Clinical trial NCT05719428: Neural Network-based Treatment Decision Support Tool in Patients With Refractory Solid Organ Malignancies
Official title: A Phase II Trial of Neural Network-based Treatment Decision Support Tool in Patients With Refractory Solid Organ Malignancies
Study type: INTERVENTIONAL
Overall status: RECRUITING
Phase: PHASE2
Conditions: Solid Organ Malignancies
Keywords: Next generation sequencing
DRUID
Lead sponsor: National University Hospital, Singapore
Sponsor class: OTHER
Collaborators: name: School of Computing, National University of Singapore
name: National University of Singapore
First posted: 2023-02-09
Enrollment: 37
Sex: ALL
Minimum age (years): 21
Maximum age (years): 99
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Allocation: NA
Intervention model: SINGLE_GROUP
Intervention model description: Patients NGS profile will be analysed with DRUID system to generate recommendations based on predicted efficacy. Patients with available archival tissue will have gene expression analysis performed to optimise DRUID recommendation. Patients will subsequently receive single agent therapy based on DRUID recommendations and criteria for therapy choice.

Patients will begin single agent therapy within 4 weeks of enrolment and continue until disease progression, maximum safe cumulative dose reached (where applicable, per standard institution practice) or unacceptable toxicity as per physician's discretion.
Primary purpose: TREATMENT

Brief summary: DRUID is a treatment decision support tool combining predictive models and public databases related to multi-gene markers, drug response screens, gene essentiality and clinical status of drugs to provide drug recommendations personalized based on an input genomic profile. We hypothesize that DRUID analysis of patients' somatic mutational profile from NGS diagnostic platform can be used as a treatment decision support tool in patients with refractory cancer without targetable mutations.

Detailed description: 2.1. Hypothesis We hypothesize that DRUID analysis of patients' somatic mutational profile from NGS diagnostic platform can be used as a treatment decision support tool in patients with refractory cancer without targetable mutations. Using algorithm selected treatment from a panel of 60 drugs we predict an objective response rate (ORR) of ≥ 25%.

2.2. Primary Objectives

• To prospectively determine if DRUID selected therapy based on NGS diagnostic profile input can lead to objective responses in refractory solid organ malignancies.

2.3. Secondary Objectives

* To assess clinical benefit rate (complete response, partial response and stable disease for ≥ 24 weeks)
* To assess progression-free and overall survival of patients treated with DRUID-guided therapy

Eligibility criteria: Inclusion Criteria:

Patients may be included in the study only if they meet all of the following criteria:

* Age ≥ 21 years.
* Histological or cytological diagnosis solid organ malignancy
* Available results of comprehensive NGS panel testing performed on either tumour tissue or blood-based assay. If results are from blood-based assay, test must have been performed in the metastatic setting.
* ECOG 0-2.
* 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 2 lines of palliative systemic therapy with no available standard therapy:
* 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
    * Haemoglobin ≥ 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
    * Signed informed consent from patient or legal representative.
* Able to comply with study-related procedures.

Exclusion Criteria:

* Treatment within the last 30 days with any investigational drug.
* Concurrent administration of any other tumour therapy, including cytotoxic chemotherapy, hormonal therapy, and immunotherapy.
* 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.
* 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.
* Non-healing wound.
* Second primary malignancy that is clinically detectable at the time of consideration for study enrolment.
* Symptomatic brain metastasis.

Primary outcomes:
measure: Objective response rate (ORR)
timeFrame: 10 months
Secondary outcomes:
measure: Clinical benefit
timeFrame: 10 months
measure: Progression free survival
timeFrame: From enrolment till disease progression or date of death or final follow-up visit (1 year).
Other outcomes:
Locations:
city: Singapore
country: Singapore
facility: Department of Hematology-Oncology, National University Hospital
Study officials:
affiliation: National University Hospital, Singapore
name: Robert John Walsh
role: PRINCIPAL_INVESTIGATOR
References:
citation: Zehir A, Benayed R, Shah RH, Syed A, Middha S, Kim HR, Srinivasan P, Gao J, Chakravarty D, Devlin SM, Hellmann MD, Barron DA, Schram AM, Hameed M, Dogan S, Ross DS, Hechtman JF, DeLair DF, Yao J, Mandelker DL, Cheng DT, Chandramohan R, Mohanty AS, Ptashkin RN, Jayakumaran G, Prasad M, Syed MH, Rema AB, Liu ZY, Nafa K, Borsu L, Sadowska J, Casanova J, Bacares R, Kiecka IJ, Razumova A, Son JB, Stewart L, Baldi T, Mullaney KA, Al-Ahmadie H, Vakiani E, Abeshouse AA, Penson AV, Jonsson P, Camacho N, Chang MT, Won HH, Gross BE, Kundra R, Heins ZJ, Chen HW, Phillips S, Zhang H, Wang J, Ochoa A, Wills J, Eubank M, Thomas SB, Gardos SM, Reales DN, Galle J, Durany R, Cambria R, Abida W, Cercek A, Feldman DR, Gounder MM, Hakimi AA, Harding JJ, Iyer G, Janjigian YY, Jordan EJ, Kelly CM, Lowery MA, Morris LGT, Omuro AM, Raj N, Razavi P, Shoushtari AN, Shukla N, Soumerai TE, Varghese AM, Yaeger R, Coleman J, Bochner B, Riely GJ, Saltz LB, Scher HI, Sabbatini PJ, Robson ME, Klimstra DS, Taylor BS, Baselga J, Schultz N, Hyman DM, Arcila ME, Solit DB, Ladanyi M, Berger MF. Mutational landscape of metastatic cancer revealed from prospective clinical sequencing of 10,000 patients. Nat Med. 2017 Jun;23(6):703-713. doi: 10.1038/nm.4333. Epub 2017 May 8.
citation: Garraway LA, Verweij J, Ballman KV. Precision oncology: an overview. J Clin Oncol. 2013 May 20;31(15):1803-5. doi: 10.1200/JCO.2013.49.4799. Epub 2013 Apr 15. No abstract available.
citation: Califano A, Alvarez MJ. The recurrent architecture of tumour initiation, progression and drug sensitivity. Nat Rev Cancer. 2017 Feb;17(2):116-130. doi: 10.1038/nrc.2016.124. Epub 2016 Dec 15.
citation: Mariappan R, Jayagopal A, Sien HZ, Rajan V. Neural Collective Matrix Factorization for integrated analysis of heterogeneous biomedical data. Bioinformatics. 2022 Sep 30;38(19):4554-4561. doi: 10.1093/bioinformatics/btac543.
citation: Nguyen T, Nguyen GTT, Nguyen T, Le DH. Graph Convolutional Networks for Drug Response Prediction. IEEE/ACM Trans Comput Biol Bioinform. 2022 Jan-Feb;19(1):146-154. doi: 10.1109/TCBB.2021.3060430. Epub 2022 Feb 3.
citation: Garnett MJ, Edelman EJ, Heidorn SJ, Greenman CD, Dastur A, Lau KW, Greninger P, Thompson IR, Luo X, Soares J, Liu Q, Iorio F, Surdez D, Chen L, Milano RJ, Bignell GR, Tam AT, Davies H, Stevenson JA, Barthorpe S, Lutz SR, Kogera F, Lawrence K, McLaren-Douglas A, Mitropoulos X, Mironenko T, Thi H, Richardson L, Zhou W, Jewitt F, Zhang T, O'Brien P, Boisvert JL, Price S, Hur W, Yang W, Deng X, Butler A, Choi HG, Chang JW, Baselga J, Stamenkovic I, Engelman JA, Sharma SV, Delattre O, Saez-Rodriguez J, Gray NS, Settleman J, Futreal PA, Haber DA, Stratton MR, Ramaswamy S, McDermott U, Benes CH. Systematic identification of genomic markers of drug sensitivity in cancer cells. Nature. 2012 Mar 28;483(7391):570-5. doi: 10.1038/nature11005.
citation: Kim S, Chen J, Cheng T, Gindulyte A, He J, He S, Li Q, Shoemaker BA, Thiessen PA, Yu B, Zaslavsky L, Zhang J, Bolton EE. PubChem 2019 update: improved access to chemical data. Nucleic Acids Res. 2019 Jan 8;47(D1):D1102-D1109. doi: 10.1093/nar/gky1033.
citation: Cancer Genome Atlas Research Network; Weinstein JN, Collisson EA, Mills GB, Shaw KR, Ozenberger BA, Ellrott K, Shmulevich I, Sander C, Stuart JM. The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet. 2013 Oct;45(10):1113-20. doi: 10.1038/ng.2764.