Development of an optimized machine learning approach to enhance brain metastatic burden assessment in preclinical models. DOI Creative Commons

Jessica Rappaport,

Quanyi Chen,

Tomi McGuire

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 23, 2024

Abstract Brain metastases (BrM) occur when malignant cells spread from a primary tumor located in other parts of the body to brain. BrM is deadly complication for cancer patients and currently lacks effective therapies. Due limited access patient samples, preclinical models remain valuable tool studying metastasis development, progression, response therapy. Thus, reliable methods quantifying metastatic burden these are crucial. Here, we describe step by new semi-automatic machine-learning approach quantify on mouse whole-brain stereomicroscope images while preserving tissue integrity. This protocol utilizes open-source, user-friendly image analysis software QuPath. The method fast, reproducible, unbiased, provides data points not always obtainable with existing strategies.

Language: Английский

Next-Generation Therapeutic Antibodies for Cancer Treatment: Advancements, Applications, and Challenges DOI
A. Raja,

Abhishek Kasana,

Vaishali Verma

et al.

Molecular Biotechnology, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 2, 2024

Language: Английский

Citations

6
177Lu-Trastuzumab Radionuclide Therapy: an Effective Approach for Resistant Brain Metastases in HER2+ Breast Cancer DOI Creative Commons
Liliana Santos, Ivanna Hrynchak, José Sereno

et al.

Research Square (Research Square), Journal Year: 2025, Volume and Issue: unknown

Published: March 12, 2025

Abstract Purpose Breast cancer (BC) is the most common malignancy in women, with HER2 amplification present 25–30% of metastatic cases. Although HER2-targeted therapies like trastuzumab have significantly improved patient outcomes, their efficacy + brain metastases (BrM) hindered by emergence resistance mechanisms. This study explores therapeutic potential radiolabeled β⁻-emitting radionuclide ¹⁷⁷Lu as a strategy to overcome BrM. Material and methods BC cell lines brain-tropic derivatives were assessed for expression sensitivity [177Lu]Lu-DOTA-Trastuzumab. In vivo models established orthotopic implantation cells primary tumor formation or intracardiac injection induce Once tumors established, [¹⁷⁷Lu]Lu-DOTA-Trastuzumab was evaluated monitoring progression via magnetic resonance imaging (MRI). [⁸⁹Zr]Zr-DFO-Trastuzumab PET performed assess expression, while blood-brain barrier (BBB) permeability using dynamic contrast-enhanced MRI. Results Brain-tropic exhibited despite maintaining expression. In contrast, [177Lu]Lu-DOTA-trastuzumab induced significant DNA damage cytotoxicity. confirmed specific radiotracer uptake A single dose effectively suppressed growth achieved complete BrM remission 40% treated animals. Heterogeneous BBB observed across lesions, potentially influencing efficacy. Conclusion These findings underscore [¹⁷⁷Lu]Lu-DOTA-trastuzumab novel BrM, offering promising approach improve outcomes BC.

Language: Английский

Citations

0
Machine learning approach to assess brain metastatic burden in preclinical models DOI

Jessica Rappaport,

Quanyi Chen,

Tomi McGuire

et al.

Methods in cell biology, Journal Year: 2024, Volume and Issue: unknown, P. 25 - 49

Published: Jan. 1, 2024

Language: Английский

Citations

2
Development of an optimized machine learning approach to enhance brain metastatic burden assessment in preclinical models. DOI Creative Commons

Jessica Rappaport,

Quanyi Chen,

Tomi McGuire

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 23, 2024

Abstract Brain metastases (BrM) occur when malignant cells spread from a primary tumor located in other parts of the body to brain. BrM is deadly complication for cancer patients and currently lacks effective therapies. Due limited access patient samples, preclinical models remain valuable tool studying metastasis development, progression, response therapy. Thus, reliable methods quantifying metastatic burden these are crucial. Here, we describe step by new semi-automatic machine-learning approach quantify on mouse whole-brain stereomicroscope images while preserving tissue integrity. This protocol utilizes open-source, user-friendly image analysis software QuPath. The method fast, reproducible, unbiased, provides data points not always obtainable with existing strategies.

Language: Английский

Citations

1