Precision Oncology Applications in Gastric Cancer logo
Transcript of Precision Oncology Applications in Gastric Cancer logo
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Patrick Tan, MD PhD
Executive Director, Genome Institute of Singapore
Professor, Duke-NUS Medical School
European Society of Medical Oncology Annual Meeting
Sept 2021
Precision Oncology Applications in Gastric Cancer
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Content of this presentation is copyright and responsibility of the author. Permission is required for re-use.
DECLARATION OF INTERESTS
First name Patrick Tan
I am named on patent applications filed by A*STAR (Agency for Science, Technology and Research) related to
the use of alternate promoters in cancer
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Today’s Topics
1) Predictive Biomarkers for Chemotherapy
- Machine Learning and Taxane Sensitivity
2) Predictive Biomarkers for Immune Checkpoint Inhibitors
- Epigenetic Use of Alternate Promoters
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Taxanes are Playing an Increasingly Prominent Role in Gastric Cancer
• FLOT4 – role of perioperative docetaxel
• JACCRO-G07 – role of adjuvant docetaxel
• PRODIGY – neoadjuvant docetaxel
• V325 – first-line docetaxel + platinum
doublet
• RAINBOW – Paclitaxel + ramucirumab,
second line GC
• KEYNOTE-061 – failure of immunotherapy
vs paclitaxel
Al-Batran et al Lancet 2019; Yoshida et al JCO 2019
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SAMIT : One of the Largest Adjuvant Trials in Gastric Cancer
Tsuburaya et al., Lancet Oncology 2014
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Training Validation Treatment Interaction
Nanostring Analysis of 500 Genes• Spindle assembly checkpoint
• Therapeutic targets in GC
• Tumour microenvironment
• Oncogenic signalling
• Frequent genomic alterations
• Immune-related
• DNA repair
• Chemotherapy benefit (from literature)
SAMIT’s 2x2 Design Enables Training, Validation and Treatment
Interaction Analysis
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A 19-gene Signature of Paclitaxel Response Identified by
Random-Forest Machine Learning
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Performance of 19-gene Paclitaxel Classifier in Validation Cohorts
Kim et al, Genome Medicine 2021
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Take-Home Messages
• Applying machine-learning to SAMIT, one of the largest adjuvant
Phase III trials in GC, identifies a 19-gene expression signature of
paclitaxel response
• The signature was validated in two independent cohorts, thereby
reflecting one of the first predictive biomarkers for taxanes in GC
• Given the increasing use of taxanes in GC in the adjuvant and
perioperative setting, this biomarker may guide clinicians in
identifying patients with GC who might benefit from taxane based
therapy
• Further validation of the 19-gene biomarker is warranted
Sundar et al., 2021 Gut
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Today’s Topics
1) Predictive Biomarkers for Chemotherapy
- Machine Learning and Taxane Sensitivity
2) Predictive Biomarkers for Immune Checkpoint Inhibitors
- Epigenetic Use of Alternate Promoters
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Gene Promoters : Critical Integrators of Regulatory Inputs
Valen and Sandelin (2011) Trends in Genetics
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Many GC Promoters Comprise
Alternative Promoters
Alternative Promoters cause Loss
of 5’ Immunogenic Regions
Qamra et al., 2017 Cancer Discovery
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Alternate Promoter Usage is Widespread across Tumor Types(18,468 samples, 33 tumor types)
Several genes have
Multiple Promoters
Demircioğlu et al, (2019) Cell
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Alternative Promoter Burden (APBscore) is a Candidate
Negative Predictive Biomarker of I/O Response
Sundar et al, Annals of Oncology 2019
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APB(High) GCs Show Decreased T-cell Markers and Infiltrate
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APB(High) Gastrointestinal Tumors Exhibit Resistance to ICIs
ICI-Treated Cohorts from
National Cancer Centre/National
University Hospital (Singapore)
Samsung Medical Centre (S. Korea)
Yonsei Cancer Centre (S. Korea)
Pharmaceutical Company (Intl)
University of Milan (Europe)
Aichi Cancer Centre (Japan)
Treated with nivolumab,
pembrolizumab, durvalumab
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Take-Home Messages
• Alternate promoter utilization is an epigenetic phenomenon
associated with immune-editing and immune-evasion
• Tumors with high alternate promoter burden (APB) are depleted of
T-cells in the tumor microenvironment and tend to be resistant to
immune checkpoint inhibition
• This phenomenon, initially identified in gastric cancer, appears to
occur in multiple tumor types
• APB is a potential predictive biomarker for immunotherapy
Sundar et al., Gut (in press)
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18
Acknowledgements• Tan laboratory
• Raghav Sundar – Lead fellow
• Vikrant Kumar - Bioinformatics
• Nisha Padmanabhan – DNA methylation
• Manjie Xing – ChIPSeq
• Kalpana Ramnarayanan – Single-cell sequencing
• Tay Su Ting – Library prep and sequencing
• Thaleia Ong – cell lines
• SAMIT trial investigators• Akira Tsuburaya• Takashi Oshima
• Jonathan Goke laboratoryo Deniz Demicioglu (Bioinformatics)
• Cancer Science Institute• Anand Jeyasekharan
• A*STAR Humanized mouse labo Chen Qingfeng and Her Zhisheng
• National Cancer Centre Singaporeo David Tai and Su Pin Choo
• Samsung Medical Centreo Jeeyun Lee and Kyoung Mee-Kim
• University of Milano Filippo Pietrantonio
• University of Maastricht /U. Leedso Heike Grabsch
• InSilico Genomicso Mark Simone