Treatment-specific risk stratification of feline lymphoma based on unsupervised clustering of flow cytometry results

March 21, 2024

Presentation Detail

Research Abstract

Introduction
Feline lymphoma is one of the most common cancers for cats with an estimated incidence rate of roughly 50–200 per 100,000. The most common treatments for the disease include the combinations therapies, namely CHOP (cyclophosphamide, doxorubicin, vincristine, prednisolone) and ChlP (chlorambucil, prednisolone). Response and survival can vary significantly across the patients even when treated by the same regimen. Up to 18-fold difference in the progression-free survival (PFS), for example, was observed among the patients treated with ChlP therapy. Given the variability in treatment outcome and plurality of regimens, there is an unmet need for a predictive technology assisting optimization of treatment for feline lymphoma.

Methods
We conducted flow cytometric (FC) analysis of the fine needle aspirates taken from 141 feline patients diagnosed by cytology with lymphoma. Expression levels of the cell surface molecules—CD4, CD5, CD8, CD14, CD18, and CD21—were measured and used together with the forward and side scatter values for immunophenotyping and clustering. The latter was done by employing the machine learning algorithm (KMeans) with k ranging from two to six.

Results
Unsupervised clustering of the FC data enabled risk stratification of the patients with respect to ChlP or CHOP therapies. The overall median PFS with respect to the two regimens were 492 and 120 days with the hazard ratios of 5.1 and 2.7 between the high and low risk subgroups, respectively.

Conclusion
We developed the predictive technology that classifies each feline lymphoma patient as low or high risk with respect to the disease progression when treated by ChlP or CHOP therapy.

Clinical significance of the results
The proposed risk-stratification can contribute to personalized treatment selection regarding ChlP and CHOP for feline lymphoma. Retrospective analysis suggests that a superior PFS may be achieved for each patient by selecting a therapy associated with low risk of disease progression.

Presentations

Boosting the power of AI-based treatment outcome predictions for canine lymphoma by combining tumor and germline genetic biomarkers and live-cell analytics

Veterinary Cancer Society Annual Conference 2024
Learn More →

Novel Genetic Biomarkers of Chemotherapeutic Response in Canine Lymphoma and Improved Predictive Power With Integration of Machine Learning

Veterinary Cancer Society Annual Conference 2024
Learn More →

Decoding Feline Lymphoma: From Diagnosis to Prognosis

Veterinary Cancer Society Annual Conference 2024
Learn More →

Improving Canine Lymphoma Treatment Outcomes by Individualizing Drug Selection using Machine-Learning-Based Predictive Models

Symposium on Artificial Intelligence in Veterinary Medicine (SAVY)
Learn More →

Dramatically increased clinical remission rates and survival times in dogs with high-grade T-cell lymphoma and relapsed B-cell lymphoma in clinical study of AI decision support

World Veterinary Cancer Congress 2024
Learn More →

AI-driven Personalized Medicine for Cancer Care

Precision Medicine World Conference 2024
Learn More →

Increased survival and remission rates in prospective study of relapsed B-cell lymphoma patients treated by oncologists using ImpriMed's AI predictions

Veterinary Cancer Society Annual Conference 2023
Learn More →

Canine Lymphoma Diagnostics Past, Present and Future: How AI technology and genetics are pushing the boundaries in veterinary medicine

Veterinary Cancer Society Annual Conference 2023
Learn More →

Impact of AI on clinical practice and outcome

Veterinary Cancer Society Annual Conference 2023
Learn More →

Identification of drug response biomarkers for canine lymphoma in large-scale NGS screen

Veterinary Cancer Society Annual Conference 2023
Learn More →

ImpriMed: AI-driven Personalized Medicine for Pet Cancer Care

Veterinary Cancer Society-Veterinary Society of Surgical Oncology Collaborative Conference 2023
Learn More →

Reinventing precision medicine: from dogs to humans

Precision Medicine World Conference 2023
Learn More →

Identification of Novel Predictive Biomarkers of Anticancer Drug Responses in Canine B-Cell Lymphoma Using Targeted NGS

Veterinary Cancer Society Annual Conference 2022
Learn More →

ImpriMed Research Update

Veterinary Cancer Society Annual Conference 2022
Learn More →

Drugs predicted to be effective using artificial intelligence (AI) double the clinical response rate in canines with relapsed B-cell lymphoma

Veterinary Cancer Society Mid-Year Conference 2022
Learn More →

ImpriMed Research Update

Veterinary Cancer Society Mid-Year Conference 2022
Learn More →

Precision medicine for pet cancer care

Animal Health, Nutrition and Technology Innovation Europe 2022
Learn More →

Predicting dynamic clinical outcomes of (L-)CHOP chemotherapy for canine lymphoma patients using an artificial intelligence model

Veterinary Cancer Society Annual Conference 2021
Learn More →

ImpriMed: Experiences from the eyes of an oncologist in clinical practice

Veterinary Cancer Society Annual Conference 2021
Learn More →

Predicting likelihood of in vivo chemotherapy response in canine lymphoma using ex vivo drug sensitivity and immunophenotyping data in a machine learning model

Veterinary Cancer Society Annual Conference 2020
Learn More →

ImpriMed: A data-driven, personalized chemotherapy drug testing service for canine blood cancer patients

Veterinary Cancer Society Annual Conference 2021
Learn More →

Individualizing chemotherapy for canine lymphoma based on ex vivo drug sensitivity test

World Veterinary Cancer Congress 2020
Learn More →

From dog to human: precision medicine for comparative oncology

Precision Medicine World Conference 2020
Learn More →

ImpriMed: precision medicine for pet cancer care

NYC Oncology Conference 2019
Learn More →

Individualizing chemotherapy for canine lymphoma based on ex vivo drug sensitivity test

Veterinary Cancer Society Annual Conference 2019
Learn More →