 1 siteSingapore
- Study dates
- 6 Dec 2021to 31 Dec 2025
- Phase
- Phase 1
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 21 to 99 years
- Women only
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- Age >= 21 years.
- Histological confirmed breast carcinoma of any subtype (any estrogen receptor, progesterone receptor and HER2 receptor status)
- ECOG 0-1.
- At least 1 tumour lesion (primary or metastatic) amenable to fresh biopsy
- At least 1 measurable tumour lesions based on RECIST 1.1 criteria
- Estimated life expectancy of at least 12 weeks.
Show all 22Show fewer
- Has documented progressive disease from last line of therapy.
- Has received at least 1 line of palliative systemic therapy
- Expected adequate organ function (bone marrow, hepatic, renal) after recovering from treatment-induced acute toxicities at the time of study treatment.
- Signed informed consent from patient or legal representative.
- Able to comply with study-related procedures.
- Patients may be included in the study only if they meet all of the following criteria:
- Adequate organ function including the following:
- Bone marrow:
- Absolute neutrophil (segmented and bands) count (ANC) >= 1.5 x 109/L
- Platelets >= 100 x 109/L
- Hemoglobin >= 8 x 109/L
- Hepatic:
- Bilirubin <= 1.5 x upper limit of normal (ULN),
- ALT or AST <= 2.5x ULN, (or <=5 X with liver metastases)
- Renal:
- Creatinine <= 1.5x ULN
Who cannot join
- Patients will be excluded from the study for any of the following reasons:
- Pregnancy.
- Breast feeding.
- Serious concomitant disorders that would compromise the safety of the patient or compromise the patient's ability to complete the study, at the discretion of the investigator.
- Active bleeding disorder or bleeding site.
- Second primary malignancy that is clinically detectable at the time of consideration for study enrollment.
Show all 20Show fewer
- History of significant neurological or mental disorder, including seizures or dementia.
- Part B - Additional inclusion and exclusion criteria to be fulfilled prior to commencement of study drug
- Patients will be excluded from the study for any of the following reasons:
- Treatment within the last 30 days with any investigational drug.
- Concurrent administration of any other tumour therapy, including cytotoxic chemotherapy, hormonal therapy, and immunotherapy at time of commencement of study drug.
- Major surgery within 28 days of study drug administration.
- Active infection that in the opinion of the investigator would compromise the patient's ability to tolerate therapy.
- Non-healing wound.
- Poorly controlled diabetes mellitus.
- Symptomatic brain metastasis.
- Contraindication to receiving specific QPOP-directed study treatment within drug that has been recommended based on QPOP analyses (e.g., poorly controlled diabetes mellitus for alpelisib, left ventricular ejection fraction of <50% for trastuzumab-emtansine)
- Received more than 2 lines of empirical therapy between tumor biopsy for organoids growth and commencement on QPOP-directed study treatment (including endocrine therapy, chemotherapy, targeted therapy or immunotherapy)
- Biopsy for organoids growth performed more than 12 months from time of study drug commencement
- Has not recovered from acute toxicities from prior anti-cancer therapy
Trial locations and contacts
1 site in 1 country.
Singapore1 site, 1 recruiting
- Recruiting
National University Hospital Singapore
Singapore
Soo Chin Lee, MBBS, MRCP, 65 6772 4629, soo_chin_lee@nuhs.edu.sg
Soo Chin Lee, MBBS, MRCP
About this study
Based on proof-of-concept study, the investigators hypothesise that the QPOP prediction model can be further extended into use in solid tumors. Using breast cancer as a model, the investigators intend to investigate the feasibility of QPOP as a clinical decision support platform to identify patient-specific drug combinations across a range of breast cancer patients. The investigators propose a pilot phase I clinical study to test the feasibility of using QPOP to guide therapy in patients with advanced breast cancer.
Read the detailed description
Breast cancer (BC) is highly heterogeneous with more than 20 genetically, morphologically and clinically distinct subtypes. Despite improved diagnosis and a wide repertoire of clinically-approved therapies, metastatic BC patients have a dismal 5-year survival rate of about 20%. Systemic treatment options for breast cancer are empiric based on large clinical studies, and the ability to tailor choices individualized to each patient may aid optimization of patient's responses to treatment.
A major obstacle for drug development is the lack of clinically relevant cell model systems. The use of conventional two-dimensional (2D) cell cultures presents limitations in the recapitulation of the primary disease characteristics. In particular, monolayer cultures have been demonstrated for not being able to recapitulate the heterogeneous nature of primary patient tumor tissues. Despite the establishment of the National Cancer Institute 60 Panel (NCI-60) in an attempt to capture the heterogeneous nature of nine different cancer types, it is still unable to comprehensively represent the diversity which cancer patients exhibit, and is currently being phased out. Studies comparing the molecular profiles of cell lines with primary tumors have reported that conventional cell lines are unable to represent all cancer subtypes. Recently, patient-derived organoids (PDOs) has been demonstrated to faithfully preserve and maintain heterogeneity of several primary cancer types including BC and are being used to evaluate drug sensitivity and their associated genomic variations. PDOs have also been shown to retain the molecular profiles of the parental tumors and can be effectively used to investigate drug responses and mechanisms ex vivo. Thus, human PDOs can provide more clinically relevant models that can recapitulate disease heterogeneity for more accurate studies of drug responses as compared to conventional cell line-based models.
Identifying the most suitable patient-specific drug combinations remains a challenge due to the complex molecular networks that contribute to feedback mechanisms of drug resistance and compensatory oncogenic drivers that limit efficacy of targeted inhibitors. Pharmacogenomics is highly useful in predicting drug sensitivity and clinical outcome and define appropriate subpopulations for specific drugs based on genetic biomarkers. These approaches are largely limited to monotherapy and cannot identify patient-specific drug combinations nor do they consider other genomic alterations that are unaccounted for by the specific biomarkers used. In order to address this critical deficit in combination therapy, the investigators have developed an experimental-analytical hybrid platform, Quadratic Phenotypic Optimisation Platform (QPOP), that can rank potential drug combination response in biological model systems. The investigators initially applied QPOP towards identifying novel drug combinations against bortezomib-resistant multiple myeloma. As the investigators have improved the efficiency of the QPOP platform and applied QPOP towards primary patient samples and patient-derived organoids, it has become clear that QPOP may be useful as a clinical decision support platform that may be able to suggest clinically actionable and suitable patient-specific drug combinations derived from drug sensitivity tests using patient-derived materials.
The investigators hypothesise that the QPOP prediction model can be further extended into use in solid tumors. Using breast cancer as a model, we intend to investigate the feasibility of QPOP as a clinical decision support platform to identity patient-specific drug combinations across a range of breast cancer patients. The Investigators propose a pilot phase I clinical study to test the feasibility of using QPOP to guide therapy in patients with advanced breast cancer.
Eligible patients will undergo a fresh biopsy of tumour lesion to obtain cells that will be used to generate patient-derived tumour organoids. After successful organoid generation, organoids will be subjected to a 12-drug panel screening including 10 fixed drugs and 2 drugs that may be selected by physician based on treatment history of individual patients. The drug panel is curated taking into account standard drugs commonly used in breast cancer treatment as well as drugs/combinations being tested at ongoing therapeutic trials at the National University Cancer Institute Singapore (NCIS) which enrolled patients can gain access to. Fixed drug panel will include chemotherapy (cisplatin, 5-fluorouracil, paclitaxel), endocrine therapy (tamoxifen, fulvestrant), HER2-directed therapy (lapatinib) and small molecule tyrosine kinase inhibitors (abemaciclib, olaparib, alpelisib, lenvatinib).
QPOP analysis results will be reported to treating physician once available to aid selection of therapy. To prevent delay in patient treatment while awaiting QPOP results, patients are allowed to receive next line of therapy at physician's discretion (termed "empirical therapy"), and QPOP analyses results can be used to guide further lines of treatment upon progression on "empirical therapy".
- Conditions
- Breast Cancer
- Being tested
- QPOP
- Keywords
- QPOP, Enhance Selection, Patient Therapy, Patient Derived Organoids, Breast Cancer
- Medical subject headings
- Breast Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
QPOPDevice
QPOP will be used as a clinical decision support platform to identity suitable patient-specific drug combinations across a range of breast cancer patients, which are derived from drug sensitivity tests using patient-derived materials.
| Study group | Role | What they receive |
|---|---|---|
| QPOP-based drug screen assay using patient tumour-derived organoids | Experimental | Patients with Histological confirmed breast carcinoma of any subtype (any estrogen receptor, progesterone receptor and HER2 receptor status) with at least 1 tumour lesion (primary or metastatic) amendable to fresh biopsy and measurable based on RECIST 1.1 criteria will undergo biopsy to obtain a sample of cancer tissue that will be used to generate Patient Derived Organoids (PDOs).
Patients' cells will be subjected to testing with 10-12 anti-cancer drugs and a table for treatment sensitivity to each drug will be derived after 8 to 12 weeks of treatment in the laboratory. Results will be reviewed at an expert panel discussion to decide on the most suitable anti-cancer drug treatment Receives: Device: QPOP |
What is being measured
The primary outcome is the main question the study is designed to answer.
Objective response rate measured by RECIST 1.1 criteria to anti-cancer therapy selected by QPOP (prospective analysis).
Measured: 3 years
Secondary outcomes (4)
Clinical benefit rate as determined by proportion of patients with complete response, partial response or stable disease as best response on RECIST 1.1 criteria (prospective analysis)
Measured: 3 years
Progression-free survival of QPOP-guided therapy as measured by RECIST 1.1 criteria (prospective analysis)
Measured: 3 years
Correlating QPOP prediction score of immediate past line of therapy and objective response rate to that therapy (retrospective correlative analysis)
Measured: 3 years
Correlating objective response rate (ORR) measured by RECIST v1.1 of the tumor lesion biopsied for QPOP analyses with QPOP guided therapy (retrospective correlative analysis)
Measured: 3 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted4 Jan 2022
- Study start6 Dec 2021
- Primary completion3 Jan 2025 (estimated)
- Study completion31 Dec 2025 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (11)Papers the sponsor has linked to this study.
- Other trials for Breast CancerStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase 1
- Primary purpose
- Treatment
- Allocation
- Not applicable
- Intervention model
- Sequential
- Model description
- This is a single-centre study based on the Simon 2-stage optimal design: 9 patients will be enrolled initially. The study will be expanded to a further 14 patients (Stage II) if 2 or more patients enrolled in stage I of the study achieve an objective response (of \>35% ) with the chemotherapeutic agent selected by the drug screen assay. Accounting for 10% attrition rate, a total of 26 evaluable patients will be included in both stages of study. QPOP-based drug screen assay will be used as a clinical decision platform to identify patient-specific drug combinations across a range of breast cancer patients.
- Masking
- None (open label)
- Enrolment
- 26 participants (estimated)
Sponsor and investigators
- Lead sponsor
- National University Hospital, Singapore
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05177432
- Sponsor's protocol ID
- BR02/03/21
- Acronym
- QUEST
- Status last verified
- Apr 2023
- First submitted
- 10 Nov 2021
- Last updated
- 18 Apr 2023
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2018 Nov;68(6):394-424. doi: 10.3322/caac.21492. Epub 2018 Sep 12.
PubMed 30207593Fong ELS, Toh TB, Yu H, Chow EK. 3D Culture as a Clinically Relevant Model for Personalized Medicine. SLAS Technol. 2017 Jun;22(3):245-253. doi: 10.1177/2472630317697251. Epub 2017 Mar 9.
PubMed 28277923Gillet JP, Varma S, Gottesman MM. The clinical relevance of cancer cell lines. J Natl Cancer Inst. 2013 Apr 3;105(7):452-8. doi: 10.1093/jnci/djt007. Epub 2013 Feb 21.
PubMed 23434901Ledford H. US cancer institute to overhaul tumour cell lines. Nature. 2016 Feb 25;530(7591):391. doi: 10.1038/nature.2016.19364. No abstract available.
PubMed 26911756Virtanen C, Ishikawa Y, Honjoh D, Kimura M, Shimane M, Miyoshi T, Nomura H, Jones MH. Integrated classification of lung tumors and cell lines by expression profiling. Proc Natl Acad Sci U S A. 2002 Sep 17;99(19):12357-62. doi: 10.1073/pnas.192240599. Epub 2002 Sep 6.
PubMed 12218176Goodspeed A, Heiser LM, Gray JW, Costello JC. Tumor-Derived Cell Lines as Molecular Models of Cancer Pharmacogenomics. Mol Cancer Res. 2016 Jan;14(1):3-13. doi: 10.1158/1541-7786.MCR-15-0189. Epub 2015 Aug 6.
PubMed 26248648Sachs N, de Ligt J, Kopper O, Gogola E, Bounova G, Weeber F, Balgobind AV, Wind K, Gracanin A, Begthel H, Korving J, van Boxtel R, Duarte AA, Lelieveld D, van Hoeck A, Ernst RF, Blokzijl F, Nijman IJ, Hoogstraat M, van de Ven M, Egan DA, Zinzalla V, Moll J, Boj SF, Voest EE, Wessels L, van Diest PJ, Rottenberg S, Vries RGJ, Cuppen E, Clevers H. A Living Biobank of Breast Cancer Organoids Captures Disease Heterogeneity. Cell. 2018 Jan 11;172(1-2):373-386.e10. doi: 10.1016/j.cell.2017.11.010. Epub 2017 Dec 7.
PubMed 29224780Vlachogiannis G, Hedayat S, Vatsiou A, Jamin Y, Fernandez-Mateos J, Khan K, Lampis A, Eason K, Huntingford I, Burke R, Rata M, Koh DM, Tunariu N, Collins D, Hulkki-Wilson S, Ragulan C, Spiteri I, Moorcraft SY, Chau I, Rao S, Watkins D, Fotiadis N, Bali M, Darvish-Damavandi M, Lote H, Eltahir Z, Smyth EC, Begum R, Clarke PA, Hahne JC, Dowsett M, de Bono J, Workman P, Sadanandam A, Fassan M, Sansom OJ, Eccles S, Starling N, Braconi C, Sottoriva A, Robinson SP, Cunningham D, Valeri N. Patient-derived organoids model treatment response of metastatic gastrointestinal cancers. Science. 2018 Feb 23;359(6378):920-926. doi: 10.1126/science.aao2774.
PubMed 29472484Shaughnessy JD Jr, Qu P, Usmani S, Heuck CJ, Zhang Q, Zhou Y, Tian E, Hanamura I, van Rhee F, Anaissie E, Epstein J, Nair B, Stephens O, Williams R, Waheed S, Alsayed Y, Crowley J, Barlogie B. Pharmacogenomics of bortezomib test-dosing identifies hyperexpression of proteasome genes, especially PSMD4, as novel high-risk feature in myeloma treated with Total Therapy 3. Blood. 2011 Sep 29;118(13):3512-24. doi: 10.1182/blood-2010-12-328252. Epub 2011 May 31.
PubMed 21628408Rashid MBMA, Toh TB, Hooi L, Silva A, Zhang Y, Tan PF, Teh AL, Karnani N, Jha S, Ho CM, Chng WJ, Ho D, Chow EK. Optimizing drug combinations against multiple myeloma using a quadratic phenotypic optimization platform (QPOP). Sci Transl Med. 2018 Aug 8;10(453):eaan0941. doi: 10.1126/scitranslmed.aan0941.
PubMed 30089632de Mel S, Rashid MBM, Zhang XY, Goh J, Lee CT, Poon LM, Chan EHL, Liu X, Chng WJ, Chee YL, Lee J, Yuen YC, Lim JQ, Chia BKH, Laurensia Y, Huang D, Pang WL, Cheah DMZ, Wong EKY, Ong CK, Tang T, Lim ST, Ng SB, Tan SY, Loi HY, Tan LK, Chow EK, Jeyasekharan AD. Application of an ex-vivo drug sensitivity platform towards achieving complete remission in a refractory T-cell lymphoma. Blood Cancer J. 2020 Jan 27;10(1):9. doi: 10.1038/s41408-020-0276-7. No abstract available.
PubMed 31988286
Study at a glance
From the registry record, last updated by the sponsor on 18 Apr 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Quadratic Phenotypic Optimisation Platform (QPOP) Utilisation to Enhance Selection of Patient Therapy Through Patient Derived Organoids in Breast Cancer
- ClinicalTrials.gov ID
- NCT05177432
- Acronym / sponsor ID
- QUEST / BR02/03/21
- Conditions
- Breast Cancer
- Keywords
- QPOP, Enhance Selection, Patient Therapy, Patient Derived Organoids, Breast Cancer
- Phase
- Phase 1
- Study type
- Interventional
- Recruitment status
- Unknown status
- Enrolment
- 26 participants (estimated)
- Lead sponsor
- National University Hospital, SingaporeUniversity, hospital or other
- Study start
- 6 Dec 2021 (actual)
- Primary completion
- 3 Jan 2025 (estimated)
- Study completion
- 31 Dec 2025 (estimated)
- First posted
- 4 Jan 2022 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
Based on proof-of-concept study, the investigators hypothesise that the QPOP prediction model can be further extended into use in solid tumors. Using breast cancer as a model, the investigators intend to investigate the feasibility of QPOP as a clinical decision support platform to identify patient-specific drug combinations across a range of breast cancer patients. The investigators propose a pilot phase I clinical study to test the feasibility of using QPOP to guide therapy in patients with advanced breast cancer.
Detailed description
Breast cancer (BC) is highly heterogeneous with more than 20 genetically, morphologically and clinically distinct subtypes. Despite improved diagnosis and a wide repertoire of clinically-approved therapies, metastatic BC patients have a dismal 5-year survival rate of about 20%. Systemic treatment options for breast cancer are empiric based on large clinical studies, and the ability to tailor choices individualized to each patient may aid optimization of patient's responses to treatment.
A major obstacle for drug development is the lack of clinically relevant cell model systems. The use of conventional two-dimensional (2D) cell cultures presents limitations in the recapitulation of the primary disease characteristics. In particular, monolayer cultures have been demonstrated for not being able to recapitulate the heterogeneous nature of primary patient tumor tissues. Despite the establishment of the National Cancer Institute 60 Panel (NCI-60) in an attempt to capture the heterogeneous nature of nine different cancer types, it is still unable to comprehensively represent the diversity which cancer patients exhibit, and is currently being phased out. Studies comparing the molecular profiles of cell lines with primary tumors have reported that conventional cell lines are unable to represent all cancer subtypes. Recently, patient-derived organoids (PDOs) has been demonstrated to faithfully preserve and maintain heterogeneity of several primary cancer types including BC and are being used to evaluate drug sensitivity and their associated genomic variations. PDOs have also been shown to retain the molecular profiles of the parental tumors and can be effectively used to investigate drug responses and mechanisms ex vivo. Thus, human PDOs can provide more clinically relevant models that can recapitulate disease heterogeneity for more accurate studies of drug responses as compared to conventional cell line-based models.
Identifying the most suitable patient-specific drug combinations remains a challenge due to the complex molecular networks that contribute to feedback mechanisms of drug resistance and compensatory oncogenic drivers that limit efficacy of targeted inhibitors. Pharmacogenomics is highly useful in predicting drug sensitivity and clinical outcome and define appropriate subpopulations for specific drugs based on genetic biomarkers. These approaches are largely limited to monotherapy and cannot identify patient-specific drug combinations nor do they consider other genomic alterations that are unaccounted for by the specific biomarkers used. In order to address this critical deficit in combination therapy, the investigators have developed an experimental-analytical hybrid platform, Quadratic Phenotypic Optimisation Platform (QPOP), that can rank potential drug combination response in biological model systems. The investigators initially applied QPOP towards identifying novel drug combinations against bortezomib-resistant multiple myeloma. As the investigators have improved the efficiency of the QPOP platform and applied QPOP towards primary patient samples and patient-derived organoids, it has become clear that QPOP may be useful as a clinical decision support platform that may be able to suggest clinically actionable and suitable patient-specific drug combinations derived from drug sensitivity tests using patient-derived materials.
The investigators hypothesise that the QPOP prediction model can be further extended into use in solid tumors. Using breast cancer as a model, we intend to investigate the feasibility of QPOP as a clinical decision support platform to identity patient-specific drug combinations across a range of breast cancer patients. The Investigators propose a pilot phase I clinical study to test the feasibility of using QPOP to guide therapy in patients with advanced breast cancer.
Eligible patients will undergo a fresh biopsy of tumour lesion to obtain cells that will be used to generate patient-derived tumour organoids. After successful organoid generation, organoids will be subjected to a 12-drug panel screening including 10 fixed drugs and 2 drugs that may be selected by physician based on treatment history of individual patients. The drug panel is curated taking into account standard drugs commonly used in breast cancer treatment as well as drugs/combinations being tested at ongoing therapeutic trials at the National University Cancer Institute Singapore (NCIS) which enrolled patients can gain access to. Fixed drug panel will include chemotherapy (cisplatin, 5-fluorouracil, paclitaxel), endocrine therapy (tamoxifen, fulvestrant), HER2-directed therapy (lapatinib) and small molecule tyrosine kinase inhibitors (abemaciclib, olaparib, alpelisib, lenvatinib).
QPOP analysis results will be reported to treating physician once available to aid selection of therapy. To prevent delay in patient treatment while awaiting QPOP results, patients are allowed to receive next line of therapy at physician's discretion (termed "empirical therapy"), and QPOP analyses results can be used to guide further lines of treatment upon progression on "empirical therapy".
Study design
- Primary purpose
- Treatment
- Allocation
- Not applicable
- Intervention model
- Sequential
- Masking
- None (open label)
- Number of arms
- 1
- Model description
- This is a single-centre study based on the Simon 2-stage optimal design: 9 patients will be enrolled initially. The study will be expanded to a further 14 patients (Stage II) if 2 or more patients enrolled in stage I of the study achieve an objective response (of >35% ) with the chemotherapeutic agent selected by the drug screen assay. Accounting for 10% attrition rate, a total of 26 evaluable patients will be included in both stages of study. QPOP-based drug screen assay will be used as a clinical decision platform to identify patient-specific drug combinations across a range of breast cancer patients.
| Arm | Type | Receives |
|---|---|---|
| QPOP-based drug screen assay using patient tumour-derived organoids | Experimental | Device: QPOPPatients with Histological confirmed breast carcinoma of any subtype (any estrogen receptor, progesterone receptor and HER2 receptor status) with at least 1 tumour lesion (primary or metastatic) amendable to fresh biopsy and measurable based on RECIST 1.1 criteria will undergo biopsy to obtain a sample of cancer tissue that will be used to generate Patient Derived Organoids (PDOs). Patients' cells will be subjected to testing with 10-12 anti-cancer drugs and a table for treatment sensitivity to each drug will be derived after 8 to 12 weeks of treatment in the laboratory. Results will be reviewed at an expert panel discussion to decide on the most suitable anti-cancer drug treatment |
Who may qualify
- Ages
- 21 to 99 years
- Sex
- Female only
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (22 listed)
- Age >= 21 years.
- Histological confirmed breast carcinoma of any subtype (any estrogen receptor, progesterone receptor and HER2 receptor status)
- ECOG 0-1.
- At least 1 tumour lesion (primary or metastatic) amenable to fresh biopsy
- At least 1 measurable tumour lesions based on RECIST 1.1 criteria
Key exclusion criteria (20 listed)
- Pregnancy.
- Breast feeding.
- Serious concomitant disorders that would compromise the safety of the patient or compromise the patient's ability to complete the study, at the discretion of the investigator.
- Active bleeding disorder or bleeding site.
- Second primary malignancy that is clinically detectable at the time of consideration for study enrollment.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| QPOPQPOP will be used as a clinical decision support platform to identity suitable patient-specific drug combinations across a range of breast cancer patients, which are derived from drug sensitivity tests using patient-derived materials. | Device | QPOP-based drug screen assay using patient tumour-derived organoids |
Primary outcome
Objective response rate measured by RECIST 1.1 criteria to anti-cancer therapy selected by QPOP (prospective analysis).
Time frame: 3 years
4 secondary outcomes
Clinical benefit rate as determined by proportion of patients with complete response, partial response or stable disease as best response on RECIST 1.1 criteria (prospective analysis)
Time frame: 3 years
Progression-free survival of QPOP-guided therapy as measured by RECIST 1.1 criteria (prospective analysis)
Time frame: 3 years
Correlating QPOP prediction score of immediate past line of therapy and objective response rate to that therapy (retrospective correlative analysis)
Time frame: 3 years
Correlating objective response rate (ORR) measured by RECIST v1.1 of the tumor lesion biopsied for QPOP analyses with QPOP guided therapy (retrospective correlative analysis)
Time frame: 3 years
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 18 Apr 2023.
- Results publication: PubMed 30207593Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2018 Nov;68(6):394-424. doi: 10.3322/caac.21492. Epub 2018 Sep 12.
- Results publication: PubMed 28277923Fong ELS, Toh TB, Yu H, Chow EK. 3D Culture as a Clinically Relevant Model for Personalized Medicine. SLAS Technol. 2017 Jun;22(3):245-253. doi: 10.1177/2472630317697251. Epub 2017 Mar 9.
- Results publication: PubMed 23434901Gillet JP, Varma S, Gottesman MM. The clinical relevance of cancer cell lines. J Natl Cancer Inst. 2013 Apr 3;105(7):452-8. doi: 10.1093/jnci/djt007. Epub 2013 Feb 21.
- Results publication: PubMed 26911756Ledford H. US cancer institute to overhaul tumour cell lines. Nature. 2016 Feb 25;530(7591):391. doi: 10.1038/nature.2016.19364. No abstract available.
- Results publication: PubMed 12218176Virtanen C, Ishikawa Y, Honjoh D, Kimura M, Shimane M, Miyoshi T, Nomura H, Jones MH. Integrated classification of lung tumors and cell lines by expression profiling. Proc Natl Acad Sci U S A. 2002 Sep 17;99(19):12357-62. doi: 10.1073/pnas.192240599. Epub 2002 Sep 6.
- Results publication: PubMed 26248648Goodspeed A, Heiser LM, Gray JW, Costello JC. Tumor-Derived Cell Lines as Molecular Models of Cancer Pharmacogenomics. Mol Cancer Res. 2016 Jan;14(1):3-13. doi: 10.1158/1541-7786.MCR-15-0189. Epub 2015 Aug 6.
- Results publication: PubMed 29224780Sachs N, de Ligt J, Kopper O, Gogola E, Bounova G, Weeber F, Balgobind AV, Wind K, Gracanin A, Begthel H, Korving J, van Boxtel R, Duarte AA, Lelieveld D, van Hoeck A, Ernst RF, Blokzijl F, Nijman IJ, Hoogstraat M, van de Ven M, Egan DA, Zinzalla V, Moll J, Boj SF, Voest EE, Wessels L, van Diest PJ, Rottenberg S, Vries RGJ, Cuppen E, Clevers H. A Living Biobank of Breast Cancer Organoids Captures Disease Heterogeneity. Cell. 2018 Jan 11;172(1-2):373-386.e10. doi: 10.1016/j.cell.2017.11.010. Epub 2017 Dec 7.
- Results publication: PubMed 29472484Vlachogiannis G, Hedayat S, Vatsiou A, Jamin Y, Fernandez-Mateos J, Khan K, Lampis A, Eason K, Huntingford I, Burke R, Rata M, Koh DM, Tunariu N, Collins D, Hulkki-Wilson S, Ragulan C, Spiteri I, Moorcraft SY, Chau I, Rao S, Watkins D, Fotiadis N, Bali M, Darvish-Damavandi M, Lote H, Eltahir Z, Smyth EC, Begum R, Clarke PA, Hahne JC, Dowsett M, de Bono J, Workman P, Sadanandam A, Fassan M, Sansom OJ, Eccles S, Starling N, Braconi C, Sottoriva A, Robinson SP, Cunningham D, Valeri N. Patient-derived organoids model treatment response of metastatic gastrointestinal cancers. Science. 2018 Feb 23;359(6378):920-926. doi: 10.1126/science.aao2774.
- Results publication: PubMed 21628408Shaughnessy JD Jr, Qu P, Usmani S, Heuck CJ, Zhang Q, Zhou Y, Tian E, Hanamura I, van Rhee F, Anaissie E, Epstein J, Nair B, Stephens O, Williams R, Waheed S, Alsayed Y, Crowley J, Barlogie B. Pharmacogenomics of bortezomib test-dosing identifies hyperexpression of proteasome genes, especially PSMD4, as novel high-risk feature in myeloma treated with Total Therapy 3. Blood. 2011 Sep 29;118(13):3512-24. doi: 10.1182/blood-2010-12-328252. Epub 2011 May 31.
- Results publication: PubMed 30089632Rashid MBMA, Toh TB, Hooi L, Silva A, Zhang Y, Tan PF, Teh AL, Karnani N, Jha S, Ho CM, Chng WJ, Ho D, Chow EK. Optimizing drug combinations against multiple myeloma using a quadratic phenotypic optimization platform (QPOP). Sci Transl Med. 2018 Aug 8;10(453):eaan0941. doi: 10.1126/scitranslmed.aan0941.
- Results publication: PubMed 31988286de Mel S, Rashid MBM, Zhang XY, Goh J, Lee CT, Poon LM, Chan EHL, Liu X, Chng WJ, Chee YL, Lee J, Yuen YC, Lim JQ, Chia BKH, Laurensia Y, Huang D, Pang WL, Cheah DMZ, Wong EKY, Ong CK, Tang T, Lim ST, Ng SB, Tan SY, Loi HY, Tan LK, Chow EK, Jeyasekharan AD. Application of an ex-vivo drug sensitivity platform towards achieving complete remission in a refractory T-cell lymphoma. Blood Cancer J. 2020 Jan 27;10(1):9. doi: 10.1038/s41408-020-0276-7. No abstract available.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A Phase 1 study testing QPOP in people with Breast Cancer, run by National University Hospital, Singapore at 1 site in Singapore.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 22 inclusion and 20 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 18 Apr 2023. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Singapore. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
What does this phase mean?
Phase 1 studies test a treatment in a small group of people for the first time or in a new way, mainly to check safety and find the right dose.
Not every study follows the same path, and some studies (observational studies, device studies) have no phase.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Soo Chin Lee (65 6772 4629, soo_chin_lee@nuhs.edu.sg), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Objective response rate measured by RECIST 1.1 criteria to anti-cancer therapy selected by QPOP (prospective analysis). (time frame: 3 years). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT05177432, which also lists 11 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 18 Apr 2023.