Oops, you're using an old version of your browser so some of the features on this page may not be displaying properly.

MINIMAL Requirements: Google Chrome 24+Mozilla Firefox 20+Internet Explorer 11Opera 15–18Apple Safari 7SeaMonkey 2.15-2.23

Poster session 09

229TiP - A phase II trial of a neural network-based treatment decision support tool in patients (pts) with refractory solid organ malignancies

Date

14 Sep 2024

Session

Poster session 09

Topics

Clinical Research;  Cancer Intelligence (eHealth, Telehealth Technology, BIG Data);  Molecular Oncology

Tumour Site

Presenters

Robert Walsh

Citation

Annals of Oncology (2024) 35 (suppl_2): S238-S308. 10.1016/annonc/annonc1576

Authors

R.J. Walsh1, A. Jayagopal2, T.Z. Tan3, J. Pitt4, R. Sundar1, S.C. Lee1, B. Goh1, V. Rajan5, D.S. Tan6, A.D. Jeyasekharan1

Author affiliations

  • 1 Department Of Haematology-oncology, National University Cancer Institute Singapore, 119074 - Singapore/SG
  • 2 Department Of Information Systems And Analytics, School of Computing, NUS - National University of Singapore, 117417 - Singapore/SG
  • 3 Genomics And Data Analytics, Cancer Science Institute (CSI) - National University of Singapore (NUS), 117599 - Singapore/SG
  • 4 National University Singapore, Cancer Science Institute (CSI) - National University of Singapore (NUS), 117599 - Singapore/SG
  • 5 Department Of Information Systems And Analytics, School of Computing, National University of Singapore, 119077 - Singapore/SG
  • 6 Medical Oncology Department, NUHS - National University Health System, 119228 - Singapore/SG

Resources

Login to get immediate access to this content.

If you do not have an ESMO account, please create one for free.

Abstract 229TiP

Background

Precision oncology in the form of one actionable mutation targeted with a specific drug/drug combination benefits a minority of pts. This results in the majority having clinical grade next generation sequencing (cNGS) results that do not impact on treatment (tx) decisions. Polygenic analysis of such cNGS data to predict efficacious tx, via drug response prediction tools (DRPs), represents a potential solution. Several DRPs utilising artificial intelligence show early promise however the majority use gene expression or whole exome sequencing input data that is not available in the clinic. An exception is DruID (Drug IDentifier), a DRP trained using cell-line and pt data that is designed to perform with limited input data from cNGS panels, utilising domain-invariant representation learning and multi-task learning. Reported aberrations are analysed by DruID giving an output of anti-cancer agents ranked by a predicted efficacy score. In this single-centre phase II trial we aim to assess the efficacy of DruID recommended tx (DruID-Tx) in pts with refectory malignancies.

Trial design

Eligible pts have a histologically confirmed solid organ malignancy with progressive disease (PD) after ≥2 lines of tx and ECOG PS 0-2. Pts cNGS results are input into DruID to generate recommendations. Agents on a pre-defined panel of generic locally approved (HSA, Singapore) anti-cancer tx, with a predicted efficacy in the 4th quartile of DruID scores generated from a reference dataset can be considered. Specific DruID-Tx options will be omitted if a subject has received them in the prior 3 lines, with remaining options subjected to panel discussion by investigators. Pts with an available DruID-Tx after this selection criteria will undergo single agent tx until PD or unacceptable toxicity. Trial primary end point is objective response rate (ORR) with the hypothesis that DruID-Tx will give an ORR ≥25%. Using a Simon 2-stage optimal design, (80% power, one-sided α of 0.1) 13 pts will be enrolled to Stage I with a further 21 pts recruited in Stage II if ≥2 pts achieve response in Stage I. Secondary endpoints include clinical benefit rate, progression free and overall survival. Recruitment for Stage I is ongoing with 5/13 pts enrolled.

Clinical trial identification

NCT05719428.

Editorial acknowledgement

Legal entity responsible for the study

NUHS.

Funding

NCIS-N2CR.

Disclosure

R.J. Walsh: Financial Interests, Personal, Advisory Board: Pfizer, Novartis; Financial Interests, Personal, Speaker, Consultant, Advisor: AstraZeneca; Non-Financial Interests, Institutional, Local PI: BMS, Merck; Financial Interests, Institutional, Speaker, Consultant, Advisor: Merck. R. Sundar: Financial Interests, Personal, Advisory Board, Advisory Board, Invited Speaker: Bristol Myers Squibb, MSD; Financial Interests, Personal, Advisory Board: Merck, Bayer, Novartis, GSK, Pierre-Fabre, Tavotek, AstraZeneca, Daiichi Sankyo, BeiGene; Financial Interests, Personal, Advisory Board, Advisory Board, Invited Speaker, Travel: Eisai; Financial Interests, Personal, Advisory Board, Advisory Board, Invited Speaker, Travel for conferences. Funding declared is over several years (<10,000 Euro per year): Taiho; Financial Interests, Personal, Invited Speaker: Eli Lilly, BMS, Roche, Taiho, AstraZeneca, DKSH, Daiichi Sankyo, BeiGene, Astellas; Financial Interests, Personal, Advisory Board, Travel for conference and Advisory Board, funding declared is over several years, S.C. Lee: Financial Interests, Personal, Advisory Board, Advisory board, speaker invitations: Pfizer, Novartis, AstraZeneca, Roche, MSD; Financial Interests, Personal, Advisory Board, Advisory Board: Eli Lilly; Financial Interests, Personal, Advisory Board: Gilead; Financial Interests, Institutional, Research Grant: Pfizer, ACT Genomics, Eisai, Taiho, MSD, Karyopharm, ASLAN Pharmaceuticals, Adagene; Financial Interests, Institutional, Local PI: Roche, Novartis, Daiichi Sankyo, BMS, AstraZeneca; Financial Interests, Personal, Steering Committee Member: AstraZeneca. B. Goh: Financial Interests, Institutional, Invited Speaker: MSD; Financial Interests, Institutional, Advisory Board: Bayer Healthcare; Financial Interests, Personal, Other, Consultancy: Adagene pharmaceutical; Financial Interests, Institutional, Research Grant, Support for investigator initiated trial: MSD; Financial Interests, Institutional, Other, Pharmaceutical support for clinical trial: BMS; Financial Interests, Institutional, Other, Pharmaceutical support for investigator initiated clinical trial: Taiho pharmaceuticals; Financial Interests, Institutional, Coordinating PI: Adagene, Bayer; Financial Interests, Institutional, Local PI: Pfizer; Financial Interests, Institutional, Local PI, Conducting clinical trial: alx; Financial Interests, Institutional, Local PI, Pharmaceutical phase 1 trial: Novartis; Non-Financial Interests, Institutional, Other, Lead clinical trial platform of the Singapore Translational Cancer Consortium: Consortium for Clinical Research and Innovation Singapore; Non-Financial Interests, Member: ASCO. D.S. Tan: Financial Interests, Personal, Invited Speaker: AstraZeneca, MSD, Merck Serono, Roche, Eisai, GSK, Takeda; Financial Interests, Personal, Advisory Board: AstraZeneca, Bayer, MSD, Eisai, Roche, Genmab, GSK, Boehringer Ingelheim; Financial Interests, Personal, Stocks/Shares: Asian Microbiome Library (AMiLi); Financial Interests, Institutional, Research Grant: Roche, Bayer, Karyopharm Therapeutics, AstraZeneca; Financial Interests, Institutional, Coordinating PI: AstraZeneca, Bergen Bio; Financial Interests, Institutional, Local PI: Zeria Pharmaceutical Co Ltd, Bayer, Byondis B.V.; Non-Financial Interests, Leadership Role, Ex society president: Gynecologic Cancer Group Singapore; Non-Financial Interests, Member of Board of Directors: Gynaecologic Cancer Intergroup (GCIG); Non-Financial Interests, Leadership Role, Ex- Chair: Asia-Pacific Gynecologic Oncology Trials Group (APGOT); Non-Financial Interests, Institutional, Product Samples, Research Study: MSD, Eisai, AstraZeneca, Cyclacel Pharmaceuticals. A.D. Jeyasekharan: Financial Interests, Personal, Speaker, Consultant, Advisor: DKSH, Roche, Gilead Sciences, Turbine, AstraZeneca, Janssen, MSD; Financial Interests, Personal, Funding: AstraZeneca, Janssen. All other authors have declared no conflicts of interest.

This site uses cookies. Some of these cookies are essential, while others help us improve your experience by providing insights into how the site is being used.

For more detailed information on the cookies we use, please check our Privacy Policy.

Customise settings
  • Necessary cookies enable core functionality. The website cannot function properly without these cookies, and you can only disable them by changing your browser preferences.