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A combination approach successfully overcame treatment resistance and led to durable responses in preclinical models of advanced breast cancer, according to researchers at The University of Texas MD Anderson Cancer Center. The targets at the center of this approach were identified by a new computational biology algorithm developed at UT MD Anderson called TargetScore, suggesting this tool could be used to develop new therapeutic strategies and advance precision oncology.

The study, published in Cancer Research, was co-led by Anil Korkut, Ph.D., associate professor of bioinformatics and computational biology.

"Our findings suggest that resistant cancer cells often adapt to find alternate pathways for survival," Korkut said. "Current precision medicine approaches don't account for these adaptive responses, which allow tumors to avoid being destroyed. Uncovering these backup routes can bring about more effective combination strategies to, ultimately, improve patient outcomes."

Many targeted therapies initially shrink tumors, but cancer cells often activate compensatory biological pathways that help them survive—a process known as adaptive response. Current treatment approaches mainly focus on targeting preexisting genetic mutations but do not account for these adaptive response mechanisms, which may include DNA repair, cell proliferation, immune evasion and epigenetic regulation.

The researchers used a computational platform developed in the Korkut lab, called TargetScore, to examine how breast cancer cells respond to treatment stress. Rather than focusing on preexisting genes, TargetScore evaluates networks of proteins and molecular signatures that work together inside tumor cells while under therapeutic stress.

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