Neoplasms

Digestive System Diseases

All Conditions

Gastrointestinal Neoplasms

Digestive System Neoplasms

Gastrointestinal Diseases

Patient with first-line gastrointestinal cancers and patient with advanced and refractory GI cancers (\>1 line of treatment), or post-progression biopsy)

QPOP will then be applied to establish the most efficacious drug combination for the specific organoid. Additional drugs other than those listed above may be screened depending on availability of cancer organoids. When patients progress after first-line treatment, QPOP generated second-line options will be informed to treating physicians and the physician will exercise his/her discretion to select the most suitable drug based on patient's comorbidities and organ function after a formal molecular/phenotype tumour board.

QPOP

Wei Peng Yong

This is a multi-cohort proof of concept study involving patients with metastatic gastrointestinal cancers. In the first cohort of treatment-naïve patients, the investigators intend to create cancer organoids for 100 subjects. Then, the investigators intend to evaluate ex-vivo prediction of treatment outcomes using QPOP (see section 4.0 for detailed sample size calculation).

Patients enrolled on study will undergo a fresh biopsy of tumour lesion to obtain cells that will be used to generate patient-derived tumour organoids. These patients will go on to receive standard of care first-line chemotherapy +/- targeted therapy. Organoids will then be subjected to up to a 14-drug panel screening. The drugs in the respective drug panel have been shown to have activity in the respective cancers and would be used in the standard-of-care setting by treating physicians.

Hypothesis: Ex-vivo sensitivity testing on patient derived tumour organoids using QPOP can identify drug combinations which may have clinical efficacy against metastatic gastrointestinal cancer.

Specific aim 1: To grow patients' gastrointestinal tumour-derived organoids.

Specific aim 2: To perform ex-vivo drug sensitivity testing on patient derived tumour organoids using QPOP for metastatic gastrointestinal cancers.

Specific aim 3: Asses the efficacy of phenotype directed therapy using QPOP to assign treatment after progression of standard of chemo for gastric cancer.

1.1 Tumour biopsies

* maximum of 2 time points - baseline upon study enrolment and upon disease progression. Whenever possible, the tumour core biopsies will be performed under image guidance.
* Up to 6 fresh core samples (each of Tumour and matched Adjacent-Normal/Non-Tumour) will be biopsied from each patient at each time-point.

1.2 Blood -A total of 50ml or 3.5 tablespoons of blood will be taken during screening of the study. A total of 40mls or 2.5 tablespoons of blood will be taken prior to each chemotherapy cycle. At the end of study, a total of 40mls or 2.5 tablespoons of blood will be collected. In total, a maximum of about 330ml or 22 tablespoons of blood will be taken during the course of this study.

Q-GAIN (Using Qpop to Predict Treatment for GAstroIntestinal caNcer)

complete and partial clinical response, including confidence intervals.

Patients enrolled on study will undergo a fresh biopsy of tumour lesion to obtain cells that will be used to generate patient-derived tumour organoids.

measured by Overall Response Rate for patients with gastric cancer.

Toxicity rating using the NCI CTC v4.03 scale

National University Hospital, Singapore

Skin and Connective Tissue Diseases

Breast Neoplasms

Antineoplastic Agents

All Drugs and Chemicals

Irinotecan

Paclitaxel

Docetaxel

Azacitidine

Albumin-Bound Paclitaxel

Tubulin Modulators

Antimitotic Agents

Topoisomerase I Inhibitors

Enzyme Inhibitors

Antimetabolites

Participants will undergo two study stages: QPOP drug selection and CURATE.AI-guided dosing modulation. In the QPOP drug selection stage, participants will undergo a baseline biopsy for organoid generation and subsequently receive treatment as per SOC. During this time, QPOP will identify optimal drug combinations for the participant based on ex vivo experiments on the participant's organoid. Participants who are identified via QPOP to potentially benefit from azacitidine in combination therapy (azacitidine + docetaxel, azacitidine + paclitaxel or azacitidine + irinotecan) will move on to the CURATE.AI-guided dosing modulation stage with treatment with azacitidine. Azacitidine treatment will begin once disease progression after SOC treatment is determined based on CT scans. Only azacitidine dose in the selected azacitidine combination will be modulated by CURATE.AI

QPOP is a mechanism-agnostic platform for optimizing drug selection. QPOP uses a quadratic equation to describe the patient-specific drug-drug interaction space based solely on experimentally derived drug-response data on individual patient's tissue sample, from which optimal drug combinations can be identified. Drug selection via QPOP allows for identification of an optimal combination therapy without the need for exhaustive testing of every combination. The first stage of the trial aims to generate a personalised QPOP drugs list for each participant based on experimentally derived data from ex vivo testing in the participant's tissue sample. Optimal drugs from a pre-specified drug pool will be recommended by the QPOP team.

CURATE.AI - a small data, AI-derived technology platform based on this discovery - allows personalised guidance of an individual's dose modulations based only on that individual's data. Additionally, CURATE.AI is mechanism-independent, and dynamically adapts to changes experienced by the participant, providing dynamic dose optimisation throughout the duration of the participant's treatment.

The second stage of the trial aims to obtain a personalised CURATE.AI profile for each participant, based on their phenotypic response to a set of drug doses from the drug combinations with azacitidine identified and recommended by the QPOP team. The doses will be recommended by the CURATE.AI team, when relevant to the clinical decision-making process. Once an actionable profile is obtained, dose recommendations are based on the profile and aimed to treat the participant.

The maximum period of involvement with this study when azacitidine may be adjusted by CURATE.AI is 18 months.

CURATE.AI

Azacitidine subcutaneous injection Day 1, 2 and Day 8, 9 + 30 mg/m2 docetaxel intravenously Day 1 and 8

Each chemotherapy cycle will be 21 days.

Azacitidine + docetaxel

Azacitidine subcutaneous injection Day 1, 2 and Day 8, 9 + 80 mg/m2 paclitaxel intravenously Day 1 and 8

Each chemotherapy cycle will be 21 days.

Azacitidine + paclitaxel

Azacitidine intravenously subcutaneous injection Day 1, 2 and Day 8, 9 + 100 mg/m2 irinotecan intravenously Day 1 and 8.

Each chemotherapy cycle will be 21 days.

Azacitidine + irinotecan

Cheng Ean Chee

Raghav Sundar

Hon Lyn Tan

Jingshan Ho

Gloria Chan

Joan Choo

Soo Chin Lee

Boon Cher Goh

Ross Soo

David Tan

Andrea Wong

Joline Lim

Robert Walsh

Jia Li Low

This pilot feasibility study aims to set the foundation to investigate the applicability of QPOP drug selection followed by CURATE.AI-guided dose optimisation of the selected azacitidine combination therapy for solid tumours using CURATE.AI within the current clinical setting.

QPOP will identify drug interactions towards optimal efficacy and cytotoxicity from the pre-specified drug pool based on ex vivo experimental data from the individual participant's tissue sample model. With these drug interactions, QPOP will identify the optimal drugs for the specific participant whose biopsy provided the cells for the ex vivo experimentation. Subsequently, CURATE.AI will be used to guide dosing for the selected combination therapy for that participant.

Individualised CURATE.AI profiles will be generated based on each participant's response to a set of drug doses. Subsequently, the personalised CURATE.AI profile will be used to recommend the efficacy-driven dose. CURATE.AI will operate only within the safety range for each drug pre-specified for each participant.

This pilot feasibility study will inform the investigators on the logistical and scientific feasibility of performing a large-scale randomised controlled trial (RCT) with the selected azacitidine combination therapy regimens and response markers. A secondary objective is to collect toxicity and efficacy data using established and exploratory response markers within and in-between cycles as exploratory outcomes.

Several drug combinations and modulation in drug dosing are given to promote cancer cell elimination in cancer patients. While advances in omics tools have led to greater understanding of the complexity of diseases such as cancer, they have also led to the understanding that large networks of molecular interactions contribute to both disease progression and therapeutic resistance. The rational design of drug combinations is a challenge because complex molecular networks contribute to feedback mechanisms of drug resistance and compensatory oncogenic drivers that limit the efficacy of targeted inhibitors. This challenge is compounded by the vast number of available drugs to identify optimal drug combinations from.

In addition to the complexities in identifying optimal drug combinations, optimal dosing remains a challenge as drug synergy is both dose, time- and patient- dependent. The final drug concentration in the body must fall within a narrow range that maximises cancer elimination while minimizing toxic side effects. The complexity of this task increases significantly with the number of drugs given in combination due to increasing parameters and stochastic behaviour of a biological system. Currently, the established approach is to select maximum tolerated doses (MTD) - the highest drug doses that do not cause unacceptable side effects. Treatment efficacy does not guide dose selection. Combined with limited personalisation, this dosing strategy often results in sub-optimal outcomes of the treatment.

In this pilot feasibility study, participants will undergo QPOP drug selection, a stage of CURATE.AI profile generation, and a stage of CURATE.AI profile-based, efficacy-driven drug dosing.

As there are no prior clinical trial cohorts using CURATE.AI in participants with solid tumours and there are existing data for breast and gastric cancer for input into QPOP, this feasibility pilot study will focus on the practicality and feasibility of using QPOP and CURATE.AI in this clinical context.

At the end of the participation of the first 10 patients, an interim analysis will be conducted using the data generated from these participants, which will include formal power and statistical sample size calculations. Based on these outcomes, the investigators will consider cohort expansion or an RCT. Specifically, the interim analysis will aid the decisions on whether to proceed with future RCTs; their design (superiority, equivalence or non-inferiority); logistical and practical aspects of running a large-scale RCT; patient population selection for the RCT; and potential applicability of CURATE.AI in a wider range of systemic therapy regimens, response markers and/or expansion of the current cohort to elicit further data on secondary endpoints and/or new randomized cohorts.

Although not standard of care treatment for breast and gastric cancer, azacitidine combination therapy is chosen by the investigators as azacitidine combination therapy as azacitidine is a potent DNA methyltransferase inhibitor (DNMT) that can increase the sensitivity of a range of metastatic or advanced solid tumours, such as breast and gastric cancer, to treatment with docetaxel, paclitaxel, or irinotecan after developing resistance. Studies have also demonstrated the possibility of low dose treatment with chemotherapeutic agents when given together with azacitidine. However, cytotoxicity of azacitidine increases with dose and exposure time, which highlights the need to rapidly identify the optimal azacitidine-containing drug combinations and for personalised dose modulation during treatment. As such, QPOP drug selection and CURATE.AI dose modulation pipeline is in the ideal position to optimise treatment with azacitidine in combination with docetaxel, paclitaxel, or irinotecan via a personalised manner to maximise efficacy while minimising toxicities.

Participants will undergo QPOP drugs selection optimisation, and those participants who are identified via QPOP to potentially benefit from azacitidine in combination with docetaxel, paclitaxel, or irinotecan will transition to the CURATE.AI stage of the trial after treatment fails. Participants who have undergone QPOP drug selection (e.g. under QGAIN (2019/00924) or NGAIN trial (2021/00009)) are allowed to enrol for the CURATE.AI modulation period of this study at the approval of the Principal Investigator and Sponsor.

CURATE.AI will facilitate personalised treatment to each of the participants by recommending optimal doses in a dynamic fashion. In this phase, only azacitidine dose in the selected azacitidine combination will be modulated by CURATE.AI. Criteria for recruitment allow a high variability in the participant population to reflect a true variability in the cases faced in the clinical practice.

10 participants diagnosed with breast or gastric cancer undergoing or planned for treatment will be enrolled. The participants will undergo QPOP drug selection optimisation, and those participants who are identified via QPOP to potentially benefit from azacitidine in combination with docetaxel, paclitaxel or irinotecan will transition to the CURATE.AI dose modulation phase of the trial. Only azacitidine dose in the selected azacitidine combination will be modulated by CURATE.AI

Optimizing and Personalising Azacitidine Combination Therapy for Treating Solid Tumours QPOP and CURATE.AI

Optimizing and Personalising Azacitidine Combination Therapy for Treating Solid Tumours Using the Quadratic Phenotypic Optimization Platform (QPOP) and an Artificial Intelligence-based Platform (CURATE.AI)

Data collection and explorative analysis of response marker in higher frequency serial measurements after modulated dosing in relation to standard frequency readings and other efficacy measures, e.g RECIST criteria

Data collection and explorative analysis of ctDNA as:

1. a response marker in serial measurements at given clinical context and after modulated dosing;
2. potential input for CURATE.AI.

A decision on whether we "successfully apply" the QPOP drug selection requires expert judgement and cannot be made based on a purely numerical process. The expert panel will consider the following factors with careful regard for the individual circumstances of each participant:

1. The goodness-of-fit of the QPOP derived equation is acceptable to allow for a reliable list of effective drug combinations;
2. The drugs list is generated sufficiently early for the participant to potentially benefit

A decision on whether we "successfully apply" the CURATE.AI profile requires expert judgement and cannot be made based on a purely numerical process. The expert panel will consider the following factors with careful regard for the individual circumstances of each participant:

1. Error/variance (biological/analytical) is sufficiently small to allow accurate predictions;
2. Profile can be generated sufficiently early for the participant to potentially benefit;
3. Dose-dependent relationship is observed;
4. Profile is actionable (i.e. fulfils the co-investigator's pre-specified safety requirements);
5. Systemic changes in the participant which require profile recalibration are rare or readily assimilated into the CURATE.AI algorithm.

percentage of participants in whom the CURATE.AI-guided cumulative dose is substantially (≥10%) different from the projected SOC cumulative dose, which is defined as the maximum dose of the modulated drug\*, azacitidine, multiplied by the number of completed chemotherapy cycles.

\* The maximum dose of the modulated drug azacitidine is 120mg/m2 once daily given on days 1-2 and days 8-9, every 21 days, in combination with weekly docetaxel, paclitaxel or irinotecan for two weeks followed by one week of rest.

The N.1 Institute for Health (N.1)

Cancer Science Institute of Singapore

Rare Diseases

Melanoma

Sarcoma

Neuroendocrine Tumors

Neuroectodermal Tumors

Neuroectodermal Tumors, Primitive

Neoplasms, Germ Cell and Embryonal

Neoplasms by Histologic Type

Neoplasms, Nerve Tissue

Nevus, Pigmented

Nevus

Nevi and Melanomas

Skin Neoplasms

Skin Diseases

Neoplasms, Connective and Soft Tissue

Soft Tissue Sarcoma

Neuroendocrine Tumor

Neuroepithelioma

Valerie Shiwen Yang, BSc, MB BChir, PhD

This is a multi-cohort proof of concept study involving patients with sarcomas or melanomas. Patient models, both two- and three-dimensional, will be derived from tumour samples. These will then be used to evaluate drug sensitivities ex vivo.

Enrolled patients will undergo resections or biopsies as part of standard-of-care, which will be used to generate patient models. Patients will receive standard-of-care systemic treatment. Patient models will also be subjected up to a 14-drug screening panel. The majority of drugs in the respective drug panels has been shown to have activity in the respective cancers and would be used in the standard-of-care setting by treating physicians.

Hypothesis: Ex vivo drug testing on patient-derived models using QPOP can identify drug sensitivities and combinations which may have clinical efficacy against sarcomas and melanomas.

Specific aim 1: To derive patient models, both two- and three-dimensional, of sarcomas and melanomas

Specific aim 2: To perform ex vivo drug testing on patient models of sarcoma and melanoma using QPOP

Specific aim 3: To assess the efficacy of phenotype directed therapy using QPOP to assign treatment after progression on standard-of-care treatment in sarcoma and melanoma

Tumour Samples - Baseline upon study enrolment and on disease progression where clinically applicable.

Enrolled patients undergoing a procedure where tumour tissue is removed for diagnostic or other clinical purposes, will be asked to donate any tissue that is in excess and not needed for diagnostic purposes, for this research study.

Using QPOP to Predict Treatment for Sarcomas and Melanomas

Q-SAM (Using QPOP to Predict Treatment for Sarcomas and Melanomas)

Patients enrolled on study will undergo resection or biopsy of fresh tumour tissue to obtain cells for the generation of models.

Complete and partial clinical response and stable disease.

Overall survival, progression-free survival.

National Cancer Centre, Singapore

Breast Diseases

Estrogens

Progesterone

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

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.

Soo Chin Lee, MBBS, MRCP

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.

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".

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.

Breast cancer study - 99.9% breast cancer patients are female. Male breast cancer is extremely rare and we do not intend to include this rare population

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

Study title	NCT ID	Conditions	Interventions	Eligibility	Organization
Q-GAIN (Using Qpop to Predict Treatment for GAstroIntestinal caNcer)	NCT04611035	Gastrointestina...	QPOP	21 Years - 99 Years	National University Hospital, Singapore
Optimizing and Personalising Azacitidine Combination Therapy for Treating Solid Tumours QPOP and CURATE.AI	NCT05381038	Solid Tumor Gastrointestina... Breast Cancer	QPOP CURATE.AI Azacitidine + d... Azacitidine + p... Azacitidine + i...	21 Years - 99 Years	National University Hospital, Singapore
Using QPOP to Predict Treatment for Sarcomas and Melanomas	NCT04986748	Sarcoma Melanoma		21 Years - 99 Years	National Cancer Centre, Singapore
Optimizing and Personalising Azacitidine Combination Therapy for Treating Solid Tumours QPOP and CURATE.AI	NCT05381038	Solid Tumor Gastrointestina... Breast Cancer	QPOP CURATE.AI Azacitidine + d... Azacitidine + p... Azacitidine + i...	21 Years - 99 Years	National University Hospital, Singapore
Quadratic Phenotypic Optimisation Platform (QPOP) Utilisation to Enhance Selection of Patient Therapy Through Patient Derived Organoids in Breast Cancer	NCT05177432	Breast Cancer	QPOP	21 Years - 99 Years	National University Hospital, Singapore

Spots Global Cancer Trial Database for qpop

Every month we try and update this database with for qpop cancer trials from around the world to help patients and their families find trials that might be right for them.
We offer this 100% free of charge and do not endorse any of the trials listed here, we hope it helps you or a loved one.

Spots Global Cancer Trial Database for qpop

Every month we try and update this database with for qpop cancer trials from around the world to help patients and their families find trials that might be right for them. We offer this 100% free of charge and do not endorse any of the trials listed here, we hope it helps you or a loved one.

Every month we try and update this database with for qpop cancer trials from around the world to help patients and their families find trials that might be right for them.
We offer this 100% free of charge and do not endorse any of the trials listed here, we hope it helps you or a loved one.