Quadratic Phenotypic Optimisation Platform (QPOP) Utilisation to Enhance Selection of Patient Therapy Through Patient Derived Organoids in Breast Cancer

Breast Cancer Clinical Trial

— QUEST  

Official title:

Quadratic Phenotypic Optimisation Platform (QPOP) Utilisation to Enhance Selection of Patient Therapy Through Patient Derived Organoids in Breast Cancer

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT05177432
• Other study ID #: BR02/03/21
• Status: Recruiting
• Phase: Phase 1
• Start date: December 6, 2021
• Est. completion date: December 31, 2025

Study information

• Verified date: April 2023
• Source: National University Hospital, Singapore
• Contact: Soo Chin Lee
• Phone: 65 6772 4629
• Email: soo_chin_lee[@]nuhs.edu.sg
• Is FDA regulated: No
• Study type: Interventional

Clinical 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 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.

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".

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 26
• Est. completion date: December 31, 2025
• Est. primary completion date: January 3, 2025
• Accepts healthy volunteers: No
• Gender: Female
• Age group: 21 Years to 99 Years

Eligibility

Part A - Inclusion and exclusion criteria to be fulfilled both prior to study enrolment and prior to commencement of study drug

Inclusion Criteria:

• 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.

• 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.

Exclusion Criteria:

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.

• 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

Inclusion criteria

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

Exclusion criteria

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

Related Conditions & MeSH terms

• Breast Cancer
• Breast Neoplasms

Intervention

Device:
• QPOP
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.

Locations

Country	Name	City	State
Singapore	National University Hospital Singapore	Singapore

Sponsors (1)

Lead Sponsor	Collaborator
National University Hospital, Singapore

Country where clinical trial is conducted

Singapore, 

References & Publications (11)

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. Epu — View Citation

de 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 A — View Citation

Fong 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. — View Citation

Gillet 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. — View Citation

Goodspeed 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. — View Citation

Ledford 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. — View Citation

Rashid 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( — View Citation

Sachs 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 — View Citation

Shaughnessy 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 hyperexp — View Citation

Virtanen 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 — View Citation

Vlachogiannis 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, — View Citation

* Note: There are 11 references in all — Click here to view all references

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Objective response rate measured by RECIST 1.1 criteria to anti-cancer therapy selected by QPOP (prospective analysis).		3 years
Secondary	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)		3 years
Secondary	Progression-free survival of QPOP-guided therapy as measured by RECIST 1.1 criteria (prospective analysis)		3 years
Secondary	Correlating QPOP prediction score of immediate past line of therapy and objective response rate to that therapy (retrospective correlative analysis)		3 years
Secondary	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)		3 years