ASCO 23 Posters

June 8, 2023

GTC presented abstracts at ASCO 2023

GTC’s posters from 2023 Convention of American Society of Clinical Oncology are now available to be downloaded. Please reach out with any questions.

Distinguishing between cancer-related mutations and clonal hematopoiesis using cell-free RNA (cfRNA) expression levels in a machine learning model

Defining the immune microenvironment
in myelodysplastic syndrome
and acute myeloid leukemia
using machine learning

Using machine learning to characterize lung cancer microenvironment and the development of a model to predict the presence of similar microenvironment in other cancers

Read more Articles and News from GTC

Skin Cancer Pittsburgh

January 7, 2026 No Comments

Tags: ASCO, asco23, genomic testing, Posters

Categories: Innovation, Publications

YOU MAY ALSO ENJOY THESE ARTICLES:

SNO 25 Posters

GTC presented abstracts at the 2025 Annual Meeting of the Society for Neuro-Oncology (SNO). GTC participated in the 30th Annual Meeting & Education Day of the Society for Neuro-Oncology (SNO),

November 24, 2025

67th ASH® Annual Meeting Posters

GTC posters at 67th ASH® Annual Meeting (2025) We are delighted to share that GTC collaborated on 12 abstracts at the ASH 2025 Annual Meeting in Orlando, December 6-9. The

November 5, 2025

Peripheral Blood Tumor Associated Cell-Free DNA Testing as a Predictor for Relapse Postallogeneic Stem Cell Transplant for Acute Myelogenous Leukemia

Authors: Vanisha Patel1,*, Maciej Kabat1, Andrew Ip2, Sukhdeep Kaur2, Hyung Chan Suh2, Christina Cho2, David Vesole2, Michele Donato2, Maher Albitar2, Scott D. Rowley2 Affiliations:1Hackensack University Medical Center, Hackensack, New Jersey2John

Genomic Tests

Liquid Biopsy

Solid Tumors

Hematologic Tumors

Physicians

Gene Tables

Patients

Research

About GTC

Newsroom

Call Us: 866-484-8870

Follow Us

Contact Us

ASCO 23 Posters

GTC presented abstracts at ASCO 2023

Distinguishing between cancer-related mutations and clonal hematopoiesis using cell-free RNA (cfRNA) expression levels in a machine learning model

Defining the immune microenvironment in myelodysplastic syndrome and acute myeloid leukemia using machine learning

Using machine learning to characterize lung cancer microenvironment and the development of a model to predict the presence of similar microenvironment in other cancers

Read more Articles and News from GTC

YOU MAY ALSO ENJOY THESE ARTICLES:

Subscribe to our Newsletter

Share this post with your friends

About GTC

Physicians

Patients

Downloads

Follow Us

Licensing

UK Reponsible Person & EU Authorized Representative

Follow Us

Licensing

UK Reponsible Person & EU Authorized Representative

Defining the immune microenvironment
in myelodysplastic syndrome
and acute myeloid leukemia
using machine learning