New methods to identify personalized drug treatments for breast cancer


Genomic landscape of PDxOs compared to PDXs and human tumors. a, Correlation heatmap illustrating genome-wide DNA methylation analysis for 11 patient-derived model sets against commonly used breast cancer cell lines. The color scale indicates the Pearson correlation coefficient. b, Eleven model sets were characterized at different time points (early and late) to assess molecular fidelity with human tumors. The heat map is divided into four sections from top to bottom: annotations, exome sequencing variant detection, CN correlations from SNP array data, and RNA-seq gene expression correlations. Mutation variants are shown with an oncoprint plot highlighting single nucleotide variants and indels for commonly mutated genes in breast cancer. Quantitative CNV correlations are presented using a heat map of Spearman correlations for log2 CN ratios at the gene level. Quantitative transcriptome correlations are presented using a heat map of Spearman correlations for log10-transformed-expectation-maximization (RSEM) RNA-Seq number estimates; NA, not applicable. c, Unsupervised clustering of the same models shown in b, with the PAM50 gene defined to classify the subtype. Credit: DOI: 10.1038/s43018-022-00337-6

For years, researchers at the Huntsman Cancer Institute at the University of Utah (U of U) have perfected a process for developing models of breast cancer using tumors donated by breast cancer patients, who they then implant into mice to study tumor behavior.

Now the research team is reporting a new, more efficient way to grow these tumors. Additionally, they describe a process for testing potential drugs to help prioritize clinical therapy choices based on unique tumor characteristics.

The study, published this week in the journal nature cancer, creates a way for researchers to narrow down the number of drugs that might be effective in each tumor based on its unique characteristics and behavior in laboratory models of cancer. Using this resource, researchers have found experimental and Food and Drug Administration-approved drugs with high efficacy compared to models. They extended this work to personalize therapy for a patient with metastatic breast cancer, resulting in a complete response for the patient and a progression-free survival period more than three times longer than her previous therapies.

“We were able to use the data to prioritize treatment options for a patient,” says co-lead author Alana Welm, Ph.D., breast cancer researcher at the Huntsman Cancer Institute and professor of oncology sciences at the U of U. “Although this therapy was unfortunately not curative, it did lead to regression of the patient’s tumor and a longer survival period.”

Welm says this unique library of tumor models is key to advancing research into aggressive breast cancers. “It is also, to our knowledge, the first time that such models have been used to influence the therapeutic choice of a patient with breast cancer in the context of a clinical trial.”

The research team included a diverse group of clinicians, laboratory researchers, and technicians from the University of Utah’s Huntsman Cancer Institute, Baylor College of Medicine, Jackson Labs, University of Connecticut, and the University of Pittsburgh. The team worked together to prioritize the advancement of research on specimens most aligned with the current challenges seen in the clinic.

A new clinical trial called FORESEE (NCT04450706) builds on the results of this study. Led by Saundra Buys, MD, chief of the division of oncology at the Huntsman Cancer Institute, the trial tests patient-derived tumor models to inform treatment choice in patients with metastatic breast cancer.

With a second trial in development, Welm says, “We will also use the models to predict recurrence for a subset of patients with newly diagnosed breast cancer, and then attempt to personalize treatment for the metastatic stage of breast cancer. disease when recurrence occurs.

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More information:
Katrin P. Guillen et al, A human breast cancer-derived xenograft and organoid platform for drug discovery and precision oncology, nature cancer (2022). DOI: 10.1038/s43018-022-00337-6

Provided by the University of Utah

Quote: New Methods to Identify Personalized Breast Cancer Drug Treatments (March 1, 2022) Retrieved March 9, 2022 from

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