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Combination Therapies and Personalized Medicine

Combination Therapies and Personalized Medicine

Combining two or more treatment methods increases the life span of cancer patients while reducing damage to vital organs or tissue from the overuse of a single treatment. Combination therapy also targets different cancer-inducing pathways, thus reducing the chances of developing resistance to treatment.
The combination of the drug acetazolamide and sulforaphane is a good example of combination therapy to treat cancer. The cells in the interior of a large tumor often die due to the hypoxic and...

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Related Experiment Video

Updated: Oct 2, 2025

Integration of Wet and Dry Bench Processes Optimizes Targeted Next-generation Sequencing of Low-quality and Low-quantity Tumor Biopsies

Integration of Wet and Dry Bench Processes Optimizes Targeted Next-generation Sequencing of Low-quality and Low-quantity Tumor Biopsies

Published on: April 11, 2016

Software Workflows and Infrastructures for Precision Oncology.

Waleed Osman¹, Alessandro Laganà²

¹Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.

Advances in Experimental Medicine and Biology

|March 1, 2022

Summary

This summary is machine-generated.

Precision oncology uses patient genetic data for targeted cancer treatments. This guide details computational tools and infrastructure for reproducible precision oncology pipelines, addressing big data challenges.

Keywords:

Bioinformatics CWL Genomics data processing Pipeline Precision oncology Software infrastructure Workflow Workflow management system

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Author Spotlight: Integrating Computational and Experimental Approaches in Precision Oncology

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Last Updated: Oct 2, 2025

Integration of Wet and Dry Bench Processes Optimizes Targeted Next-generation Sequencing of Low-quality and Low-quantity Tumor Biopsies

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Published on: April 11, 2016

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Author Spotlight: Integrating Computational and Experimental Approaches in Precision Oncology

Author Spotlight: Integrating Computational and Experimental Approaches in Precision Oncology

Published on: December 1, 2023

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Precision oncology leverages high-throughput sequencing for patient profiling.
Analyzing large genomic datasets requires robust computational tools.
Reproducibility and data handling are key challenges in precision oncology workflows.

Purpose of the Study:

To describe tools for designing and implementing precision oncology pipelines.
To highlight technical considerations for effective tool utilization.
To guide the development of precision oncology software workflows and infrastructure.

Main Methods:

Review of existing computational tools for genomic data analysis.
Discussion of infrastructure requirements for large-scale data processing.
Emphasis on software workflow design for reproducibility.

Main Results:

Identification of various tools for precision oncology pipeline development.
Outlined technical considerations for efficient data analysis.
Provided a framework for building reproducible bioinformatics workflows.

Conclusions:

Effective implementation of precision oncology requires careful selection of computational tools.
Robust infrastructure and reproducible software workflows are essential for success.
Addressing data handling challenges ensures reliable genomic profiling for cancer treatment.